CN106844538A - A kind of many attribute sort methods and device for being applied to Internet of Things search - Google Patents
A kind of many attribute sort methods and device for being applied to Internet of Things search Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/957—Browsing optimisation, e.g. caching or content distillation
- G06F16/9577—Optimising the visualization of content, e.g. distillation of HTML documents
Abstract
Include with device the invention discloses a kind of many attribute sort methods for being applied to Internet of Things search：Obtain searching request, contextual information and the sensor information of user terminal；Contextual information is pre-processed with sensor information, multidimensional property information of the generation with attribute weight；Multidimensional property information is quickly screened, is retained sequence element；User terminal is accurately sorted and is sent to sequence element；Obtain the feedback of user terminal and adjust attribute weight.In the foundation of the sequence that user context perception information and sensor context aware information can be counted search engine by the present invention, rational reasoning and calculation is carried out for context aware information, so as to obtain best suiting the ordering attribute and element of current context, objective reality, data compaction, calculate quick, and receive user feedback, lift experience.
Description
Technical field
The present invention relates to wireless communication field, particularly relate to a kind of many attribute sort methods for being applied to Internet of Things search with
Device.
Background technology
With the development of Internet technology in recent years, the widespread deployment of the awareness apparatus such as sensor, the information for interconnecting
Shared to be not only satisfied with webpage and computer aspect, the demand of network more extend to entity world.It is connected to
Sensor device start information interconnection physically, it is shared mutually to control, hundreds of millions of sensing datas are produced, constitute huge
Internet of Things.With the application and development based on Internet of Things, propulsion, how accurately, intelligently using physical world entity information into
It is the urgent problem for solving, in this context, the search service of internet of things oriented is with the information industry of intelligent sensing net
Development, arises at the historic moment, and as the vital link of Internet of Things, and the ordering strategy in Internet of Things search is the most key one
Ring.
Sensor entity search is a very important ring in prior internet of things application layer face, realizes Internet of Things application
Technology foundation stone.Therefore, real-time entity search is realized in the information of Internet of Things magnanimity, the entity required for being accurately positioned user
Information is that Internet of Things searches for key issue urgently to be resolved hurrily.
Traditional search engine is substantially that universal search draws, and the result that different users searches for same problem return is all
The same, and Internet of Things search is wisdom search, the Search Results of return different according to different users and its residing situation
It is also different.Judge that the mode channel of user search intent is different from conventional search in Internet of Things search, except traditional
Outside search word text input, the information that can also be obtained by the awareness apparatus in the network situation letter current to analyze user
Breath, so as to intelligently provide the user more accurately information and service according to the current situation of user.
Mostly be the feature according to one-dimensional attribute in existing Internet of Things sortord, according to a certain attribute carry out ascending order or
Internet of Things entity information, for example, be converted to info web by the arrangement of descending, by the similitude of keyword or data to webpage
Element is ranked up；And in existing multi attribute search sequence, user's pogoniasis directly inputs the numerical value of each attribute weight or relative
Weight importance, the ordering score for carrying out next step is calculated, or the method for acquisition weight is single, the getting sth into one's head property of this mode
By force, autgmentability difference is, it is necessary to user has certain Internet of Things stock of knowledge；The service architecture of searching order be centralized two-layer or
Three-tier architecture, is unfavorable for capturing satisfactory element in the physical entity of magnanimity, causes network congestion or brings huge
Communication pressure, is unfavorable for the large-scale use of real-time searching order service；Sequence is computationally intensive, and it is a large amount of that Internet of Things search is returned
Element sort for meeting preliminary requirement, untreated to be directly ranked up calculating, time-consuming for algorithm, consumes resource many；With
Ranking results are returned to user by family result feedback-less, it is impossible to catch user to the satisfaction of ranking results whether, or feedback form
Single, assumption property is strong, causes user to repeat to submit ordering requirements to, server resource and Internet resources is caused to waste, while nothing
Different element importance under the different search scenes of method difference, it is impossible to reach the purpose of wisdom sequence.
For getting sth into one's head property of multi attribute search weight order in the prior art is strong, communication pressure big, time-consuming for algorithm, use
The problem of family result feedback-less, not yet there is effective solution at present.
The content of the invention
In view of this, the purpose of the embodiment of the present invention is to propose a kind of many attribute sequence sides for being applied to Internet of Things search
Method and device, user context perception information and sensor context aware information can be counted the foundation of the sequence of search engine
In, rational reasoning and calculation is carried out for context aware information, so as to obtain best suiting the ordering attribute and element of current context,
Objective reality, data compaction calculates quick, and receives user feedback, lifts experience.
Based on above-mentioned purpose, a kind of many attribute sort methods for being applied to Internet of Things search are the embodiment of the invention provides,
Including：
Obtain searching request, contextual information and the sensor information of user terminal；
Contextual information is pre-processed with sensor information, multidimensional property information of the generation with attribute weight；
Multidimensional property information is quickly screened, is retained sequence element；
User terminal is accurately sorted and is sent to sequence element；
Obtain the feedback of user terminal and adjust attribute weight.
In some embodiments, the searching request of acquisition user terminal is：It is actively entered by user, obtaining search please
Ask；Obtain user terminal contextual information be with sensor information：The gateway connected from user terminal sensor obtains the biography
The log-on message of the description information table generation of sensor, and the contextual information and sensor letter of user terminal are extracted from log-on message
Breath.
In some embodiments, the description information table for gateway be each access user terminal sensor generation
, the routed path information of sensor and attribute information, the log-on message of the description information table generation include it is following at least it
One：Time delay, response time, dump energy, service life, expense, geographical position, sensor ID, sensor senses scope.
In some embodiments, described that contextual information is pre-processed with sensor information, generation has Attribute Weight
The multidimensional property information of weight includes：
Data mart modeling is carried out to contextual information and sensor information and is converted to the form for being easy to calculate, including by azimuth information
Direction of motion vector information is converted to acceleration information, direction of motion vector information is converted into the angle number of degrees letter in direction
Cease, latitude and longitude information and positional information are converted into range information；
Contextual information after data mart modeling is changed carries out maximum distance cluster with sensor information, reduces the dimension of data
And user context perception properties information, sensor context aware attribute information and other sensors attribute information are generated as having
The multidimensional property information of attribute weight.
In some embodiments, described that multidimensional property information is quickly screened, retaining sequence element includes：
All properties weight is arranged according to order from high to low, and specifies each attribute weight successively；
By multidimensional property information, according to appointed attribute weight order, order is arranged from high to low, and rejects queue end
Multiple multidimensional property information, the multidimensional property information content being removed is the multidimensional property information content that is not yet removed and be intended to
Retain the difference of sequence number of elements and the product of appointed attribute weight size；
Each attribute weight is specified to perform previous step successively, the multidimensional property information that will be not yet removed is retained as row
Order elements.
In some embodiments, described pair of sequence element carries out accurate sequence includes：
Convergentization treatment is carried out to sequence element, makes value and its result effect positive correlation of all sequence elements；
Sequence element to convergentization carries out dynamic normalization treatment, normalization set square of the generation with fixed interval
Battle array；
Positive ideal solution and minus ideal result are asked to each attribute with attribute weight matrix according to normalization set matrix；
Distance according to each element to positive ideal solution and minus ideal result scores, and sequence element is entered according to fraction size
Row sequence.
In some embodiments, the sequence element to convergentization carries out dynamic normalization treatment, and generation has fixed area
Between normalization set matrix include：
Obtain the attribute set of sequence element each attribute, and extract maximum of the sequence element on each attribute with most
Small value；
The attribute set of each attribute of the sequence element of convergentization is obtained, and extracts the sequence element of convergentization and belonged at each
All values in property；
Sequence element is taken in each attribute according to all values renewal of the sequence element of convergentization on each attribute is extracted
On maxima and minima；
For each value on each attribute of sequence element of convergentization generates normalized value, normalized value be the value with most
The difference of small value, the business with the difference of maxima and minima；
Calculate all normalized values and generate normalization set matrix.
In some embodiments, the feedback for obtaining user terminal and adjust attribute weight and include：
User terminal selecting and the sequence element information set for browsing are obtained, the set of sequence element information includes user terminal
Order interested in sequence element；
Feedback cluster is carried out to sequence element information set using self-adaptive features weight clustering algorithm, user terminal is obtained
Attribute sequence interested；
User is judged according to user terminal order interested in sequence element and user terminal attribute interested
Whether ranking results are satisfied with, if it is not, then according to user terminal selecting element and the discrepancy adjustment attribute weight of sequence element.
In some embodiments, the use self-adaptive features weight clustering algorithm is carried out to sequence element information set
Feedback cluster, obtaining user terminal attribute sequence interested includes：
Sequence element is clustered using self-adaptive features weight clustering algorithm, obtains multiple cluster centres；
Feedback cluster is carried out to sequence element information set using self-adaptive features weight clustering algorithm, feedback cluster is obtained
Center；
Feedback cluster centre and the respective distance of multiple cluster centres are calculated respectively, and distance is arranged by ascending order
Sequence, the attribute corresponding to the smaller cluster centre of distance is user terminal attribute interested.
Based on above-mentioned purpose, the another aspect of the embodiment of the present invention additionally provides a kind of electronic equipment, including at least one
Processor；And, the memory being connected with least one processor communication；Wherein, have can be by institute for the memory storage
State the instruction of at least one computing device, the instruction by least one computing device so that described at least one
Processor is able to carry out the above method.
From the above it can be seen that many attribute sort methods for being applied to Internet of Things search provided in an embodiment of the present invention
With dress by using searching request, contextual information and the sensor information for obtaining user terminal, pretreatment generation has Attribute Weight
The multidimensional property information of weight, quick screening retains sequence element, is accurately sorted and be sent to user terminal, obtains user's end
The feedback at end simultaneously adjusts the technological means of attribute weight, can be by user context perception information and sensor context aware information meter
In the foundation of the sequence for entering search engine, rational reasoning and calculation is carried out for context aware information, worked as so as to obtain best suiting
The ordering attribute and element of preceding situation, objective reality, data compaction calculate quick, and receive user feedback, and lifting uses body
Test.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
The flow chart of many attribute sort methods for being applied to Internet of Things search that Fig. 1 is provided for the present invention；
The electronic equipment of many attribute sort methods of Internet of Things search is applied to described in the execution that Fig. 2 is provided for the present invention
The hardware structure diagram of one embodiment.
Specific embodiment
To make the object, technical solutions and advantages of the present invention become more apparent, below in conjunction with specific embodiment, and reference
Accompanying drawing, further describes to the embodiment of the present invention.
It should be noted that the statement of all uses " first " and " second " is for differentiation two in the embodiment of the present invention
The entity of individual same names non-equal or the parameter of non-equal, it is seen that " first " " second " should not only for the convenience of statement
The restriction to the embodiment of the present invention is interpreted as, subsequent embodiment is no longer illustrated one by one to this.
Based on above-mentioned purpose, the embodiment of the present invention the on one side, it is proposed that one kind can for different user or not
The node of same type be applied to many attribute sequence sides for being applied to Internet of Things search of many attributes sequence of Internet of Things search
One embodiment of method.Fig. 1 is illustrated that the of many attribute sort methods for being applied to Internet of Things search of present invention offer
The schematic flow sheet of one embodiment.
As shown in figure 1, many attribute sort methods for being applied to Internet of Things search include：
Step S101, obtains searching request, contextual information and the sensor information of user terminal；
Step S103, pre-processes to contextual information with sensor information, multidimensional property of the generation with attribute weight
Information；
Step S105, is quickly screened to multidimensional property information, retains sequence element；
Step S107, is accurately sorted and is sent to user terminal to sequence element；
Step S109, obtains the feedback of user terminal and adjusts attribute weight.
In some embodiments, the searching request of acquisition user terminal is：It is actively entered by user, obtaining search please
Ask；Obtain user terminal contextual information be with sensor information：The gateway connected from user terminal sensor obtains the biography
The log-on message of the description information table generation of sensor, and the contextual information and sensor letter of user terminal are extracted from log-on message
Breath.
In some embodiments, the description information table for gateway be each access user terminal sensor generation
, the routed path information of sensor and attribute information, the log-on message of the description information table generation include it is following at least it
One：Time delay, response time, dump energy, service life, expense, geographical position, sensor ID, sensor senses scope.
In some embodiments, described that contextual information is pre-processed with sensor information, generation has Attribute Weight
The multidimensional property information of weight includes：
Data mart modeling is carried out to contextual information and sensor information and is converted to the form for being easy to calculate, including by azimuth information
Direction of motion vector information is converted to acceleration information, direction of motion vector information is converted into the angle number of degrees letter in direction
Cease, latitude and longitude information and positional information are converted into range information；
Contextual information after data mart modeling is changed carries out maximum distance cluster with sensor information, reduces the dimension of data
And user context perception properties information, sensor context aware attribute information and other sensors attribute information are generated as having
The multidimensional property information of attribute weight.
In some embodiments, described that multidimensional property information is quickly screened, retaining sequence element includes：
All properties weight is arranged according to order from high to low, and specifies each attribute weight successively；
By multidimensional property information, according to appointed attribute weight order, order is arranged from high to low, and rejects queue end
Multiple multidimensional property information, the multidimensional property information content being removed is the multidimensional property information content that is not yet removed and be intended to
Retain the difference of sequence number of elements and the product of appointed attribute weight size；
Each attribute weight is specified to perform previous step successively, the multidimensional property information that will be not yet removed is retained as row
Order elements.
In some embodiments, described pair of sequence element carries out accurate sequence includes：
Convergentization treatment is carried out to sequence element, makes value and its result effect positive correlation of all sequence elements；
Sequence element to convergentization carries out dynamic normalization treatment, normalization set square of the generation with fixed interval
Battle array；
Positive ideal solution and minus ideal result are asked to each attribute with attribute weight matrix according to normalization set matrix；
Distance according to each element to positive ideal solution and minus ideal result scores, and sequence element is entered according to fraction size
Row sequence.
In some embodiments, the sequence element to convergentization carries out dynamic normalization treatment, and generation has fixed area
Between normalization set matrix include：
Obtain the attribute set of sequence element each attribute, and extract maximum of the sequence element on each attribute with most
Small value；
The attribute set of each attribute of the sequence element of convergentization is obtained, and extracts the sequence element of convergentization and belonged at each
All values in property；
Sequence element is taken in each attribute according to all values renewal of the sequence element of convergentization on each attribute is extracted
On maxima and minima；
For each value on each attribute of sequence element of convergentization generates normalized value, normalized value be the value with most
The difference of small value, the business with the difference of maxima and minima；
Calculate all normalized values and generate normalization set matrix.
In some embodiments, the feedback for obtaining user terminal and adjust attribute weight and include：
User terminal selecting and the sequence element information set for browsing are obtained, the set of sequence element information includes user terminal
Order interested in sequence element；
Feedback cluster is carried out to sequence element information set using self-adaptive features weight clustering algorithm, user terminal is obtained
Attribute sequence interested；
User is judged according to user terminal order interested in sequence element and user terminal attribute interested
Whether ranking results are satisfied with, if it is not, then according to user terminal selecting element and the discrepancy adjustment attribute weight of sequence element.
In some embodiments, the use self-adaptive features weight clustering algorithm is carried out to sequence element information set
Feedback cluster, obtaining user terminal attribute sequence interested includes：
Sequence element is clustered using self-adaptive features weight clustering algorithm, obtains multiple cluster centres；
Feedback cluster is carried out to sequence element information set using self-adaptive features weight clustering algorithm, feedback cluster is obtained
Center；
Feedback cluster centre and the respective distance of multiple cluster centres are calculated respectively, and distance is arranged by ascending order
Sequence, the attribute corresponding to the smaller cluster centre of distance is user terminal attribute interested.
From above-described embodiment as can be seen that many attribute sequence sides for being applied to Internet of Things search provided in an embodiment of the present invention
Method has attribute weight by using searching request, contextual information and the sensor information for obtaining user terminal, pretreatment generation
Multidimensional property information, quick screening retains sequence element, accurately sorted and be sent to user terminal, obtains user terminal
Feedback and adjust the technological means of attribute weight, user context perception information and sensor context aware information can be counted
In the foundation of the sequence of search engine, rational reasoning and calculation is carried out for context aware information, so as to obtain best suiting currently
The ordering attribute and element of situation, objective reality, data compaction calculate quick, and receive user feedback, lift experience.
The embodiment of the present invention also proposed one kind and can carry out being applied to thing for different user or different types of user
Second embodiment for being applied to many attribute sort methods that Internet of Things is searched for that many attributes of search of networking sort.
The many attribute sort methods for being applied to Internet of Things search include：
Step S101, obtains searching request, contextual information and the sensor information of user terminal.
The sensor search service framework that the embodiment of the present invention is proposed, more intuitively can use for all users, improve
The experience of user.Service architecture includes 3 key elements：Sensor, gateway and server.
Gateway is responsible for the inquiry request of processing server as intermediate treatment link, receives the perception number that sensor sends
According to.For preferably storage sensor contextual information and management of sensor, for each sensor creates description on gateway
Information table, stores the routed path information and all of attribute information of sensor, both including highly dynamic situation attribute, example
Such as time delay, response time and dump energy etc., and basic static information, such as geographical position, the unique ID numberings of sensor, road
Footpath information.Table is described for each sensor, gateway all can send a log-on message, contain in log-on message to server
Be sensor static information.
Gateway can manage the sensor being distributed locally, be connected to sensor group into network in.Server is connected to
Some gateways, direct communication is carried out with gateway, and the indirect control to sensor is carried out by gateway.When new sensor is connected
During in network：New sensor sends log-on message to gateway first, including sensor type, geographical position and by producing
The standard transducer that manufacturer is formed describes fileinfo (including sensing range, dump energy, service life, the expense of sensor
Etc. contextual information).
Server is supplied to the web windows and mobile client window of user input query, receives the query search of user
Demand, while server also manages each gateway node.When server receives the log-on message of service describing table, it is carried first
The information on geographical position and ID of sensor are taken out, is that description information is set up in the geographical position of sensor.It is geographical position
Ontology describing is set up, convenient generation with user geographical position contacts, and calculates the two distance etc., conveniently counts, calculates samely
Sensor information in reason domain, is also convenient for managing the contextual information of current geographic domain inner sensor and user.Then according to search
Condition, is linked to the search-type level in corresponding geographical position domain, and the distributed network gate under the type level is sensed
The collection of device information.
Input includes two parts, and a part is input into for display, employs traditional keyword input mode, and user input is searched
Some sensor types, attribute information needed for rope sequence, another part are implicit input for input.Implicit input refer to
In the case that family need not be input into, user's relevant context perception information is implicitly obtained.For example by mobile terminal search window and
Wearable device etc. obtains the contextual informations such as user's current geographic position information, movement locus, and user is carried out by contextual information
It is simple to sort out, so as to obtain the corresponding weight order of each attribute, then attribute weight can be adjusted by user feedback, so that
Progressively obtain the ranking results that user is most inclined to.
Step S103, pre-processes to contextual information with sensor information, multidimensional property of the generation with attribute weight
Information.
Sorted search condition submission after, sort reference multidimensional property need to be processed after, including initial data plus
Work changes the cluster with multidimensional property.The working process of data includes azimuth information, acceleration information to direction of motion vector
Conversion, the conversion of positional information to distance property, the similitude cluster of element to be sorted reduces the amount of calculation of sort algorithm,
The time performance of sort algorithm can further be improved.
Attribute of the invention pretreatment is mainly and carry out into being about to original context aware information and attribute sensor information
The conversion of data form and the conversion of type feature, two sides of dimension reduction of main conversion and attribute including message structure
Face, the conversion of message structure refers to the processing of situation attribute information and attribute sensor initial data to be processed as being easy to calculating be other
Form, such as GPS longitudes, latitude information be processed as distance property, and direction vector is processed as the angle number of degrees in direction etc.；Attribute dimension
Degree reduce be primarily referred to as will participate in sequence calculate multidimensional like attribute be converted to less dimensional attribute, for example by Parking Fee,
Service fee, charging expense are converted to total cost attribute etc., advantageously reduce the complexity of calculating, the timeliness of boosting algorithm
Energy.
Context aware attribute is divided into the context aware attribute of user context perception properties and sensor.User context perceives category
Property source be mainly the sensor informations such as the sensor and wearable device at user's movement APP ends, the feelings that users' mobile end is obtained
Border attribute includes movement locus by display input vectorMobile terminal sensing is taken if nothing is explicitly entered
The information of deviceUsed as the direction of motion, user's acceleration, subscriber calendar is arranged etc., and this sentences (uc1,uc2,…,
ucn) represent user situation perception information.Wherein, GPS information generally requires the standoff distance attribute for being converted into two GPS locations
To carry out calculating treatment, distance calculates the longitude and latitude distance algorithm with reference to google map：
Quantization between movement locus vector is compared, mainly cosine similarity algorithm：
The sensor context aware attribute of Internet of Things is some, has different context awares under different search need scenes
Information, such as traffic index, weather, distance etc., this is sentencedCharacterize internet of things sensors scene sense
Know information.After obtaining user's search input and context aware information, system judges by context aware information, draws specific
Search scene, so as to find out the ordering attribute information of this search species in ontology model, this is sentencedTable
Show.
In practical application scene, often the attribute for sorting can comprehensively be considered to obtain optimal result, as much as possible
Enumerate all of correlative factor, and sort when calculating then can attribute variable it is excessive, and the degree of correlation is high between variable, to analysis with
Model is set up and brings certain influence.So ordering attribute with reference to attribute it is excessive in the case of, can use clustering methodology, to phase
Clustered like the index of attribute, be classified as same index, reduced amount of calculation, to ranking results and had no significant effect.
Clustering methodology is a kind of method of research " things of a kind come together, people of a mind fall into the same group " in mathematical statistics, mainly there is hierarchical clustering method and iteration
Clustering procedure, the difference according to object of classification is divided into the cluster analysis of Q types and the major class of R types cluster analysis two, and the computational methods of cluster have
Direct Cluster Analysis, beeline clustering procedure, maximum distance clustering procedure, it is simple direct herein using maximum distance clustering procedure, use
Maximum distance weighs the distance between data.Its method and step is as follows：
1) coefficient correlation is calculated：
Note variable xjValue (x1j,x2j,…,xnj)T∈Rn(j=1,2 ..., m), variable xkValue (x1k,x2k,…,
xnk)T∈Rn(k=1,2 ..., m), then variable xjWith xkSample correlation coefficient be：
2) clustered with longest distance method.Define 2 variables distance be:
Wherein, djk=1- | rjk|。
Property parameters number after cluster tails off, and the attribute information for finally participating in sequence is divided into three parts, user context
Perception properties, sensor context aware attribute and other sensors attribute.The set of all properties is represented with CP now, will be all
Weight unification with (w1,w2,…,wm) table all properties weight.
Step S105, is quickly screened to multidimensional property information, retains sequence element.
By showing that input and implicit input obtain demand, movement locus and user current location, fortune that user searches for
The context informations such as dynamic intention, acceleration, and then judge to meet sensor distributed areas border and the kind of sensor of user's request,
Search need is then sent to service end, service end according to the sensor region label and type label for meeting demand, in level
The gateway with satisfactory sensor information is navigated in framework, crawl is all from the sensor information table of gateway storage
Meet the sensor information of type and area label, return it to service end.Assuming that returning to N altogetherallIndividual satisfactory sensing
Device element, number of elements N nowallMuch larger than the information number N that last sequence returns to user, in order to carry out more quickly
Sequence is calculated, and present invention design introduces quick screening module, by preliminary rejecting differ with user's request it is larger wait to sort it is first
Element, reduces the relative element for not meeting user's searching order intention, mitigates the amount of calculation of accurate sequence link, improves many attribute rows
The integration algorithm performance of sequence strategy, while also ensure that a certain degree of sequence accuracy.
The process of screening is by all of element N to be sortedallIt is individual, according to attribute weight highest element P1Quality, by
It is excellent to be ranked up to difference, some elements for sorting rearward are removed, then according to the excellent of attribute weight time single-element high
Bad sequence, removes some rankings most element rearward, the step is performed successively, until being left N number of element to be sorted.
Assuming that returning to NallIndividual satisfactory sensor element, number of elements N nowallMuch larger than the letter for finally returning to
Breath number N, in order to be ranked up calculating more quickly, the system increased quick screening module, and will tentatively reject needs with user
The element to be sorted for asking difference larger, reduces the relative element for not meeting user's searching order intention.The calculating step of quick screening
Suddenly it is：
By all of element N to be sortedallIt is individual, according to a certain element P of certain attribute weight highest0Quality, by it is excellent to
Difference is ranked up；Then last M is comep1Individual element is removed from element to be sorted, Mp1Computational methods be：
By remaining NrestIndividual element to be sorted, according to a certain element P of weight time attribute high1Good and bad standard, by excellent to bad
It is ranked up；From NrestContinue to remove Mp2Individual element, wherein Mp2Calculating start with：
By that analogy, until being left N number of element to be sorted.
After quick screening module is calculated, N number of element to be sorted is had, the sequence of this project with reference to many attribute sequences and calculate
Method, and connected applications scene, are partially improved to the sequence general-purpose algorithm.In this minor sort, have and N number of treat ranked object M
(m=M) individual ordering attribute, corresponding weight is respectively (w1,w2,…,wm)。
Step S107, is accurately sorted and is sent to user terminal to sequence element.
The accurate sort algorithm strategy of design is invented, is that the N number of of return after accurate screening is most consistent with sort criteria
Element, the accurate calculating of many attribute sequences is carried out to it.Algorithm becomes to the element N number of to be sorted with M attribute first
Assimilation is processed, and because the investigation standard of element property is different, some property values are more big more excellent, and some property values are smaller more excellent, also
Other attributes are then moderate and excellent, carry out convergentization treatment to different elements, it is convergent after element value be all unified value
It is more big more excellent.
It is the dynamic normalization treatment of attribute after convergentization treatment, because the interval of different attribute is not to fix not
Become, with the development of science and technology, for different attribute sensors and context aware attribute, its maximum and minimum value can occur
Dynamic change, so this sentences the newest interval value obtained from sensor network as normalized interval, with this dynamic
Scope is normalized calculating for normalized value range.
After dynamic normalization, by the element property values matrix multiple after weight matrix and normalization, after being weighted
Element property value matrix to be sorted, each attribute to matrix obtains positive ideal solution and minus ideal result, calculates each and waits to sort
The distance of element and plus-minus ideal solutions, when element not only near it is positive it is preferable simultaneously but also away from minus ideal result when, the sequence of the element is
It is optimal.On this basis, the present invention is sorted to element, and element is returned into user terminal after sequence.
Specifically, for N number of element to be sorted, the sequence of this project with reference to many attribute sort algorithms, and connected applications
Scene, is partially improved to the sequence general-purpose algorithm.In this minor sort, have and N number of treat that the individual sequences of ranked object M (m=M) belong to
Property, corresponding weight is respectively (w1,w2,…,wm)。
The treatment of Criterion Attribute convergentization, for some attributes, value is more high then to influence better to ranking results, and for some
Attribute, such as charging expense/unit, then more low more excellent, also another attribute, is moderate then excellent, so doing following point
Class treatment：
Normalization --- the dynamic normalization of convergent rear data
For N number of element to be sorted, each there are M attribute, attribute set CP=(cp1,cp2,…,cpm)
1) the peak cp for obtaining the attribute of certain attribute definition is obtained from the configuration file of sensor networki highestWith
cpi lowest, Part II be obtain it is convergent after each property value
2) judge, ifThenSimilarly judge cpi lowest；
3) for each in a set CPOutput normalization set NCP,
For normalization set NCP, by N number of element M attribute, N × Metzler matrix is constituted, the i-th row is element NCP to be sortedi
Property value
To normalized Matrix Multiplication with weight matrix W
W is the weight matrix of M × M, wherein w1+w2+…+wm=1；
C=NCP × W (9)
For each attribute in M attribute in Matrix C, maximum is used as the attribute in taking N number of element to be sorted
Ideal solution, thus constitutes positive ideal solution N × Metzler matrix C*=[c* 1,c* 2,…,c* m], the minimum value conduct in N number of element to be sorted
The minus ideal result of the attribute, thus constitutes 1 × Metzler matrix C '=[c '1,c′2,…,c′m]。
Positive ideal solution
Minus ideal result cj'=mini|ciji| j=1,2 ..., n
Calculate the distance of N number of element to be sorted, each element to positive ideal solution and minus ideal result：
Calculate the score value f of each element to be sortedi, according to sorting from big to small
Step S109, obtains the feedback of user terminal and adjusts attribute weight.
N number of ranking results are returned to mobile client by system, and user can check unit to be selected by browsing clickthrough
The detail information of element, can carry out interested or uninterested mark to element, end user may be selected some result or
Some results meet actual demand, and the result of user behavior is returned to service end by system.The feedback model of service end is to user
Feedback result is analyzed comprehensively, is compared by Kmeans focusing solutions analysis, judge user to the satisfaction of ranking results whether,
Calculate to ranking results property value gap simultaneously, Mobile state adjustment is entered to attribute weight, until user is full to ranking results feedback
Meaning.
Feedack includes：Select and browsed wherein several element Bs=[b1,b2,b3,…,bj], j≤N, in B
Comprising each element be all one-dimension array, in array include some variables, including element of interest sort order.To user
The result of selection and browse the sequence number of element and judged, using clustering algorithm, judge satisfaction of the user for ranking results,
Judge whether the weight that sequence is calculated needs adjustment.
The calculating for feeding back cluster uses self-adaptive features weight Kmeans clustering algorithms, and Kmeans algorithms are most classical
Clustering algorithm, the algorithm can carry out effectively classification for large data collection and efficiency high, scalability are strong, with timeliness
Can be good, the advantages of realize simple and quick.
Determination is needed to be divided into K classes in Kmeans algorithms, this is fed back with 5 for interval is classified, K >=4, if user feedback number
Group theThe point c of name elementy=cyj, (y=1,2 ..., m) as barycenter, select user
Select carries out classification judgement with element interested.User is 6 ranks to the feedback result fuzzy classification of result, respectively form institute
Show, being that user is very satisfied to ranking results spends 6 levels being gradually reduced.
First, Kmeans clusters are carried out to ranking results, is classified as K1 classes, sequence number of times is corresponded to respectively forward to rearward
K1 rank.
Then, by user feedback data B=[b1,b2,b3,…,bj], j≤N, j object is clustered, and obtains feedback knot
The barycenter of fruit, calculates the K1 distance of classification of itself and sequence, so as to infer that the classification minimum with its distance is that user is most interested in
Classification.
The sequence number of times of the classification being most interested in by user, further infers that out that whether user is satisfied with to ranking results.With
Family classification interested, sequence number of times is more forward, then it is assumed that user is more satisfied to ranking results.
When judging that user is dissatisfied to ranking results, element weights need to be adjusted.Adjustment is main to be selected according to user
Select element and the differentiation of the optimal element that initially sorts, the weight to attribute is adjusted.It is optimal with the element for ranking the first
, difference attribute set from big to small is d=(d1,d2,…,dm), the attribute gathered D according to the diversity factor of attribute is with Δ
=0.05 is adjusted for interval.Weight raising is proportionally carried out to contribution attribute, remaining weight is according to original before adjustment
Ratio is reduced, and carries out feeding back to the new weights of user in lower minor sort, and adjustment is re-started according to user feedback,
Threshold value until reaching regulation, i.e. user are in interval that is very satisfied and feeling quite pleased to ranking results.
From above-described embodiment as can be seen that many attribute sequence sides for being applied to Internet of Things search provided in an embodiment of the present invention
Method has attribute weight by using searching request, contextual information and the sensor information for obtaining user terminal, pretreatment generation
Multidimensional property information, quick screening retains sequence element, accurately sorted and be sent to user terminal, obtains user terminal
Feedback and adjust the technological means of attribute weight, user context perception information and sensor context aware information can be counted
In the foundation of the sequence of search engine, rational reasoning and calculation is carried out for context aware information, so as to obtain best suiting currently
The ordering attribute and element of situation, objective reality, data compaction calculate quick, and receive user feedback, lift experience.
Based on above-mentioned purpose, the 3rd aspect of the embodiment of the present invention, it is proposed that be applied to Internet of Things described in one kind execution
One embodiment of the electronic equipment of many attribute sort methods of search.
The electronic equipment that many attribute sort methods of Internet of Things search are applied to described in the execution is included at least one
Reason device；And the memory being connected with least one processor communication；Wherein, the memory storage have can by it is described extremely
A few instruction for computing device, the instruction is by least one computing device, so that described at least one is processed
Device is able to carry out any one method as described above.
As shown in Fig. 2 being the electronic equipment for performing the method for speech processing in the real time phone call for providing of the invention
The hardware architecture diagram of one embodiment.By taking electronic equipment as shown in Figure 2 as an example, include at one in the electronic equipment
Reason device 201 and a memory 202, and can also include：Input unit 203 and output device 204.
Processor 201, memory 202, input unit 203 and output device 204 can be by bus or other modes
Connection, in Fig. 2 as a example by being connected by bus.
Memory 202 can be used to store non-volatile software journey as a kind of non-volatile computer readable storage medium storing program for executing
Sequence, non-volatile computer executable program and module, are applied to Internet of Things search as described in the embodiment of the present application
Corresponding programmed instruction/the module of many attribute sort methods.Processor 201 is non-volatile in memory 202 by running storage
Property software program, instruction and module so that the various function application of execute server and data processing, that is, realize above-mentioned side
The many attribute sort methods for being applied to Internet of Things search of method embodiment.
Memory 202 can include storing program area and storage data field, wherein, storing program area can store operation system
Application program required for system, at least one function；Storage data field can be stored according to many attributes for being applied to Internet of Things search
Collator uses created data etc..Additionally, memory 202 can include high-speed random access memory, can be with
Including nonvolatile memory, for example, at least one disk memory, flush memory device or other non-volatile solid state memories
Part.In certain embodiments, memory 202 is optional including the memory remotely located relative to processor 201, and these are remotely deposited
Reservoir can be by network connection to node.The example of above-mentioned network includes but is not limited to internet, intranet, local
Net, mobile radio communication and combinations thereof.
Input unit 203 can receive the numeral or character information of input, and produce and be applied to many of Internet of Things search
The key signals input that the user of attribute collator is set and function control is relevant.Output device 204 may include display screen etc.
Display device.
One or more of module storages, when being performed by the processor 201, are held in the memory 202
The many attribute sort methods for being applied to Internet of Things search in the above-mentioned any means embodiment of row.
Any one implementation of the electronic equipment of many attribute sort methods of Internet of Things search is applied to described in the execution
Example, can reach the identical or similar effect of corresponding foregoing any means embodiment.
One of ordinary skill in the art will appreciate that all or part of flow in realizing above-described embodiment method, can be
Related hardware is instructed to complete by computer program, described program can be stored in a computer read/write memory medium
In, the program is upon execution, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, described storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access
Memory, RAM) etc..The embodiment of the computer program, can reach corresponding foregoing any means embodiment identical
Or similar effect.
Additionally, typically, device, equipment described in the disclosure etc. can be various electric terminal equipments, such as mobile phone, individual
Digital assistants (PDA), panel computer (PAD), intelligent television etc., or large-scale terminal device, such as server, therefore this
Disclosed protection domain should not limit as certain certain types of device, equipment.Client described in the disclosure can be with electricity
The combining form of sub- hardware, computer software or both is applied in above-mentioned any one electric terminal equipment.
Additionally, the computer program for being also implemented as being performed by CPU according to disclosed method, the computer program
Can store in a computer-readable storage medium.When the computer program is performed by CPU, limit in disclosed method is performed
Fixed above-mentioned functions.
Additionally, above method step and system unit can also utilize controller and cause controller reality for storing
The computer-readable recording medium of the computer program of existing above-mentioned steps or Elementary Function is realized.
In addition, it should be appreciated that computer-readable recording medium (for example, memory) as herein described can be volatile
Property memory or nonvolatile memory, or both volatile memory and nonvolatile memory can be included.As example
Son and it is nonrestrictive, nonvolatile memory can include read-only storage (ROM), programming ROM (PROM), electrically programmable
ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory.Volatile memory can include arbitrary access
Memory (RAM), the RAM can serve as external cache.Nonrestrictive as an example, RAM can be with more
The form of kind is obtained, such as synchronous random access memory (DRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate SDRAM
(DDR SDRAM), enhancing SDRAM (ESDRAM), synchronization link DRAM (SLDRAM) and direct RambusRAM (DRRAM).Institute
The storage device of disclosed aspect is intended to the memory of including but not limited to these and other suitable type.
Those skilled in the art will also understand is that, the various illustrative logical blocks with reference to described by disclosure herein, mould
Block, circuit and algorithm steps may be implemented as the combination of electronic hardware, computer software or both.It is hard in order to clearly demonstrate
This interchangeability of part and software, the function with regard to various exemplary components, square, module, circuit and step it is entered
General description is gone.This function is implemented as software and is also implemented as hardware depending on concrete application and applying
To the design constraint of whole system.Those skilled in the art can in a variety of ways realize described for every kind of concrete application
Function, but this realize that decision should not be interpreted as causing a departure from the scope of the present disclosure.
Various illustrative logical blocks, module and circuit with reference to described by disclosure herein can be utilized and are designed to
The following part of function described here is performed to realize or perform：General processor, digital signal processor (DSP), special collection
Into circuit (ASIC), field programmable gate array (FPGA) or other PLDs, discrete gate or transistor logic, divide
Any combinations of vertical nextport hardware component NextPort or these parts.General processor can be microprocessor, but alternatively, treatment
Device can be any conventional processors, controller, microcontroller or state machine.Processor can also be implemented as computing device
Combination, for example, the combination of DSP and microprocessor, multi-microprocessor, one or more microprocessors combination DSP core or any
Other this configurations.
The step of method or algorithm with reference to described by disclosure herein can be directly contained in hardware in, held by processor
In capable software module or in combination of the two.Software module may reside within RAM memory, flash memory, ROM storages
Device, eprom memory, eeprom memory, register, hard disk, removable disk, CD-ROM or known in the art it is any its
In the storage medium of its form.Exemplary storage medium is coupled to processor so that processor can be from the storage medium
Middle reading information writes information to the storage medium.In an alternative, the storage medium can be with processor collection
Into together.Processor and storage medium may reside within ASIC.ASIC may reside within user terminal.In a replacement
In scheme, processor and storage medium can be resident in the user terminal as discrete assembly.
In one or more exemplary designs, the function can be real in hardware, software, firmware or its any combination
It is existing.If realized in software, can be stored the function as one or more instructions or code in computer-readable
Transmitted on medium or by computer-readable medium.Computer-readable medium includes computer-readable storage medium and communication media,
The communication media includes any medium for helping that computer program is sent to another position from position.Storage medium
It can be any usable medium that can be accessed by a general purpose or special purpose computer.It is nonrestrictive as an example, the computer
Computer-readable recording medium can include RAM, ROM, EEPROM, CD-ROM or other optical disc memory apparatus, disk storage equipment or other magnetic
Property storage device, or can be used for carrying or storage form program code and can for needed for instruction or data structure
Any other medium accessed by universal or special computer or universal or special processor.Additionally, any connection can
It is properly termed as computer-readable medium.If for example, using coaxial cable, optical fiber cable, twisted-pair feeder, digital subscriber line
(DSL) or such as infrared ray, radio and microwave wireless technology come from website, server or other remote sources send software,
Then the wireless technology of above-mentioned coaxial cable, optical fiber cable, twisted-pair feeder, DSL or such as infrared elder generations, radio and microwave is included in
The definition of medium.As used herein, disk and CD include compact disk (CD), laser disk, CD, digital versatile disc
(DVD) the usual magnetically reproduce data of, floppy disk, Blu-ray disc, wherein disk, and CD is using laser optics ground reproduce data.On
The combination for stating content should also be as being included in the range of computer-readable medium.
Disclosed exemplary embodiment, but disclosed exemplary embodiment should be noted, it should be noted that without departing substantially from
On the premise of the scope of the present disclosure that claim is limited, may be many modifications and change.According to disclosure described herein
The function of the claim to a method of embodiment, step and/or action are not required to be performed with any particular order.Although additionally, this public affairs
The element opened can be described or required in individual form, it is also contemplated that it is multiple, it is unless explicitly limited odd number.
It should be appreciated that it is used in the present context, unless context clearly supports exception, singulative "
It is individual " (" a ", " an ", " the ") be intended to also include plural form.It is to be further understood that "and/or" used herein is
Finger includes any of or more than one project listed in association and is possible to combine.
Above-mentioned embodiment of the present disclosure sequence number is for illustration only, and the quality of embodiment is not represented.
One of ordinary skill in the art will appreciate that realizing that all or part of step of above-described embodiment can be by hardware
To complete, it is also possible to instruct the hardware of correlation to complete by program, described program can be stored in a kind of computer-readable
In storage medium, storage medium mentioned above can be read-only storage, disk or CD etc..
Those of ordinary skill in the art should be understood：The discussion of any of the above embodiment is exemplary only, not
It is intended to imply that the scope of the present disclosure (including claim) is limited to these examples；Under the thinking of the embodiment of the present invention, the above
Can also be combined between technical characteristic in embodiment or different embodiments, and there is the present invention as described above and implemented
Many other changes of the different aspect of example, for simplicity, they are provided not in details.Therefore, it is all in the embodiment of the present invention
Spirit and principle within, any omission, modification, equivalent, improvement for being made etc. should be included in the embodiment of the present invention
Within protection domain.
Claims (10)
1. it is a kind of to be applied to many attribute sort methods that Internet of Things is searched for, it is characterised in that including：
Obtain searching request, contextual information and the sensor information of user terminal；
Contextual information is pre-processed with sensor information, multidimensional property information of the generation with attribute weight；
Multidimensional property information is quickly screened, is retained sequence element；
User terminal is accurately sorted and is sent to sequence element；
Obtain the feedback of user terminal and adjust attribute weight.
2. method according to claim 1, it is characterised in that the searching request for obtaining user terminal is：By with householder
Dynamic input, obtains searching request；Obtain user terminal contextual information be with sensor information：Connect from user terminal sensor
The gateway for connecing obtains the log-on message of the description information table generation of the sensor, and the feelings of user terminal are extracted from log-on message
Environment information and sensor information.
3. method according to claim 2, it is characterised in that the description information table is user that gateway is that each is accessed
End sensor is generated, the routed path information of sensor and attribute information, the log-on message of the description information table generation
Including at least one of：Time delay, response time, dump energy, service life, expense, geographical position, sensor ID, sensing
Device sensing range.
4. method according to claim 1, it is characterised in that described that pre- place is carried out to contextual information and sensor information
Reason, multidimensional property information of the generation with attribute weight includes：
Data mart modeling is carried out to contextual information and sensor information and is converted to the form for being easy to calculate, including by azimuth information and added
Velocity information is converted to direction of motion vector information, direction of motion vector information is converted into the angle number of degrees information in direction, inciting somebody to action
Latitude and longitude information is converted into range information with positional information；
Contextual information after data mart modeling is changed carries out maximum distance cluster with sensor information, reduces dimension and the life of data
Into user context perception properties information, sensor context aware attribute information and other sensors attribute information as with attribute
The multidimensional property information of weight.
5. method according to claim 1, it is characterised in that described quickly to be screened to multidimensional property information, retains
Sequence element includes：
All properties weight is arranged according to order from high to low, and specifies each attribute weight successively；
By multidimensional property information, according to appointed attribute weight order, order is arranged from high to low, and rejects many of queue end
Individual multidimensional property information, the multidimensional property information content being removed is the multidimensional property information content that is not yet removed and be intended to retain
The difference of the number of elements that sorts and the product of appointed attribute weight size；
Each attribute weight is specified to perform previous step successively, the multidimensional property information that will be not yet removed is retained as sequence unit
Element.
6. method according to claim 5, it is characterised in that described pair of sequence element carries out accurate sequence to be included：
Convergentization treatment is carried out to sequence element, makes value and its result effect positive correlation of all sequence elements；
Sequence element to convergentization carries out dynamic normalization treatment, normalization set matrix of the generation with fixed interval；
Positive ideal solution and minus ideal result are asked to each attribute with attribute weight matrix according to normalization set matrix；
Distance according to each element to positive ideal solution and minus ideal result scores, and sequence element is arranged according to fraction size
Sequence.
7. method according to claim 6, it is characterised in that the sequence element to convergentization is carried out at dynamic normalization
Reason, normalization set matrix of the generation with fixed interval includes：
The attribute set of each attribute of sequence element is obtained, and extracts maximum of the sequence element on each attribute and minimum
Value；
The attribute set of sequence element each attribute of convergentization is obtained, and extracts the sequence element of convergentization on each attribute
All values；
All values of the sequence element on each attribute according to convergentization is extracted update to take and sort element on each attribute
Maxima and minima；
For each value on each attribute of sequence element of convergentization generates normalized value, normalized value is the value and minimum value
Difference, the business with the difference of maxima and minima；
Calculate all normalized values and generate normalization set matrix.
8. method according to claim 1, it is characterised in that the feedback of the acquisition user terminal simultaneously adjusts attribute weight
Including：
User terminal selecting and the sequence element information set for browsing are obtained, the set of sequence element information includes user terminal to row
Order elements order interested；
Feedback cluster is carried out to sequence element information set using self-adaptive features weight clustering algorithm, user terminal sense is obtained emerging
The attribute sequence of interest；
Judge user to row according to user terminal order interested in sequence element and user terminal attribute interested
Whether sequence result is satisfied with, if it is not, then according to user terminal selecting element and the discrepancy adjustment attribute weight of sequence element.
9. method according to claim 8, it is characterised in that the use self-adaptive features weight clustering algorithm is to sequence
Element information set carries out feedback cluster, and obtaining user terminal attribute sequence interested includes：
Sequence element is clustered using self-adaptive features weight clustering algorithm, obtains multiple cluster centres；
Feedback cluster is carried out to sequence element information set using self-adaptive features weight clustering algorithm, in acquisition feedback cluster
The heart；
Feedback cluster centre and the respective distance of multiple cluster centres are calculated respectively, and distance is sorted from low to high,
Attribute corresponding to the smaller cluster centre of distance is user terminal attribute interested.
10. a kind of electronic equipment, it is characterised in that including at least one processor；And, it is logical with least one processor
Believe the memory of connection；Wherein, have can be by the instruction of at least one computing device, the instruction for the memory storage
By at least one computing device, so that at least one processor is able to carry out such as any one of claim 1-9
Described method.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107733694A (en) * | 2017-09-25 | 2018-02-23 | 苏州耕耘无忧物联科技有限公司 | The automatic analysis method of internet of things oriented real time data |
| CN107958081A (en) * | 2017-12-14 | 2018-04-24 | 泰康保险集团股份有限公司 | Information processing method and device |
| CN111435514A (en) * | 2019-01-15 | 2020-07-21 | 北京京东尚科信息技术有限公司 | Feature calculation method and device, sorting method and device, and storage medium |
| CN111835866A (en) * | 2020-07-24 | 2020-10-27 | 成都西加云杉科技有限公司 | Data search method, device, system, medium and Internet of things terminal |
| CN112540977A (en) * | 2019-09-23 | 2021-03-23 | 伊姆西Ip控股有限责任公司 | Method, apparatus and article of manufacture for managing multiple users of an application system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101334796A (en) * | 2008-02-29 | 2008-12-31 | 浙江师范大学 | Personalized and synergistic integration network multimedia search and enquiry method |
| CN102880617A (en) * | 2011-07-15 | 2013-01-16 | 无锡物联网产业研究院 | Internet-of-things entity searching method and system |
-
2016
- 2016-12-30 CN CN201611255599.9A patent/CN106844538A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101334796A (en) * | 2008-02-29 | 2008-12-31 | 浙江师范大学 | Personalized and synergistic integration network multimedia search and enquiry method |
| CN102880617A (en) * | 2011-07-15 | 2013-01-16 | 无锡物联网产业研究院 | Internet-of-things entity searching method and system |
Non-Patent Citations (4)
| Title |
|---|
| CHARITH PERERA 等: "Context Aware Computing for The Internet of Things:A Survey", 《 IEEE COMMUNICATIONS SURVEYS & TUTORIALS》 * |
| 于海宁 等: "物联网中物理实体搜索服务的研究", 《电信科学》 * |
| 周新: "情境感知的物联网信息检索技术研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 周玮: "我国第四方物流企业盈利模式研究", 《中国优秀硕士学位论文全文数据库 经济与管理科学辑》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107733694A (en) * | 2017-09-25 | 2018-02-23 | 苏州耕耘无忧物联科技有限公司 | The automatic analysis method of internet of things oriented real time data |
| CN107958081A (en) * | 2017-12-14 | 2018-04-24 | 泰康保险集团股份有限公司 | Information processing method and device |
| CN107958081B (en) * | 2017-12-14 | 2021-11-30 | 泰康保险集团股份有限公司 | Information processing method and device |
| CN111435514A (en) * | 2019-01-15 | 2020-07-21 | 北京京东尚科信息技术有限公司 | Feature calculation method and device, sorting method and device, and storage medium |
| CN111435514B (en) * | 2019-01-15 | 2024-04-09 | 北京京东尚科信息技术有限公司 | Feature calculation method and device, ranking method and device, and storage medium |
| CN112540977A (en) * | 2019-09-23 | 2021-03-23 | 伊姆西Ip控股有限责任公司 | Method, apparatus and article of manufacture for managing multiple users of an application system |
| CN111835866A (en) * | 2020-07-24 | 2020-10-27 | 成都西加云杉科技有限公司 | Data search method, device, system, medium and Internet of things terminal |
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