CN117291464A - Observation data evaluation method and related device - Google Patents
Observation data evaluation method and related device Download PDFInfo
- Publication number
- CN117291464A CN117291464A CN202311243421.2A CN202311243421A CN117291464A CN 117291464 A CN117291464 A CN 117291464A CN 202311243421 A CN202311243421 A CN 202311243421A CN 117291464 A CN117291464 A CN 117291464A
- Authority
- CN
- China
- Prior art keywords
- index
- groups
- sets
- value
- target
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000011157 data evaluation Methods 0.000 title abstract description 18
- 238000011156 evaluation Methods 0.000 claims abstract description 45
- 238000004364 calculation method Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 9
- 238000013441 quality evaluation Methods 0.000 abstract description 19
- 238000012544 monitoring process Methods 0.000 description 50
- 239000005433 ionosphere Substances 0.000 description 10
- 238000004458 analytical method Methods 0.000 description 9
- 238000013461 design Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 9
- 230000000694 effects Effects 0.000 description 8
- 230000008859 change Effects 0.000 description 7
- 238000013500 data storage Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 230000036541 health Effects 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 101000653426 Homo sapiens Very-long-chain enoyl-CoA reductase Proteins 0.000 description 1
- 238000012300 Sequence Analysis Methods 0.000 description 1
- 102100030747 Very-long-chain enoyl-CoA reductase Human genes 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The embodiment of the invention discloses an observation data evaluation method and a related device, wherein the method comprises the following steps: and respectively carrying out co-trend on the index values in the n groups of index value sets by acquiring the n groups of index value sets, correspondingly obtaining n groups of first target sets, carrying out dimensionless treatment on the index values in the n groups of first target sets, correspondingly obtaining n groups of second target sets, calculating the weight coefficient of each index item according to the n groups of second target sets, and calculating the comprehensive evaluation value corresponding to each group of index value sets according to the weight coefficient of each index item and the n groups of second target sets. The invention can realize the quality evaluation of multiple groups of index value sets respectively, and can evaluate the quality in a smaller range, thereby improving the effectiveness of the quality evaluation.
Description
Technical Field
The invention relates to the technical field of satellite navigation, in particular to an observation data evaluation method and a related device.
Background
In recent years, with the continuous development and application of global satellite navigation system (GNSS) technology, GNSS quality monitoring is becoming a hot research direction in the basic mapping field. The currently used GNSS data quality inspection software can meet the basic requirements of quality monitoring, but with the increasing diversity of GNSS frequency bands, the robustness of quality monitoring needs to be improved, and meanwhile, abnormal ionosphere activities can generate extremely unstable influence on radio signals, so that the amplitude and phase of the radio signals fluctuate when the radio signals pass through abnormal ionosphere, and ionosphere scintillation is caused. Ionospheric scintillation can also have a serious or even destructive impact on GNSS. Therefore, in order to improve accuracy of GNSS data acquisition, it is necessary to systematically and reliably systematically perform quality comprehensive assessment on GNSS data so as to effectively correct observation data, and effectively improve data processing accuracy and efficiency, so as to provide important basic assurance for subsequent data processing.
Disclosure of Invention
The invention mainly aims to provide an observation data evaluation method, an observation data evaluation device, computer equipment and a storage medium, which can solve the problem of low efficiency of comprehensively evaluating the quality of GNSS data in the prior art.
To achieve the above object, a first aspect of the present invention provides an observation data evaluation method, the method comprising:
acquiring n groups of index value sets, wherein each group of index value sets comprises index values corresponding to m index items, and the index items are technical indexes calculated according to observation data monitored by a global navigation satellite system;
respectively carrying out co-trend on index values in the n groups of index value sets to correspondingly obtain n groups of first target sets; respectively carrying out dimensionless treatment on index values in the n groups of first target sets to correspondingly obtain n groups of second target sets;
and calculating the weight coefficient of each index item according to the n groups of second target sets, and calculating the comprehensive evaluation value corresponding to each group of index value sets according to the weight coefficient of each index item and the n groups of second target sets.
With reference to the first aspect, in one possible implementation manner, the performing dimensionless mapping on the index values in the n groups of first target sets respectively, correspondingly obtaining n groups of second target sets includes:
wherein,indicating index values corresponding to the jth index in the ith group of second target sets; j takes on values from 1 to m, i takes on values from 1 to n;
indicating index values corresponding to the jth index in the ith group of first target sets;
Max j representing the maximum value of index values corresponding to the jth index in the n first target sets;
Min j refers to the minimum value of index values corresponding to the jth index in the n first target sets.
With reference to the first aspect, in one possible implementation manner, the calculating a weight coefficient of each index item according to the n second target sets includes: calculating entropy values of all index items according to the n groups of second target sets; calculating the difference coefficient of each index item according to the entropy value of each index item; and calculating the weight coefficient of each index item according to the difference coefficient of each index item.
With reference to the first aspect, in one possible implementation manner, the calculating, according to the weight coefficient of each index item and the n groups of second target sets, a comprehensive evaluation value corresponding to each group of index value sets includes:
wherein E is i For the comprehensive evaluation value of the i-th set of index values,index value corresponding to the j-th index in the second target set of the i-th group,/th index>To represent the minimum value, omega, of index values corresponding to the jth index in the n first target sets j And a weight coefficient representing the j-th index.
With reference to the first aspect, in a possible implementation manner, calculating entropy values of each index item according to the n groups of second target sets includes:
wherein S is j The entropy value representing the j-th index,
with reference to the first aspect, in one possible implementation manner, the calculating the difference coefficient of each index item according to the entropy value correspondence of each index item includes:
CV j ＝1-S j
wherein CV j A difference coefficient representing the j-th index, S j And (5) representing the entropy value of the j-th index.
With reference to the first aspect, in one possible implementation manner, the calculating a weight coefficient of each index item according to the difference coefficient of each index item includes:
wherein omega j Weight coefficient, CV representing the j-th index j And the difference coefficient of the j index is represented.
To achieve the above object, a second aspect of the present invention provides an observation data evaluating apparatus, comprising:
the acquisition module is used for: the method comprises the steps of acquiring n groups of index value sets, wherein each group of index value sets comprises index values corresponding to m index items, and the index items are technical indexes calculated according to data monitored by a global navigation satellite system;
the calculation module: the index values in the n groups of index value sets are subjected to co-trend respectively, and n groups of first target sets are correspondingly obtained; respectively carrying out dimensionless treatment on index values in the n groups of first target sets to correspondingly obtain n groups of second target sets;
and an evaluation module: and the comprehensive evaluation value corresponding to each index value set is calculated according to the weight coefficient of each index item and the n groups of second target sets.
To achieve the above object, a third aspect of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring n groups of index value sets, wherein each group of index value sets comprises index values corresponding to m index items, and the index items are technical indexes calculated according to observation data monitored by a global navigation satellite system;
respectively carrying out co-trend on index values in the n groups of index value sets to correspondingly obtain n groups of first target sets; respectively carrying out dimensionless treatment on index values in the n groups of first target sets to correspondingly obtain n groups of second target sets;
and calculating the weight coefficient of each index item according to the n groups of second target sets, and calculating the comprehensive evaluation value corresponding to each group of index value sets according to the weight coefficient of each index item and the n groups of second target sets.
To achieve the above object, a fourth aspect of the present invention provides a computer device including a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
acquiring n groups of index value sets, wherein each group of index value sets comprises index values corresponding to m index items, and the index items are technical indexes calculated according to observation data monitored by a global navigation satellite system;
respectively carrying out co-trend on index values in the n groups of index value sets to correspondingly obtain n groups of first target sets; respectively carrying out dimensionless treatment on index values in the n groups of first target sets to correspondingly obtain n groups of second target sets;
and calculating the weight coefficient of each index item according to the n groups of second target sets, and calculating the comprehensive evaluation value corresponding to each group of index value sets according to the weight coefficient of each index item and the n groups of second target sets.
The embodiment of the invention has the following beneficial effects:
the invention provides an observation data evaluation method, which is characterized in that n groups of index value sets are obtained, wherein index items are technical indexes calculated according to data monitored by a global navigation satellite system, each group of index value sets comprises index values corresponding to m index items, the index values in the n groups of index value sets are subjected to co-trend respectively to correspondingly obtain n groups of first target sets, the index values in the n groups of first target sets are subjected to dimensionless treatment respectively to correspondingly obtain n groups of second target sets, weight coefficients of the index items are calculated according to the n groups of second target sets, and comprehensive evaluation values corresponding to the index value sets are calculated according to the weight coefficients of the index items and the n groups of second target sets. The invention calculates the corresponding comprehensive evaluation values according to the multiple index value sets to realize the quality evaluation of the multiple index value sets respectively, and can evaluate the quality in a smaller range, thereby improving the effectiveness of the quality evaluation.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Wherein:
FIG. 1 is a flow chart of an observation data evaluation method according to an embodiment of the invention;
FIG. 2 is a block diagram of an observation data evaluation apparatus according to an embodiment of the present invention;
FIG. 3 is a block diagram of an observation data evaluation system according to an embodiment of the present invention;
fig. 4 is a block diagram of a signal quality monitoring module according to an embodiment of the present invention;
fig. 5 is a block diagram of a computer device in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The present invention provides an observation data evaluation method, which is used for evaluating the observation data of a global satellite navigation system (GNSS) to realize the evaluation of GNSS signal quality, wherein in the present embodiment, the GNSS system mainly comprises four large global satellite navigation systems (beidou satellite navigation system, global positioning system GPS, galileo and russian developed satellite navigation system GLONASS), and regional satellite navigation systems (quasi-zenith satellite system QZSS and indian regional satellite navigation system IRNSS, etc.), specifically, the evaluation of the observation data quality can be realized by evaluating the index items monitored according to the observation data, wherein the observation data can be GNSS monitoring station information, navigation messages (satellite navigation messages, which are messages describing the running state parameters of the navigation satellites by the navigation satellites, including system time, ephemeris, almanac, correction parameters of satellite clocks, navigation satellite health conditions, ionosphere delay model parameters, etc.), observation files, etc., the index items are technical indexes calculated according to the observation data of the global satellite systems, such as pseudo-range data, vds, ionosphere integrity, ionosphere ratio, carrier wave stability, op-state error, ionosphere carrier, etc., the multipath carrier wave health index, and the like.
Referring to fig. 1, fig. 1 is a flow chart of an observation data evaluation method according to an embodiment of the present invention, as shown in fig. 1, the method specifically includes the following steps:
step S101, acquiring n groups of index value sets.
Each index value set comprises index values corresponding to m index items. The index value refers to a value corresponding to the index item, specifically, in this embodiment, n groups of index value sets may be divided according to a preset rule, in one possible implementation manner, n groups of index value sets may be divided according to a preset time period, and n groups of index value sets may be divided according to a preset time period, because the index item is monitored in real time, it is possible that more than one index value is monitored in a period, therefore, an average value of index values corresponding to each index item monitored in the preset time period is obtained, a target average value of each corresponding index item is obtained, the target average value is used as an index value corresponding to each index item in the index value sets, an index value set corresponding to the preset time period is obtained, and in one possible implementation manner, the index value sets may be ordered according to a sequence of the preset time period. For example, every 1 month is a preset time period, if the monitored time is from 1 month 1 to 12 months 31, and the total time is 1 year, the index item is A, B, C, the index values corresponding to A in 1 month are obtained, the index values corresponding to A are A1, A2 and A3, the index values corresponding to B are B1, B2 and B3, the index values corresponding to C are C1, C2 and C3, the average value of A1, A2 and A3 is obtained, the first average value is obtained, the average value of B1, B2 and B3 is obtained, obtaining a second average value, averaging C1, C2 and C3 to obtain a third average value, taking the first average value, the second average value and the third average value as A1 st index value set, processing index values of 2 months, 3 months, the third month and 12 months similarly to the generation of the 1 st index value set, and correspondingly obtaining A2 nd index value set, A3 rd index value set, the third month and the 12 th index value set.
In another possible implementation manner, n groups of index value sets may be divided according to the GNSS monitoring stations, average values of index values corresponding to the index items correspondingly monitored in each GNSS monitoring station may be respectively obtained, target average values of the index items monitored by each GNSS monitoring station may be obtained, and the target average values of the index items monitored by each GNSS monitoring station may be respectively used as index values corresponding to the index items in the corresponding index value set.
Because the generating methods of the index value sets corresponding to each GNSS monitoring station are the same, the target GNSS monitoring station is taken as an example, namely, the target GNSS monitoring station is any GNSS monitoring station in the GNSS system, the average value of the index values corresponding to the index items monitored by the target GNSS monitoring station is respectively calculated, the target average value of the index items is obtained, and the target average value is taken as the index value corresponding to the index item in the index value set, so that the index value set corresponding to the target GNSS monitoring station is obtained. For example, the total number of the GNSS monitoring stations Z1, Z2 and Z3 is A, B, C, the index items corresponding to the monitoring a in the GNSS monitoring station Z1 are obtained, the index values corresponding to the monitoring a are A1, A2 and A3, the index values corresponding to the monitoring B are B1, B2 and B3, the index values corresponding to the monitoring C are C1, C2 and C3, the average values of A1, A2 and A3 are obtained to obtain a first average value, the average values of B1, B2 and B3 are obtained to obtain a second average value, the average values of C1, C2 and C3 are obtained to obtain a third average value, the first average value, the second average value and the third average value are set of 1 st set of index values, and the index values of the GNSS monitoring station Z2 and the GNSS monitoring station Z3 are processed to obtain a set of 2 nd set of index values and a set of 3.
Step S102, respectively carrying out co-trend on index values in the n groups of index value sets to correspondingly obtain n groups of first target sets; and respectively carrying out dimensionless treatment on index values in the n groups of first target sets to correspondingly obtain n groups of second target sets.
And respectively carrying out co-trend on the index values in the n groups of index value sets to correspondingly obtain n groups of first target sets, wherein in a comprehensive index system, when the data integrity rate is larger, the cycle slip rate is larger, the multipath value is smaller, the ionosphere delay change rate is smaller, the pseudo range and carrier phase observation precision is smaller, the spatial position precision factor is smaller, and the ionosphere scintillation index is smaller, the observation data quality is better, so that index items which characterize the observation data quality better are required to be larger in index value and are subjected to inverse method are required to be converted into minimum index values, namely index items such as the data integrity rate and cycle slip rate are required to be subjected to inverse method to be converted into minimum index values, specifically, index values of preset index items in the n groups of index value sets are respectively subjected to co-trend on the index values of the preset index items, corresponding to correspondingly obtain n groups of first target sets, wherein the preset index items are index items which characterize the observation data quality better are larger in the corresponding index value, namely the preset index items are the maximum index items, and the maximum index items refer to the index items which are good in data quality, such as the complete and the cycle slip rate. Respectively converting index values of preset index items in n groups of index value sets into minimum index values according to a reciprocal method, and correspondingly obtaining n groups of first target sets.
The calculation formula for converting the index value of the preset index item into the minimum index value is as follows:
wherein x is i 0，j Index value x representing preset index item i 1，j A minimum index value representing a converted preset index item.
For example, the 1 st set of index values includes A1, B1, and C1, where A1 is an index value corresponding to a maximum index item, and then A1 is converted into a minimum index valueThen the first target set corresponding to the 1 st index value set is obtained as (/>B1、C1)。
And respectively carrying out dimensionless treatment on index values in the n groups of first target sets to correspondingly obtain n groups of second target sets. Sequencing the index items to obtain an index item sequence, wherein the index values of the comprehensive evaluation are all dimensionless values, and processing the trended index values according to the following formula:
wherein,index values representing the correspondence of the jth index in the sequence of index items in the ith set of second targets, i.e +.>Indicating index values corresponding to the jth index in the ith group of second target sets, wherein the jth index is identical to the jth index; j takes on values from 1 to m, i takes on values from 1 to n; />Index values representing the correspondence of the jth index in the sequence of index items in the ith set of first targets, i.e +.>Indicating index values corresponding to the jth index in the ith group of first target sets; max (Max) j Representing the maximum value of index values corresponding to the jth index in the index item sequence in the n first target sets; min (Min) j And the minimum value of index values corresponding to the jth index in the index item sequence in the n first target sets is represented.
For example, if the index item sequence is A, B, C and there are 3 first target sets, the 1 st first target set is A1, B1, C1, the 2 nd first target set is A2, B2, C2, the 3 rd first target set is A3, B3, C3, wherein A1 > A2 > A3, thenIndex value representing A corresponding to the second target set of group 1,/index value>Denoted A1, max 1 Is A1, min 1 A3.
Step S103, calculating the weight coefficient of each index item according to the n groups of second target sets, and calculating the comprehensive evaluation value corresponding to each group of index value sets according to the weight coefficient of each index item and the n groups of second target sets.
The method comprises the following specific steps of:
step S1031, calculating entropy values of all index items according to the n groups of second target sets.
Calculating the entropy value S of the jth index by the following method j ：
Wherein S is j The entropy value representing the j-th index,
step S1032, calculating the difference coefficient of each index item according to the entropy value of each index item.
Calculating the difference coefficient CV of the jth index according to the following formula j ：
CV j ＝1-S j
Wherein CV j A difference coefficient representing the j-th index, S j And (5) representing the entropy value of the j-th index.
Step S1033, calculating the weight coefficient of each index item according to the difference coefficient of each index item.
Calculating the weight coefficient omega of the jth index by the following method j ：
Wherein omega j Weight coefficient, CV representing the j-th index j The difference coefficient of the j-th index is represented, and m represents the total number of index items.
The smaller the comprehensive evaluation value E is calculated according to the following formula, the better the observed data quality is:
wherein E is i For the comprehensive evaluation value of the i-th set of index values,index value indicating the correspondence of the jth index in the ith set of second targets, ++>To represent the minimum value, omega, of index values corresponding to the jth index in the n first target sets j And a weight coefficient representing the j-th index.
Further, after the comprehensive evaluation value of the index value set is obtained, whether the observed data corresponding to the index value set needs to be corrected can be judged according to the size of the comprehensive evaluation value, in a possible implementation manner, the threshold value can be determined according to the accurate requirement through comparing the comprehensive evaluation value with a preset threshold value, as the smaller E is the better the quality of the observed data, when the comprehensive evaluation value is larger than the threshold value, the observed data corresponding to the index value set corresponding to the comprehensive evaluation value is corrected, so that the quality and the accuracy of the observed data are improved, and therefore the accuracy of the index value is improved, and important basic assurance is provided for the subsequent data processing based on the observed data and the index value.
Based on the method, the corresponding comprehensive evaluation value of the index value sets can be calculated, so that the quality evaluation of the index value sets can be respectively carried out, the quality evaluation can be carried out in a smaller range, the effectiveness of the quality evaluation is improved, and in addition, the comprehensive quality evaluation of the index value sets can be realized by calculating the weight coefficient of each index item in the index value sets and the second target set.
In order to better implement the method, an embodiment of the present invention provides an observation data evaluation apparatus, referring to fig. 2, fig. 2 is a block diagram of an observation data evaluation apparatus provided in the embodiment of the present invention, as shown in fig. 2, the apparatus 20 includes:
the acquisition module 201: the index value calculation method is used for obtaining n groups of index value sets, wherein each group of index value sets comprises index values corresponding to m index items, and the index items are technical indexes calculated according to observation data monitored by a global navigation satellite system.
The calculation module 202: the index values in the n groups of index value sets are subjected to co-trend respectively, and n groups of first target sets are correspondingly obtained; and respectively carrying out dimensionless treatment on index values in the n groups of first target sets to correspondingly obtain n groups of second target sets.
The evaluation module 203: and the comprehensive evaluation value corresponding to each index value set is calculated according to the weight coefficient of each index item and the n groups of second target sets.
In one possible design, the calculation module 202 calculates the index value corresponding to the jth index in the ith second target set according to the following formula:
wherein,indicating index values corresponding to the jth index in the ith group of second target sets; j takes on values from 1 to m, i takes on values from 1 to n; />Indicating index values corresponding to the jth index in the ith group of first target sets; max (Max) j Representing the maximum value of index values corresponding to the jth index in the n first target sets; min (Min) j Refers to the minimum value of index values corresponding to the jth index in the n first target sets.
In one possible design, the evaluation module 203 is specifically configured to: calculating entropy values of all index items according to the n groups of second target sets; calculating the difference coefficient of each index item according to the entropy value of each index item; and calculating the weight coefficient of each index item according to the difference coefficient of each index item.
In one possible design, the evaluation module 203 specifically calculates the entropy value of each index term using the following formula:
wherein S is j The entropy value representing the j-th index,
in one possible design, the evaluation module 203 specifically calculates the difference coefficient of each index term using the following formula:
CV j ＝1-S j
wherein CV j A difference coefficient representing the j-th index, S j And (5) representing the entropy value of the j-th index.
In one possible design, the evaluation module 203 specifically calculates the weight coefficient of each index term using the following formula:
wherein omega j Weight coefficient, CV representing the j-th index j And the difference coefficient of the j index is represented.
In one possible design, the evaluation module 203 specifically calculates the comprehensive evaluation value corresponding to each set of index values by using the following formula:
wherein E is i For the comprehensive evaluation value of the i-th set of index values,index value corresponding to the j-th index in the second target set of the i-th group,/th index>To represent the minimum value, omega, of index values corresponding to the jth index in the n first target sets j And a weight coefficient representing the j-th index. The fuzzy operator is expressed as
Based on the device, the comprehensive quality evaluation of the index value set can be realized, so that the effectiveness of the observation data quality evaluation result is improved.
In this embodiment, an observation data evaluation system is further provided, referring to fig. 3, and fig. 3 is a block diagram of a structure of an observation data evaluation system provided in an embodiment of the present invention, as shown in fig. 3, where the system includes a signal receiving and reading module 101, a signal quality monitoring module 102, a signal quality evaluation module 103, and a data storage module 104, where the signal receiving and reading module 101, the signal quality detecting module 102, the signal quality evaluation module 103, and the data storage module 104 are sequentially connected in communication.
The signal receiving and reading module 101 is configured to receive and read the raw observation data sent by the receiver, and transmit the raw observation data to the signal quality monitoring module 102.
The signal quality monitoring module 102 is configured to receive the original observation data, obtain an index item according to the original observation data, obtain a monitoring result, and transmit the monitoring result to the signal quality evaluation module 103 and the data storage module 104.
The signal quality evaluation module 103 is configured to perform comprehensive evaluation on the observation data by receiving and analyzing the monitoring result, and send the comprehensive evaluation value to the data storage module 104.
The data storage module 104 is configured to store the monitoring result of the signal quality monitoring module and the comprehensive evaluation result of the quality evaluation module.
Specifically, the signal receiving and reading module 101 transmits the received and read original observation data to the signal quality monitoring module 102, the signal quality monitoring module 102 monitors the data and obtains a monitoring result, the signal quality evaluation module 103 performs comprehensive evaluation on the monitoring result, and finally the monitoring result and the comprehensive evaluation result are transmitted to the data storage module 104 for storage, so that the GNSS signal quality can be accurately reflected.
Further, the system supports quality monitoring of the following navigation systems such as a Beidou satellite navigation system, a Global Positioning System (GPS), a Galileo satellite navigation system (Galileo), a Russian developed satellite navigation system (GLONASS), an India regional navigation satellite system (NavIC) and the like: beidou authorization signals (B1A, B3Q, B3A, B AE), beidou public signals (B1I, B3I, B1C, B a, B2B; GPS: L1C/A, L2P, L1C, L2C, L5), galileo (E1, E5a, E5B), GLONASS (G1, G2), nacIC (L5).
Further, the raw observation data received and read by the signal receiving and reading module 101 includes at least one of longitude and latitude of the GNSS monitoring station, altitude of the GNSS monitoring station, carrier phase observation value, pseudo-range observation value, carrier-to-noise ratio observation value, or navigation message data.
Referring to fig. 4, fig. 4 is a block diagram of a signal quality monitoring module according to an embodiment of the present invention, where, as shown in fig. 4, the signal quality monitoring module 102 includes a module for calculating index items, such as positioning error, data integrity rate, cycle slip rate, multipath effect, ionospheric delay change rate, pseudo-range error, carrier error, undersea point (latitude, longitude of the undersea point, angular distance between the satellite and the rising intersection point), geometric precision factor, and ionospheric scintillation index.
The device specifically comprises a GNSS navigation signal single-frequency and double-frequency positioning and resolving unit 1021.
Specifically, the single-frequency and double-frequency positioning resolving unit 1021 of the GNSS navigation signals is configured to implement single-frequency and double-frequency pseudo-range single-point positioning resolving of different navigation signals of the navigation system such as beidou, GPS, galileo, GLONASS, navIC and the like according to the original observation data, and calculate a positioning coordinate and a positioning error, where a calculation formula of the positioning error is as follows:
in sigma x ,σ y ,σ z Indicating the positioning errors in the x, y and z directions of the receiver, respectively.
Specifically, the signal quality monitoring module 102 further includes a GNSS observation data quality analysis unit 1022, and the GNSS observation data quality analysis unit 1022 is configured to perform data quality analysis and evaluation according to the original observation data, implement statistics of data integrity rate, cycle slip rate, multipath effect, ionospheric delay change rate, and implement time sequence analysis of cycle slip, multipath effect, and ionospheric delay change rate.
Specifically, the data integrity rate is the ratio of the actual observation number to the theoretical observation number, and the calculation formula is as follows:
in the formula, n is the total number of satellites observed in the observation period, j is the serial number of the observed satellite, I s Representing the observed data integrity rate of the system s, s representing the serial number of the satellite navigation system (e.g., GPS, galileo, GLONASS) in the GNSS system,for the actual total number of observations of system s satellite j during the observation period,/for>Is the theoretical total number of observations of the system s satellite j in the observation period.
Specifically, the cycle slip rate is the ratio of the cycle slip number to the actual observed number, and the calculation formula is as follows:
in the formula, n is the total number of satellites observed in the observation period, j is the serial number of the observed satellites, o/slps s For the cycle slip ratio of system s during the observation period,for the actual number of observations of system s satellite j during the observation period,/->Zhou Tiaoshu for system s satellite j to occur during the observation period.
Specifically, the multipath effect refers to a phenomenon that a signal is reflected and delayed to arrive when going from a navigation satellite to a receiver, and the calculation formula is as follows:
wherein lambda is 1 、λ 2 Two preset carrier wavelengths, MP, of a GNSS system respectively 1 、MP 2 Multipath effects, P, of two preset frequency bands respectively 1 、P 2 Pseudo-range observables of two preset frequency bands respectively, L 1 、L 2 Carrier phase observables of two preset frequency bands respectively, < MP 1 ＞、＜MP 2 Each is the mean of the multipath effects over the observation period.
Specifically, the ionospheric delay change rate refers to the change amount of ionospheric delay in unit time, and the calculation formula is as follows:
in the formula, TEC N The total ionospheric electron content (ionosphere TEC), in TECu,is a proportionality coefficient, f 1 、f 2 Respectively corresponding to two preset carrier wave wavelengths, t N TECR is the observation time of the nth epoch N The ionospheric delay change rate at time N is given in TECu/s.
Specifically, the signal quality monitoring module 102 further includes a GNSS observation data accuracy analysis unit 1023, and the GNSS observation data accuracy analysis unit 1023 is configured to perform data accuracy analysis and evaluation according to the original observation data, implement statistics of measurement errors of the pseudo range and the carrier phase, and implement measurement accuracy analysis of the pseudo range and the carrier phase, that is, monitor the pseudo range error and the carrier error.
The calculation formulas of the pseudo-range error and the carrier error are as follows:
/>
in sigma P 、σ L Pseudo-range noise and carrier noise respectively,respectively is epoch t a Three-difference combined observations of pseudoranges and carrier phases, where t a Representing the a-th epoch, epoch t a Can be the current epoch, i.e. +.>Respectively is epoch t a Pseudo-range error and carrier error, +.>Respectively is epoch t a Pseudo-range observance quantity, carrier phase observance quantity and N of lower satellite j P 、N L Respectively is epoch t a Lower receiver satellite j pseudo-range three-difference combined observed quantity, carrier phase three-difference combined observed quantity, t i Refers to the ith epoch.
Specifically, the signal quality monitoring module 102 further includes a GNSS constellation state analysis unit 1024, where the GNSS constellation state analysis unit 1024 is configured to perform constellation state evaluation according to the original observation data, so as to implement statistics of a satellite under-satellite point trajectory and a satellite health state, where a calculation formula of the satellite under-satellite point is:
wherein:as the latitude of the point below the satellite, lambda the longitude of the point below the satellite, i is the orbital inclination angle, u is the angular distance between the satellite and the intersection point at time t, omega is the right ascent point, w c The rotation angular velocity of the earth, omega is the amplitude angle of a near point, f is the true near point angle at the moment t, t 0 Representing the reference epoch of the ephemeris.
Specifically, the signal quality monitoring module 102 further includes a GNSS constellation geometric precision factor monitoring unit 1025, where the GNSS constellation geometric precision factor monitoring unit 1025 is configured to evaluate the geometric constellations of the GNSS constellation according to the original observation data, to implement evaluation of geometric precision factors (PDOP/HDOP/VDOP) and satellite visible number indexes, where the geometric precision factors are an important index for measuring the positioning precision of the satellite system, and can be obtained by a design matrix of the observation data, where the design matrix can be expressed as:
where n is the number of satellites, u, v and w are the coefficients corresponding to the position coordinate components x, y and z in the observation equation, respectively, and the last column is the coefficient of the receiver clock error parameter.
Further, the geometric precision factor can be obtained from the design matrix, and the calculation formula is as follows:
Q＝(A T A) -1
in the formula, PDOP is a three-dimensional position precision factor, HDOP is a horizontal component precision factor, and VDOP is a vertical component precision factor.
Specifically, the signal quality monitoring module 102 further includes a GNSS ionospheric scintillation monitoring unit 1026, where the GNSS ionospheric scintillation monitoring unit 1026 is configured to calculate the GNSS ionospheric scintillation index S4 according to the raw observation data, and evaluate the intensity of the ionospheric scintillation activity. The GNSS ionospheric scintillation index S4 is defined as the standard deviation of normalized signal strength. A typical receiver cannot directly provide an output of signal strength data, but only an output of carrier to noise ratio CNR. The carrier to noise ratio CNR is used to derive the approximate signal strength and thus the S4 index. Let signal strength be SI, signal-to-noise ratio (SNR) be expressed as SNR, then snr=10 CNR/10 From this, the equivalent signal strength at time k can be obtained as:
wherein f s Is the sampling frequency.
Further, the S4 index is defined as:
where SI is the signal strength and < > represents the arithmetic mean.
Specifically, the signal quality evaluation module 103 is used for evaluating the comprehensive quality of the GNSS raw data, and the signal quality evaluation module 103 implements the comprehensive quality evaluation of the GNSS raw data by executing the observation data evaluation method in the present invention.
FIG. 5 illustrates an internal block diagram of a computer device in one embodiment. The computer device may specifically be a terminal or a server. As shown in fig. 5, the computer device includes a processor, a memory, and a network interface connected by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program which, when executed by a processor, causes the processor to carry out all the steps of the above-described method. The internal memory may also have stored therein a computer program which, when executed by a processor, causes the processor to perform all the steps of the method described above. It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is presented comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the aforementioned method.
In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, causes the processor to perform the steps of the aforementioned method.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method of observation data assessment, the method comprising:
acquiring n groups of index value sets, wherein each group of index value sets comprises index values corresponding to m index items, and the index items are technical indexes calculated according to observation data monitored by a global navigation satellite system;
respectively carrying out co-trend on index values in the n groups of index value sets to correspondingly obtain n groups of first target sets; respectively carrying out dimensionless treatment on index values in the n groups of first target sets to correspondingly obtain n groups of second target sets;
and calculating the weight coefficient of each index item according to the n groups of second target sets, and calculating the comprehensive evaluation value corresponding to each group of index value sets according to the weight coefficient of each index item and the n groups of second target sets.
2. The method of claim 1, wherein the performing dimensionless number of the index values in the n groups of first target sets respectively, and correspondingly obtaining n groups of second target sets, includes:
wherein,indicating index values corresponding to the jth index in the ith group of second target sets; j takes on values from 1 to m, i takes on values from 1 to n;
indicating index values corresponding to the jth index in the ith group of first target sets;
Max j representing the maximum value of index values corresponding to the jth index in the n first target sets;
Min j refers to the minimum value of index values corresponding to the jth index in the n first target sets.
3. The method of claim 1, wherein calculating the weight coefficients of the index items from the n second target sets comprises:
calculating entropy values of all index items according to the n groups of second target sets;
calculating the difference coefficient of each index item according to the entropy value of each index item;
and calculating the weight coefficient of each index item according to the difference coefficient of each index item.
4. The method according to claim 1, wherein the calculating the comprehensive evaluation value corresponding to each set of index values according to the weight coefficient of each index item and the n sets of second target sets includes:
wherein E is i For the comprehensive evaluation value of the i-th set of index values,index value corresponding to the j-th index in the second target set of the i-th group,/th index>To represent the minimum value, omega, of index values corresponding to the jth index in the n first target sets j And a weight coefficient representing the j-th index.
5. A method according to claim 3, wherein said calculating entropy values of the index entries from the n sets of second targets comprises:
wherein S is j The entropy value representing the j-th index,
6. a method according to claim 3, wherein the calculating the difference coefficient of each index item according to the entropy value of each index item comprises:
CV j ＝1-S j
wherein CV j A difference coefficient representing the j-th index, S j And (5) representing the entropy value of the j-th index.
7. A method according to claim 3, wherein said calculating the weight coefficient of each index item based on the difference coefficient of each index item comprises:
wherein omega j Weight coefficient, CV representing the j-th index j And the difference coefficient of the j index is represented.
8. An observation data evaluating apparatus, the apparatus comprising:
the acquisition module is used for: the method comprises the steps of acquiring n groups of index value sets, wherein each group of index value sets comprises index values corresponding to m index items, and the index items are technical indexes calculated according to observation data monitored by a global navigation satellite system;
the calculation module: the index values in the n groups of index value sets are subjected to co-trend respectively, and n groups of first target sets are correspondingly obtained; respectively carrying out dimensionless treatment on index values in the n groups of first target sets to correspondingly obtain n groups of second target sets;
and an evaluation module: and the comprehensive evaluation value corresponding to each index value set is calculated according to the weight coefficient of each index item and the n groups of second target sets.
9. A computer readable storage medium storing a computer program, which when executed by a processor causes the processor to perform the steps of the method according to any one of claims 1 to 7.
10. A computer device comprising a memory and a processor, wherein the memory stores a computer program which, when executed by the processor, causes the processor to perform the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311243421.2A CN117291464A (en) | 2023-09-25 | 2023-09-25 | Observation data evaluation method and related device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311243421.2A CN117291464A (en) | 2023-09-25 | 2023-09-25 | Observation data evaluation method and related device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117291464A true CN117291464A (en) | 2023-12-26 |
Family
ID=89243874
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311243421.2A Pending CN117291464A (en) | 2023-09-25 | 2023-09-25 | Observation data evaluation method and related device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117291464A (en) |
-
2023
- 2023-09-25 CN CN202311243421.2A patent/CN117291464A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107765275B (en) | Wide-area differential positioning method, device, terminal and computer readable storage medium | |
| CN107607969B (en) | Four-system pseudo range positioning method based on DCB correction | |
| US8223068B2 (en) | Method and system for logging position data | |
| CN113325446B (en) | Multimode common-frequency GNSS carrier phase time transfer method and system | |
| CN114994727B (en) | Equipment for realizing high-precision time calibration and satellite positioning | |
| CN109633703B (en) | Beidou navigation passive positioning method for responding to sheltered scene | |
| CN111290004A (en) | Pseudo-range differential positioning method, pseudo-range differential positioning device, electronic equipment and storage medium | |
| Rodriguez-Solano et al. | Protection level of the trimble RTX positioning engine for autonomous applications | |
| Dong et al. | Absolute calibration of the TOPEX/Poseidon altimeters using UK tide gauges, GPS, and precise, local geoid-differences | |
| CN113124919A (en) | Abnormity monitoring method and device for reference station and related equipment | |
| Vollath et al. | Network RTK–concept and performance | |
| KR101429474B1 (en) | Differential GPS augmentation system and method using satellite constellation information | |
| Hadas et al. | Galileo High Accuracy Service in real-time PNT, geoscience and monitoring applications | |
| CN117826200A (en) | PPP-B2B-based marine real-time precise positioning method, system and medium | |
| Tang et al. | GNSS localization propagation error estimation considering environmental conditions | |
| Guo et al. | Evaluation on measurement performance of low-cost GNSS receivers | |
| CN117291464A (en) | Observation data evaluation method and related device | |
| CN106707311A (en) | GPS based enhanced GLONASS RTK (Real-time Kinematic) positioning method | |
| Gioia et al. | Inter-system Bias: Stability and impact on multi-constellation positioning | |
| Sulaiman et al. | Global Positioning System Performance Assessment with Precise Point Positioning and Relative Positioning | |
| Lim et al. | Monitoring QZSS CLAS-based VRS-RTK Positioning Performance | |
| CN111399019B (en) | Satellite positioning method and device in GNSS | |
| CN109141214B (en) | Deformation monitoring multi-source evidence printing method based on Beidou meteorological model | |
| Jamieson | Investigation of Multi-constellation RTK GNSS Survey Productivity and Coordinate Accuracy | |
| Öğütçü | Assessing the Contribution of Galileo to GPS+ GLONASS Single Point Positioning Navigation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |