CN105912574B - A kind of Spatial data query verification method that multi-user determines - Google Patents
A kind of Spatial data query verification method that multi-user determines Download PDFInfo
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Abstract
The invention discloses the Spatial data query verification methods that a kind of multi-user determines, including spatial data handling step S1, inquiry data to give step S2, inquiry initialization step S3, data query step S4 and data authentication step S5；The present invention provides the Spatial data query verification methods that a kind of multi-user determines, in the scene that can be applied to multi-user's decision, the user of one group obtains a good result according to respective place and hobby, one kind is provided effectively for the inquiring and authenticating of the spatial data of the big data quantity based on multi-user under big data environment, completely, correct method.
Description
Technical field
The present invention relates to the Spatial data query verification methods that a kind of multi-user determines.
Background technique
With universal based on venue services, various tissues, company even personal (referred to as DOs) starts to collect and possess
A large amount of spatial data；The so a large amount of spatial data of processing is that data owner brings technology and economically huge is chosen
War；In the management and query process of data, DOs tends to for data to be contracted out to third party, rather than establishes themselves
Technical team and basic framework；Because alleviating great burden of the DOs in data management processes, the potential offer of data outsourcing
More effective and save the cost service；In data sub-contract management, the data of oneself are delegated to ISP (SP), SP by D0
It establishes and indexes for data, the query requirement of feedback user；Because server is in the administration authority scope of D0, server can be with
Distort the query result of return, therefore, user be necessary to ensure that query result meet three conditions: validity, correctness, completely
Property.
Number of patent application: CN201510101056.0 discloses a kind of space querying integrality based on Merkle tree construction
Verification method, this method propose support and have inquired on existing adaptive H ilbert curve quarter tree node generated
The construction method of the Merkle tree construction of integrity verification, and the integrity verification method of range query and KNN inquiry is proposed, make
Integrity verification result provided by the present invention the case where there is no wrong reports with failing to report so that ISP is difficult to pair
The query result of user carries out malice change；The method of the present invention can mention under the service mode of spatial data outsourcing for user
Integrity verification function is inquired with KNN for efficiently verifying structural generation function and true scope inquiry, to guarantee sky
Between query service quality.A kind of space querying integrity verification method of Merkle tree construction is only provided, is had to data
Effect property is related to correctness there is no related, is also not carried out the feedback that inquiry is determined to multi-user.
Number of patent application: CN201310132565.0 discloses a kind of data dynamic operation verifiability based on Hash tree
Method is connected with auditing by third party mechanism TPA three parts by communication network by user USER, cloud computation data center CDC
Composition；One side of proposition that USER is requested as data storage service, it is desirable to by one's own data file storage to cloud computing
Among the cloud storage space of data center；USER is also possible to enterprise customer either personal user；CDC is responsible for response and uses
The data storage service at family is requested, oneself huge data center is arrived in the data file storage of user according to certain rules,
And the management service of data file is responsible for；TPA is as reliable auditing by third party mechanism, by the commission of USER to being stored in
The data file of CDC data center carries out the examination of integrality and consistency；The present invention solve under cloud computing environment for
The validation problem of user data file integrality and consistency；The verifying to the integrality and consistency of data is only provided, not
There is the feedback realized and determine inquiry to multi-user.
Existing data query method such as k-Nearest Neighbors inquiry and Skyline inquiry all can not be applied to
In the scene that multi-user determines, i.e., the user of one group cannot obtain a best result according to respective place and hobby.
Summary of the invention
The Spatial data query determined it is an object of the invention to overcome the deficiencies of the prior art and provide a kind of multi-user is tested
Card method can be applied in the scene that multi-user determines, the user of a group obtains one according to respective place and hobby
Well as a result, query result has effectively, completely, correct advantage.
The purpose of the present invention is achieved through the following technical solutions: the Spatial data query that a kind of multi-user determines is tested
Card method, comprising the following steps:
S1. spatial data handling: the site objects of spatial data are grouped according to space attribute, each grouping conduct
One external square of minimum forms MR-tree using each external square of minimum as a leaf node, generates and correspond to for each leaf node
Index and store corresponding numerical value；MR-tree is a kind of Merkle Hash tree comprising multiple intermediate nodes, intermediate node
In include one or more leaf nodes, these leaf nodes are all its child nodes；
S2. inquiry data are given: given quaternary array Q={ U, W, P, k }, U represent the user group of inquiry, and P represents user
The ground point set of group；W represents user and defines hobby set, and k is the site objects quantity that user needs；
U={ u0, u1... ui..., un-1, u in formulai, indicate that i+1 user in user group U, n indicate in user group U
The number of user,
P={ p0, p1..., pi..., ps, pi, i=1,2 in formula, 3.....s indicate i-th of ground in ground point set P
Point object, s indicate the number of site objects in ground point set P,
W={ w0, w1..., wi..., wn-1, w in formulai, i=1,2,3.....n-1, indicate i-th of user-defined love
It is good；
wi={ wI, 0, wI, 1..., wI, m, wI, 0The space attribute weight of hobby, { w are defined for i-th of userI, 1...,
wI, mIt is the non-spatial attributes weight that i-th of user defines hobby.
It is defined as follows concept:
Non-space dominates: given two place p and p ' are provided if p ' is poor unlike p on all non-spatial attributes
P ' non-space dominates p, symbolically are as follows:
Space dominates: a given user set U ' and two place p and p ', if p ' non-space dominates p, and p ' ratio
P will be close apart from all users, we then provide above there is p ' domination p in user's set U '；
Non-space weight dominates: the weight of the non-spatial attributes of hobby the set W, p of given one place p and all users
And expression are as follows: ◇ (p):
Two place p and p ' are given, if ◇ (p ') is big unlike ◇ (p), then it is assumed that p ' non-space weight dominates p；
Space weight dominates: the power of hobby the set W, p of given two place p, p ' and all users to all users
Weight are as follows:
If adsum(p ') is unlike adsum(p) big, it is believed that the space p ' weight dominates p；
Weight dominates: a given user collects U, all consumer taste collection W and two place p ' and p, if p ' is not propped up
With p, p ' non-space weight dominates p, and the space p ' weight dominates p, then it is assumed that collects p ' weight on U in user and dominates p；
The added value of one node: vsum(p)=adsum(p)+◇(p)；
The added value of one range:
After given Q={ U, W, P, k }, desired result is that final k result is stored in result set R, and is met not by P
Other places dominate or weight dominates；
S3. inquiry initialization: query results R and identifying object collection VO is defined, and by query results R and identifying object
Collection VO is initialized as sky；Intermediate node where the calculating each grouping of MR-tree and the leaf node v where each placesum；
S4. it data query: defines a heap H and comes with vsumAscending order scan the node of MR-tree: first by MR-tree's
Root node is put into heap H and identifying object collection VO, is scanned each time, all pops up the heap top element of heap H, is carried out to heap top element
Inspection is handled identifying object collection VO, query results R and heap H according to inspection result, and until heap H is sky, completion is primary
Basic query is checked and is counted in R | | R | | the size with user's defined parameters k:
(1) if | | R | | be greater than k, by place according to vsumNumerical values recited sequence, k value obtains final result collection before taking
R′；
(2) if | | R | | be equal to k, directly using R as final result collection R '；
(3) if | | R | | be less than k, carry out next round inquiry, until | | R | | be not less than k:
S5. data verification: according to final result collection R ' and when VO, verifying the validity of final result collection R ', integrality and
Correctness.
The numerical value of each leaf node storage described in step S1 are as follows:
H=hash (p1|p2|...|pi...|pt),
P in formulai, i=1,2,3.....t indicate i-th of site objects in the corresponding grouping of leaf node, and ' | ' indicates over the ground
Point object carries out cascade operation, and t indicates the number of site objects in the leaf node.
The MR-tree has multiple intermediate nodes, and each intermediate node includes one or more leaf node.
If one place p is dominated or weight dominates another place p and has: vsum(p) < vsum(P')；If one place
P, which is dominated, or weight dominates area s must then have: vsum(p) < vsum(S), any one location-based vsumIt is traversed by ascending order
MR-tree, the place for being added to candidate result collection R will be put into final result certainly and concentrate R '.
Heap top element is checked in step S4 and carries out respective handling and is divided into following four situation:
(1) if heap top element is dominated by the element in existing R or weight dominates, heap top element is put into VO；
(2) if heap top element is not dominated by the element in existing R or weight dominates, and heap top element is MR-
Its child node is added in heap H by the intermediate node of tree；
(3) if heap top element is not dominated by the element in existing R or weight dominates, and heap top element is leaf segment
Point, all places for including by it are added in heap H；
(4) if heap top element is not dominated by the element in existing R or weight dominates；Heap top element is place pair
As it is just directly placed into R.
The step S5 includes following sub-step:
S51. it checks and prepares: result set R and corresponding VO being grouped and are ordered as verifying in each round inquiry and is made
It is good to prepare, when poll-final, three kinds of data: site objects are contained in final identifying object collection VO；The number of minimum external square
Value intermediate node corresponding with the external square of minimum；The query information U and W of all users；
S52. validity check: checking the numerical value that root node is calculated according to final identifying object collection VO, then with former number
Value is compared, and judges whether the numerical value being calculated and former numerical value are identical:
(1) if identical, R ' has validity；
(2) if it is not the same, then R ' does not have validity；
S53. Correctness checking: each grouping G in the place number and R ' of final result collection R ' is checkedi, to judge R '
Correctness；
S54. integrity checking: each grouping G in R ' is checkediWith VO corresponding in VOi, to judge the integrality of R ', such as
G in fruit R 'iComplete, then R ' has integrality.
The step S53 includes following sub-step:
S531. check whether final result collection R ' meets condition one: final result collection R ' place number is defined equal to user
Parameter k:
(1) if meeting condition one, go to step S532；
(2) if being unsatisfactory for condition one, final result collection R ' does not have correctness；
S532. each grouping G in R ' is checkedi, judge GiWhether condition two: G is metiIn place do not dominate mutually,
Not mutual weight dominates:
(1) if meeting condition two, GiHas correctness, go to step S533；
(2) if being unsatisfactory for condition two, final result collection R ' does not have correctness:
S533. each site objects in final result collection R ' are checked, judge whether to meet condition three: high-grade grouping
Site objects can dominate or weight dominates the site objects of inferior grade grouping:
(1) if meeting condition three, final result collection R ' has correctness；
(2) if being unsatisfactory for condition three, final result collection R ' does not have correctness.
The step S54 includes following sub-step:
S541. judge VOiIn site objects, if be all satisfied condition four: by corresponding GiIn site objects dominate or power
It dominates again；
(1) if meeting condition four, G is corresponded toiHas integrality；
(2) if being unsatisfactory for condition four, go to step S542；
S542. judge VOiIn site objects, if meet condition five: the site objects for being unsatisfactory for condition four,
Added value is higher than GiIn all site objects added value:
(1) if meeting condition five, G is corresponded toiHas integrality；
(2) if being unsatisfactory for condition five, G is corresponded toiDo not have integrality.
Spatial data query and verifying based on multi-user, make query process and prove generating result then it is each the result is that
Effectively, correctly and completely, meet the data owner's for needing to handle high amount spatial data under big data environment
Demand, and Spatial data query and verification method based on Merkle Hash tree are innovatively proposed, improve inquiry speed
Spend and reduce the size for returning the result collection；And highly effective rate and robustness under different parameters setting.
The beneficial effects of the present invention are: being looking into for the spatial data of the big data quantity based on multi-user under big data environment
It askes and verifying provides one kind effectively, completely, correct method, and have faster than original general data querying method
Inquiry velocity, the shorter response time and it is smaller return the result collection, reduce the spending of user.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is the scanning process figure during data query of the invention；
Fig. 3 is one schematic diagram of the embodiment of the present invention.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing, but protection scope of the present invention is not limited to
It is as described below.
As shown in Figure 1, the Spatial data query verification method that a kind of multi-user determines, comprising the following steps:
S1. spatial data handling: the site objects of spatial data are grouped according to space attribute, each grouping conduct
One external square of minimum forms MR-tree using each external square of minimum as a leaf node, generates and correspond to for each leaf node
Index and store corresponding numerical value；MR-tree is a kind of Merkle Hash tree comprising multiple intermediate nodes, intermediate node
In include one or more leaf nodes, these leaf nodes are all its child nodes；
S2. inquiry data are given: given quaternary array Q={ U, W, P, k }, U represent the user group of inquiry, and P represents user
The ground point set of group；W represents user and defines hobby set, and k is the site objects quantity that user needs；
U={ u0, ui... ui..., un-1, u in formulai, indicate that i+1 user in user group U, n indicate in user group U
The number of user,
P={ p0, p1..., pi..., ps, p in formulai, i=1,2,3.....s, indicate i-th of place in ground point set P
Object, s indicate the number of site objects in ground point set P,
W={ w0, w1..., wi..., wn-1, w in formulai, i=1,2,3.....n-1, indicate i-th of user-defined love
It is good；
wi={ wI, 0, wI, 1..., wI, m, wI, 0The space attribute weight of hobby, { w are defined for i-th of userI, 1...,
wI, mIt is the non-spatial attributes weight that i-th of user defines hobby.
It is defined as follows concept:
Non-space dominates: given two place p and p ' are provided if p ' is poor unlike p on all non-spatial attributes
P ' non-space dominates p, symbolically are as follows:
Space dominates: a given user set U ' and two place p and p ', if p ' non-space dominates p, and p ' ratio
P will be close apart from all users, we then provide above there is p ' domination p in user's set U '；
Non-space weight dominates: the weight of the non-spatial attributes of hobby the set W, p of given one place p and all users
And expression are as follows: ◇ (p):
Two place p and p ' are given, if ◇ (p ') is big unlike ◇ (p), then it is assumed that p ' non-space weight dominates p；
Space weight dominates: the power of hobby the set W, p of given two place p, p ' and all users to all users
Weight are as follows:
If adsum(p ') is unlike adsum(p) big, it is believed that the space p ' weight dominates p；
Weight dominates: a given user collects U, all consumer taste collection W and two place p ' and p, if p ' is not propped up
With p, p ' non-space weight dominates p, and the space p ' weight dominates p, then it is assumed that collects p ' weight on U in user and dominates p；
The added value of one node: vsum(p)=adsum(p)+◇(p)；
The added value of one range:
After given Q={ U, W, P, k }, desired result is that final k result is stored in result set R, and is met not by P
Other places dominate or weight dominates；
S3. inquiry initialization: query results R and identifying object collection V0 is defined, and by query results R and identifying object
Collection V0 is initialized as sky；Intermediate node where the calculating each grouping of MR-tree and the leaf node v where each placesum；
S4. it data query: defines a heap H and comes with vsumAscending order scan the node of MR-tree: as shown in Fig. 2, first
The root node of MR-tree is put into heap H and identifying object collection VO, is scanned each time, all pops up the heap top element of heap H, it is right
Heap top element, which is checked, is handled identifying object collection VO, query results R and heap H according to inspection result, until heap H is
Sky completes a basic query, checks and counts in R | | R | | the size with user's defined parameters k:
(1) if | | R | | be greater than k, by place according to vsumNumerical values recited sequence, k value obtains final result collection before taking
R′；
(2) if | | R | | be equal to k, directly using R as final result collection R '；
(3) if | | R | | be less than k, carry out next round inquiry, until | | R | | be not less than k；
S5. data verification: according to final result collection R ' and when VO, verifying the validity of final result collection R ', integrality and
Correctness.
The numerical value of each leaf node storage described in step S1 are as follows:
H=hash (p1|p2|...|pi...|pt),
P in formulai, i=1,2,3.....t indicate i-th of site objects in the corresponding grouping of leaf node, and ' | ' indicates over the ground
Point object carries out cascade operation, and t indicates the number of site objects in the leaf node.
The MR-tree has multiple intermediate nodes, and each intermediate node includes one or more leaf node.
If one place p is dominated or weight dominates another place p and has: vsum(p) < vsum(p')；If one place
P, which is dominated, or weight dominates area s must then have: vsum(p) < vsum(S), any one location-based vsumIt is traversed by ascending order
MR-tree, the place for being added to candidate result collection R will be put into final result certainly and concentrate R '.
As shown in Fig. 2, being checked in step S4 heap top element and carrying out respective handling and be divided into following four situation:
(1) if heap top element is dominated by the element in existing R or weight dominates, heap top element is put into VO；
(2) if heap top element is not dominated by the element in existing R or weight dominates, and heap top element is MR-
Its child node is added in heap H by the intermediate node of tree；
(3) if heap top element is not dominated by the element in existing R or weight dominates, and heap top element is leaf segment
Point, all places for including by it are added in heap H；
(4) if heap top element is not dominated by the element in existing R or weight dominates；Heap top element is place pair
As it is just directly placed into R.
The step S5 includes following sub-step:
S51. it checks and prepares: result set R and corresponding VO being grouped and are ordered as verifying in each round inquiry and is made
It is good to prepare, when poll-final, three kinds of data: site objects are contained in final identifying object collection V0；The number of minimum external square
Value intermediate node corresponding with the external square of minimum；The query information U and W of all users；
S52. validity check: checking the numerical value that root node is calculated according to final identifying object collection VO, then with former number
Value is compared, and judges whether the numerical value being calculated and former numerical value are identical:
(1) if identical, R ' has validity；
(2) if it is not the same, then R ' does not have validity；
S53. Correctness checking: each grouping G in the place number and R ' of final result collection R ' is checkedi, to judge R '
Correctness；
S54. integrity checking: each grouping G in R ' is checkediWith VO corresponding in VOi, to judge the integrality of R ', such as
G in fruit R 'iComplete, then R ' has integrality.
The step S53 includes following sub-step:
S531. check whether final result collection R ' meets condition one: final result collection R ' place number is defined equal to user
Parameter k:
(1) if meeting condition one, go to step S532；
(2) if being unsatisfactory for condition one, final result collection R ' does not have correctness；
S532. each grouping G in R ' is checkedi, judge GiWhether condition two: G is metiIn place do not dominate mutually,
Not mutual weight dominates:
(1) if meeting condition two, GiHas correctness, go to step S533；
(2) if being unsatisfactory for condition two, final result collection R ' does not have correctness:
S533. each site objects in final result collection R ' are checked, judge whether to meet condition three: high-grade grouping
Site objects can dominate or weight dominates the site objects of inferior grade grouping:
(1) if meeting condition three, final result collection R ' has correctness；
(2) if being unsatisfactory for condition three, final result collection R ' does not have correctness.
The step S54 includes following sub-step:
S541. judge VOiIn site objects, if be all satisfied condition four: by corresponding GiIn site objects dominate or power
It dominates again；
(1) if meeting condition four, G is corresponded toiHas integrality；
(2) if being unsatisfactory for condition four, go to step S542；
S542. judge VOiIn site objects, if meet condition five: the site objects for being unsatisfactory for condition four,
Added value is higher than GiIn all site objects added value:
(1) if meeting condition five, G is corresponded toiHas integrality；
(2) if being unsatisfactory for condition five, G is corresponded toiDo not have integrality.
Spatial data query and verifying based on multi-user, make query process and prove generating result then it is each the result is that
Effectively, correctly and completely, meet the data owner's for needing to handle high amount spatial data under big data environment
Demand, and Spatial data query and verification method based on Merkle Hash tree are innovatively proposed, improve inquiry speed
Spend and reduce the size for returning the result collection；And highly effective rate and robustness under different parameters setting.
Embodiment one, as shown in figure 3, data owner (Data Owner, abbreviation DO) is by spatial data according to step S1
It is grouped, using each grouping as leaf node, carries out the processing of Merkle Hash tree foundation, and generate one for each grouping
Numerical value, and data are supplied to third-party ISP (Service Provider, abbreviation SP) by treated；When multi-purpose
When data inquiry request is initiated to ISP in family (User), according to the user group U that request data is inquired, the place of user group
Set P；User, which defines, likes set W, the site objects quantity k that user needs, formation quaternary array (quaternary formula) Q=U, W,
P, k }, ISP carries out data initialization, data query and data verification further according to step S3~S5, will be finally obtained
As a result each user is fed back to, so that multi-user has obtained a good query result according to respective place and hobby.
Claims (8)
1. the Spatial data query verification method that a kind of multi-user determines, it is characterised in that: the following steps are included:
S1. spatial data handling: being grouped the site objects of spatial data according to space attribute, and each grouping is used as one
Minimum external square forms MR-tree using each external square of minimum as a leaf node, generates corresponding rope for each leaf node
Draw and stores corresponding numerical value；
S2. inquiry data are given: given quaternary array Q={ U, W, P, k }, U represent the user group of inquiry, and P represents user group
Ground point set；W represents user and defines hobby set, and k is the site objects quantity that user needs；
S3. inquiry initialization: query results R and identifying object collection VO is defined, and by query results R and identifying object collection VO
It is initialized as sky；Calculate the added value v of the intermediate node where each grouping of MR-treesum(N), the leaf segment where each place
The added value v of pointsum(N') and the added value v in each placesum(p)；Wherein, N represents intermediate node, N ' represents leaf node, P generation
Table place；
S4. it data query: defines a heap H and comes with vsum(N)、vsum(N') and vsum(p) ascending order scans the section of MR-tree
The root node of MR-tree: being first put into heap H and identifying object collection VO, scan each time by point, all by the heap top element bullet of heap H
Out, heap top element is checked and identifying object collection VO, query results R and heap H is handled according to inspection result, until
Heap H is sky, completes a basic query, checks and counts in R | | R | | the size with user's defined parameters k:
(1) if | | R | | be greater than k, by place according to place added value vsum(p) numerical values recited sorts, and k value obtains most before taking
Whole result set R '；
(2) if | | R | | be equal to k, directly using R as final result collection R '；
(3) if | | R | | be less than k, carry out next round inquiry, until | | R | | be not less than k；
S5. according to final result collection R ' and VO, the validity of final result collection R ', integrality and correctness data verification: are verified.
2. the Spatial data query verification method that a kind of multi-user according to claim 1 determines, it is characterised in that: step
The numerical value of each leaf node storage described in S1 are as follows:
H=hash (p1|p2|...|pi...|pt),
P in formulai, i=1,2,3.....t indicate i-th of site objects in the corresponding grouping of leaf node, and ' | ' indicates that point carries out over the ground
Cascade operation, t indicate the number of site objects in the leaf node.
3. the Spatial data query verification method that a kind of multi-user according to claim 1 determines, it is characterised in that: described
MR-tree have multiple intermediate nodes, each intermediate node includes one or more leaf node.
4. the Spatial data query verification method that a kind of multi-user according to claim 1 determines, it is characterised in that: any
One location-based vsum(p) MR-tree is traversed by ascending order, the place for being added to query results R, which will be put into, most to terminate
Fruit collects in R '.
5. the Spatial data query verification method that a kind of multi-user according to claim 1 determines, it is characterised in that: step
Heap top element is checked in S4 and carries out respective handling and is divided into following four situation:
(1) if heap top element is dominated by the element in existing R or weight dominates, heap top element is put into VO；
(2) if heap top element is not dominated by the element in existing R or weight dominates, and heap top element is MR-tree
Its child node is added in heap H by intermediate node；
(3) if heap top element is not dominated by the element in existing R or weight dominates, and heap top element is leaf node, will
All places that it includes are added in heap H；
(4) if heap top element is not dominated by the element in existing R or weight dominates；Heap top element is site objects, will
It is just directly placed into R.
6. the Spatial data query verification method that a kind of multi-user according to claim 1 determines, it is characterised in that: described
Step S5 include following sub-step:
S51. it checks and prepares: query results R and corresponding VO being grouped and are ordered as verifying in each round inquiry and is made
It is good to prepare, when poll-final, three kinds of data: site objects are contained in final identifying object collection VO；The number of minimum external square
Value intermediate node corresponding with the external square of minimum；The query information U and W of all users；
S52. validity check: checking the numerical value that root node is calculated according to final identifying object collection VO, then with former numerical value into
Row compares, and judges whether the numerical value being calculated and former numerical value are identical:
(1) if identical, R ' has validity；
(2) if it is not the same, then R ' does not have validity；
S53. Correctness checking: each grouping G in the place number and R ' of final result collection R ' is checkedi, to judge that R's ' is correct
Property；
S54. integrity checking: each grouping G in R ' is checkediWith VO corresponding in VOi, to judge the integrality of R ', if R '
In GiComplete, then R ' has integrality.
7. the Spatial data query verification method that a kind of multi-user according to claim 6 determines, it is characterised in that: described
Step S53 include following sub-step:
S531. check whether final result collection R ' meets condition one: final result collection R ' place number is equal to user's defined parameters
k:
(1) if meeting condition one, go to step S532；
(2) if being unsatisfactory for condition one, final result collection R ' does not have correctness；
S532. each grouping G in R ' is checkedi, judge GiWhether condition two: G is metiIn place do not dominate mutually, also not phase
Mutual weight dominates:
(1) if meeting condition two, GiHas correctness, go to step S533；
(2) if being unsatisfactory for condition two, final result collection R ' does not have correctness:
S533. each site objects in final result collection R ' are checked, judge whether to meet condition three: the place of high-grade grouping
Object can dominate or weight dominates the site objects of inferior grade grouping:
(1) if meeting condition three, final result collection R ' has correctness；
(2) if being unsatisfactory for condition three, final result collection R ' does not have correctness.
8. the Spatial data query verification method that a kind of multi-user according to claim 6 determines, it is characterised in that: described
Step S54 include following sub-step:
S541. judge VOiIn site objects, if be all satisfied condition four: by corresponding GiIn site objects dominate or weight branch
Match；
(1) if meeting condition four, G is corresponded toiHas integrality；
(2) if being unsatisfactory for condition four, go to step S542；
S542. judge VOiIn site objects, if meet condition five: the site objects for being unsatisfactory for condition four, add
Value is higher than GiIn all site objects added value:
(1) if meeting condition five, G is corresponded toiHas integrality；
(2) if being unsatisfactory for condition five, G is corresponded toiDo not have integrality.
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