CN108108643B - Radio frequency identification anti-collision optimal Q value calculation method and device - Google Patents
Radio frequency identification anti-collision optimal Q value calculation method and device Download PDFInfo
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Abstract
The invention relates to the field of radio frequency identification, and provides a method and a device for calculating an optimal Q value of radio frequency identification anti-collision so as to quickly calculate the optimal Q value of radio frequency identification anti-collision and improve the efficiency of label inventory. The method comprises the following steps: acquiring tag collision times C, time slot idle number F and a Q value used in checking in each round of checking on tags; inputting label collision times C, time slot idle number F and Q value used in inventory into neural network net as parametersbpTo calculate the optimal Q value; neural network net using dynamic Q value adjustment algorithmbpAnd adjusting the calculated optimal Q value so that the adjusted Q value is further closest to the optimal Q value in the anti-collision algorithm. According to the technical scheme provided by the invention, on one hand, the calculated Qt is closest to the optimal Q value in the anti-collision algorithm; on the other hand, the time cost for further adjusting Qt to be closest to the best Q value in the collision avoidance algorithm is small, thereby improving the inventory efficiency for the tags as a whole.
Description
Technical Field
The invention belongs to the field of radio frequency identification, and particularly relates to an optimal Q value calculation method and device for radio frequency identification anti-collision.
Background
Radio Frequency IDentification (RFID) technology is a new technology for automatic IDentification, and is widely applied to various fields such as warehouse logistics, indoor positioning, book management and the like by virtue of various advantages. The RFID utilizes a radio frequency mode to perform non-contact bidirectional communication to exchange data so as to achieve the aim of identification. However, when 2 or more than 2 tags simultaneously communicate with a reader (reader), since the tags of the passive RFID system share one transmission channel, collision between the tags will cause the tags not to normally communicate with the reader.
Existing anti-collision algorithms in the RFID field include dynamic Q value adjustment algorithms. The algorithm dynamically adjusts the Q value according to the label collision condition or label collision and time slot idle condition in each counting period when counting the labels until obtaining the best Q value, namely, the time slot number is close to the Q value when the label number, at this time, the time slot number is equal to 2 in numberQ。
However, the existing dynamic Q value adjustment algorithm has the disadvantages that either the Q value really belonging to the optimal Q value cannot be obtained in the inventory process, that is, the obtained optimal Q value is not optimal and is too different from the theoretical optimal Q value, or the time cost for obtaining the optimal Q value is too high, that is, it takes a long time to obtain the optimal Q value, thereby affecting the efficiency of label inventory.
Disclosure of Invention
The invention provides a method and a device for calculating an optimal Q value of radio frequency identification anti-collision, which are used for quickly calculating the optimal Q value of radio frequency identification anti-collision and improving the efficiency of label checking.
The invention provides a method for calculating an optimal Q value of radio frequency identification anti-collision, which comprises the following steps:
acquiring tag collision times C, time slot idle number F and a Q value used in checking in each round of checking on tags;
inputting the label collision times C, the time slot idle number F and the Q value used in inventory into a neural network net as parametersbpTo calculate the optimal Q value, said neural network netbpAfter training, Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in checkingtThe neural network of, the QtThe Q value closest to the optimal Q value in the anti-collision algorithm;
adapting the neural network net to a dynamic Q-value adjustment algorithmbpAnd adjusting the calculated optimal Q value so that the adjusted Q value is further closest to the optimal Q value in the anti-collision algorithm.
With reference to the first aspect of the present invention, in a first implementation manner of the first aspect, the inputting the number of tag collisions C, the number of slot idles F, and a Q value used in inventory into a neural network as parameters to derive an optimal Q value includes:
s1, inputting label collision times C, time slot idle number F and Q value used in inventory into the neural network net as parametersbpQ 'is obtained by estimation't；
S2, judging whether to continue using the neural network to calculate the optimal Q value, if so, continuing using the neural network netbpAnd (4) calculating the optimal Q value, then the flow jumps to S1, otherwise, the Q 'is used'tAs said neural network netbpAnd (5) calculating the optimal Q value.
With reference to the first aspect, in a second implementation manner of the first aspect, the applying a dynamic Q value adjusting algorithm to the neural network netbpAdjusting the calculated optimal Q value to make the adjusted Q value closest to the optimal Q value in the anti-collision algorithm, comprising:
s' 1, connecting the neural network netbpTaking the calculated optimal Q value as the Q value of the dynamic Q value adjustment algorithm to perform one-round counting on the label;
s '2, judging whether the process of S' 1 is finished or not, if so, finishing one wheel disc point of the label, and taking the Q value at the end of the wheel disc point as the Q valueClosest to the best Q value in the anti-collision algorithm, otherwise, fine-tuning the neural network netbpAnd continuously counting the labels for a new round by taking the calculated optimal Q value or the Q value used for finely adjusting the previous round when the labels are counted by adopting the dynamic Q value adjustment algorithm as the Q value of the dynamic Q value adjustment algorithm until the process of counting the labels is finished.
With reference to the first aspect of the present invention, the first implementation manner of the first aspect, or the second implementation manner of the first aspect, in a third implementation manner of the first aspect, the method further includes:
before acquiring label collision times C, time slot idle number F and Q value used in inventory in each round of inventory of labels, training the neural network netbpSo that said trained neural netbpThe Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in the checkingt。
The invention provides a device for estimating the optimal Q value of radio frequency identification anti-collision, which comprises:
the acquisition module is used for acquiring the label collision times C, the time slot idle number F and a Q value used in counting in each round of counting of the labels;
a calculation module for inputting the label collision frequency C, the time slot idle number F and the Q value used in inventory into the neural network net as parametersbpTo calculate the optimal Q value, said neural network netbpAfter training, Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in checkingtThe neural network of, the QtThe Q value closest to the optimal Q value in the anti-collision algorithm;
an adjustment module for adjusting the neural network net using a dynamic Q-value adjustment algorithmbpAnd adjusting the calculated optimal Q value so that the adjusted Q value is further closest to the optimal Q value in the anti-collision algorithm.
With reference to the second aspect of the present invention, in a first implementation manner of the second aspect, the estimation module includes:
an input unit for inputting the tag collision number C, the slot idle number F, and a Q value used in inventory to the neural network net as parametersbpQ 'is obtained by estimation't；
A first judgment unit for judging whether to continue using the neural network netbpCalculating the optimal Q value if the input unit continues to use the neural network netbpCalculating the optimal Q value, the input unit continues to input the label collision frequency C, the time slot idle number F and the Q value used in inventory as parameters into the neural network netbpQ 'is obtained by estimation'tOtherwise, with the Q'tAs said neural network netbpAnd (5) calculating the optimal Q value.
With reference to the second aspect of the present invention, in a second implementation manner of the second aspect, the adjusting module includes:
an inventory unit for inventorying the neural network netbpTaking the calculated optimal Q value as the Q value of the dynamic Q value adjustment algorithm to perform one-round counting on the label;
a second judging unit, configured to judge whether a process executed by the inventory unit is finished, if so, the inventory unit finishes a wheel disc point performed on the tag, and takes a Q value at the end of inventory as an optimal Q value in the closest anti-collision algorithm, otherwise, the inventory unit finely adjusts the neural network netbpAnd continuously counting the labels for a new round by taking the calculated optimal Q value or the Q value used for finely adjusting the previous round when the labels are counted by adopting the dynamic Q value adjustment algorithm as the Q value of the dynamic Q value adjustment algorithm until the process of counting the labels is finished.
With reference to the second aspect of the present invention, the first embodiment of the second aspect, or the second embodiment of the second aspect, in a third embodiment of the second aspect, the apparatus further comprises:
a training module, configured to train the neural network net before the obtaining module obtains the tag collision number C, the slot idle number F, and the Q value used in the inventory for each round of the inventory of the tagbpSo that after said trainingNeural network netbpThe Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in the checkingt。
A third aspect of the invention provides a computing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring tag collision times C, time slot idle number F and a Q value used in checking in each round of checking on tags;
inputting the label collision times C, the time slot idle number F and the Q value used in inventory into a neural network net as parametersbpTo calculate the optimal Q value, said neural network netbpAfter training, Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in checkingtThe neural network of, the QtThe Q value closest to the optimal Q value in the anti-collision algorithm;
adapting the neural network net to a dynamic Q-value adjustment algorithmbpAnd adjusting the calculated optimal Q value so that the adjusted Q value is further closest to the optimal Q value in the anti-collision algorithm.
With reference to the third aspect of the present invention, in a first implementation manner of the third aspect, the inputting the number of tag collisions C, the number of time slot idleness F, and a Q value used in inventory into a neural network as parameters to derive an optimal Q value includes:
s1, inputting label collision times C, time slot idle number F and Q value used in inventory into the neural network net as parametersbpQ 'is obtained by estimation't；
S2, judging whether to continue using the neural network to calculate the optimal Q value, if so, continuing using the neural network netbpAnd (4) calculating the optimal Q value, then the flow jumps to S1, otherwise, the Q 'is used'tAs said neural network netbpAnd (5) calculating the optimal Q value.
With reference to the third aspect of the present invention, in a second implementation manner of the third aspect, the dynamic Q is adoptedValue adjustment algorithm for the neural network netbpAdjusting the calculated optimal Q value to make the adjusted Q value closest to the optimal Q value in the anti-collision algorithm, comprising:
s' 1, connecting the neural network netbpTaking the calculated optimal Q value as the Q value of the dynamic Q value adjustment algorithm to perform one-round counting on the label;
s '2, judging whether the process of the S' 1 is finished or not, if so, finishing a wheel disc point of the label, taking a Q value at the end of the wheel disc point as an optimal Q value in the closest anti-collision algorithm, and otherwise, finely adjusting the neural network netbpAnd continuously counting the labels for a new round by taking the calculated optimal Q value or the Q value used for finely adjusting the previous round when the labels are counted by adopting the dynamic Q value adjustment algorithm as the Q value of the dynamic Q value adjustment algorithm until the process of counting the labels is finished.
With reference to the third aspect of the present invention, the first implementation manner of the third aspect, or the second implementation manner of the third aspect, in a third implementation manner of the third aspect, the method further includes:
before acquiring label collision times C, time slot idle number F and Q value used in inventory in each round of inventory of labels, training the neural network netbpSo that said trained neural netbpThe Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in the checkingt。
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium, in which a computer program is stored, the computer program, when executed by a processor, implementing the steps of the method:
acquiring tag collision times C, time slot idle number F and a Q value used in checking in each round of checking on tags;
inputting the label collision times C, the time slot idle number F and the Q value used in inventory into a neural network net as parametersbpTo calculate the optimal Q value, said neural network netbpCan be trained according to the input label collision frequency,Calculating Q from the number of free time slots and the Q value used in the inventorytThe neural network of, the QtThe Q value closest to the optimal Q value in the anti-collision algorithm;
adapting the neural network net to a dynamic Q-value adjustment algorithmbpAnd adjusting the calculated optimal Q value so that the adjusted Q value is further closest to the optimal Q value in the anti-collision algorithm.
With reference to the fourth aspect of the present invention, in a first implementation manner of the fourth aspect, the inputting the number of tag collisions C, the time slot idle number F, and a Q value used in inventory into a neural network as parameters to calculate an optimal Q value includes:
s1, inputting label collision times C, time slot idle number F and Q value used in inventory into the neural network net as parametersbpQ 'is obtained by estimation't；
S2, judging whether to continue using the neural network to calculate the optimal Q value, if so, continuing using the neural network netbpAnd (4) calculating the optimal Q value, then the flow jumps to S1, otherwise, the Q 'is used'tAs said neural network netbpAnd (5) calculating the optimal Q value.
With reference to the fourth aspect, in a second implementation manner of the fourth aspect, the applying a dynamic Q value adjusting algorithm to the neural network netbpAdjusting the calculated optimal Q value to make the adjusted Q value closest to the optimal Q value in the anti-collision algorithm, comprising:
s' 1, connecting the neural network netbpTaking the calculated optimal Q value as the Q value of the dynamic Q value adjustment algorithm to perform one-round counting on the label;
s '2, judging whether the process of the S' 1 is finished or not, if so, finishing a wheel disc point of the label, taking a Q value at the end of the wheel disc point as an optimal Q value in the closest anti-collision algorithm, and otherwise, finely adjusting the neural network netbpThe calculated optimal Q value or the Q value used when the dynamic Q value adjustment algorithm is finely adjusted for checking the label in the previous round is taken as the Q value of the dynamic Q value adjustment algorithm to continue to check the label in a new round until the process of checking the label is finishedAnd (4) bundling.
With reference to the fourth aspect of the present invention, the first implementation manner of the fourth aspect, or the second implementation manner of the fourth aspect, in a third implementation manner of the fourth aspect, the method further includes:
before acquiring label collision times C, time slot idle number F and Q value used in inventory in each round of inventory of labels, training the neural network netbpSo that said trained neural netbpThe Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in the checkingt。
As can be seen from the above technical solutions of the present invention, on one hand, the neural network is a neural network that is trained and can calculate the Q value closest to the optimal Q value in the anti-collision algorithm according to the input tag collision number, slot idle number, and Q value used in inventory, and therefore, when the obtained parameters such as the tag collision number C, slot idle number F, and Q value used in inventory are input into the neural network that has been trained in this way, the calculated Q value is calculatedtHas been closest to the optimal Q value in the collision avoidance algorithm; on the other hand, Q is calculated by using the trained neural networktThe best Q value in the anti-collision algorithm is closest, and then the dynamic Q value adjusting algorithm is adopted to adjust QtMake adjustments ("even" means that it is possible and not necessary to apply the dynamic Q adjustment algorithm to Q againtMake adjustments) the time cost spent would be small, i.e., Q would be quickly pulledtAnd further adjusting the Q value to be closest to the optimal Q value in the anti-collision algorithm, so that the inventory efficiency of the labels is improved as a whole.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating an implementation of an optimal Q value calculation method for radio frequency identification collision avoidance according to an embodiment of the present invention;
FIG. 2 is a trained neural network net provided by an embodiment of the present inventionbpThe calculated optimal Q value is compared with the theoretical optimal Q value;
FIG. 3 is a trained neural net provided in another embodiment of the present inventionbpThe calculated optimal Q value is compared with the theoretical optimal Q value;
FIG. 4 is a trained neural net provided in another embodiment of the present inventionbpThe calculated optimal Q value is compared with the theoretical optimal Q value;
fig. 5 is a schematic diagram illustrating the comparison between the efficiency of the technical solution provided by the embodiment of the present invention for tag inventory and the efficiency of the existing impinj algorithm for tag inventory;
fig. 6 is a schematic structural diagram of an optimal Q value estimator for rfid collision avoidance according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an optimal Q value estimator for rfid collision avoidance according to another embodiment of the present invention;
fig. 8 is a schematic structural diagram of an optimal Q value estimator for rfid collision avoidance according to another embodiment of the present invention;
FIG. 9-a is a schematic structural diagram of an optimal Q value estimator for RFID collision avoidance according to another embodiment of the present invention;
FIG. 9-b is a schematic structural diagram of an optimal Q value estimator for RFID collision avoidance according to another embodiment of the present invention;
FIG. 9-c is a schematic structural diagram of an optimal Q value estimator for RFID collision avoidance according to another embodiment of the present invention;
fig. 10 is a schematic structural diagram of a computing device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart illustrating an implementation process of the method for calculating an optimal Q value of radio frequency identification collision avoidance according to the embodiment of the present invention, and an execution main body of the method may be a tag reader (reader) in the RFID technology. The method illustrated in fig. 1 mainly comprises the following steps S101 to S103, which are explained in detail below:
s101, acquiring the number C of label collision, the idle time slot number F and a Q value used in counting in each round of counting of the labels.
It should be noted that, the concept of the Q value mentioned in the embodiments of the present invention belongs to the concept of the Q value in the existing dynamic Q value adjustment algorithm, and reference may be made to the relevant documents of the existing dynamic Q value adjustment algorithm, which is not described herein again. In the embodiment of the present invention, the Q value used in the initial inventory of the tag may be obtained by initializing a reader of the RFID tag, and the Q value used in the subsequent inventory may be an optimal Q value estimated by a neural network, or a Q value obtained by further adjusting the optimal Q value by a dynamic frame time slot algorithm (DFSA), for example, a dynamic Q value adjustment algorithm.
In an embodiment of the invention, before step S101, the neural network net may be trainedbpTo make the trained neural network netbpQ can be calculated according to the input label collision frequency, the time slot idle number and the Q value used in the checkingtI.e. the Q value closest to the best Q value in the collision avoidance algorithm. Training neural network netbpInvolving the neural net before trainingbpConstruction of and training followed by neural network netbpThe building process may be to build a neural network with two hidden layers, such as an Error Back Propagation (Error Back Propagation) neural network (abbreviated as BP neural network), by using some mathematical software, such as MatlabNetwork), selecting the Q value, the tag collision times and the time slot idle times used in the tag inventory as input parameters, and selecting the optimal Q value Qt as an output parameter. In establishing neural network netbpThen, a predetermined number of groups obtained by actual measurement, for example, 150 groups of data, may be normalized and used as input parameters to the neural network net, with the Q value used in the tag inventory, the number of tag collisions, and the number of slot idleness as a group of data (that is, each group of data includes three parameters, i.e., the Q value used in the tag inventory, the number of tag collisions, and the number of slot idleness)bpAnd (5) training. Neural network net after trainingbpThe verification of (2) may be that 30 additionally obtained data (30 data out of the above-mentioned 150 data) are actually measured and used for verifying the neural network net after trainingbpThe accuracy of the best Q estimation is shown in fig. 2, fig. 3 and fig. 4, in which only two of the 30 sets of data indicate the neural network netbpThe difference between the calculated Q value and the optimal Q value is 1, the comparison results are respectively shown in figure 2 and figure 4, and the rest 28 groups of data show the neural network netbpThe calculated Q value was the same as the optimum Q value, and the comparison result is shown in FIG. 3.
S102, inputting label collision frequency C, time slot idle number F and Q value used in inventory into neural network net as parametersbpTo calculate the optimal Q value, wherein the neural network netbpAfter training, Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in checkingtA neural network of, here, QtIs the Q value closest to the best Q value in the collision avoidance algorithm.
According to the principle of a dynamic frame time slot algorithm (DFSA), such as a dynamic Q value adjustment algorithm, when a tag is checked, too much or too little time slot idle is not an optimal choice, and too much time slot idle results in a waste of resources and a relatively high time cost although the probability of tag collision is relatively reduced, while too little time slot idle results in an increased probability of tag collision and a reduced checking efficiency. Therefore, when the number of slots (the value of which is equal to 2)Q) Efficiency of inventorying when number of labels is closeIs the highest. In the embodiment of the invention, the neural network netbpAfter training, Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in checkingt(QtQ value closest to the optimal Q value in the collision avoidance algorithm), and thus, the Q value used when the number of tag collisions C, the slot free number F, and the inventory are input as parameters to the neural network netbpIn the network, the neural network netbpCalculated QtIs the Q value closest to the best Q value in the collision avoidance algorithm. For example, if the theoretically calculated optimal Q value of the anti-collision algorithm is 5, the neural network net will be usedbpCalculated QtPossibly equal to 4, 5 or 6, equal to the theoretically calculated optimal Q value or only differing by 1.
As an embodiment of the present invention, the number of tag collisions C, the slot free number F, and the Q value used at the time of inventory are input as parameters to the neural network netbpThe calculation of the optimal Q value can be realized by the following steps S1 and S2:
s1, inputting label collision frequency C, time slot idle number F and Q value used in inventory into neural network net as parametersbpQ 'is obtained by estimation't。
As per the description of the preceding embodiments, here via the neural network netbpEstimated Q'tThe reason is the optimum Q value.
S2, judging whether to continue using the neural network netbpCalculating optimal Q value, and if neural network net is continuously usedbpIf the optimal Q value is estimated, the flow goes to S1, otherwise, the Q 'obtained in the step S1 is used'tAs a neural network netbpAnd (5) calculating the optimal Q value.
Method for determining whether to continue using neural network netbpCalculating the optimal Q value, in the embodiment of the present invention, one basis is that if the neural network net is usedbpThe calculated optimal Q value tends to be stable, i.e. for each round of inventory of the label, the neural network net is usedbpIf the calculated optimal Q value is basically consistent, the neural network net can be usedbpCalculating the optimal Q value to be the last oneQ 'obtained'tAs a neural network netbpCalculating optimal Q value, otherwise, repeating the processes of S1 and S2 until determining no need to continue using neural network netbpThe optimum Q value is calculated.
S103, adopting a dynamic Q value adjustment algorithm to carry out adjustment on the neural network netbpAnd adjusting the calculated optimal Q value so that the adjusted Q value is further closest to the optimal Q value in the anti-collision algorithm.
As mentioned previously, the neural network netbpCalculating Q according to parameters such as input label collision times C, time slot idle number F and Q value used in inventorytThe reason would be the Q value closest to the best Q value in the collision avoidance algorithm. However, neural network netbpDerived QtThere is still room for further adjustment. For example, the optimum Q value is 5, the neural network netbpDerived Q t4 or 6, and thus, the neural network net can also be adjusted by adopting a dynamic Q value adjusting algorithmbpAnd adjusting the calculated optimal Q value so that the adjusted Q value is further closest to the optimal Q value in the anti-collision algorithm.
As an embodiment of the invention, a dynamic Q value adjustment algorithm is adopted for the neural network netbpThe calculated optimal Q value is adjusted so that the adjusted Q value is closest to the optimal Q value in the anti-collision algorithm, which can be realized by the following steps S '1 and S' 2:
s' 1, connecting neural network to netbpAnd taking the calculated optimal Q value as the Q value of the dynamic Q value adjustment algorithm to perform one-round inventory on the label.
For example, neural network netbpAnd if the calculated optimal Q value is 4, taking 4 as the Q value of the dynamic Q value adjustment algorithm to perform one round of inventory on the label.
S '2, judging whether the process of the S' 1 is finished or not, if so, finishing a wheel disc point of the label, taking a Q value at the end of the wheel disc point as an optimal Q value in the closest anti-collision algorithm, and otherwise, finely adjusting the neural network netbpThe calculated optimal Q value or the Q value used for finely adjusting the previous round of checking the label by adopting the dynamic Q value adjustment algorithm is taken as the Q value of the dynamic Q value adjustment algorithm to continueAnd performing a new round of checking on the label until the process of checking the label is finished.
As for the time for determining when the process of S '1 is finished, it may be determined according to the predetermined time for counting, or it may be determined according to the labels of the target number of counting, for example, if the time for the predetermined counting label is 5 seconds, the process of S' 1 is finished after 5 seconds; for another example, if the number of the tags of the predetermined inventory target number is 10, the process of S' 1 is terminated after 10 tags are inventory, and so on.
In an embodiment of the invention, the neural network net is fine-tunedbpThe calculated optimal Q value or the Q value used for counting the labels by adopting a dynamic Q value adjustment algorithm in the previous round is finely adjusted, and the amplitude of each adjustment can be 1. For example, assuming that the theoretically calculated optimal Q value is 6, the neural network netbpThe calculated optimal Q value or the Q value used when the label is checked by adopting a dynamic Q value adjusting algorithm in the previous round is 4, and the theoretically calculated optimal Q value can be adjusted by two times according to an adjusting strategy that the adjusting amplitude of each time is 1. As can be seen from the above example, because of the neural network netbpCalculating Q according to the input label collision times C, the time slot idle number F and the Q value used in checkingtThe optimal Q value calculated by the theory can be reached by fine adjustment after being relatively close to the optimal Q value calculated by the theory, so that the Q value can be adjusted to the optimal Q value in a short time compared with the prior art. For example, if the theoretically calculated optimal Q value is 13, and if the initial Q value is 4 according to the prior art, the adjustment is performed in a manner that the adjustment range is 1 each time, 9 times of adjustment are required to adjust the optimal Q value, which is very time-consuming, whereas according to the technical solution provided by the present invention, the neural network net is usedbpDerived QtMay already be 10, 12 or 13, according to the adjustment strategy of which the adjustment range is 1, at most, two times of adjustment, at least 0 times of adjustment (i.e. no adjustment) are needed to achieve the optimal Q value, and the consumed time is obviously much shorter than that of the prior art, so that the inventory efficiency of the tag is greatly improved, as shown in fig. 5, the technical scheme provided by the present invention, i.e. the algorithm of the present invention is one of the algorithm of the present invention and the prior artAccording to the method for calculating the optimal Q, for example, when the initial Q value of the impinj algorithm is set to be 1, under the condition that other conditions are the same, the efficiency comparison result of the label counting is performed by the technical scheme provided by the invention and the impinj algorithm, and obviously, for the same number of labels, the label counting speed of the technical scheme provided by the invention is far higher than that of the impinj algorithm.
As can be seen from the foregoing method for estimating the optimal Q value for rfid anti-collision illustrated in fig. 1, on one hand, the neural network is a neural network that is trained and can estimate the Q value closest to the optimal Q value in the anti-collision algorithm according to the input tag collision count, the slot idle count, and the Q value used in inventory, and therefore, when the obtained parameters such as the tag collision count C, the slot idle count F, and the Q value used in inventory are input into the neural network thus trained, the estimated Q value is obtainedtHas been closest to the optimal Q value in the collision avoidance algorithm; on the other hand, Q is calculated by using the trained neural networktThe best Q value in the anti-collision algorithm is closest, and then the dynamic Q value adjusting algorithm is adopted to adjust QtMake adjustments ("even" means that it is possible and not necessary to apply the dynamic Q adjustment algorithm to Q againtMake adjustments) the time cost spent would be small, i.e., Q would be quickly pulledtAnd further adjusting the Q value to be closest to the optimal Q value in the anti-collision algorithm, so that the inventory efficiency of the labels is improved as a whole.
Fig. 6 is a schematic diagram of an optimal Q value estimator for rfid collision avoidance according to an embodiment of the present invention. The device illustrated in fig. 6 may be a tag reader in RFID technology, and for convenience of description, only the parts relevant to the present invention are shown. The optimal Q value estimator for rfid collision avoidance illustrated in fig. 6 mainly includes an obtaining module 601, an estimating module 602, and an adjusting module 603, which are described in detail as follows:
an obtaining module 601, configured to obtain, in each round of inventory performed on a tag, a tag collision number C, a time slot idle number F, and a Q value used in inventory;
a calculation module 602, configured to input the tag collision frequency C, the time slot idle number F, and a Q value used in inventory as parameters into the serverVia the network netbpTo calculate the optimal Q value, wherein the neural network netbpAfter training, Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in checkingtA neural network of, here, QtThe Q value closest to the optimal Q value in the anti-collision algorithm;
an adjusting module 603 for adjusting the neural network net using a dynamic Q value adjustment algorithmbpAnd adjusting the calculated optimal Q value so that the adjusted Q value is further closest to the optimal Q value in the anti-collision algorithm.
It should be noted that, since the apparatus provided in the embodiment of the present invention is based on the same concept as the method embodiment of the present invention, the technical effect brought by the apparatus is the same as the method embodiment of the present invention, and specific contents may refer to the description in the method embodiment of the present invention, and are not described herein again.
The estimation module 602 illustrated in fig. 6 may include an input unit 701 and a first determining unit 702, such as the optimal Q value estimation apparatus for rfid collision avoidance illustrated in fig. 7, where:
an input unit 701 for inputting the tag collision number C, the slot idle number F, and a Q value used at the time of inventory to the neural network net as parametersbpCalculating to obtain Q't;
a first judging unit 702 for judging whether to continue using the neural network netbpCalculating the optimal Q value if the input unit 701 continues to use the neural network netbpCalculating the optimal Q value, the input unit 701 continues to input the tag collision number C, the slot idle number F and the Q value used in the inventory as parameters into the neural network netbpQ 'is obtained by estimation'tOtherwise, by Q'tAs a neural network netbpAnd (5) calculating the optimal Q value.
The adjusting module 603 illustrated in fig. 6 may include an inventory unit 801 and a second determining unit 802, such as the optimal Q value estimator for rfid collision avoidance illustrated in fig. 8, wherein:
an inventory unit 801 for connecting the neural network netbpThe calculated optimal Q value is used as the Q value of the dynamic Q value adjustment algorithm to carry out label adjustmentOne wheel is counted;
a second determining unit 802, configured to determine whether a process executed by the inventory unit is finished, if so, the inventory unit 801 finishes one wheel disc point performed on the tag, and takes a Q value at the end of inventory as an optimal Q value in the closest anti-collision algorithm, otherwise, the inventory unit 801 fine-tunes the neural network netbpAnd continuously counting the labels for a new round by taking the calculated optimal Q value or the Q value used for finely adjusting the previous round when the labels are counted by adopting the dynamic Q value adjustment algorithm as the Q value of the dynamic Q value adjustment algorithm until the process of counting the labels is finished.
The optimal Q value estimator for rfid collision avoidance of any of fig. 6 to 8 may further include a training module 901, such as the optimal Q value estimator for rfid collision avoidance of any of fig. 9-a to 9-c. The training module 901 is configured to train the neural network net before the obtaining module 601 obtains the tag collision number C, the slot idle number F, and the Q value used in the inventory in each round of inventory performed on the tagbpTo make the trained neural network netbpQ can be calculated according to the input label collision frequency, the time slot idle number and the Q value used in the checkingtI.e. the Q value closest to the best Q value in the collision avoidance algorithm.
Fig. 10 is a schematic structural diagram of a computing device according to an embodiment of the present invention. As shown in fig. 10, the computing device 10 of this embodiment may be a tag reader in RFID technology, and the computing device 10 mainly includes: a processor 100, a memory 101 and a computer program 102 stored in the memory 101 and executable on the processor 100, such as a program for an optimal Q value estimation method for rfid collision avoidance. The processor 100 executes the computer program 102 to implement the steps in the above-mentioned embodiment of the method for estimating the optimal Q value for rfid collision avoidance, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor 100, when executing the computer program 102, implements the functions of each module/unit in each device embodiment described above, such as the functions of the acquiring module 601, the calculating module 602, and the adjusting module 603 shown in fig. 6.
The Processor 100 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 101 may be an internal storage unit of the computing device 10, such as a hard disk or a memory of the computing device 10. Memory 101 may also be an external storage device of computing device 10, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) Card, Flash memory Card (Flash Card), etc. provided on computing device 10. Further, memory 101 may also include both internal storage units of computing device 10 and external storage devices. The memory 101 is used to store computer programs and other programs and data required by the computing device. The memory 101 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus/computing device and method may be implemented in other ways. For example, the above-described apparatus/computing device embodiments are merely illustrative, and for example, a division of modules or units is merely a logical division, and an actual implementation may have another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the method of the embodiments of the present invention may also be implemented by instructing related hardware by a computer program, where the computer program of the optimal Q value estimation method for rfid collision avoidance may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the method may be implemented, that is, the number C of tag collisions, the number F of slot idles, and the Q value used in counting are obtained in each round of counting performed on tags; inputting label collision times C, time slot idle number F and Q value used in inventory into neural network net as parametersbpTo calculate the optimal Q value; neural network net using dynamic Q value adjustment algorithmbpAdjusting the calculated optimal Q value to make the adjusted Q value further closest to the optimal Q value in the anti-collision algorithm, wherein the neural network netbpAfter training, Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in checkingtOf a neural network, QtIs the Q value closest to the best Q value in the collision avoidance algorithm. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory(ROM), Random Access Memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media, among others. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals. The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. An optimal Q value calculation method for radio frequency identification collision avoidance, the method comprising:
acquiring tag collision times C, time slot idle number F and a Q value used in checking in each round of checking on tags;
inputting the label collision times C, the time slot idle number F and the Q value used in inventory into a neural network net as parametersbpTo calculate the optimal Q value, said neural network netbpAfter training, Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in checkingtThe neural network of, the QtThe Q value closest to the optimal Q value in the anti-collision algorithm;
adapting the neural network net to a dynamic Q-value adjustment algorithmbpDerived QtThe value is adjusted so that the resulting Q is adjustedtThe value is further closest to the optimal Q value in the collision avoidance algorithm.
2. The method for estimating the optimal Q value for rfid collision avoidance according to claim 1, wherein the inputting the number of tag collisions C, the number of time slot idleness F, and the Q value used in the inventory into a neural network as parameters to estimate the optimal Q value comprises:
s1, inputting label collision times C, time slot idle number F and Q value used in inventory into the neural network net as parametersbpQ 'is obtained by estimation't；
S2, judging whether to continue using the neural network to calculate the optimal Q value, if so, continuing using the neural network netbpAnd (4) calculating the optimal Q value, then the flow jumps to S1, otherwise, the Q 'is used'tAs said neural network netbpAnd (5) calculating the optimal Q value.
3. The method for calculating optimal Q value for RFID collision avoidance according to claim 1, wherein said neural network net is adjusted by dynamic Q value adjustment algorithmbpAdjusting the calculated optimal Q value to make the adjusted Q value closest to the optimal Q value in the anti-collision algorithm, comprising:
s' 1, connecting the neural network netbpTaking the calculated optimal Q value as the Q value of the dynamic Q value adjustment algorithm to perform one-round counting on the label;
s '2, judging whether the process of the S' 1 is finished or not, if so, finishing a wheel disc point of the label, taking a Q value at the end of the wheel disc point as an optimal Q value in the closest anti-collision algorithm, and otherwise, finely adjusting the neural network netbpAnd continuously counting the labels for a new round by taking the calculated optimal Q value or the Q value used for finely adjusting the previous round when the labels are counted by adopting the dynamic Q value adjustment algorithm as the Q value of the dynamic Q value adjustment algorithm until the process of counting the labels is finished.
4. The method for estimating the optimal Q value of radio frequency identification collision avoidance according to any one of claims 1 to 3, wherein the method further comprises:
before acquiring label collision times C, time slot idle number F and Q value used in inventory in each round of inventory of labels, training the neural network netbpSo that said trained neural netbpThe Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in the checkingt。
5. An optimal Q value estimator for RFID collision avoidance, comprising:
the acquisition module is used for acquiring the label collision times C, the time slot idle number F and a Q value used in counting in each round of counting of the labels;
a calculation module for inputting the label collision frequency C, the time slot idle number F and the Q value used in inventory into the neural network net as parametersbpTo calculate the optimal Q value, said neural network netbpAfter training, Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in checkingtThe neural network of, the QtThe Q value closest to the optimal Q value in the anti-collision algorithm;
an adjustment module for adjusting the neural network net using a dynamic Q-value adjustment algorithmbpDerived QtThe value is adjusted so that the resulting Q is adjustedtThe value is further closest to the optimal Q value in the collision avoidance algorithm.
6. The radio frequency identification collision avoidance optimal Q value estimator as claimed in claim 5, wherein said estimator module comprises:
an input unit for inputting the tag collision number C, the slot idle number F, and a Q value used in inventory to the neural network net as parametersbpQ 'is obtained by estimation't；
A first judgment unit for judging whether to continue using the neural network netbpCalculating the optimal Q value if the input unit continues to use the neural network netbpCalculating the optimal Q value, the input unit continues to input the label collision frequency C, the time slot idle number F and the Q value used in inventory as parameters into the neural network netbpQ 'is obtained by estimation'tOtherwise, with the Q'tAs said neural network netbpAnd (5) calculating the optimal Q value.
7. The radio frequency identification collision avoidance best Q estimator of claim 5, wherein the adjusting module comprises:
an inventory unit for inventorying the neural network netbpTaking the calculated optimal Q value as the Q value of the dynamic Q value adjustment algorithm to perform one-round counting on the label;
a second judging unit, configured to judge whether a process executed by the inventory unit is finished, if so, the inventory unit finishes a wheel disc point performed on the tag, and takes a Q value at the end of inventory as an optimal Q value in the closest anti-collision algorithm, otherwise, the inventory unit finely adjusts the neural network netbpAnd continuously counting the labels for a new round by taking the calculated optimal Q value or the Q value used for finely adjusting the previous round when the labels are counted by adopting the dynamic Q value adjustment algorithm as the Q value of the dynamic Q value adjustment algorithm until the process of counting the labels is finished.
8. The apparatus for estimating optimal Q for rfid collision avoidance according to any one of claims 5 to 7, wherein the apparatus further comprises:
a training module, configured to train the neural network net before the obtaining module obtains the tag collision number C, the slot idle number F, and the Q value used in the inventory for each round of the inventory of the tagbpSo that said trained neural netbpThe Q can be calculated according to the input label collision times, the time slot idle number and the Q value used in the checkingt。
9. A computing device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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