CN106844561B - R-TBF-based RFID (radio frequency identification) redundant data cleaning method - Google Patents
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Abstract
The invention discloses an R-TBF-based RFID redundant data cleaning method, which comprises the steps of initializing a filter, wherein the filter comprises an integer array M for storing data TIME attributes, a hash function, a mapping function, a Map set P, a TIME threshold tau and an intensity threshold α, judging redundancy of current data X, and performing redundant cleaning according to { ID, TIME and RSSI } format transmission and cleaning rules, and finally processing the current data X.
Description
Technical Field
The invention relates to the technical field of data cleaning, in particular to an R-TBF-based RFID redundant data cleaning method.
Background
The radio frequency identification technology has wide application in the fields of logistics, supply chains and the like due to the characteristics of non-contact, non-line-of-sight and the like, and particularly, the application of the RFID technology is more common along with the development of modern computers and intelligent storage construction. RFID data is an important component of RFID applications, and the quality of RFID data has a significant impact on the application of RFID technology. In practical RFID application, due to the characteristics of non-contact and non-line-of-sight, when a reader-writer is not close to a target tag, a large amount of data of the target tag is generated, and the data have certain redundancy; in addition, in practical application, a plurality of readers often work simultaneously, a large amount of redundant data can be generated for the same target tag in similar time, the generation of the redundant data cannot be avoided in the whole RFID application process, and the existence of the redundant data also limits the popularization of RFID application.
In addition, in RFID applications, RFID data mostly has the characteristic of mobility, which provides a greater challenge to the processing of the RFID data, so the main problem facing the cleaning of the RFID redundant data is how to clean a large amount of RFID data streams in real time in a short time and a small space, which puts higher requirements on the execution time and the occupied space of the cleaning algorithm.
At present, a plurality of methods for cleaning RFID redundant data are provided, Alonso proposes an extensible data stream cleaning model ESP based on statement query, but all data to be processed need to be stored, the dynamic requirements of RFID data streams are not met, and a large amount of memory space is occupied; in addition, Bloom Filter (hereinafter abbreviated as BF) was proposed in 1970, BF has been widely used in the field of data cleaning at present due to its characteristics of low memory ratio, efficient query and the like, while metro uses BF to detect redundant data, and since BF has no deletion function, when the amount of data is large enough, it will be filled up and fail. In addition, the Bloom Filter judges whether the data is redundant according to the existence of the data, for a large amount of data in practical application, useful data information of the data needs to be stored aiming at the same label instead of only one piece of data information, and single piece of data information has one-sidedness and uncertainty, so that the traditional Bloom Filter does not meet the requirement of practical application.
Chun-Hee Lee et al first proposed TBF (time Bloom Filter) to eliminate redundant data by using time information, and although the redundancy problem of RFID data on the time attribute was solved, data could be cleaned to a certain extent and valid information could be retained, RFID data has intensity attribute besides the time attribute, and the intensity attribute also has important role in each RFID application, and redundant data on the time attribute is not necessarily redundant on the intensity attribute for determination, so that cleaning data based on TBF is easy to lose many valid intensity attribute information, and for RFID application, the cleaning effect based on TBF is poor, and considering the incomplete factors and reducing the quality of data to a certain extent affects the effective utilization of subsequent application on RFID data. Similarly, the DTBF-based RFID redundant data cleansing method and system proposed in patent application No. CN201610212717.1 can solve the data cleansing problem when the size of the data stream is uncertain, but cannot consider the influence of the intensity factor in the data attributes on the cleansing effect, so that the cleansing effect is not good due to the insufficient consideration factor although the data can be cleansed. In an actual application scene, due to the fact that the coverage range of a reader-writer is large, data information of a corresponding label can be read when a mobile RFID patrol vehicle does not reach the position opposite to the label, the information is typically characterized by being short in time and low in strength, if the data is judged whether to be redundant or not only depending on time, effective strength information of the same label at the similar time is lost, the authenticity of the data cannot be truly reflected, and the real position of the data cannot be restored. Therefore, an R-TBF-based RFID redundant data cleansing method is needed.
Disclosure of Invention
The invention aims to provide an R-TBF-based RFID redundant data cleaning method; according to the cleaning method, on the basis of an original cleaning strategy, through redefining the time and intensity screening rules, the intensity is considered while the time is considered, and therefore the data cleaning effect is improved, and the data quality is improved.
The purpose of the invention is realized by the following technical scheme:
the invention provides an R-TBF-based RFID redundant data cleaning method, which comprises the following steps:
step 1: initializing a filter, the initializing including:
11) an integer array M for storing data time attributes, the size of the integer array M being M;
12) k hash functions h for mapping data tag information to an array of integers1…hk；
13) A mapping function HK for mapping the integer array subscript value to the Map set key value;
14) the Map set P is used for storing the data intensity attribute, and the size of the Map set P changes along with the size of the data volume;
15) a time threshold τ and an intensity threshold α that determine whether the time attribute and the intensity attribute of the data are redundant, respectively;
the value range of the time threshold τ is: 300 ms-600 ms;
the value range of the intensity threshold value α is 2 dB-5 dB;
step 2: carrying out redundancy judgment on current data X, wherein the current data X is transmitted according to a { ID, TIME, RSSI } format and is subjected to redundancy cleaning according to the following cleaning rule:
wherein the ID represents a package tag number; TIME represents the timestamp when this tag number was read; RSSI represents the strength value of the tag when it is read;
21) firstly, mapping X.ID in current data X to k different positions of an integer array M through k hash functions, judging whether k positions are assigned, if at least one of the k positions is not assigned, indicating that the data X is not processed, directly updating X.TIME to the k positions, and updating X.RSSI to the designated position according to a mapping function HK:
the value of j is a subscript of a selected k position in the integer array, X.HK is calculated according to a binary bit weight rule and is used as a key of a Map set, X.RSSI is used as a corresponding value of the Map set, and the RSSI value of X is stored;
22) if all the k positions are assigned, the data with the same X.ID are processed, redundancy judgment needs to be carried out on the current data X, and the redundancy judgment is carried out by comparing X.TIME with M [ h ]i(X.ID)]And the size of X.RSSI and X.HK.RSSI determines the redundancy of the time attribute and the strength attribute;
and step 3: and after the current data X is processed, repeating the step 2, and processing the next data by using the same cleaning rule.
Further, the size M of the integer array M of the data time attribute in step 1 is calculated according to the following formula:
wherein n is input dataThe size of the amount of the active ingredients is small,
p represents the probability that a unit in the integer array is still empty after k.n times of mapping, and k is the number of the hash functions.
Further, the hash function h in step 11…hkThe number k of (a) is calculated according to the following formula:
k·n<m；
wherein n is the size of the input data volume, and m is the size of the integer array;
the formula for k is:
wherein n is the size of the input data volume, m is the size of the integer array, and P represents the probability that a unit in the integer array is still empty after k.n times of mapping.
Further, the mapping function HK in step 1 is calculated according to the following formula:
wherein j represents a certain unit of the integer array selected by the hash function, the subscript of the selected integer array unit performs binary decimal conversion operation according to the weight of the position of the subscript, and k is the number of the hash function.
Further, the size of the Map set P in step 1 varies with the size of the data volume.
Due to the adoption of the technical scheme, the invention has the following advantages:
the R-TBF-based RFID redundant data cleaning method provided by the invention considers two limiting conditions of time factor and intensity factor to correspondingly clean data, improves the cleaning effect, improves the data quality, reduces the data authenticity to the maximum extent and provides powerful guarantee for the effective utilization of subsequent data through primary timestamp cleaning and secondary intensity value cleaning.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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The drawings of the present invention are described below.
FIG. 1 is a schematic view of a cleaning process.
Fig. 2 is an algorithm flow chart.
Detailed Description
The invention is further illustrated by the following figures and examples.
Example 1
As shown in the figure, the R-TBF-based RFID redundant data cleaning method provided by the embodiment solves the problems of poor cleaning effect and mistaken deletion of useful data caused by insufficient constraint conditions in the conventional TBF-based RFID data cleaning strategy, further improves the data quality, restores the authenticity of data, and provides a strong guarantee for effective utilization of subsequent data; on the basis of an RFID data cleaning strategy based on TBF, the consideration factor is adjusted from single time to two factors of time and intensity, because in an actual application scene, because the coverage range of a reader-writer is large, the data information of a corresponding label can be read when a mobile RFID patrol car does not reach the position right opposite to the label, the information has the typical characteristics of small time and small intensity, if the data is judged whether to be redundant only by depending on the time, the effective intensity information of the same label at the similar time is lost, the authenticity of the data can not be truly reflected, and the real position of the label can not be restored.
Therefore, in the embodiment, on the basis of the original cleaning strategy, the time and intensity screening rule is redefined, and the intensity is considered while the time is considered, so that the data cleaning effect is improved, and the data quality is improved. The specific contents are as follows:
step 1: initializing a filter, the initializing including:
11) an integer array M for storing data time attributes, the size of the integer array M being M;
the magnitude of m is calculated according to the following formula:
wherein n is the size of the input data volume,
p represents the probability that a certain unit in the integer array is still empty after k.n times of mapping, and k is the number of the hash function;
12) k hash functions h for mapping data tag information to an array of integers1…hk；
The magnitude of k is such that the following inequality is satisfied:
k·n<m；
wherein n is the size of the input data volume, and m is the size of the integer array;
the formula for k is:
wherein n is the size of the input data volume, m is the size of the integer array, and P represents the probability that a certain unit in the integer array is still empty after k.n times of mapping;
13) a mapping function HK for mapping the integer array subscript value to the Map set key value;
the HK function is formulated as follows:
j represents a certain unit of the integer array selected by the hash function, the formula represents that the subscript of the selected integer array unit performs binary decimal conversion operation according to the weight of the position of the selected integer array unit, and k is the number of the hash function.
14) The Map set P is used for storing the data intensity attribute, and the size of the Map set P changes along with the size of the data volume;
15) a time threshold τ and an intensity threshold α that determine whether the time attribute and the intensity attribute of the data, respectively, are redundant.
The value range of the time threshold τ is: 300 ms-600 ms;
the value range of the intensity threshold value α is 2 dB-5 dB;
step 2: and (3) carrying out redundancy judgment on the current data X, transmitting the data X according to a { ID, TIME, RSSI } format and carrying out redundancy cleaning according to the following cleaning rules:
21) firstly, mapping X.ID to k different positions of an integer array M through k hash functions, judging whether k positions are assigned, if at least one of the k positions is not assigned, indicating that data X is not processed, directly updating X.TIME to the k positions, and updating X.RSSI to a specified position according to a mapping function HK:
the value of j is a subscript of a selected k position in the integer array, X.HK is calculated according to a binary bit weight rule and is used as a key of a Map set, X.RSSI is used as a corresponding value of the Map set, and the RSSI value of X is stored;
22) if all the k positions are assigned, the data X with the same X.ID are processed, redundancy judgment needs to be carried out on the data X, and the redundancy judgment is carried out by comparing X.TIME with M [ h ]i(X.ID)]And the size of the X.RSSI and the X.HK.RSSI determines the redundancy of the time attribute and the strength attribute;
and step 3: after the data X is processed, the step 2 is repeated, and the next data is processed by using the same cleaning rule.
Example 2
The following is specifically described with reference to the cleaning process shown in fig. 2, and the cleaning process provided in this embodiment mainly includes the following steps:
step 1: initializing a filter, the initializing including:
11) an integer array M for storing data time attributes, the size of the integer array M being M;
the magnitude of m is calculated according to the following formula:
wherein n is the size of the input data volume,
p represents the probability that a certain unit in the integer array is still empty after k.n times of mapping, and k is the number of the hash function;
12) k hash functions h for mapping data tag information to an array of integers1…hk；
The magnitude of k is such that the following inequality is satisfied:
k·n<m；
wherein n is the size of the input data volume, and m is the size of the integer array;
the formula for k is:
wherein n is the size of the input data volume, and m is the size of the integer array;
13) a mapping function HK for mapping the integer array subscript value to the Map set key value;
the HK function is formulated as follows:
j represents a certain unit of the integer array selected by the hash function, the formula represents that the subscript of the selected integer array unit performs binary decimal conversion operation according to the weight of the position of the selected integer array unit, and k is the number of the hash function;
14) the Map set P is used for storing the data intensity attribute, and the size of the Map set P changes along with the size of the data volume;
25) a time threshold τ and an intensity threshold α that determine whether the time attribute and the intensity attribute of the data, respectively, are redundant.
The time threshold value tau is 300 ms-600 ms, the intensity threshold value α is 2 dB-5 dB, the specific values of the embodiment are 350ms, 400ms, 450ms, 500ms, 2.5dB, 3dB, 3.5dB and 4 dB.
Step 2: and (3) carrying out redundancy judgment on the current data X, transmitting the data X according to a { ID, TIME, RSSI } format and carrying out redundancy cleaning according to the following cleaning rules:
21) firstly, mapping X.ID to k different positions of an integer array M through k hash functions, judging whether k positions are assigned, if at least one of the k positions is not assigned, indicating that data X is not processed, directly updating X.TIME to the k positions, and updating X.RSSI to a specified position according to a mapping function HK:
the value of j is a subscript of a selected k position in the integer array, X.HK is calculated according to a binary bit weight rule and is used as a key of a Map set, X.RSSI is used as a corresponding value of the Map set, and the RSSI value of X is stored;
22) if all the k positions are assigned, the data X with the same X.ID are processed, redundancy judgment needs to be carried out on the data X, and X.TIME and M [ h ] are compared firstlyi(X.ID)]If, if
X.TIME-M[hi(X.ID)]>τ；
The data X is not redundant data in time attribute and then x.rssi and x.hk.rssi are compared if
X.RSSI-X.HK.RSSI>α；
Data X is also not redundant in intensity attribute, so both the time attribute and intensity attribute of data X are updated to the latest value if
X.RSSI-X.HK.RSSI<α；
The data X is redundant data on the intensity attribute, so that only the time attribute of the data X is updated to the latest value, and the original intensity attribute value is reserved;
3) and step 22), performing redundancy judgment on the data X, and comparing X.TIME with M [ h ]i(X.ID)]If, if
X.TIME-M[hi(X.ID)]<τ；
The data X is redundant data in time attribute and then x.rssi and x.hk.rssi are compared if x.rssi is redundant in time attribute
X.RSSI-X.HK.RSSI>α；
The data X is not redundant data on the intensity attribute, so the intensity attribute of the data X is only updated to the latest value, the original time attribute value is reserved, and if the intensity attribute is not redundant data, the intensity attribute of the data X is updated to the latest value
X.RSSI-X.HK.RSSI<α；
And if the data X is also redundant data in the intensity attribute, directly eliminating the data X as the redundant data.
And step 3: after the data X is processed, the step 2 is repeated, and the next data is processed by using the same cleaning rule.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered in the protection scope of the present invention.
Claims (3)
1. An R-TBF-based RFID redundant data cleaning method is characterized in that: the method comprises the following steps:
step 1: initializing a filter, the initializing including:
11) an integer array M for storing data time attributes, the size of the integer array M being M;
12) k hash functions h for mapping data tag information to an array of integers1…hk；
13) A mapping function HK for mapping the integer array subscript value to the Map set key value;
14) the Map set P is used for storing the data intensity attribute, and the size of the Map set P changes along with the size of the data volume;
15) a time threshold τ and an intensity threshold α that determine whether the time attribute and the intensity attribute of the data are redundant, respectively;
the value range of the time threshold τ is: 300 ms-600 ms;
the value range of the intensity threshold value α is 2 dB-5 dB;
step 2: carrying out redundancy judgment on current data X, wherein the current data X is transmitted according to a { ID, TIME, RSSI } format and is subjected to redundancy cleaning according to the following cleaning rule:
wherein the ID represents a package tag number; TIME represents the timestamp when this tag number was read; RSSI represents the strength value of the tag when it is read;
21) firstly, mapping X.ID in current data X to k different positions of an integer array M through k hash functions, judging whether k positions are assigned, if at least one of the k positions is not assigned, indicating that the data X is not processed, directly updating X.TIME to the k positions, and updating X.RSSI to the designated position according to a mapping function HK:
the value of j is a subscript of a selected k position in the integer array, X.HK is calculated according to a binary bit weight rule and is used as a key of a Map set, X.RSSI is used as a corresponding value of the Map set, and the RSSI value of X is stored;
22) if all the k positions are assigned, the data with the same X.ID are processed, redundancy judgment needs to be carried out on the current data X, and the redundancy judgment is carried out by comparing X.TIME with M [ h ]i(X.ID)]And the size of X.RSSI and X.HK.RSSI determines the redundancy of the time attribute and the strength attribute;
and step 3: and after the current data X is processed, repeating the step 2, and processing the next data by using the same cleaning rule.
2. An R-TBF-based RFID redundant data cleansing method according to claim 1, characterized in that: the size M of the integer array M of the data time attribute in step 1 is calculated according to the following formula:
wherein n is the size of the input data volume,
p represents the probability that a unit in the integer array is still empty after k.n times of mapping, and k is the number of the hash functions.
3. An R-TBF-based RFID redundant data cleansing method according to claim 1, characterized in that: the hash function h in the step 11…hkThe number k of (a) is calculated according to the following formula:
k·n<m；
wherein n is the size of the input data volume, and m is the size of the integer array;
the formula for k is:
wherein n is the size of the input data volume, m is the size of the integer array, and P represents the probability that a unit in the integer array is still empty after k.n times of mapping.
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