Multi-classification label information collection method and system based on fixed-length coding

Abstract

The invention discloses a multi-classification label information collection method and system based on fixed-length coding. The method comprises the following steps: establishing a multi-classification system model consisting of a commercial RFID reader and a tag, and defining a definition of a multi-classification tag information collection problem based on the model; an efficient multi-classification information collection scheme compatible with commercial RFID equipment is designed, and classification information is efficiently mapped into a tag memory through a compact fixed-length classification coding mode; and then generating a corresponding mask according to the target query classification, designing a Select command conforming to the commercial equipment standard, quickly screening and identifying the matched tag, and realizing efficient classification tag information collection. The method is suitable for a medium-scale system of which the classification number does not exceed the commercial tag memory limit. The fixed-length coding scheme is simple and easy to implement, and meanwhile, the query command can be quickly generated to accurately identify the labels of the target classification. The invention provides a more efficient method for classification, management and tracking of article information.

Description

Technical Field

[0001] The present invention relates to the fields of target recognition and item information collection, and particularly to a method and system for collecting tag information corresponding to target classification based on commercial devices in a multi-class commercial RFID system. Background Art

[0002] RFID (Radio Frequency Identification) technology is a non-contact automatic identification technology that realizes the information identification of target objects attached with RFID tags through the wireless radio frequency signal and the spatial coupling transmission characteristics. Compared with traditional identification technologies such as barcodes, RFID has significant advantages such as non-contact identification, multi-target concurrent reading, batch identification, and high-speed data acquisition. Therefore, RFID technology is widely used in multiple fields, including logistics management, production line monitoring, traffic control, and unmanned retail. The popularization of RFID not only improves the level of information management but also provides technical support for the in-depth development of intelligent applications.

[0003] In a retail store or warehouse managed by RFID, goods are usually grouped into different classifications according to their attributes (such as product type, brand, season, etc.), forming a multi-class RFID system. In a multi-class RFID system, each item can belong to multiple classifications simultaneously. For example, a red cashmere sweater can be classified as "red series" according to its color, "cashmere products" according to its material, and "autumn and winter models" according to the season. Such a multi-class RFID system greatly facilitates the management of target objects and can efficiently complete tasks for specific classifications, such as detecting slow-moving goods, inventory counting, or promotion activity management. This fully meets the needs of the real world: managers usually pay more attention to the information of goods in specific classifications rather than each item in the store or warehouse. For example, managers can quickly obtain the information of all red and autumn / winter cashmere sweaters by querying a specific classification, so as to replenish stock or adjust the display, avoiding the inefficient method of scanning each tag one by one.

[0004] The key to achieving efficient classification-based item management lies in quickly collecting and processing tag information that meets the target classification. However, the current information collection methods for multi-class RFID systems face significant technical bottlenecks in commercial systems, mainly reflected in the following aspects: (1) Existing methods do not fully consider compatibility with the EPC global Class1 Gen2 standard and are difficult to be directly applied to commercial RFID devices; (2) Most studies only target the simple scenario where a tag belongs to a single classification and cannot meet the demand for precise management of tag information in complex multi-class scenarios. Therefore, there is an urgent need for a method that can efficiently collect multi-class tag information to meet the actual needs of commercial RFID systems. Summary of the Invention

[0005] Objective of the Invention: Aiming at the deficiencies of the existing technology, the present invention proposes a method and system for collecting tag information based on fixed-length coding for a multi-class commercial RFID system, also known as a method and system for collecting multi-class tag information based on fixed-length coding. This technology uses existing commercial devices to query tags belonging to specific categories, optimizes the classification management of tag information by collecting tag information of specific categories, thereby improving the efficiency and accuracy of item management and operation.

[0006] To achieve the above objective of the invention, a method for collecting multi-class tag information based on fixed-length coding disclosed by the present invention includes the following steps:

[0007] Establish a multi-class RFID system model consisting of a set of tags and a reader. Each tag in the model has a unique ID for identifying the item to which it is attached; the tag set is represented as T = {t 1 , t 2 , …, t n}, where n is the number of tags; the classification set is represented as c = {c 1 , c 2 , …, c m}, where m is the number of classifications; each tag t i ∈ T belongs to a classification subset c i , where c i ∈ c; each classification c j ∈ c contains a tag subset T j , where T j ∈ T;

[0008] According to the established multi-class RFID system model, define the multi-class tag information collection problem as: given a set of tags T, a set of classifications C, the maximum number of classifications m a to which each tag belongs, and the target query classification set C t , where how to encode and write the classification information associated with each tag, and collect the information of the tags in T whose classifications match the query elements in C t ;

[0009] According to the defined multi-class tag information collection problem, design a classification coding scheme to encode the information of all classifications into binary strings of fixed length;

[0010] According to the coding scheme, design a mask for the target classification, generate a corresponding mask string based on the target query classification set, and mask the tags that conform to the target query classification set;

[0011] Design the Select command according to the generated mask, select the set of tags that match the target query classification, and identify these tags, so as to achieve multi-class label information collection.

[0012] Furthermore, design a classification coding scheme to encode the information of all classifications into a binary string of fixed length, including:

[0013] According to the number of classifications m in the system, encode all classification information into a binary string of fixed length m bits;

[0014] According to whether the tag belongs to the i-th classification, set the specific value of the i-th bit in the binary string, where 1 ≤ i ≤ m; if it is, the i-th bit is set to 1; otherwise, it is set to 0.

[0015] Furthermore, design the mask of the target classification according to the coding scheme, and generate the corresponding mask string based on the target query classification set, including:

[0016] Define the target query classification set as C t ={C 1 ,C 2 ,…,C x}, where x is the number of elements in C t , and each element C i is called a sub-target classification set, which contains single classifications, where 1 ≤ i ≤ x, and C i represents the membership classification of the target tag being queried, and the target tag contains all classifications in C i ;

[0017] Define the continuous classification segment as CCS. CCS is a subset composed of continuous classification IDs, where the tags must belong to all classifications within this classification segment; formally, given a set of classifications C = {c x′ c i …c x c y …c j c y′}, where i < j, when x + 1 = y (i + 1 ≤ x < y ≤ j - 1) and c x′ to c j and c i to c y′ are both discontinuous, the classifications c i to c j form a continuous classification segment CCS, and its length is j - i + 1;

[0018] Sort all classifications in C i , and for each C i , design masks respectively based on the continuous classification segment and single classification, generate the mask set of each C i , and each Ci The union of the individual mask sets of t constitutes the overall mask set of C.

[0019] Furthermore, for each C i , mask sets for each C are generated respectively based on continuous classification segments and single classification design masks, including: i

[0020] For each continuous classification segment CCS, a binary string with all 1s of the corresponding length is set as the mask according to the length of the continuous classification segment, and for single classification, the mask is a single "1";

[0021] C i 's mask set is composed of the union of the masks of all CCSs and all single classification masks.

[0022] Furthermore, Select commands are designed according to the generated masks, including:

[0023] According to the RFID ultra-high frequency international standard C1G2, use Session 2 of the Query command to query tags, that is, the first target field of the Select command is set to 2, and the tag query status is A or B;

[0024] For the first mask in the mask set of C i , the corresponding Select command is Flag←AB:S(2,a = 0,3,p 1 ,|C i [1]|,C i [1]), where a = 0 indicates that the tag status that conforms to the mask is A, and other tags are B, C i [1] is the first mask of C i , and p 1 is the starting position of the first classification in the tag memory of C i ;

[0025] When considering querying all classifications in C i as a whole, for the jth mask C i in C i [j], j>1, the Select command is where a = 2 indicates that the tag status that conforms to the mask remains unchanged, and the tag status that does not conform to the mask is B, is the starting position of the o i th classification code in the tag memory of C j ;

[0026] When considering selecting each classification in C i separately, for C iThe j-th mask C in i [j], the Select command is where a = 1 means that the tag that conforms to the mask sets its status to A, and the tag that does not conform to the mask keeps its original status unchanged; is the starting position of the j-th classification in the tag memory encoding of C i .

[0027] Furthermore, for the j-th mask of C i , compare the first bit of the encoding information of the classification with the order o i in C j , where the calculation formula of o j is:

[0028]

[0029] where, is the length of the k j consecutive classification segments before the j-th mask, is the number of consecutive classification segments before the j-th mask.

[0030] Furthermore, select the set of tags that conform to the target query classification and identify these tags, including:

[0031] The reader sends the Select command mask tag according to the specific requirements of C i , sets the status of the tag that conforms to the mask to A, and sets the status of the tag that does not conform to the mask to B; then the reader sends the Query command to query these tags with the status of A, and collects the information of the tags belonging to the target classification.

[0032] The present invention also provides a multi-classification tag information collection system based on fixed-length encoding, including: an RFID reader, a plurality of RFID tags, and a processing device, and the processing device is configured to execute the steps of the multi-classification tag information collection method based on fixed-length encoding as described above.

[0033] The present invention also provides a computer device, including: one or more processors; a memory; and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the program is executed by the processor, it implements the steps of the multi-classification tag information collection method based on fixed-length encoding as described above.

[0034] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the multi-classification tag information collection method based on fixed-length encoding as described above.

[0035] Beneficial effects: (1) In response to the actual requirements of multi-class label information collection in commercial RFID systems, the present invention proposes an efficient information collection method based on fixed-length coding. Compared with existing methods, the fixed-length coding scheme of the present invention is applicable to medium-scale systems where the number of classifications does not exceed the memory capacity of commercial RFID tags. This coding method represents classification information in a fixed length, with simple coding rules and low implementation costs, which helps to quickly generate selection commands (Select commands), significantly improving the screening efficiency of target tags. At the same time, by reducing the number of target classifications involved in the Select command, the complexity and communication overhead of tag selection are effectively reduced, thereby improving the overall efficiency of information collection. (2) By designing a compact and standardized fixed-length coding scheme, the present invention is fully compatible with commercial RFID devices, capable of quickly screening out tags of target classifications and effectively excluding interference from non-target classification tags. Compared with the method of scanning all tags, the present invention significantly reduces the time and communication burden of tag identification, providing an efficient and reliable multi-class label information collection strategy for commercial RFID systems, which helps to optimize system performance and reduce operating costs. Description of the Drawings

[0036] Figure 1 is a prototype system for label information collection based on fixed-length coding for commercial multi-class RFID systems.

[0037] Figure 2 is a flowchart of a method for collecting multi-class label information based on fixed-length coding.

[0038] Figure 3 is a schematic diagram of mask design based on fixed-length coding. Detailed Embodiments

[0039] The technical solutions of the present invention will be further described below in conjunction with the drawings.

[0040] The present invention constructs a prototype system for collecting multi-class label information based on commercial RFID devices, as Figure 1 shown. On the left side of the figure are the specifications of the RFID devices used. The reader model is Alien F800, which supports the bit mask function. The tag models are Alien 9962 and Alien 9940 with Higgs9 chips. On the right side of the figure is the constructed prototype system. Tags are used in this system to identify the items they are attached to. The reader is connected to a directional antenna with a gain of 9dBic, model Laird S9028, and operating frequency of 920MHz. It communicates with the tags through the antenna, reads the tag memory data, and transmits the data to the computer control terminal for processing. Based on this system, the present invention provides a method for collecting multi-class label information based on fixed-length coding. The process of this method is executed by a computer, and the computer controls the communication between the reader and the tags, thereby completing the collection of tag information, asFigure 2 As shown, it includes the following content.

[0041] Step 1, establish a multi-class RFID system model consisting of a set of tags and a reader on the computer side. Each tag in the system has a unique ID that identifies the item to which it is attached. The RFID reader communicates with the tags through selection and query operations. In the system, an item can belong to multiple classes, so a tag can be associated with multiple classes. The number of classes is determined by the system size and the application, and is usually much smaller than the number of tags. The tags that meet the query conditions are called target tags. The tag set is represented as T = {t 1 , t 2 , …, t n}, where n is the number of tags; the class set is represented as C = {c 1 , c 2 , …, c m}, where m is the number of classes. Each tag t i ∈ T belongs to a class subset c i , where c i ∈ C. Each class c j ∈ C contains a tag subset T j , where T j ∈ T;

[0042] Step 2, according to the multi-class RFID system model created in Step 1, define the multi-class tag information collection problem as: given a set of tags T, a set of classes C, the maximum number of classes m a associated with each tag, and the target query class set C t , where how to effectively encode and write the class information associated with each tag, and efficiently collect the information of the tags in T whose classes match the query elements in C t ;

[0043] Step 3, according to the multi-class tag information collection problem defined in Step 2, design a compact class encoding scheme to encode the information of all classes into a fixed-length binary string.

[0044] According to the implementation manner of the present invention, Step 3 specifically includes the following steps:

[0045] Step 3-1, encode all class information into a fixed-length binary string of m bits according to the number of classes m in the system;

[0046] Step 3-2: Set the specific value of the \(i\)-th bit (\(1\leq i\leq m\)) in the binary string according to whether the tag belongs to the \(i\)-th category. If so, the \(i\)-th bit is set to 1; otherwise, it is set to 0. As an example, assume the category set in the system is \(C = \{c 1 ,c 2 ,c 3 ,c 4 ,c 5 \}, and the tag \(t\) belongs to both categories \(c 2 \) and \(c 5 \). Then, the 2nd and 5th bits in the category information of the tag are set to 1, and the rest are set to 0. Therefore, the category code stored in the memory of tag \(t\) is \(c 1 c 2 c 3 c 4 c 5 = 01001.

[0047] Step 4: Design a mask for the target category according to the encoding scheme in Step 3, generate a corresponding mask string based on the target query category set, and the mask conforms to the tags in the target query category set;

[0048] According to the embodiments of the present invention, Step 4 specifically includes the following steps:

[0049] Step 4-1: Define the target query category set as \(C t = \{C 1 ,C 2 ,…,C x \}, where \(x\) is the number of elements in \(C t . Each element \(C i \) (\(1\leq i\leq x\)) is called a sub-target category set, which contains a single category, and \(C i \) represents the membership category of the target tag to be queried, and the target tag should include all categories in \(C i .

[0050] Step 4-2: Define the Consecutive Category Segment (CCS) as a subset composed of consecutive category IDs, where the tag must belong to all categories within this category segment. Formally, given a set of categories \(c = \{c x′ c i …c x c y …c j c y1 \) (\(i < j\)), when \(x + 1 = y\) (\(i + 1\leq x < y\leq j - 1\)) and \(c x′ \) to \(c j \) and \(c i \) to \(c y′ \) are both discontinuous, the category \(ci to c j constitute a continuous classification segment CCS with a length of j - i + 1;

[0051] Step 4 - 3, to determine the comparison positions of each mask in the Select command, generate masks by sorting all the classifications in C i . Then for each C i , design masks based on the continuous classification segments and single classifications respectively, and generate corresponding mask sets for each C i ;

[0052] Specifically, it includes:

[0053] Step 4 - 3 - 1, for each continuous classification segment, set a binary string with all 1s of the corresponding length as the mask, and for a single classification, the mask is a single "1". Taking Figure 3 as an example, there are 12 classifications c 1-12 in the system, and the target query classification set is C t = { { c 2}, { c 4 c 5 c 8}, { c 10 , c 11}}. Among them, the target query classification set meets the actual requirements. Managers can identify products with specific attributes by querying labels with multi - classification membership relationships. For example, ({Retailer: Amazon}, {Brand: Apple Inc., Product Type: iPad, Color: Space Gray}, {Release Date: 2023, 2024}). For multi - category elements without commas (such as { c 4 c 5 c 8}), labels containing all the specified classifications will be regarded as matching. For multi - category elements with commas (such as { c 10 , c 11}), labels containing at least one of the specified classifications are considered matching. As Figure 3 shown, the mask for the single classification { c 2} is "1", the mask for the continuous classification segment { c 10 c 11} is "11", and the masks for the multi - classification { c 4 c 5 c 8} are "11" and "1".

[0054] Step 4 - 3 - 2, the mask set of C i is composed of the union of the masks of all CCSs and the masks of all single classifications.

[0055] Step 4 - 4, each Ci The union of the individual mask sets of t constitutes the overall mask set of C.

[0056] Step 5: According to the mask design Select command generated in Step 4, select the set of tags that conform to the target query classification, and further identify these tags, thereby realizing the collection of multi-class label information.

[0057] According to the RFID ultra-high frequency international standard C1G2, use Session 2 of the Query command to query the tags, that is, the first target field of the Select command is set to 2, and the tag query status is A or B;

[0058] For C i For the first mask in the mask set of, the corresponding Select command is Flag←AB:S(2,a = 0,3,p 1 ,|C i [1]|,C i [1]), where a = 0 indicates that the tag status conforming to the mask is A, and other tags are B, C i [1] is the first mask of C i and p 1 is the starting position of the first classification in C i in the tag memory. After the reader sends this Select command, starting from the p 1 th bit of the tag memory, the tags that exactly match the string specified by C i [1] set their query status to A, while the remaining tags set their status to B. When the mask set of C i contains multiple masks, for the remaining masks, the Select command is similar, but the mask string and mask position need to be adjusted accordingly.

[0059] First, consider the case of querying all classifications in C i as a whole, which requires that the tag must contain all classifications in C i . For the jth (j>1) mask of C i , compare the first bit of the coding information of the classification with the order of o i in C j , where the calculation formula of o j is:

[0060]

[0061] where is the length of the k j th consecutive classification segment before the jth mask, is the number of consecutive classification segments before the jth mask. It is the starting position of the o-th classification code in C in the tag memory. i in the o-th j For the j-th mask C i in C, the Select command is i [j], the Select command is where a = 2 means that the tag status that conforms to the mask remains unchanged, and the tag status that does not conform to the mask is B.

[0062] Then consider the case of separately selecting each classification in C i , which requires that the tag contains at least one classification in C i For the j-th mask C i in C, the Select command is i [j], the Select command is where a = 1 means that the tag that conforms to the mask sets its status to A, and the tag that does not conform to the mask remains unchanged. It is the starting position of the j-th classification in the tag memory encoding of C i .

[0063] Next, take Figure 3 as an example to further illustrate the design of the Select command. In Figure 3 , the target query classification set is C t ={{c 2},{c 4 c 5 c 8},{c 10 ,c 11}}{. For the first target query classification subset {c 2}, the corresponding Select command is Flag←AB:S(2,a = 0,3,2,1,1), which compares the mask "1" with the classification code of the 2nd bit in the tag memory MemBank-3 (a memory block that supports user-defined data). The tags t 3 and t 6 match the mask and are selected. For the target query classification subset {c 4 c 5 c 8}, query the tags that belong to both classifications c 4 c 5 c 8 . The masks are "11" and "1", and the corresponding Select commands are Flag←A-:S(2,a = 1,3,4,2,11) and Flag←-B:S(2,a = 2,3,2,8,1) respectively. Only the tag t 3 matches both masks. For the target query classification {c 10 ,c 11}, it means querying tags that contain at least c10 or c 11 The tags of 11 with a mask of two single '1's are compared with the classification codes at the 10th and 11th bits in the tag memory. The corresponding Select commands are Flag←A-:S(2,a=1,3,10,1,1) and Flag←A-:S(2,a=1,3,11,1,1), as Figure 3 shown, the tags t 1 , t 3 and t 5 match the mask and are selected. Among them, the tags t 3 and t 5 both belong to the classifications c 10 and c 11 , and the tag t 1 belongs to the classification c 11 .

[0064] The reader sends the Select command mask tags according to the specific requirements of C i , sets the status of the tags that match the mask to A, and sets the status of the tags that do not match the mask to B. In this way, the target tags and non-target tags are separated, and the tags that meet the target query classification maintain the status A. Subsequently, the reader sends a Query command to query these tags with the status A and collects the information of the tags belonging to the target classification.

[0065] The present invention also provides a multi-classification tag information collection system based on fixed-length coding, including: an RFID reader, a plurality of RFID tags, and a processing device, and the processing device is configured to execute the steps of the above-mentioned multi-classification tag information collection method based on fixed-length coding.

[0066] The present invention also provides a computer device, including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the program is executed by the processor, the steps of the above-mentioned multi-classification tag information collection method based on fixed-length coding are implemented.

[0067] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned multi-classification tag information collection method based on fixed-length coding are implemented.

[0068] The present invention provides a method and a system for collecting tag information for a multi-class commercial RFID system. There are many methods and ways to specifically implement this technical solution. The above description is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. Each component not clearly defined in this embodiment can be implemented by using the prior art.

Claims

1. A method for collecting multi-classification label information based on fixed-length coding, characterized in that: The method comprises the following steps: A multi-classification RFID system model consisting of a set of tags and a reader is established. In the model, each tag has a unique ID to identify the item it is attached to. The tag set is represented by T = {t1, t2, …, t n }, where n is the number of labels; the classification set is represented by C = {c1, c2, ..., c m }, where m is the number of categories; each label t i ∈T belongs to a classification subset c i , where c i ∈C; each category c j ∈C contains a label subset T j , where T j ∈T; According to the established multi-classification RFID system model, the multi-classification tag information collection problem is defined as: given a set of tags T, a set of categories C, the maximum number of categories to which each tag belongs is m a And the target query classification set C t ,in How to encode and write the classification information associated with each label and collect the classification information in T and C t The information of the tag that matches the query element; According to the defined multi-classification label information collection problem, a classification encoding scheme is designed to encode all classification information into a fixed-length binary string; Design a target classification mask according to the encoding scheme, generate a corresponding mask string based on the target query classification set, and the mask conforms to the label of the target query classification set; The Select command is designed based on the generated mask to select the set of labels that meet the target query classification and identify these labels, thereby realizing the collection of multi-classification label information.

2. The method according to claim 1, characterized in that Design a classification encoding scheme to encode all classified information into a fixed-length binary string, including: According to the number of categories m in the system, all classification information is encoded into a binary string with a fixed length of m bits; According to whether the label belongs to the i-th category, the specific value of the i-th bit in the binary string is set, 1≤i≤m; if yes, the i-th bit is set to 1; otherwise, it is set to 0.

3. The method according to claim 1, characterized in that Design the target classification mask according to the encoding scheme, and generate the corresponding mask string based on the target query classification set, including: Define the target query classification set as C t ={C1,C2,…,C x }, where x is C t The number of elements in C i It is called the sub-target classification set, which contains single classification, 1≤i≤x, C i Indicates the category of the target label being queried. The target label contains C i All categories in; Define the Continuous Classification Segment as CCS. CCS is a subset composed of consecutive classification IDs, where the labels must belong to all classifications within the segment. Formally, given a set of classifications C = {c x′ c i …c x c y …c j c y′}, i < j, when x + 1 = y (i + 1 ≤ x < y ≤ j - 1) and c x′ to c j and c i to c y′ are both non - consecutive, the classifications c i to c j constitute a continuous classification segment CCS, with a length of j - i + 1; C i Sort all the categories in i , respectively, based on continuous classification segments and single classification design masks, generating each C i The mask set for each C i The union of the individual mask sets of t The overall mask set.

4. The method according to claim 3, characterized in that For each C i , respectively, based on continuous classification segments and single classification design masks, generating each C i The mask set includes: For each continuous classification segment CCS, a binary string of all 1s is set as a mask according to the length of the continuous classification segment, and for a single classification, the mask is a single "1"; C i The mask set of consists of the union of all CCS masks and all single-category masks.

5. The method according to claim 1, characterized in that: Design the Select command based on the generated mask, including: According to the RFID UHF international standard C1G2, use Session 2 of the Query command to query the tag, that is, the first target field of the Select command is set to 2, and the tag query status is A or B; For C i The first mask in the mask set, the corresponding Select command is Flag←AB:S(2,a=0,3,p1,|C i [1]|,C i [1]), where a=0 means that the state of the label that matches the mask is A, and the other labels are B, C i [1] is C i The first mask of p1 is the label memory C i The starting position of the first category in ; When considering C i When all the categories in C are queried as a whole, i The jth mask C in i [j], j>1, Select command is Where a=2 means that the state of the label that meets the mask remains unchanged, and the state of the label that does not meet the mask is B. is in the tag memory C i No. o j The starting position of the classification code; When considering choosing C alone i For each category in C i The jth mask C in i [j], Select command is Where a=1 means that the labels that meet the mask will be set to state A, and the labels that do not meet the mask will remain in their original state; It is C i The starting position of the j-th category in the label memory encoding.

6. The method according to claim 5, characterized in that For C i The j-th mask of C i The classification order is o j The first bit of the coded information, where o j The calculation formula is: in, is the kth mask before the jth j The length of consecutive classification segments, is the number of consecutive classification segments before the j-th mask.

7. The method according to claim 1, characterized in that Select a set of tags that match the target query classification and identify these tags, including: By the reader according to C i The reader sends a Select command to mask the tags according to the specific needs, sets the status of the tags that meet the mask to A, and sets the status of the tags that do not meet the mask to B; then the reader sends a Query command to query the tags with status A and collect information belonging to the target classification tags.

8. A multi-classification label information collection system based on fixed-length coding, characterized in that: include: An RFID reader, several RFID tags and a processing device, wherein the processing device is configured to execute the steps of the fixed-length coding-based multi-classification tag information collection method according to any one of claims 1 to 7.

9. A computer device, characterized in that: include: one or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the multi-classification label information collection method based on fixed-length coding as described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for collecting multi-classification label information based on fixed-length coding as described in any one of claims 1 to 7 are implemented.

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