CN114003956A - Spatial data analysis scheduling system and method applying big data analysis - Google Patents

Spatial data analysis scheduling system and method applying big data analysis Download PDF

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CN114003956A
CN114003956A CN202111295713.1A CN202111295713A CN114003956A CN 114003956 A CN114003956 A CN 114003956A CN 202111295713 A CN202111295713 A CN 202111295713A CN 114003956 A CN114003956 A CN 114003956A
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CN114003956B (en
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张业红
顾行发
刘向东
黄祥志
石宇航
陈佳玮
朱玉婷
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Jiangsu Tianhui Spatial Information Research Institute Co ltd
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Abstract

The invention discloses a spatial data analysis scheduling system and a spatial data analysis scheduling method applying big data analysis, wherein the system comprises a spatial data acquisition module, a data receiving module, a spatial data analysis module and a result feedback module; the spatial data acquisition module is used for acquiring real-time spatial data of a target object and carrying out topology processing on the spatial data of the target object to obtain topology related data; the data receiving module is used for receiving the topology related data of the spatial data acquisition module and carrying out encryption processing to obtain encrypted spatial information; the spatial data analysis module is used for analyzing and processing the encrypted spatial information transmitted by the data receiving module; the result feedback module is used for feeding back the result analyzed by the spatial data analysis module to the resource database unit; the invention reasonably schedules the spatial data by analyzing the density of the spatial data so as to lead the spatial data to achieve the purpose of reasonable storage state and resource utilization.

Description

Spatial data analysis scheduling system and method applying big data analysis
Technical Field
The invention relates to the technical field of data analysis, in particular to a spatial data analysis scheduling system and method applying big data analysis.
Background
At present, in the field of spatial data analysis, pure traditional analysis and big data analysis exist, and in practical application, the analysis route is either based on the traditional analysis route or the big data analysis route, but in the aspect of resource utilization of spatial information, a lot of resources are improperly utilized, the resources cannot be reasonably and effectively utilized, and the situation that the space for information storage is influenced by spatial data distribution happens occasionally; in addition, uneven spatial data information also causes interference and influence on each other, and influences on the accuracy of data to a certain extent, so that effective and reasonable scheduling and utilization of the spatial data information are effective means for solving the problem of complex spatial data at present; there are also security issues in spatial data transmission that are currently in need of resolution.
Disclosure of Invention
The present invention provides a spatial data analysis scheduling system and method using big data analysis to solve the above problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: the spatial data analysis scheduling system applying big data analysis comprises a spatial data acquisition module, a data receiving module, a spatial data analysis module and a result feedback module; the spatial data acquisition module is used for acquiring real-time spatial data of a target object and carrying out topology processing on the spatial data of the target object to obtain topology related data; the data receiving module is used for receiving the topology related data of the spatial data acquisition module and carrying out encryption processing to obtain encrypted spatial information; the spatial data analysis module is used for analyzing and processing the encrypted spatial information transmitted by the data receiving module; and the result feedback module is used for feeding back the analysis result of the spatial data analysis module to the resource database unit.
Further, the spatial data acquisition module comprises a target detection unit and a resource database retrieval unit;
the target detection unit is used for realizing real-time acquisition of basic spatial data information of the target object in a mode that the spatial data acquisition module detects the target object;
the resource database retrieval unit is used for the spatial data acquisition module to extract the same spatial data information from the resource database unit in the result feedback module for direct utilization;
the spatial data acquisition module firstly utilizes the resource database retrieval unit to retrieve the target object, and if the resource database unit has the same target object, the spatial data is directly utilized; if the resource database unit does not have the same data as the target object, the spatial data acquisition module further starts the target detection unit to acquire the basic spatial data information of the target object.
The spatial data acquisition module is firstly provided with a resource database retrieval unit for effectively utilizing the existing data resources, so that the data processing time is saved; when the same data is not retrieved from the resource database, the data is acquired and then transmitted to the resource database unit after being processed, so that the data in the resource database is richer.
Furthermore, the spatial data analysis module comprises a data extraction unit and an analysis scheduling unit;
the data extraction unit decrypts the spatial data information encrypted by the data receiving unit and extracts the decrypted spatial data to obtain initial spatial block data; the analysis scheduling unit can simultaneously analyze the spatial information in the spatial data analysis module and the resource information in the resource database unit in the data feedback module, and perform data analysis scheduling on the data in the spatial data analysis module and the resource database unit in the data feedback module.
The spatial data analysis module is provided with a data extraction unit which can extract and utilize the effective information in the data receiving unit, and the analysis scheduling unit processes and analyzes the information in the data extraction unit and schedules the data to be scheduled, so that the data information in the spatial block can be shared.
The invention also provides a spatial data analysis scheduling method applying the big data analysis, which comprises the following steps:
step S100: acquiring spatial data information, and if the spatial data information is existing, directly referring to the spatial data information; if the space data information is not the existing space data information, topology processing is carried out after the space data information is acquired, and topology related data are obtained;
step S200: continuously encrypting the topology related data obtained by processing in the step S100 to obtain encrypted processing data;
step S300: decrypting the encrypted data obtained in the step S200, and extracting information in the topology related data to obtain initial space block data; carrying out spatial analysis on the initial spatial block data to judge whether scheduling is needed; obtaining second space block data information through analysis scheduling processing, wherein data which is not analyzed and scheduled to be processed is first space block data information;
step S400: comparing the first spatial block data or the second spatial block data in the step S300 with the resource database, respectively; if the first spatial block data or the second spatial block data can be matched with the same spatial data, recording the frequency of the spatial data; and if the first spatial block data or the second spatial block data are not matched with the same spatial data, the resource database updates and records the first spatial block data or the second spatial block data.
The importance degree of the spatial information can be effectively judged by recording the frequency of the matched spatial data, and the resource abundance of the database can be increased by updating and recording the unmatched spatial information, so that the aim of reasonably sharing the resources is fulfilled.
Further, in step S100, topology processing is performed on the acquired spatial data information to obtain topology-related data, where the topology-related data includes a topology relationship, and the specific process is as follows:
step S110: the acquired spatial data information comprises image structure information and land distribution information, wherein the image structure information is an irregular polygon image formed by adjacent m lands with different areas, and m isThe land parcel distribution information includes a reference number P of the land parcel as a natural numbermAdjacent point N of land blocks with different areasiAnd the vector relation between adjacent points
Figure BDA0003336507440000031
Figure BDA0003336507440000032
Step S120: performing topology processing on the image structure information and the parcel distribution information in the step S110 to obtain a topological relation, wherein the topological relation comprises a vector relation
Figure BDA0003336507440000033
And parcel number PmTopological relation between, i.e. PmCorrespond to
Figure BDA0003336507440000034
And adjacent point NiWith a landmark block number PmTopological relation between, i.e. PmCorresponding to the starting point Ni+dAnd endpoint Ni+eB, c, d and e all belong to natural numbers;
the spatial plot data information is converted into the relation between the vector and the plot, so that the spatial information in the plot can be effectively set in relation to the vector password standard and the proportion problem of the vectors contained in different plots can be judged;
step S130: relating the different vectors in step S120
Figure BDA0003336507440000035
And parcel number PmThe topological relation between the two is taken as a set A, the orientation quantity relation
Figure BDA0003336507440000036
Are respectively marked as a set BiRespectively calculating the specific gravities of different elements
Figure BDA0003336507440000037
The position information of the land blocks to which the land blocks belong can be obviously distinguished by using the ratio of different vectors to measure the total proportion of different vectors contained in different land blocks by using the overall vector relation set in the space blocks as denominators, namely, the land blocks are preliminarily judged to have the number of the adjacent land blocks and the proportion of the contained space data information.
Step S140: based on the specific gravity G of different elements in step S130iThe land parcel is marked with the number PmSpecific gravity G of different elements iniAnd adding and summing, namely dividing the sum into an m space block set according to the proportion relation after summing, wherein the m space block set is a first space block and a second space block which have the summing proportion sequenced from large to small.
The target detection unit carries out topology processing on the spatial data information, so that the problem that the extraction deviation of the spatial data information is large due to the complex terrain can be avoided, the storage space of the spatial data can be effectively saved by utilizing the topology processing, and the relation between the spatial data can be reflected more simply; and one plot label at least corresponds to one vector relationship, the vector relationships of different plot labels are different, but the contained elements may be repeated, because the plots and the plots are in an adjacent state, the specific weight of the vector elements can be calculated to obtain which plot contains the most vector relationship, and the spatial plots containing the most vectors are sequenced, so that the spatial plots containing different levels of vectors are encrypted, and effective protection is achieved.
Further, the encryption processing data includes a spatial data similarity password, and the specific process of step S200 is as follows:
step S210: receiving the m-space block set information in step S140, and extracting the vector relationship of each space block in the m-space block set
Figure BDA0003336507440000041
Setting a known data vector password as a system;
step S220: respectively setting space data similarity passwords for each space block in the m space block set, wherein the space data similarity passwords comprise known data vector passwords set by a system
Figure BDA0003336507440000042
Spatial data vector password with unknown input unlock content
Figure BDA0003336507440000043
System setting known data vector cipher
Figure BDA0003336507440000044
Vector relationships for spatial blocks
Figure BDA0003336507440000045
Step S230: the cosine similarity is used for measuring the space data similarity, and the cosine similarity is used for measuring the known data vector password set by the system by calculating the cosine included angle between vectors
Figure BDA0003336507440000046
Spatial data vector password with unknown input unlock content
Figure BDA0003336507440000047
The similarity between them; is provided with
Figure BDA0003336507440000048
And
Figure BDA0003336507440000049
the similarity between them is
Figure BDA00033365074400000410
Then
Figure BDA00033365074400000411
The calculation is as follows:
Figure BDA00033365074400000412
wherein
Figure BDA00033365074400000413
The system is set to the modulus of the known data vector cipher,
Figure BDA00033365074400000414
modulo of a spatial data vector password that unlocks the content for unknown input.
Whether the password is correct or not can be effectively converted into mathematical calculation by utilizing cosine similarity, the password is set for vector data in each land block, and the vector proportion in each land block is different, so the difficulty degree of each vector corresponding to the password is different, the password can be solved only when the vector direction and the vector size preset by the system are completely the same as the input vector, the angle deviation is not equal to the unlocking triggering value set by the system, the unlocking triggering value set secondly is only possible, and the safety of the password is improved.
Further, the specific process in step S300 is as follows:
step S310: decrypting the data similarity password in step S220, and inputting the relation with the vector
Figure BDA00033365074400000415
Same space data vector cipher
Figure BDA00033365074400000416
So that
Figure BDA00033365074400000417
When the password is not decrypted, the password is decrypted;
step S320: extracting information in the space block after password unlocking to obtain initial space block data, wherein the initial space block data comprises adjacent points N in the corresponding space blockiWith a landmark block number PmTopological relation between them;
step S330: based on the adjacent point N in the spatial block in step S320iWith a landmark block number PmThe topological relation between the adjacent points N in all the space blocks is calculated by the density calculationiCalculation of average Density and Adjacent Point N within spatial BlockiPractice ofAnd (4) calculating the density.
The password is unlocked by utilizing the set trigger unlocking numerical value, only one numerical value improves the safety of data extraction, and the density value calculation after information extraction is used for further evaluating the acquired spatial data information, so that the resource utilization rate is improved.
Further, the specific process in step S330 is as follows:
step S331: establishing a two-dimensional coordinate axis for the irregular polygon image obtained in the step S110; the coordinates of a point on the irregular polygon image are (f)i,gi)；
Step S332: based on the coordinates (f) in step S331i,gi) Selecting the minimum Minf of the abscissa of the point on the irregular polygon imageiA point on the Y coordinate axis and the minimum value Ming of the ordinateiEstablishing a rectangular coordinate system for one point on the X coordinate axis; defining the minimum Minf of the abscissaiMaximum value Maxf to abscissaiIs the length of the rectangle, defines the minimum value Ming of the ordinateiMaximum value Maxg to ordinateiIs the width of the rectangle, the coordinates within the rectangle containing all coordinates (f) within the irregular polygoni,gi)；
The adjacent points of the irregular polygon are set on the X axis and the Y axis, quantitative analysis of the adjacent points of the polygon in a two-dimensional coordinate system is facilitated, all data of the irregular polygon belong to the first quadrant and are positive values, and convenience is brought to calculation.
Step S333: maximum value Maxf based on abscissa in step S332iMinimum MinfiAnd maximum value Maxg of ordinateiMinimum MingiCalculating the area of the rectangle as WMoment＝(Maxfi-Minfi)×(Maxgi-Mingi) The rectangular area WMomentQuartering to obtain four new rectangular areas W', recording adjacent points N in all spatial blocksiThe number of coordinates of (a) is set C, using the formula: determining a single new lower adjacency of the rectangular area W ═ C/WPoint NiThe average density γ of;
the rectangular area of the whole body to which the polygon belongs is solved to carry out four equal parts, so that the analysis of the space blocks contained in the local rectangular area is facilitated, and each new rectangular area is equal, so that the deviation of the calculation density caused by different denominators can be avoided.
Step S334: let the new rectangle in step S332 be RjJ ═ {1,2,3,4}, four new rectangles are named as rectangles R clockwise from the origin of coordinates1Rectangle R2Rectangle R3And rectangle R4And is provided with a rectangle R1Has vertex coordinates of (0,0) and (0, Maxg)i/2)、(Maxfi/2,Maxgi/2)、(Maxfi2, 0); rectangle R2Has a vertex coordinate of (0, Maxg)i/2)、(0,Maxgi)、(Maxfi/2,Maxgi)、(Maxfi/2,Maxgi2); rectangle R3Has a vertex coordinate of (Maxf)i/2,Maxgi/2)、(Maxfi/2,Maxgi)、(Maxfi,Maxgi)、(MaxfiMax/2); rectangle R4Has a vertex coordinate of (Maxf)i/2,0)、(Maxfi/2,Maxgi/2)、(Maxfi,Maxgi/2)、(Maxfi,0)；
The position coordinates of the four vertexes of the new rectangle are expressed, so that the analysis of the positions of the single adjacent points is facilitated, and the rectangle of the adjacent points is better determined.
Step S335: based on the adjacent point N in step S332iActual coordinates (f) ofi,gi) Respectively to the abscissa fiAnd ordinate giF is judged to be 0 < >i＜＜MaxfiAnd 0 < gi＜＜MaxgiObtaining the actual coordinates (f)i,gi) Belonging to a rectangle RjAnd storing the actual coordinates belonging to the rectangle into the corresponding rectangle set Rj(ii) a Using the formula:
Figure BDA0003336507440000051
get each new rectangle RjOfAdjacent point NiDensity value
Figure BDA0003336507440000052
And respectively comparing the abscissa and the ordinate of the adjacent point with the vertex coordinates of the four rectangles to judge the position of the adjacent point, and then respectively calculating the density in each rectangle to obtain the result, namely the density value of the adjacent point of the space block contained in each rectangle.
Step S336: the actual adjacent point N in step S335iDensity value
Figure BDA0003336507440000061
Respectively connected with adjacent points NiComparing the average density gamma of the two; if present, is
Figure BDA0003336507440000062
Then the rectangle RjData information of the contained space block is scheduled to
Figure BDA0003336507440000063
Is rectangular RjObtaining data information of a second space block from the contained space block; if each rectangle RjAre all internally provided with
Figure BDA0003336507440000064
The first space block data information is directly obtained without scheduling.
Setting spatial data information in a coordinate system, which is beneficial to quantitative analysis of adjacent points, wherein the adjacent points can be regarded as information key points in data acquisition, and when the information key points are densely distributed in a certain spatial area, the information key points can interfere with each other or data information of different resources cannot be reasonably utilized by other spaces, so that scheduling of the spatial data information is set; the scheduling basis is the density value of the space adjacent point, the acquired space data is integrally planned, the acquired space image data is not a regular image, so that the image regularization is favorable for performing density analysis on each space block to acquire an average value, the average value is used as a measurement standard, the calculation of average density values corresponding to different areas is favorable for performing effective measurement when facing space images under different conditions, the whole area is divided to calculate the actual density of each space, and information in a space block with too high density can be scheduled to a space block with lower density, so that the space data information resource is reasonably utilized, and the space block with lower density can continuously and effectively store data information.
Compared with the prior art, the invention has the following beneficial effects: the method firstly solves the problem that the spatial data is inaccurate in information due to external factors such as terrain and landform during extraction, secondly performs encryption processing on the spatial data in the transmission process, prevents the spatial data information from leaking, effectively protects the spatial data, and finally performs density analysis on the spatial information, reasonably utilizes useful resources, reduces the resource burden of high-density spatial blocks, and possibly causes interference due to complex and variable data.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a spatial data analysis scheduling system and method employing big data analysis in accordance with the present invention;
FIG. 2 is a flowchart illustrating the overall steps of the spatial data analysis scheduling system and method using big data analysis according to the present invention;
FIG. 3 is a method step of performing topology processing on the acquired spatial data information according to the spatial data analysis scheduling system and method applying big data analysis of the present invention;
FIG. 4 is a method step of spatial data similarity password setting for the spatial data analysis scheduling system and method applying big data analysis according to the present invention;
FIG. 5 is a method step of determining whether scheduling is required for the initial spatial block data according to the spatial data analysis scheduling system and method applying big data analysis of the present invention;
FIG. 6 is a step of a method for calculating the average density and actual density of spatial blocks in the system and method for spatial data analysis scheduling using big data analysis according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
Referring to fig. 1-6, the present invention provides the following technical solutions: the spatial data analysis scheduling system applying big data analysis comprises a spatial data acquisition module, a data receiving module, a spatial data analysis module and a result feedback module; the spatial data acquisition module is used for acquiring real-time spatial data of a target object and carrying out topology processing on the spatial data of the target object to obtain topology related data; the data receiving module is used for receiving the topology related data of the spatial data acquisition module and carrying out encryption processing to obtain encrypted spatial information; the spatial data analysis module is used for analyzing and processing the encrypted spatial information transmitted by the data receiving module; and the result feedback module is used for feeding back the analysis result of the spatial data analysis module to the resource database unit.
The spatial data acquisition module comprises a target detection unit and a resource database retrieval unit;
the target detection unit is used for realizing real-time acquisition of basic spatial data information of the target object in a mode that the spatial data acquisition module detects the target object;
the resource database retrieval unit is used for the spatial data acquisition module to extract the same spatial data information from the resource database unit in the result feedback module for direct utilization;
the spatial data acquisition module firstly utilizes the resource database retrieval unit to retrieve the target object, and if the resource database unit has the same target object, the spatial data is directly utilized; if the resource database unit does not have the same data as the target object, the spatial data acquisition module further starts the target detection unit to acquire the basic spatial data information of the target object.
The spatial data acquisition module is firstly provided with a resource database retrieval unit for effectively utilizing the existing data resources, so that the data processing time is saved; when the same data is not retrieved from the resource database, the data is acquired and then transmitted to the resource database unit after being processed, so that the data in the resource database is richer.
The spatial data analysis module comprises a data extraction unit and an analysis scheduling unit;
the data extraction unit decrypts the spatial data information encrypted by the data receiving unit and extracts the decrypted spatial data to obtain initial spatial block data; the analysis scheduling unit can simultaneously analyze the spatial information in the spatial data analysis module and the resource information in the resource database unit in the data feedback module, and perform data analysis scheduling on the data in the spatial data analysis module and the resource database unit in the data feedback module.
The spatial data analysis module is provided with a data extraction unit which can extract and utilize the effective information in the data receiving unit, and the analysis scheduling unit processes and analyzes the information in the data extraction unit and schedules the data to be scheduled, so that the data information in the spatial block can be shared.
The invention also provides a spatial data analysis scheduling method applying the big data analysis, which comprises the following steps:
step S100: acquiring spatial data information, and if the spatial data information is existing, directly referring to the spatial data information; if the space data information is not the existing space data information, topology processing is carried out after the space data information is acquired, and topology related data are obtained;
in step S100, topology processing is performed on the acquired spatial data information to obtain topology-related data, where the topology-related data includes a topology relationship, and the specific process is as follows:
step S110: set acquired nullThe inter-data information includes image structure information and plot distribution information, the image structure information is irregular polygon image formed by m plots with different areas adjacent to each other, m is natural number, and the plot distribution information includes the mark P of the plotmAdjacent point N of land blocks with different areasiAnd the vector relation between adjacent points
Figure BDA0003336507440000081
Figure BDA0003336507440000082
Step S120: performing topology processing on the image structure information and the parcel distribution information in the step S110 to obtain a topological relation, wherein the topological relation comprises a vector relation
Figure BDA0003336507440000083
And parcel number PmTopological relation between, i.e. PmCorrespond to
Figure BDA0003336507440000084
And adjacent point NiWith a landmark block number PmTopological relation between, i.e. PmCorresponding to the starting point Ni+dAnd endpoint Ni+eB, c, d and e all belong to natural numbers;
the spatial plot data information is converted into the relation between the vector and the plot, so that the spatial information in the plot can be effectively set in relation to the vector password standard and the proportion problem of the vectors contained in different plots can be judged;
step S130: relating the different vectors in step S120
Figure BDA0003336507440000085
And parcel number PmThe topological relation between the two is taken as a set A, the orientation quantity relation
Figure BDA0003336507440000086
Are respectively marked as a set BiRespectively calculating the specific gravities of different elements
Figure BDA0003336507440000087
Using the integral vector relation set in the space block as denominator, calculating the occupation ratio of different vectors to measure the total proportion of different vectors contained in different blocks, so as to obviously distinguish the position information of the land block, i.e. preliminarily judging the number of adjacent land blocks and the proportion of contained space data information;
step S140: based on the specific gravity G of different elements in step S130iThe land parcel is marked with the number PmSpecific gravity G of different elements iniAnd adding and summing, namely dividing the sum into an m space block set according to the proportion relation after summing, wherein the m space block set is a first space block and a second space block which have the summing proportion sequenced from large to small.
For example, the irregular polygon image in the acquired image structure information is a pentagon image, and the pentagon image is formed by mutually adjoining 4 land blocks with different areas; the land parcel labels are respectively P1、P2、P3、P4Land parcel P1The corresponding vector relationship is
Figure BDA0003336507440000091
Land parcel P2The corresponding vector relationship is
Figure BDA0003336507440000092
Land parcel P3The corresponding vector relationship is
Figure BDA0003336507440000093
Land parcel P4The corresponding vector relationship is
Figure BDA0003336507440000094
The set A is
Figure BDA0003336507440000095
Collection
Figure BDA0003336507440000096
Collection
Figure BDA0003336507440000097
Collection
Figure BDA0003336507440000098
Collection
Figure BDA0003336507440000099
Collection
Figure BDA00033365074400000910
Collection
Figure BDA00033365074400000911
Figure BDA00033365074400000912
Collection
Figure BDA00033365074400000913
Collection
Figure BDA00033365074400000914
Collection
Figure BDA00033365074400000915
Collection
Figure BDA00033365074400000916
Collection
Figure BDA00033365074400000917
Computing
Figure BDA00033365074400000918
G3＝G4＝G7＝G82/11, the weights of all elements in each plot are added to obtain the weight of each plot and the weight is sorted.
The target detection unit carries out topology processing on the spatial data information, so that the problem that the extraction deviation of the spatial data information is large due to the complex terrain can be avoided, the storage space of the spatial data can be effectively saved by utilizing the topology processing, and the relation between the spatial data can be reflected more simply; and one plot label at least corresponds to one vector relationship, the vector relationships of different plot labels are different, but the contained elements may be repeated, because the plots and the plots are in an adjacent state, the specific weight of the vector elements can be calculated to obtain which plot contains the most vector relationship, and the spatial plots containing the most vectors are sequenced, so that the spatial plots containing different levels of vectors are encrypted, and effective protection is achieved.
Step S200: continuously encrypting the topology related data obtained by processing in the step S100 to obtain encrypted processing data; the encryption processing data includes a spatial data similarity password, and the specific process of step S200 is as follows:
step S210: receiving the m-space block set information in step S140, and extracting the vector relationship of each space block in the m-space block set
Figure BDA00033365074400000919
Setting a known data vector password as a system;
step S220: respectively setting space data similarity passwords for each space block in the m space block set, wherein the space data similarity passwords comprise known data vector passwords set by a system
Figure BDA00033365074400000920
Spatial data vector password with unknown input unlock content
Figure BDA00033365074400000921
System setting known data vector cipher
Figure BDA00033365074400000922
Vector relationships for spatial blocks
Figure BDA00033365074400000923
Step S230: the similarity of spatial data is measured by cosine similarity, and the cosine similarity is measured by calculating cosine included angle between vectorsUniformly setting known data vector cipher
Figure BDA00033365074400000924
Spatial data vector password with unknown input unlock content
Figure BDA00033365074400000925
The similarity between them; is provided with
Figure BDA00033365074400000926
And
Figure BDA00033365074400000927
the similarity between them is
Figure BDA00033365074400000928
Then
Figure BDA00033365074400000929
The calculation is as follows:
Figure BDA00033365074400000930
wherein
Figure BDA0003336507440000101
The system is set to the modulus of the known data vector cipher,
Figure BDA0003336507440000102
modulo of a spatial data vector password that unlocks the content for unknown input.
For example: when vector
Figure BDA0003336507440000103
With input unlock vector password
Figure BDA0003336507440000104
Directions being the same and coincident, then representing vectors
Figure BDA0003336507440000105
With input unlock vector password
Figure BDA0003336507440000106
The included angle of (A) is 0, namely the input password completely accords with the set password;
whether the password is correct or not can be effectively converted into mathematical calculation by utilizing cosine similarity, the password is set for vector data in each land block, and the vector proportion in each land block is different, so the difficulty degree of each vector corresponding to the password is different, the password can be solved only when the vector direction and the vector size preset by the system are completely the same as the input vector, the angle deviation is not equal to the unlocking triggering value set by the system, the unlocking triggering value set secondly is only possible, and the safety of the password is improved.
Step S300: decrypting the encrypted data obtained in the step S200, and extracting information in the topology related data to obtain initial space block data; carrying out spatial analysis on the initial spatial block data to judge whether scheduling is needed; obtaining second space block data information through analysis scheduling processing, wherein data which is not analyzed and scheduled to be processed is first space block data information; the specific process in step S300 is as follows:
step S310: decrypting the data similarity password in step S220, and inputting the relation with the vector
Figure BDA0003336507440000107
Same space data vector cipher
Figure BDA0003336507440000108
So that
Figure BDA0003336507440000109
When the password is not decrypted, the password is decrypted;
step S320: extracting information in the space block after password unlocking to obtain initial space block data, wherein the initial space block data comprises adjacent points N in the corresponding space blockiWith a landmark block number PmRubbing with each otherFlapping relation;
step S330: based on the adjacent point N in the spatial block in step S320iWith a landmark block number PmThe topological relation between the adjacent points N in all the space blocks is calculated by the density calculationiCalculation of average Density and Adjacent Point N within spatial BlockiAnd (4) calculating the actual density.
The password is unlocked by utilizing the set trigger unlocking numerical value, only one numerical value improves the safety of data extraction, and the density value calculation after information extraction is used for further evaluating the acquired spatial data information, so that the resource utilization rate is improved.
The specific process in step S330 is as follows:
step S331: establishing a two-dimensional coordinate axis for the irregular polygon image obtained in the step S110; the coordinates of a point on the irregular polygon image are (f)i,gi)；
Step S332: based on the coordinates (f) in step S331i,gi) Selecting the minimum Minf of the abscissa of the point on the irregular polygon imageiA point on the Y coordinate axis and the minimum value Ming of the ordinateiEstablishing a rectangular coordinate system for one point on the X coordinate axis; defining the minimum Minf of the abscissaiMaximum value Maxf to abscissaiIs the length of the rectangle, defines the minimum value Ming of the ordinateiMaximum value Maxg to ordinateiIs the width of the rectangle, the coordinates within the rectangle containing all coordinates (f) within the irregular polygoni,gi)；
The adjacent points of the irregular polygon are set on the X axis and the Y axis, quantitative analysis of the adjacent points of the polygon in a two-dimensional coordinate system is facilitated, all data of the irregular polygon belong to the first quadrant and are positive values, and convenience is brought to calculation.
Step S333: maximum value Maxf based on abscissa in step S332iMinimum MinfiAnd maximum value Maxg of ordinateiMinimum MingiCalculating the area of the rectangle as WMoment＝(Maxfi-Minfi)×(Maxgi-Mingi) The rectangular area WMomentQuartering to obtain four new rectangular areas W', recording adjacent points N in all spatial blocksiThe number of coordinates of (a) is set C, using the formula: obtaining a single new lower adjacent point N with a rectangular area W ═ C/WiThe average density γ of;
the rectangular area of the whole body to which the polygon belongs is solved to carry out four equal parts, so that the analysis of the space blocks contained in the local rectangular area is facilitated, and each new rectangular area is equal, so that the deviation of the calculation density caused by different denominators can be avoided.
Step S334: let the new rectangle in step S332 be RjJ ═ {1,2,3,4}, four new rectangles are named as rectangles R clockwise from the origin of coordinates1Rectangle R2Rectangle R3And rectangle R4And is provided with a rectangle R1Has vertex coordinates of (0,0) and (0, Maxg)i/2)、(Maxfi/2,Maxgi/2)、(Maxfi2, 0); rectangle R2Has a vertex coordinate of (0, Maxg)i/2)、(0,Maxgi)、(Maxfi/2,Maxgi)、(Maxfi/2,Maxgi2); rectangle R3Has a vertex coordinate of (Maxf)i/2,Maxgi/2)、(Maxfi/2,Maxgi)、(Maxfi,Maxgi)、(MaxfiMax/2); rectangle R4Has a vertex coordinate of (Maxf)i/2,0)、(Maxfi/2,Maxgi/2)、(Maxfi,Maxgi/2)、(Maxfi,0)；
The position coordinates of the four vertexes of the new rectangle are expressed, so that the analysis of the positions of the single adjacent points is facilitated, and the rectangle of the adjacent points is better determined.
Step S335: based on the adjacent point N in step S332iActual coordinates (f) ofi,gi) Respectively to the abscissa fiAnd ordinate giF is judged to be 0 < >i＜＜MaxfiAnd 0 < gi＜＜MaxgiObtaining the actual coordinates (f)i,gi) Belonging to a rectangle RjAnd storing the actual coordinates belonging to the rectangle into the corresponding rectangle set Pj(ii) a Using the formula:
Figure BDA0003336507440000111
get each new rectangle RjActual adjacent point NiDensity value
Figure BDA0003336507440000112
And respectively comparing the abscissa and the ordinate of the adjacent point with the vertex coordinates of the four rectangles to judge the position of the adjacent point, and then respectively calculating the density in each rectangle to obtain the result, namely the density value of the adjacent point of the space block contained in each rectangle.
Step S336: the actual adjacent point N in step S335iDensity value
Figure BDA0003336507440000121
Respectively connected with adjacent points NiComparing the average density gamma of the two; if present, is
Figure BDA0003336507440000122
Then the rectangle RjData information of the contained space block is scheduled to
Figure BDA0003336507440000123
Is rectangular RjObtaining data information of a second space block from the contained space block; if each rectangle RjAre all internally provided with
Figure BDA0003336507440000124
The first space block data information is directly obtained without scheduling.
For example, an XY axis coordinate system is established, and the coordinates of the adjacent points of the pentagon are respectively: n is a radical of1(0,2),N2(1.5,1),N3(1.5,4.5),N4(2,2),N5(3.5,0),N6(3.5,5),N7(4,1),N8(5,3.5), four vertices of the rectangle are (0,0), (0,5), (5,5) and (0,5)5,0), the area W of the rectangleMomentNew rectangle area W 'is 6.25, calculated average density γ C/W' is 8/6.25 is 1.28; memory rectangle R1Has coordinates of (0,0), (0,2.5), (2.5,0), and a rectangle R2Has coordinates of (0,2.5), (0,5), (2.5 ), and a rectangle R3Has coordinates of (2.5 ), (2.5,5), (5,2.5), and a rectangle R4The coordinates of (2.5 ), (2.5,0), (5,2.5) and (5,0), comparing the adjacent point with the coordinates of the four rectangles, judging the rectangle to which the adjacent point belongs, if P is1＝{N1,N2,N4}，P2＝{N3}，P3＝{N6,N8}，P4＝{N5,N7}, calculating
Figure BDA0003336507440000125
Figure BDA0003336507440000126
And if the actual densities are all smaller than the average density, the data of the first space block does not need to be scheduled.
Setting spatial data information in a coordinate system, which is beneficial to quantitative analysis of adjacent points, wherein the adjacent points can be regarded as information key points in data acquisition, and when the information key points are densely distributed in a certain spatial area, the information key points can interfere with each other or data information of different resources cannot be reasonably utilized by other spaces, so that scheduling of the spatial data information is set; the scheduling basis is the density value of the space adjacent point, the acquired space data is integrally planned, the acquired space image data is not a regular image, so that the image regularization is favorable for performing density analysis on each space block to acquire an average value, the average value is used as a measurement standard, the calculation of average density values corresponding to different areas is favorable for performing effective measurement when facing space images under different conditions, the whole area is divided to calculate the actual density of each space, and information in a space block with too high density can be scheduled to a space block with lower density, so that the space data information resource is reasonably utilized, and the space block with lower density can continuously and effectively store data information.
Step S400: comparing the first spatial block data or the second spatial block data in the step S300 with the resource database, respectively; if the first spatial block data or the second spatial block data can be matched with the same spatial data, recording the frequency of the spatial data; and if the first spatial block data or the second spatial block data are not matched with the same spatial data, the resource database updates and records the first spatial block data or the second spatial block data.
The importance degree of the spatial information can be effectively judged by recording the frequency of the matched spatial data, and the resource abundance of the database can be increased by updating and recording the unmatched spatial information, so that the aim of reasonably sharing the resources is fulfilled.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. The spatial data analysis scheduling system applying big data analysis is characterized by comprising a spatial data acquisition module, a data receiving module, a spatial data analysis module and a result feedback module; the spatial data acquisition module is used for acquiring real-time spatial data of a target object and carrying out topology processing on the spatial data of the target object to obtain topology related data; the data receiving module is used for receiving the topology related data of the spatial data acquisition module and carrying out encryption processing to obtain encrypted spatial information; the spatial data analysis module is used for analyzing and processing the encrypted spatial information transmitted by the data receiving module; and the result feedback module is used for feeding back the result analyzed by the spatial data analysis module to the resource database unit.
2. The system and method for spatial data analysis scheduling using big data analysis according to claim 1, wherein: the spatial data acquisition module comprises a target detection unit and a resource database retrieval unit;
the target detection unit is used for acquiring basic spatial data information of a target object in real time in a mode that the spatial data acquisition module detects the target object;
the resource database retrieval unit is used for the spatial data acquisition module to extract the same spatial data information from the resource database unit in the result feedback module for direct utilization;
the spatial data acquisition module firstly utilizes the resource database retrieval unit to retrieve the target object, and if the resource database unit has the same target object, the spatial data is directly utilized; if the resource database unit does not have the same data as the target object, the spatial data acquisition module further starts the target detection unit to acquire the basic spatial data information of the target object.
3. The system and method for spatial data analysis scheduling using big data analysis according to claim 1, wherein: the spatial data analysis module comprises a data extraction unit and an analysis scheduling unit;
the data extraction unit decrypts the spatial data information encrypted by the data receiving unit and extracts the decrypted spatial data to obtain initial spatial block data; the analysis scheduling unit may simultaneously analyze the spatial information in the spatial data analysis module and the resource information in the resource database unit in the data feedback module, and perform data analysis scheduling on data in the spatial data analysis module and the resource database unit in the data feedback module.
4. The spatial data analysis scheduling method applying big data analysis is characterized by comprising the following steps of:
step S100: acquiring spatial data information, and if the spatial data information is existing, directly referring to the spatial data information; if the space data information is not the existing space data information, topology processing is carried out after the space data information is acquired, and topology related data are obtained;
step S200: continuously encrypting the topology related data obtained by processing in the step S100 to obtain encrypted processing data;
step S300: decrypting the encrypted data obtained in the step S200, and extracting information in the topology related data to obtain initial space block data; carrying out spatial analysis on the initial spatial block data to judge whether scheduling is needed; obtaining second space block data information through analysis scheduling processing, wherein data which is not analyzed and scheduled to be processed is first space block data information;
step S400: comparing the first spatial block data or the second spatial block data in the step S300 with a resource database, respectively; if the first spatial block data or the second spatial block data can be matched with the same spatial data, recording the frequency of the spatial data; and if the first spatial block data or the second spatial block data are not matched with the same spatial data, the resource database updates and records the first spatial block data or the second spatial block data.
5. The spatial data analysis scheduling method applying big data analysis according to claim 4, wherein: in the step S100, topology processing is performed on the acquired spatial data information to obtain topology-related data, where the topology-related data includes a topology relationship, and the specific process is as follows:
step S110: the acquired spatial data information comprises image structure information and land distribution information, wherein the image structure information is an irregular polygonal image formed by mutually adjoining m land parcels with different areas, m is a natural number, and the land distribution information comprises a mark P of the land parcelmAdjacent point N of land blocks with different areasiAnd the vector relation between adjacent points
Figure FDA0003336507430000026
i＝{1,2,3......n}；
Step S120: performing topology processing on the image structure information and the parcel distribution information in the step S110 to obtain the topological relation, where the topological relation includes a vector relation
Figure FDA0003336507430000021
And parcel number PmTopological relation between, i.e. PmCorrespond to
Figure FDA0003336507430000027
And adjacent point NiWith a landmark block number PmTopological relation between, i.e. PmCorresponding to the starting point Ni+dAnd endpoint Ni+eB, c, d and e all belong to natural numbers;
step S130: relating the different vectors in the step S120
Figure FDA0003336507430000028
And parcel number PmThe topological relation between the two is taken as a set A, the orientation quantity relation
Figure FDA0003336507430000029
Are respectively marked as a set BiRespectively calculating the specific gravities of different elements
Figure FDA0003336507430000025
Step S140: based on the specific gravity G of different elements in the step S130iThe land parcel is marked with the number PmSpecific gravity G of different elements iniAnd adding and summing, namely dividing the sum into an m space block set according to the proportion relation after summing, wherein the m space block set is a first space block and a second space block which have the summing proportion sequenced from large to small.
6. The spatial data analysis scheduling method applying big data analysis according to claim 5, wherein: the encryption processing data includes a spatial data similarity password, and the specific process of step S200 is as follows:
step S210: receiving the m-space block set information in step S140, and extracting the vector relationship of each space block in the m-space block set
Figure FDA00033365074300000316
Setting a known data vector password as a system;
step S220: respectively setting space data similarity passwords for each space block in the m space block set, wherein the space data similarity passwords comprise known data vector passwords set by a system
Figure FDA0003336507430000034
Spatial data vector password with unknown input unlock content
Figure FDA0003336507430000035
The system sets a known data vector cipher
Figure FDA0003336507430000036
Vector relationships for spatial blocks
Figure FDA0003336507430000037
Step S230: using cosine similarity metric spaceData similarity, cosine similarity measures the system setting known data vector password by calculating the cosine angle between vectors
Figure FDA0003336507430000038
Spatial data vector password with unknown input unlock content
Figure FDA0003336507430000039
The similarity between them; is provided with
Figure FDA00033365074300000310
And
Figure FDA00033365074300000311
the similarity between them is
Figure FDA00033365074300000312
Then
Figure FDA00033365074300000313
The calculation is as follows:
Figure FDA0003336507430000031
7. the spatial data analysis scheduling method applying big data analysis according to claim 6, wherein: the specific process in step S300 is as follows:
step S310: decrypting the data similarity password in the step S220, and inputting the relation with the vector
Figure FDA00033365074300000317
Same space data vector cipher
Figure FDA00033365074300000315
So that
Figure FDA0003336507430000032
When the password is not decrypted, the password is decrypted;
step S320: extracting information in the space block after password unlocking to obtain initial space block data, wherein the initial space block data comprises adjacent points N in the corresponding space blockiWith a landmark block number PmTopological relation between them;
step S330: based on the adjacent point N in the spatial block in the step S320iWith a landmark block number PmThe topological relation between the adjacent points N in all the space blocks is calculated by the density calculationiCalculation of average Density and Adjacent Point N within spatial BlockiAnd (4) calculating the actual density.
8. The spatial data analysis scheduling method applying big data analysis according to claim 7, wherein: the specific process in step S330 is as follows:
step S331: establishing a two-dimensional coordinate axis for the irregular polygon image obtained in the step S110; the coordinates of a point on the irregular polygon image are set to (f)i,gi)；
Step S332: based on the coordinates (f) in the step S331i,gi) Selecting the minimum Minf of the abscissa of the point on the irregular polygon imageiA point on the Y coordinate axis and the minimum value Ming of the ordinateiEstablishing a rectangular coordinate system for one point on the X coordinate axis; defining the minimum Minf of the abscissaiMaximum value Maxf to abscissaiIs the length of the rectangle, defines the minimum value Ming of the ordinateiMaximum value Maxg to ordinateiIs the width of a rectangle, the coordinates within the rectangle containing all coordinates (f) within an irregular polygoni,gi)；
Step S333: a maximum value Maxf based on the abscissa in the step S332iMinimum MinfiAnd maximum value Maxg of ordinateiMinimum MingiCalculating the area of the rectangle as WMoment＝(Maxfi-Minfi)×(Maxgi-Mingi) The rectangular area WMomentQuartering to obtain four new rectangular areas W', recording adjacent points N in all spatial blocksiThe number of coordinates of (a) is set C, using the formula: obtaining a single new lower adjacent point N with a rectangular area W ═ C/WiThe average density γ of;
step S334: note that the new rectangle in step S332 is RjJ ═ {1,2,3,4}, four new rectangles are named as rectangles R clockwise from the origin of coordinates1Rectangle R2Rectangle R3And rectangle R4And is provided with a rectangle R1Has vertex coordinates of (0,0) and (0, Maxg)i/2)、(Maxfi/2,Maxgi/2)、(Maxfi2, 0); rectangle R2Has a vertex coordinate of (0, Maxg)i/2)、(0,Maxgi)、(Maxfi/2,Maxgi)、(Maxfi/2,Maxgi2); rectangle R3Has a vertex coordinate of (Maxf)i/2,Maxgi/2)、(Maxfi/2,Maxgi)、(Maxfi,Maxgi)、(MaxfiMax/2); rectangle R4Has a vertex coordinate of (Maxf)i/2,0)、(Maxfi/2,Maxgi/2)、(Maxfi,Maxgi/2)、(Maxfi,0)；
Step S335: based on the adjacent point N in the step S332iActual coordinates (f) ofi,gi) Respectively to the abscissa fiAnd ordinate giF is judged to be 0 < >i＜＜MaxfiAnd 0 < gi＜＜MaxgiObtaining the actual coordinates (f)i,gi) Belonging to a rectangle RjAnd storing the actual coordinates belonging to the rectangle into the corresponding rectangle set Pj(ii) a Using the formula:
Figure FDA0003336507430000041
get each new rectangle RjActual adjacent point NiDensity value
Figure FDA0003336507430000042
Step S336: the actual adjacent point N in the step S335 is determinediDensity value
Figure FDA0003336507430000043
Respectively connected with adjacent points NiComparing the average density gamma of the two; if present, is
Figure FDA0003336507430000044
Then the rectangle RjData information of the contained space block is scheduled to
Figure FDA0003336507430000045
Is rectangular RjObtaining data information of a second space block from the contained space block; if each rectangle RjAre all internally provided with
Figure FDA0003336507430000046
The first space block data information is directly obtained without scheduling.
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