A smart RFID electronic tag system and its working method

Abstract

The present invention discloses an intelligent RFID electronic tag system and a working method thereof, and the present invention relates to the technical field of electronic tags. Scanning encryption unit: a first target obtains information data of a second target. The intelligent RFID electronic tag system and the control method thereof, by taking the first scanning area of ​​the electronic tag scanner as the basis and changing the scanning area in the subsequent scanning process of the electronic tag scanner, establish an encryption method between the electronic tag scanner and the RFID electronic tag, and set area restrictions, effectively enhance the security of the RFID electronic tag during use, and divide the position state of the RFID electronic tag into a static position and a dynamic position, and can provide positioning prompts for the RFID electronic tag. Compared with the traditional real-time positioning method, the prompt effect is better, and the RFID electronic tag is scanned at set intervals in conjunction with the scanning, so that the search work is faster and more convenient, and the error rate of commodity placement can also be significantly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic tags, in particular to an intelligent RFID electronic tag system and a working method thereof. Background Art

[0002] Referring to the Chinese patent, the logistics management system based on RFID electronic tags (Announcement No.: CN104463519A, Announcement Date: 2015-03-25) uses RFID to count the collection. Just use a handheld RFID reader to scan along the shelf up and down, and the file information stored in the RFID tag in advance will be presented one by one. This not only greatly reduces the labor intensity of the staff, but also simplifies the work process; using RFID technology, you only need to enter the file you want to find in the RFID reader and scan along the shelf in turn. Once a shelf error is found or the required file is found, an audible and visual alarm will be used immediately, which makes the search work very convenient and can also significantly reduce the rate of wrong shelving and disordered shelving.

[0003] The existing RFID electronic tag management process still has other defects, for example: the area scanned by the electronic tag scanner is fixed, so fixed information can be read, and the security is enhanced by relying on the encryption mode, which is too costly, and a special encryption method cannot be established between the electronic tag scanner and the RFID electronic tag. At the same time, the position status monitoring of the RFID electronic tag is not in real-time positioning mode, and the fault tolerance rate is relatively low. Therefore, the present invention proposes an intelligent RFID electronic tag system and its working method to solve the above-mentioned problems. Summary of the invention

[0004] (I) Technical problems solved by the corner

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent RFID electronic tag system and a working method thereof, which solves the problems mentioned in the above background technology.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent RFID electronic tag system, comprising:

[0008] Scanning encryption unit: The first target obtains the information data of the second target, based on the first scanning area of ​​the first target, and changes the scanning area in the subsequent scanning process of the first target, so that the first target and the second target establish an encryption method, wherein the first target is specifically an electronic tag scanner, and the second target is specifically an RFID electronic tag;

[0009] Positioning analysis unit: distinguishing the position state of the second target, specifically dividing it into a static position and a dynamic position, and then analyzing the state of the second target according to the set static position and dynamic position, wherein the static position and the dynamic position can be adjusted and switched;

[0010] Positioning scanning unit: sets the sorting position of the second target, and then scans at intervals in the set order to analyze whether the sorting position of the second target is correct, wherein the sorting position is set from large to small or from small to large.

[0011] Preferably, the scanning area is changed in the subsequent scanning process of the first target in the scanning encryption unit so that the encryption mode is established between the first target and the second target in the specific manner as follows:

[0012] S1: First, the scanning area of ​​the first target is divided into Hj, where j is the serial number of the scanning area. Then, when the first target scans the RFID electronic tag of the second target, the first scanning area Hj is identified, and the first target is associated with the second target.

[0013] S2: Then set the scanning time threshold YT. Within the YT time, identify the scanning areas Hj in turn, and combine them after the scanning areas Hj are completely identified, so that the scanning areas Hj are in a complete state. When the scanning area Hj is in a direct scanning state, the RFID electronic tag will not be identified.

[0014] Preferably, in S2, when the scanning area Hj is combined after it is completely identified, the order of combination is the adjustment order, the first scan sets the order of the next scanning area, the second scan sets the order of the next scanning area, and so on, wherein the scanning combination order is random.

[0015] Preferably, in S2, when the scanning area Hj is identified sequentially within the YT time, the range area HU is set. Within the range area, the first target directly identifies the second target when scanning the RFID electronic tag. When it exceeds the range area HU, the scanning area Hj is a sequential identification process.

[0016] Preferably, the specific manner in which the positioning analysis unit analyzes the state of the second target according to the set static position and dynamic position is:

[0017] P1: First, the status of the RFID electronic tag is set to static position JZ and dynamic position DJ. When the static position JZ and dynamic position DJ change, the supervisory end of the RFID electronic tag will issue an early warning prompt;

[0018] P2: Then set the time judgment threshold FT of the static position JZ and the dynamic position DJ. When the static position JZ changes to the dynamic position DJ or the dynamic position DJ changes to the static position JZ, the early warning positioning is performed after the time reaches the time judgment threshold FT.

[0019] Preferably, after the time in P2 reaches the time judgment threshold FT, the specific method of performing early warning positioning is to mark the position of the RFID electronic tag and mark it with a red dot, wherein the position of the RFID electronic tag is located in combination with the map.

[0020] Preferably, the positioning scanning unit performs scanning at intervals in a set order, and the specific method for analyzing whether the sorting position of the second target is correct is: first scan the first RFID electronic tag A1, and then scan the last RFID electronic tag An after it is correct. After determining the number n, scan RFID electronic tags at intervals of s at this time, and issue an early warning prompt if the serial number is wrong.

[0021] The present invention discloses a smart RFID electronic tag working method, which specifically comprises the following steps:

[0022] Step 1: The first target obtains information data of the second target, based on the first scanning area of ​​the first target, and changes the scanning area in the subsequent scanning process of the first target, so that the first target and the second target establish an encryption method;

[0023] Step 2: Distinguish the position state of the second target, specifically into a static position and a dynamic position, and then analyze the state of the second target according to the set static position and dynamic position;

[0024] Step 3: Set the sorting position of the second target, and then scan at intervals according to the set sequence to analyze whether the sorting position of the second target is correct.

[0025] Preferably, the step three is to globally optimize the sorting position of the second target by combining a genetic algorithm and a particle swarm optimization algorithm (PSO); specifically comprising the following steps:

[0026] Step 1: Before starting the sorting position optimization, it is necessary to generate multiple initial solutions of sorting positions as a population; these initial solutions are generated randomly and based on existing empirical data;

[0027] Define the problem parameters: set the number of second targets n, that is, the total number of targets that need to be sorted; set the population size m, that is, the number of individuals in the initial population;

[0028] Initial population generation method: The initial solution for each sorting position is randomly generated to ensure the diversity of the population; based on historical data and experience, a more reasonable initial solution is generated based on these data, thereby improving optimization efficiency;

[0029] Step 2: Selection, crossover and mutation operations of genetic algorithm

[0030] 2.1 Select Operation

[0031] Evaluate the individuals in the initial population and select individuals with higher fitness as parents of the next generation; fitness is evaluated based on the accuracy of sorting position and scanning efficiency;

[0032] Fitness function:

[0033]

[0034] Among them, f(P i ) represents individual P i The fitness of E(P i ) represents individual P i The sorting position error and scanning efficiency loss of the individual are reduced; the higher the fitness value, the better the sorting position of the individual;

[0035] The selection method is Roulette Wheel Selection, that is, the selection probability is proportional to the fitness;

[0036]

[0037] Among them, P select (P i ) represents individual P i The probability of being selected as a parent; f(P i ) represents individual P i The fitness of f(P j ) represents individual P j Adaptability; To seek peace;

[0038] 2.2 Crossover Operation

[0039] The parent individuals after the selection operation generate new individuals through the crossover operation; the crossover operation is carried out through partial mapping crossover (PMX), sequential crossover (OX) and cyclic crossover (CX);

[0040] Partial Mapping Crossover (PMX): Select two parent individuals P parent1 and P parent2 , randomly select a crossover interval [a, b]; exchange some genes of the parent individuals in the interval [a, b] to generate two offspring individuals P child1 and P chi ld2 ;

[0041] 2.3 Mutation Operation

[0042] Perform mutation operations on the newly generated offspring individuals to increase the diversity of the population; the mutation operation is achieved by randomly exchanging the order of two positions;

[0043] Mutation method: Randomly select individual P i The two positions k and l in the , exchange the elements of these two positions, and generate a new individual P' i ;

[0044] 2.4 Selection Operation Based on Tournament Selection

[0045] In order to more effectively select individuals with higher fitness, the tournament selection method is used instead of the traditional roulette selection method; the tournament selection randomly selects several individuals from the population to compete and selects the individual with the highest fitness as the parent;

[0046] Selection operation steps: randomly select k individuals from the population to form a tournament group; select the individual with the highest fitness in the tournament group as the parent; repeat the above steps until all parent individuals are selected;

[0047] Fitness function:

[0048]

[0049] Among them, f(P i ) represents individual P i The fitness of E(P i ) represents individual P i Sorting position error and scanning efficiency loss;

[0050] Selection probability: In each tournament group, select the individual with the highest fitness

[0051]

[0052] Among them, tournament represents the set of individuals in the tournament group; P select (P i ) represents individual P i The probability of being selected as a parent; f(P j ) represents individual P j Adaptability;

[0053] 2.5 Adaptive mutation operation

[0054] In order to increase the diversity of the population and avoid falling into the local optimal solution, an adaptive mutation operation is introduced; the adaptive mutation operation dynamically adjusts the mutation probability according to the fitness of the individual;

[0055] Mutation operation steps: Dynamically adjust the mutation probability P according to the individual fitness mutation ;

[0056] Randomly select individual P i The two positions k and l in the , exchange the elements of these two positions, and generate a new individual P' i ;

[0057] Mutation probability formula:

[0058]

[0059] Among them, P mutation (P i ) represents individual P i The mutation probability, P base is the baseline mutation probability, f(P i ) is the individual P i The fitness of is the maximum fitness in the population;

[0060] Mutation operation formula: For individual P i =[x1,x2,…,x n ], randomly select positions k and l to exchange:

[0061] P' i =swap(P i ,k,l)

[0062] Among them, swap(P i ,k,l) ​​represents the exchange of P i The elements at positions k and l generate new individuals P' i ;

[0063] Step 3: After the genetic algorithm generates new sorting position solutions, the particle swarm optimization algorithm (PSO) is used to further optimize these sorting position solutions;

[0064] 3.1 Initializing the particle swarm

[0065] The offspring individuals generated by the genetic algorithm are used as the initial position and velocity of the particle swarm;

[0066] Particle position x i : represents the sorting position of the i-th particle;

[0067] Particle speed v i : represents the speed of the i-th particle, initialized to a small random value;

[0068] 3.2 Update particle speed and position

[0069] In each iteration, the particle's velocity and position are updated according to the particle's current velocity, individual optimal position, and global optimal position;

[0070] Speed ​​update formula:

[0071] v i (t+1)=ωv i (t)+c1r1(p i (t)-x i (t))+c2r2(g(t)-x i (t))

[0072] Among them, v i (t) represents the velocity of the ith particle at time t, v i (t+1) represents the velocity of the i-th particle at time t+1. ω is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers, and p i (t) is the individual optimal position of the ith particle, g(t) is the global optimal position; i (t) represents the position of the i-th particle at time t;

[0073] Position update formula:

[0074] x i (t+1)=x i (t)+v i (t+1)

[0075] Among them, x i (t) represents the position of the i-th particle at time t; x i (t+1) represents the position of the i-th particle at time t+1.

[0076] 3.3 Finding the Global Optimal Solution

[0077] In each iteration, the global optimal solution is updated until the preset number of iterations is reached or the fitness function converges;

[0078] The optimal position of an individual i : The optimal sorting position found by particle i so far;

[0079] Global optimal position g: the global optimal sorting position found among all particles;

[0080] 3.4 Particle swarm based on chaos initialization

[0081] In order to increase the diversity of the initial particle swarm and avoid falling into the local optimal solution at an early stage, a chaotic initialization method is adopted; the chaotic sequence has ergodicity and randomness, and can better cover the search space;

[0082] Chaos initialization step: Generate initial position and velocity using chaos mapping; Map the chaotic sequence to the particle's position and velocity range;

[0083] Chaotic mapping formula: Using Logistic mapping to generate chaotic sequences

[0084] x k+1 = r·x k ·(1-x k )

[0085] Among them, x k is the kth value of the chaotic sequence, r is the parameter of the chaotic system, which generally ensures that the system is in a completely chaotic state;

[0086] Chaos initialization formula:

[0087] Initial position x i (0)

[0088] x i (0) = x min +x k ·(x max -x min )

[0089] Among them, x min and x max are the minimum and maximum values ​​of the position, respectively, x k is the chaotic sequence value;

[0090] Initial velocity v i (0):

[0091] v i (0) = v min +x k ·(v max -v min )

[0092] Among them, v min and v max are the minimum and maximum values ​​of the velocity, respectively, x k is the chaotic sequence value;

[0093] Step 4: Apply the sorting position optimized by the genetic algorithm and the PSO algorithm to the interval scanning to ensure the accuracy of the sorting position;

[0094] 4.1 Define interval scanning parameters

[0095] Set the scanning time interval Δt, that is, the time interval between each scan; define the range R of each scan; define the number of targets n, that is, the total number of targets that need to be sorted and scanned;

[0096] 4.2 Applying the optimized sort position to interval scanning

[0097] The sorting position P after optimization by genetic algorithm and PSO algorithm opt , expressed as:

[0098] P opt =[p1,p2,…,p n ]

[0099] Among them, p i represents the optimized ranking position of the i-th target;

[0100] 4.3 Performing interval scanning

[0101] According to the optimized sorting position P opt , perform interval scanning to ensure that each target is scanned in an optimized order;

[0102] (III) Beneficial effects

[0103] The present invention provides an intelligent RFID electronic tag system and a working method thereof. Compared with the prior art, it has the following beneficial effects:

[0104] The intelligent RFID electronic tag system and its working method, by taking the first scanning area of ​​the electronic tag scanner as the basis and changing the scanning area in the subsequent scanning process of the electronic tag scanner, establish an encryption method between the electronic tag scanner and the RFID electronic tag, and set area restrictions, effectively enhance the security of the RFID electronic tag during use, and divide the position state of the RFID electronic tag into a static position and a dynamic position, and can provide positioning prompts for the RFID electronic tag. Compared with the traditional real-time positioning method, the prompt effect is better, and the RFID electronic tag is used to scan at a set sequence interval, so that the search work is faster and more convenient, and the error rate of commodity placement can be significantly reduced. At the same time, the genetic algorithm and the particle swarm optimization algorithm (PSO) are combined to globally optimize the sorting position of the second target; compared with the traditional real-time positioning method, the prompt effect is better, and the RFID electronic tag is used to scan at a set sequence interval, so that the search work is faster and more convenient, and the error rate of commodity placement can be significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 This is a block diagram of the smart RFID electronic tag system shown in the embodiment of the present application;

[0106] Figure 2A schematic diagram of the structure of an electronic device shown in an embodiment of the present application;

[0107] Figure 3 This is a flow chart of the intelligent RFID electronic tag control method shown in the embodiment of the present application;

[0108] Figure 4 This is a flow chart of the intelligent algorithm in step three of the intelligent RFID electronic tag control method shown in the embodiment of the present application. DETAILED DESCRIPTION

[0109] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0110] See also Figure 1-Figure 3 , the embodiment of the present invention provides four technical solutions:

[0111] Embodiment 1

[0112] An intelligent RFID electronic tag system, comprising:

[0113] Scanning encryption unit: The first target obtains the information data of the second target, based on the first scanning area of ​​the first target, and changes the scanning area in the subsequent scanning process of the first target, so that the first target and the second target establish an encryption method, wherein the first target is specifically an electronic tag scanner, and the second target is specifically an RFID electronic tag; the scanning encryption unit changes the scanning area in the subsequent scanning process of the first target, so that the first target and the second target establish an encryption method in a specific manner as follows:

[0114] S1: First, the scanning area of ​​the first target is divided into Hj, where j is the serial number of the scanning area. Then, when the first target scans the RFID electronic tag of the second target, the first scanning area Hj is identified, and the first target is associated with the second target.

[0115] S2: Then set the scanning time threshold YT. Within the YT time, identify the scanning area Hj in turn, and combine after the scanning area Hj is completely identified, so that the scanning area Hj is in a complete state. When the scanning area Hj is in a direct scanning state, the RFID electronic tag is not identified. In S2, when the scanning area Hj is combined after it is completely identified, the order of combination is the adjustment order. The first scan sets the order of the next scanning area, and the next scan sets the order of the next scanning area, and so on. The scanning combination order is random. In S2, when the scanning area Hj is identified in turn within the YT time, the range area HU is set. Within the range area, the first target directly identifies the second target when the RFID electronic tag scans it. When it exceeds the range area HU, the scanning area Hj is a sequential identification process.

[0116] Positioning analysis unit: distinguish the position state of the second target, specifically into static position and dynamic position, and then analyze the state of the second target according to the set static position and dynamic position, wherein the static position and the dynamic position can be adjusted and switched; the specific manner in which the position analysis unit analyzes the state of the second target according to the set static position and dynamic position is:

[0117] P1: First, the status of the RFID electronic tag is set to static position JZ and dynamic position DJ. When the static position JZ and dynamic position DJ change, the supervisory end of the RFID electronic tag will issue an early warning prompt;

[0118] P2: Then set the time judgment threshold FT of the static position JZ and the dynamic position DJ. When the static position JZ changes to the dynamic position DJ or the dynamic position DJ changes to the static position JZ, the warning positioning is performed after the time reaches the time judgment threshold FT. After the time reaches the time judgment threshold FT in P2, the specific method of performing the warning positioning is to mark the position of the RFID electronic tag and mark it with a red dot, where the position of the RFID electronic tag is located in combination with the map.

[0119] Positioning scanning unit: Set the sorting position of the second target, and then scan it at intervals according to the set sequence to analyze whether the sorting position of the second target is correct, where the sorting position is set from large to small or from small to large. The specific method of scanning at intervals according to the set sequence in the positioning scanning unit to analyze whether the sorting position of the second target is correct is as follows: first scan the first RFID electronic tag A1, and then scan the last RFID electronic tag An after it is correct. After determining the number n, scan RFID electronic tags at intervals of s at this time, and issue an early warning prompt if the sequence number is wrong.

[0120] Embodiment 2

[0121] An intelligent RFID electronic tag system, comprising:

[0122] Scanning encryption unit: The first target obtains the information data of the second target, based on the first scanning area of ​​the first target, and changes the scanning area in the subsequent scanning process of the first target, so that the first target and the second target establish an encryption method, wherein the first target is specifically an electronic tag scanner, and the second target is specifically an RFID electronic tag;

[0123] Positioning analysis unit: distinguish the position state of the second target, specifically into a static position and a dynamic position, and then analyze the state of the second target according to the set static position and dynamic position, wherein the static position and the dynamic position can be adjusted and switched;

[0124] Positioning scanning unit: Set the sorting position of the second target, and then scan it at intervals according to the set sequence to analyze whether the sorting position of the second target is correct, wherein the sorting position is set from large to small or from small to large.

[0125] Embodiment 3

[0126] A smart RFID electronic tag working method specifically includes the following steps:

[0127] Step 1: The first target obtains information data of the second target, based on the first scanning area of ​​the first target, and changes the scanning area in the subsequent scanning process of the first target, so that the first target and the second target establish an encryption method;

[0128] Step 2: Distinguish the position state of the second target, specifically into a static position and a dynamic position, and then analyze the state of the second target according to the set static position and dynamic position;

[0129] Step 3: Set the sorting position of the second target, and then scan at intervals according to the set sequence to analyze whether the sorting position of the second target is correct.

[0130] like Figure 4 In step 3, the ranking position of the second target is globally optimized by combining the genetic algorithm and the particle swarm optimization algorithm (PSO); the specific steps are:

[0131] Step 1: Before starting the sorting position optimization, it is necessary to generate multiple initial solutions of sorting positions as a population; these initial solutions are generated randomly and based on existing empirical data;

[0132] Define the problem parameters: set the number of second targets n, that is, the total number of targets that need to be sorted; set the population size m, that is, the number of individuals in the initial population;

[0133] Initial population generation method: The initial solution for each sorting position is randomly generated to ensure the diversity of the population; based on historical data and experience, a more reasonable initial solution is generated based on these data, thereby improving optimization efficiency;

[0134] Step 2: Selection, crossover and mutation operations of genetic algorithm

[0135] 2.1 Select Operation

[0136] Evaluate the individuals in the initial population and select individuals with higher fitness as parents of the next generation; fitness is evaluated based on the accuracy of sorting position and scanning efficiency;

[0137] Fitness function:

[0138]

[0139] Among them, f(P i ) represents individual P i The fitness of E(P i ) represents individual P i The sorting position error and scanning efficiency loss of the individual are reduced; the higher the fitness value, the better the sorting position of the individual;

[0140] The selection method is Roulette Wheel Selection, that is, the selection probability is proportional to the fitness;

[0141]

[0142] Among them, P select (P i ) represents individual P i The probability of being selected as a parent; f(P i ) represents individual P i The fitness of f(P j ) represents individual P j Adaptability; To seek peace;

[0143] 2.2 Crossover Operation

[0144] The parent individuals after the selection operation generate new individuals through the crossover operation; the crossover operation is carried out through partial mapping crossover (PMX), sequential crossover (OX) and cyclic crossover (CX);

[0145] Partial Mapping Crossover (PMX): Select two parent individuals P parent1 and P parent2 , randomly select a crossover interval [a, b]; exchange some genes of the parent individuals in the interval [a, b] to generate two offspring individuals P child1 and P chi ld2 ;

[0146] 2.3 Mutation Operation

[0147] Perform mutation operations on the newly generated offspring individuals to increase the diversity of the population; the mutation operation is achieved by randomly exchanging the order of two positions;

[0148] Mutation method: Randomly select individual P i The two positions k and l in the , exchange the elements of these two positions, and generate a new individual P' i ;

[0149] 2.4 Selection Operation Based on Tournament Selection

[0150] In order to more effectively select individuals with higher fitness, the tournament selection method is used instead of the traditional roulette selection method; the tournament selection randomly selects several individuals from the population to compete and selects the individual with the highest fitness as the parent;

[0151] Selection operation steps: randomly select k individuals from the population to form a tournament group; select the individual with the highest fitness in the tournament group as the parent; repeat the above steps until all parent individuals are selected;

[0152] Fitness function:

[0153]

[0154] Among them, f(P i ) represents individual P i The fitness of E(P i ) represents individual P i Sorting position error and scanning efficiency loss;

[0155] Selection probability: In each tournament group, select the individual with the highest fitness

[0156]

[0157] Among them, tournament represents the set of individuals in the tournament group; P select (P i ) represents individual P i The probability of being selected as a parent; f(P j ) represents individual P j Adaptability;

[0158] 2.5 Adaptive mutation operation

[0159] In order to increase the diversity of the population and avoid falling into the local optimal solution, an adaptive mutation operation is introduced; the adaptive mutation operation dynamically adjusts the mutation probability according to the fitness of the individual;

[0160] Mutation operation steps: Dynamically adjust the mutation probability P according to the individual fitness mutation ;

[0161] Randomly select individual P i The two positions k and l in the , exchange the elements of these two positions, and generate a new individual P' i ;

[0162] Mutation probability formula:

[0163]

[0164] Among them, P mutation (P i ) represents individual P i The mutation probability, P base is the baseline mutation probability, f(P i ) is the individual P i The fitness of is the maximum fitness in the population;

[0165] Mutation operation formula: For individual P i =[x1,x2,…,x n ], randomly select positions k and l to exchange:

[0166] P' i =swap(P i ,k,l)

[0167] Among them, swap(P i ,k,l) ​​represents the exchange of P i The elements at positions k and l generate new individuals P' i ;

[0168] Step 3: After the genetic algorithm generates new sorting position solutions, the particle swarm optimization algorithm (PSO) is used to further optimize these sorting position solutions;

[0169] 3.1 Initializing the particle swarm

[0170] The offspring individuals generated by the genetic algorithm are used as the initial position and velocity of the particle swarm;

[0171] Particle position x i : represents the sorting position of the i-th particle;

[0172] Particle speed v i : represents the speed of the i-th particle, initialized to a small random value;

[0173] 3.2 Update particle speed and position

[0174] In each iteration, the particle's velocity and position are updated according to the particle's current velocity, individual optimal position, and global optimal position;

[0175] Speed ​​update formula:

[0176] v i (t+1)=ωv i (t)+c1r1(p i (t)-x i (t))+c2r2(g(t)-x i (t))

[0177] Among them, v i (t) represents the velocity of the ith particle at time t, v i (t+1) represents the velocity of the i-th particle at time t+1. ω is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers, and p i (t) is the individual optimal position of the ith particle, g(t) is the global optimal position; i (t) represents the position of the i-th particle at time t;

[0178] Position update formula:

[0179] x i (t+1)=x i (t)+v i (t+1)

[0180] Among them, x i (t) represents the position of the i-th particle at time t; x i (t+1) represents the position of the i-th particle at time t+1.

[0181] 3.3 Finding the Global Optimal Solution

[0182] In each iteration, the global optimal solution is updated until the preset number of iterations is reached or the fitness function converges;

[0183] The optimal position of an individual i : The optimal sorting position found by particle i so far;

[0184] Global optimal position g: the global optimal sorting position found among all particles;

[0185] 3.4 Particle swarm based on chaos initialization

[0186] In order to increase the diversity of the initial particle swarm and avoid falling into the local optimal solution at an early stage, a chaotic initialization method is adopted; the chaotic sequence has ergodicity and randomness, and can better cover the search space;

[0187] Chaos initialization step: Generate initial position and velocity using chaos mapping; Map the chaotic sequence to the particle's position and velocity range;

[0188] Chaotic mapping formula: Using Logistic mapping to generate chaotic sequences

[0189] x k+1 = r·x k ·(1-x k )

[0190] Among them, x k is the kth value of the chaotic sequence, r is the parameter of the chaotic system, which generally ensures that the system is in a completely chaotic state;

[0191] Chaos initialization formula:

[0192] Initial position x i (0)

[0193] x i (0) = x min +x k ·(x max -x min )

[0194] Among them, x min and x max are the minimum and maximum values ​​of the position, respectively, x k is the chaotic sequence value;

[0195] Initial velocity v i (0):

[0196] v i (0) = v min +x k ·(v max -v min )

[0197] Among them, v min and v max are the minimum and maximum values ​​of the velocity, respectively, x k is the chaotic sequence value;

[0198] Step 4: Apply the sorting position optimized by the genetic algorithm and the PSO algorithm to the interval scanning to ensure the accuracy of the sorting position;

[0199] 4.1 Define interval scanning parameters

[0200] Set the scanning time interval Δt, that is, the time interval between each scan; define the range R of each scan; define the number of targets n, that is, the total number of targets that need to be sorted and scanned;

[0201] 4.2 Applying the optimized sort position to interval scanning

[0202] The sorting position P after optimization by genetic algorithm and PSO algorithm opt , expressed as:

[0203] P opt =[p1,p2,…,p n ]

[0204] Among them, p i represents the optimized ranking position of the i-th target;

[0205] 4.3 Performing interval scanning

[0206] According to the optimized sorting position P opt , performing interval scans to ensure that each target is scanned in an optimized order.

[0207] Embodiment 4

[0208] Corresponding to the aforementioned application function implementation method embodiment, the present application also provides an electronic device and corresponding embodiments.

[0209] See also Figure 2 , the electronic device 1000 includes a memory 1010 and a processor 1020 .

[0210] The processor 1020 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0211] The memory 1010 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 1020 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 1010 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 1010 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0212] The memory 1010 stores executable codes, and when the executable codes are processed by the processor 1020 , the processor 1020 can execute part or all of the methods described above.

[0213] The scheme of the present application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the description of each embodiment has its own emphasis. For the part that is not described in detail in a certain embodiment, refer to the relevant description of other embodiments. Those skilled in the art should also know that the actions and modules involved in the description are not necessarily required for the present application. In addition, it can be understood that the steps in the method of the embodiment of the present application can be adjusted in order, merged and deleted according to actual needs, and the modules in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0214] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0215] Alternatively, the present application can also be implemented as a non-temporary machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) on which executable code (or computer program, or computer instruction code) is stored. When the executable code (or computer program, or computer instruction code) is executed by a processor of an electronic device (or electronic device, server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0216] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the application herein may be implemented as electronic hardware, computer software, or combinations of both.

[0217] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system and method according to multiple embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a part of a module, a program segment or a code, and the part of the module, the program segment or the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or the flow chart, and the combination of the square boxes in the block diagram and / or the flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0218] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. An intelligent RFID electronic tag system, characterized in that: include: Scanning encryption unit: The first target obtains the information data of the second target, based on the first scanning area of ​​the first target, and changes the scanning area in the subsequent scanning process of the first target, so that the first target and the second target establish an encryption method, wherein the first target is specifically an electronic tag scanner, and the second target is specifically an RFID electronic tag; Positioning analysis unit: distinguishing the position state of the second target, specifically dividing it into a static position and a dynamic position, and then analyzing the state of the second target according to the set static position and dynamic position, wherein the static position and the dynamic position can be adjusted and switched; Positioning scanning unit: setting the sorting position of the second target, and then scanning at intervals according to the set sequence to analyze whether the sorting position of the second target is correct, wherein the sorting position is set from large to small or from small to large; The specific method of changing the scanning area during the subsequent scanning process of the first target in the scanning encryption unit so that the first target and the second target establish an encryption method is: S1: First, the scanning area of ​​the first target is divided into Hj, where j is the serial number of the scanning area. Then, when the first target scans the RFID electronic tag of the second target, the first scanning area Hj is identified, and the first target is associated with the second target. S2: Then set the scanning time threshold YT. Within the YT time, identify the scanning area Hj in turn, and combine after the scanning area Hj is completely identified, so that the scanning area Hj is in a complete state. When the scanning area Hj is in a direct scanning state, the RFID electronic tag will not be identified. In said S2, when the scanning area Hj is combined after being completely identified, the order of combination is the adjustment order, the first scan sets the order of the next scanning area, the second scan sets the order of the next scanning area, and so on, wherein the scanning combination order is random; In the above S2, when the scanning area Hj is identified sequentially within the YT time, the range area HU is set. Within the range area, the first target directly identifies the second target when scanning the RFID electronic tag. When the range area HU is exceeded, the scanning area Hj is a sequential identification process; The specific manner in which the positioning analysis unit analyzes the state of the second target according to the set static position and dynamic position is: P1: First, the status of the RFID electronic tag is set to static position JZ and dynamic position DJ. When the static position JZ and dynamic position DJ change, the supervisory end of the RFID electronic tag will issue an early warning prompt; P2: Then set the time judgment threshold FT of the static position JZ and the dynamic position DJ. When the static position JZ changes to the dynamic position DJ or the dynamic position DJ changes to the static position JZ, after the time reaches the time judgment threshold FT, perform early warning positioning; After the time in P2 reaches the time judgment threshold FT, the specific method of early warning positioning is to mark the position of the RFID electronic tag and mark it with a red dot, wherein the position of the RFID electronic tag is located in combination with the map; The positioning scanning unit performs scanning at intervals according to the set sequence, and the specific method for analyzing whether the sorting position of the second target is correct is: first scan the first RFID electronic tag A1, and then scan the last RFID electronic tag An after it is correct. After determining the number n, scan RFID electronic tags at intervals of s at this time, and issue an early warning prompt if the sequence number is wrong.

2. A smart RFID electronic tag working method, characterized in that: The specific steps include: Step 1: The first target obtains information data of the second target, based on the first scanning area of ​​the first target, and changes the scanning area in the subsequent scanning process of the first target, so that the first target and the second target establish an encryption method; Step 2: Distinguish the position state of the second target, specifically into a static position and a dynamic position, and then analyze the state of the second target according to the set static position and dynamic position; Step 3: Set the sorting position of the second target, and then scan at intervals according to the set sequence to analyze whether the sorting position of the second target is correct; Step 3: Globally optimize the sorting position of the second target by combining the genetic algorithm and the particle swarm optimization algorithm PSO; specifically, the following steps are included: Step 1: Before starting the sorting position optimization, it is necessary to generate multiple initial solutions of sorting positions as a population; these initial solutions are generated randomly and based on existing empirical data; Define the problem parameters: set the number of second targets n, that is, the total number of targets that need to be sorted; set the population size m, that is, the number of individuals in the initial population; Initial population generation method: The initial solution for each sorting position is randomly generated to ensure the diversity of the population; based on historical data and experience, a more reasonable initial solution is generated based on these data, thereby improving optimization efficiency; Step 2: selection operation, crossover operation and mutation operation of genetic algorithm; 2.1 Select Operation Evaluate the individuals in the initial population and select individuals with higher fitness as parents of the next generation; fitness is evaluated based on the accuracy of sorting position and scanning efficiency; Fitness function: Among them, f(P i ) represents individual P i The fitness of E(P i ) represents individual P i The sorting position error and scanning efficiency loss of the individual are reduced; the higher the fitness value, the better the sorting position of the individual; The selection method is roulette selection, that is, the probability of selection is proportional to the fitness; Among them, P select (P i ) represents individual P i The probability of being selected as a parent; f(P i ) represents individual P i The fitness of f(P j ) represents individual P j Adaptability; To seek peace; 2.2 Crossover Operation After the selection operation, the parent individuals generate new individuals through crossover operation; the crossover operation is carried out through partial mapping crossover PMX, sequential crossover OX and cyclic crossover CX. Partial mapping crossover PMX: Select two parent individuals P parent1 and P parent2 , randomly select a crossover interval [a, b]; exchange some genes of the parent individuals in the interval [a, b] to generate two offspring individuals P child1 and P child2 ; 2.3 Mutation Operation Perform mutation operations on the newly generated offspring individuals to increase the diversity of the population; the mutation operation is achieved by randomly exchanging the order of two positions; Mutation method: Randomly select individual P i The two positions k and l in the , exchange the elements of these two positions, and generate a new individual P' i ; 2.4 Selection Operation Based on Tournament Selection In order to more effectively select individuals with higher fitness, the tournament selection method is used instead of the traditional roulette selection method; the tournament selection randomly selects several individuals from the population to compete and selects the individual with the highest fitness as the parent; Selection operation steps: randomly select k individuals from the population to form a tournament group; select the individual with the highest fitness in the tournament group as the parent; repeat the above steps until all parent individuals are selected; fitness function: Among them, f(P i ) represents individual P i The fitness of E(P i ) represents individual P i Sorting position error and scanning efficiency loss; Selection probability: In each tournament group, select the individual with the highest fitness Among them, tournament represents the set of individuals in the tournament group; P select (P i ) represents individual P i The probability of being selected as a parent; f(P j ) represents individual P j Adaptability; 2.5 Adaptive mutation operation In order to increase the diversity of the population and avoid falling into the local optimal solution, an adaptive mutation operation is introduced; the adaptive mutation operation dynamically adjusts the mutation probability according to the fitness of the individual; the mutation operation steps are: Dynamically adjust the mutation probability P according to the individual's fitness mutation ; Randomly select individual P i The two positions k and l in the , exchange the elements of these two positions, and generate a new individual P' i ; Mutation probability formula: Among them, P mutation (P i ) represents individual P i The mutation probability, P base is the baseline mutation probability, f(P j ) is the individual P j Adaptability; is the maximum fitness in the population; Mutation operation formula: For individual P i =[x1,x2,…,x n ], randomly select positions k and l to exchange: P′ i =swap(P i ,k,l) Among them, swap(P i ,k,l) ​​represents the exchange of P i The elements at positions k and l generate new individuals P' i ; Step 3: After the genetic algorithm generates new sorting position solutions, the particle swarm optimization algorithm PSO is used to further optimize these sorting position solutions; 3.1 Initializing the particle swarm The offspring individuals generated by the genetic algorithm are used as the initial position and velocity of the particle swarm; Particle position x i : represents the sorting position of the i-th particle; Particle speed v i : represents the speed of the i-th particle, initialized to a small random value; 3.2 Update particle speed and position In each iteration, the particle's velocity and position are updated according to the particle's current velocity, individual optimal position, and global optimal position; Speed ​​update formula: v i (t+1)=ωv i (t)+c1r1(p i (t)-x i (t))+c2r2(g(t)-x i (t)) Among them, v i (t) represents the velocity of the ith particle at time t, v i (t+1) represents the velocity of the i-th particle at time t+1. ω is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers, and p i (t) is the individual optimal position of the ith particle, g(t) is the global optimal position; i (t) represents the position of the i-th particle at time t; Position update formula: x i (t+1)=x i (t)+v i (t+1) Among them, x i (t) represents the position of the i-th particle at time t; x i (t+1) represents the position of the i-th particle at time t+1; 3.3 Finding the Global Optimal Solution In each iteration, the global optimal solution is updated until the preset number of iterations is reached or the fitness function converges; The optimal position of an individual i : The optimal sorting position found by particle i so far; Global optimal position g: the global optimal sorting position found among all particles; 3.4 Particle swarm based on chaos initialization In order to increase the diversity of the initial particle swarm and avoid falling into the local optimal solution at an early stage, a chaotic initialization method is adopted; the chaotic sequence has ergodicity and randomness, and can better cover the search space; Chaos initialization step: Generate initial position and velocity using chaos mapping; Map the chaotic sequence to the particle's position and velocity range; Chaotic mapping formula: Using Logistic mapping to generate chaotic sequences x k+1 =r·x k ·(1-x k ) Among them, x k is the kth value of the chaotic sequence, r is the parameter of the chaotic system, which generally ensures that the system is in a completely chaotic state; Chaos initialization formula: Initial position x i (0) x i (0)=x min +x k ·(x max -x min ) Among them, x min and x max are the minimum and maximum values ​​of the position, respectively, x k is the chaotic sequence value; Initial velocity v i (0): v i (0)=v min +x k ·(v max -v mim ) Among them, v min and v max are the minimum and maximum values ​​of the velocity, respectively, x k is the chaotic sequence value; Step 4: Apply the sorting position optimized by the genetic algorithm and the PSO algorithm to the interval scanning to ensure the accuracy of the sorting position; 4.1 Define interval scanning parameters Set the scanning time interval Δt, that is, the time interval between each scan; define the range R of each scan; define the number of targets n, that is, the total number of targets that need to be sorted and scanned; 4.2 Applying the optimized sort position to interval scanning The sorting position P after optimization by genetic algorithm and PSO algorithm opt , expressed as: P opt =[p1,p2,...,p n ] Among them, pi represents the optimized ranking position of the i-th target; 4.3 Performing interval scanning According to the optimized sorting position P opt , performing interval scans to ensure that each target is scanned in an optimized order.

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