Internet of Things data information funnel extraction method based on hierarchical naming prefix

Abstract

The invention discloses an Internet of Things data information funnel extraction method based on hierarchical naming prefixes, and relates to the technical field of Internet of Things, a hierarchical data naming system is constructed, the data similarity is measured through the prefix distance of data names, and the longer the prefix distance of the data names is, the higher the semantic coincidence degree of data objects is; data objects are dynamically screened based on an information funnel mechanism, the semantic coincidence degree between samples is calculated through the longest common prefix length of data names, and the samples with the lowest coincidence degree with an existing sample set and the smallest prefix data range to which the samples belong are preferentially selected, so that the information value is maximized, and information redundancy is compressed; and according to a hierarchical similarity principle and an information revenue decline principle. According to the method, dynamic screening and sorting are carried out through a hierarchical naming system and an information funnel mechanism, the problems of data semantic recognition, high calculation complexity, insufficient information coverage and structural naming missing of the Internet of Things are solved, data redundancy is reduced, and information utility is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an Internet of Things data information funnel extraction method based on hierarchical naming prefixes. Background Art

[0002] In the field of Internet of Things, the efficient transmission and processing of massive sensor data faces core challenges such as high data redundancy and duplication of semantic information. Existing data deduplication and aggregation technologies mainly rely on hash matching and similarity clustering algorithms. However, the former can only identify exact duplications at the binary level and cannot capture semantic similarity. The latter requires global calculation of the similarity between data, resulting in computational complexity growing exponentially with the data size, making it difficult to apply to resource-constrained IoT devices.

[0003] The key breakthrough in technological evolution comes from the theoretical innovation of Named Data Networking (NDN). NDN embeds data semantics into the name structure through a hierarchical naming paradigm of "region / device / type", realizing the aggregated transmission of semantically related data for the first time.

[0004] However, the existing NDN solution also has shortcomings. It lacks hierarchical explicit modeling in the naming system, resulting in inaccurate prefix similarity calculation. At the same time, the lack of a quantitative mechanism for data coverage leads to the blind accumulation of redundant data and the distortion of pricing basis, which makes it difficult to support the structural defects of the refined data trading market.

[0005] Therefore, this application proposes an IoT data information funnel extraction method based on hierarchical naming prefixes to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide an Internet of Things data information funnel extraction method based on hierarchical naming prefixes to address the shortcomings of the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for extracting IoT data information from a funnel based on hierarchical naming prefixes, comprising:

[0008] Construct a hierarchical data naming system and measure data similarity by the prefix distance of data names. The longer the prefix distance of data names, the higher the semantic overlap of data objects.

[0009] Dynamically screen data objects based on the information funnel mechanism, calculate the semantic overlap between samples by the longest common prefix length of the data name, and give priority to samples with the lowest overlap with the existing sample set and the smallest prefix data range to maximize information value and reduce information redundancy;

[0010] According to the hierarchical similarity principle and the principle of diminishing returns of information, the marginal utility of data objects is dynamically calculated to determine the optimal transmission order.

[0011] In a preferred embodiment, the step of constructing the hierarchical data naming system includes:

[0012] Based on the named data network theory, the data name is constructed into a tree-like hierarchical structure consisting of three levels: region, sub-region, and node. The hierarchical path is formed by a unified separator between the levels.

[0013] Based on the hierarchical path, the semantic overlap is measured by the longest common prefix length of the data name, and the tree structure nodes can be dynamically expanded, modified or merged according to data collection requirements or transmission feedback.

[0014] In a preferred embodiment, the marginal utility calculation method of the information funnel mechanism includes:

[0015] Comparing the longest common prefix length of two data objects relative to a given data set, the data object with a shorter prefix has a greater marginal utility;

[0016] If the longest common prefix lengths are equal, the data name subspace occupancy is compared, and the data object with smaller occupancy has greater marginal utility.

[0017] In a preferred embodiment, the recursive merging algorithm based on the tree-like hierarchical structure includes:

[0018] Input the application root name prefix R determined based on the tree-like hierarchical structure, the sender data set I, and the receiver name tree T used to record the receiver namespace occupancy status;

[0019] Traverse the receiver name tree T from the bottom up, calculate the priority of nodes at each level, merge the child nodes to determine the priority of internal nodes. The specific merging rules are as follows:

[0020] Select the data object with the shortest common prefix length with the receiver's name tree T from the sender's data set I and give it the highest priority;

[0021] Select the data object of the sender data set I corresponding to the branch with the least number of occupied numbers in the receiver name tree T to balance the tree hierarchy;

[0022] After adding the new priority data object to the corresponding branch of the receiver name tree T, update the occupied numbers of each branch;

[0023] Returns the priority order of data objects in the sender's dataset I as the basis for data transmission.

[0024] In a preferred embodiment, in the recursive merging algorithm, when the data objects have the same size and weight, the recursive merging algorithm generates an optimal transmission order based on the priority rule of the tree-like hierarchical structure.

[0025] In a preferred embodiment, the system architecture of the hierarchical data naming system includes:

[0026] The application layer is used to perform operations such as perceiving data objects and naming the perceived data objects;

[0027] The information funnel layer is in communication with the application layer and is configured to maximize the transmission of application-independent information and select data objects to be transmitted from the data objects named by the application layer through a dynamic priority sorting mechanism;

[0028] The named data network layer is in communication with the information funnel layer and transmits the data objects selected by the information funnel layer based on the hierarchical data naming system.

[0029] In a preferred embodiment, a receiver feedback mechanism is also included to adjust the transmission order of data objects in the sender's data set I in real time according to the occupancy of the receiver's name tree T.

[0030] In a preferred embodiment, the hierarchical similarity principle and the principle of diminishing information returns include:

[0031] Hierarchical similarity rule, under the hierarchical data naming system, the similarity between data objects is measured by the longest common prefix length of the data names. The longer the longest common prefix length of two data objects, the higher their semantic overlap;

[0032] The rule of diminishing utility is that for a set of data objects with the same name prefix in a hierarchical data naming system, as the number of transmitted data objects in the set increases, the information utility brought by each data object in the set tends to decrease.

[0033] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0034] 1. The present invention addresses the problems of high data redundancy and repeated semantic information in massive sensor data in the Internet of Things, the inability of existing hash comparisons to identify semantically similar data, and the high computational complexity of clustering algorithms. By constructing a tree-like hierarchical naming system, the semantic overlap is measured by the prefix distance of the data name, and based on the information funnel mechanism, high-value data with the lowest overlap with existing samples and the smallest prefix space coverage is dynamically screened out, significantly reducing data redundancy and improving information density and transmission efficiency.

[0035] 2. The present invention adopts a recursive merging algorithm that traverses the name tree from the bottom up. It replaces the global similarity calculation of the traditional clustering algorithm by giving priority to the data objects with the shortest common prefix and the dual rule sorting that balances the occupancy of the tree branches. It generates the optimal transmission order based on the priority rules of the tree hierarchical structure, avoiding the high computational complexity problem caused by the need for global calculation of the similarity between data in the traditional clustering algorithm, and realizing redundant filtering with low computational complexity.

[0036] 3. Relying on the hierarchical similarity principle and the principle of diminishing utility, the present invention dynamically calculates the marginal utility of data objects and prioritizes samples with short prefixes and small subspace occupancy to determine the transmission order. This not only maximizes the breadth and uniqueness of information coverage, but also provides an objective basis for IoT data pricing based on the richness of sample information. It solves the problems of the existing NDN naming system lacking hierarchical explicit modeling and the lack of a quantitative mechanism for data coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0038] Figure 1 Flow chart of the method of the present invention.

[0039] Figure 2 Schematic diagram of the system architecture of the hierarchical data naming system of the present invention. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] Example 1, please refer to Figure 1-2 As shown, the method for extracting IoT data information from a funnel based on hierarchical naming prefixes described in this embodiment includes the following steps:

[0042] Overview:

[0043] To address the pricing problem of sparse data in the Internet of Things, a data value set evaluation theory based on the information funnel mechanism is proposed. This theory addresses the phenomenon that semantic information in a large amount of Internet of Things data is highly redundant and the sample value decreases monotonically with the expansion of the basic set. By reducing data redundancy through information aggregation and filtering, objective value evaluation is carried out based on the information richness of the data set rather than the absolute sample size.

[0044] This theory is based on the theories of hash comparison, similarity clustering algorithm (such as "Data Deduplication for IoT" in "IEEE Transactions on Knowledge and Data Engineering" 2020) and named data networking (Chapter 4 of the ACM monograph "Named Data Networking"), and proposes an information funnel mechanism. It constructs structured data naming through metadata information such as regional coverage, and uses the overlap of data naming prefixes to determine the semantic overlap between samples to compress the information redundancy in the data. In the funnel screening process, it follows the hierarchical similarity principle and the principle of diminishing information returns to dynamically select the samples with the highest marginal value in the potential set (that is, the samples with the lowest overlap with the existing sample set). When multiple data have the same similarity, the prefix space with small coverage is given priority to achieve higher semantic coverage.

[0045] As an efficient and general mechanism for extracting information from IoT data, the information funnel theory can significantly reduce data redundancy, improve information density and transmission efficiency compared to traditional data collection and transmission methods, and provide a more objective and accurate pricing object for IoT data pricing, that is, pricing based on the information richness in the sample set.

[0046] S1. Build a hierarchical data naming system and measure data similarity by the prefix distance of data names. The longer the prefix distance of data names, the higher the semantic overlap of data objects.

[0047] In one embodiment, the steps of constructing a hierarchical data naming system include:

[0048] S11. Based on the named data network theory, the data name is constructed into a tree-like hierarchical structure consisting of three levels: region, sub-region, and node. The hierarchical path is formed by a unified separator between the levels.

[0049] S12. Based on the hierarchical path, the semantic overlap is measured by the longest common prefix length of the data name, and it supports dynamic expansion, modification or merging of tree structure nodes according to data collection requirements or transmission feedback.

[0050] As shown in steps S11-S12 above, by constructing a tree-like hierarchical structure consisting of three levels: region, sub-region, and node, and forming a hierarchical path with a unified separator, the data name is incorporated into the structured namespace, which can significantly improve data organization efficiency and cross-domain interoperability. The longest common prefix length of the hierarchical path can accurately quantify the semantic overlap of data objects and can efficiently identify and compress redundant information. At the same time, it supports dynamic expansion, modification, or merging of tree nodes based on collection requirements or transmission feedback, so that the naming system can flexibly adapt to changes in IoT scenarios and reduce transmission prefix matching overhead. The hierarchical naming system works in conjunction with the dynamic mechanism to achieve full-link optimization from data naming to transmission. While reducing data transmission volume, network bandwidth occupancy, and storage costs, it provides underlying semantic support for the marginal utility calculation and recursive merging algorithm of the information funnel, thereby improving the automation, accuracy, and scenario adaptability of IoT data processing.

[0051] In one embodiment, the tree-based recursive merging algorithm includes:

[0052] S13, inputting the application root name prefix R determined based on the tree-like hierarchical structure, the sender data set I, and the receiver name tree T used to record the receiver namespace occupancy status;

[0053] S14. Traverse the recipient name tree T from bottom to top, calculate the priority of nodes at each level, merge the sub-nodes to determine the priority of internal nodes. The specific merging rules are as follows:

[0054] S141. Select the data object with the shortest common prefix length with the receiver's name tree T from the sender's data set I and assign it the highest priority.

[0055] S142. Select the data object corresponding to the sender data set I in the branch with the least number of occupied numbers in the receiver name tree T, and balance the tree hierarchy;

[0056] S143. After adding the new priority data object to the corresponding branch of the recipient name tree T, update the occupied numbers of each branch;

[0057] S15. Return the priority order of data objects in the sender's data set I as the basis for data transmission.

[0058] As in the above steps S13-S15, by selecting the data object with the shortest common prefix length with the receiver's name tree T from the sender's data set I and assigning it the highest priority (S141), data with low semantic overlap with the receiver's existing data can be transmitted first. This allows each data transmission to bring more new information, avoiding the transmission of a large amount of redundant data, thereby increasing the information value of the data transmission. At the same time, this method can also reduce unnecessary data transmission volume and improve the overall data transmission efficiency. In particular, in IoT data transmission, bandwidth resources can be effectively utilized, thereby improving the information value and efficiency of data transmission.

[0059] The algorithm selects the data object corresponding to the sender data set I in the branch with the fewest occupied numbers in the receiver name tree T (S142), and updates the occupied numbers of each branch after adding the new priority data object to the corresponding branch of the receiver name tree T (S143). This operation helps to balance the tree hierarchy, preventing some branches of the tree from being too large and others from being too sparse. The balanced tree structure can improve the speed of data query and processing, making the entire data transmission and storage system more stable and efficient, and better able to meet the management and transmission needs of large-scale Internet of Things data.

[0060] The algorithm ultimately returns the priority order of the data objects in the sender's dataset I (S15), providing a clear and reliable basis for data transmission. The sender can transmit data according to this priority order, and the receiver can also better receive and process data according to this order. This orderly data transmission method reduces confusion and conflicts during the data transmission process, improves the accuracy and reliability of data transmission, and ensures the normal circulation and application of data in the Internet of Things system.

[0061] In one embodiment, in the recursive merging algorithm, when the data objects have the same size and weight, the recursive merging algorithm generates an optimal transmission order based on a priority rule of a tree-like hierarchical structure.

[0062] As in the above-mentioned recursive merging algorithm, when the data objects have the same size and weight, the recursive merging algorithm generates the optimal transmission order based on the priority rules of the tree-like hierarchical structure, which can significantly improve transmission efficiency and resource utilization, avoid blind transmission, give priority to the transmission of new information, reduce redundancy, save network bandwidth and reduce latency. At the same time, it can ensure data integrity and consistency, allow data to arrive logically and orderly, facilitate the recipient's processing and storage, reduce the risk of loss and confusion, and enhance the scalability and adaptability of the system. It can dynamically adjust the order as the data scale and complexity changes, quickly determine the priority of new data objects, and ensure stable and efficient operation of the system.

[0063] In one embodiment, the system architecture of the hierarchical data naming system includes:

[0064] S16, application layer, for performing operations of perceiving data objects and naming the perceived data objects;

[0065] S17, an information funnel layer, in communication with the application layer, configured to maximize the transmission of application-independent information and select data objects to be transmitted from the data objects named by the application layer through a dynamic priority sorting mechanism;

[0066] S18. The named data network layer is connected to the information funnel layer for communication, and transmits the data objects selected by the information funnel layer based on the hierarchical data naming system.

[0067] As shown in steps S13-S15 above, the system architecture of the hierarchical data naming system achieves full process optimization of IoT data from perception, screening to transmission through layered decoupling and coordination mechanisms;

[0068] First, the application layer uses standardized naming operations to map data objects perceived by heterogeneous devices into a unified tree-like hierarchical path (such as the "region / subregion / node" structure). This eliminates naming differences between different vendors and protocols, transforming the perceived data of the physical world into structured information with clear semantic labels. This provides a unified semantic foundation for data processing at subsequent levels, significantly improving data interoperability and manageability across devices and systems.

[0069] Secondly, the information funnel layer uses an application-independent dynamic priority sorting mechanism to calculate marginal utility based on the longest common prefix length and subspace occupancy of data names. This allows it to accurately filter out data objects with high information value and low redundancy from the dataset named by the application layer. This mechanism transforms data transmission from "full-volume transfer" to "on-demand selection," compressing the transmission volume without losing critical information, reducing network bandwidth usage and transmission energy consumption. It is particularly suitable for bandwidth-constrained edge computing or low-power IoT scenarios.

[0070] Finally, the named data network layer relies on the tree structure of the hierarchical naming system to directly convert the semantic labels of data objects into transmission routing basis, and realizes efficient data addressing and distribution through prefix matching of hierarchical paths, avoiding the limitations of "location binding" in traditional IP networks, making data transmission closer to the logical ownership of the content itself, improving routing efficiency and network resource utilization. At the same time, the decoupled design of the three-layer architecture (the application layer is responsible for perception and naming, the information funnel layer is responsible for intelligent screening, and the network layer is responsible for efficient transmission) makes the system flexible and scalable, and the functions of each layer can be upgraded independently without affecting the overall architecture, adapting to the dynamic changes of device types, data scale and transmission requirements in IoT scenarios.

[0071] S2. Dynamically screen data objects based on the information funnel mechanism. Calculate the semantic overlap between samples by the longest common prefix length of the data names. Prioritize samples with the lowest overlap with the existing sample set and the smallest prefix data range to maximize information value and reduce information redundancy.

[0072] In one embodiment, the marginal utility calculation method of the information funnel mechanism includes:

[0073] S21. Compare the longest common prefix lengths of two data objects relative to a given data set. The data object with a shorter prefix has a greater marginal utility.

[0074] S22. If the longest common prefix lengths are equal, compare the occupancy of the data name subspaces. The data object with a smaller occupancy has a greater marginal utility.

[0075] As in steps S21-S22 above, the semantic overlap between samples is measured by calculating the longest common prefix length of the data names, and samples with the lowest overlap with the existing sample set are preferentially selected. This ensures that each filtered data can bring more new content to the overall information, avoiding the interference of a large amount of redundant data. Therefore, the most valuable information can be accurately selected from a pile of information, so that the final data set obtained contains as much unique information as possible, thereby maximizing the value of the information. In actual application scenarios, such as in the Internet of Things environment, faced with massive sensor data, this mechanism can quickly filter out truly valuable data, providing strong support for subsequent data analysis and decision-making;

[0076] At the same time, the information funneling mechanism further compresses information redundancy by prioritizing samples with the smallest prefix data range. This means that during the screening process, data with a wide coverage and high content duplication is excluded, retaining only representative and unique data. This significantly reduces the amount of data that needs to be transmitted and processed, thereby reducing data transmission costs, including network bandwidth usage and transmission time. This data redundancy compression is particularly important in resource-limited environments, such as low-bandwidth network environments or battery-powered devices, effectively improving system operating efficiency and resource utilization.

[0077] The marginal utility calculation method of the information funnel mechanism provides an effective decision-making basis for data screening. By comparing the longest common prefix length, data objects with shorter prefixes are prioritized. Because these objects have less semantic overlap with existing data, they can bring more new information and therefore have greater marginal utility. When the longest common prefix lengths are equal, the data name subspace occupancy is compared and the object with the smaller occupancy is selected. This further ensures that the screened data has higher uniqueness and value.

[0078] In summary, the hierarchical and refined funnel mechanism adopted above makes the data screening process more accurate and reasonable, can quickly find the optimal data combination in a complex data environment, and improves the performance and efficiency of the entire information processing system.

[0079] S3. Based on the hierarchical similarity principle and the principle of diminishing information returns, the marginal utility of data objects is dynamically calculated to determine the optimal transmission order.

[0080] In one embodiment, the hierarchical similarity principle and the principle of diminishing information returns include:

[0081] S31. Hierarchical similarity rule. Under the hierarchical data naming system, the similarity between data objects is measured by the longest common prefix length of the data names. The longer the longest common prefix length of two data objects, the higher their semantic overlap.

[0082] S32. Utility Diminishing Rule: For a set of data objects with the same name prefix in a hierarchical data naming system, as the number of transmitted data objects in the set increases, the information utility brought by each data object in the set tends to decrease.

[0083] As shown in the above steps S31-S32, under the hierarchical data naming system, the hierarchical similarity rule measures the similarity between data objects by the longest common prefix length of the data names, which can accurately identify data with high semantic overlap and avoid repeated transmission of redundant information, thereby effectively saving network bandwidth resources and improving transmission efficiency. As for the diminishing utility rule, in a set of data objects with the same name prefix, as the number of transmitted data objects increases, the information utility brought by each data object decreases, so that the algorithm can give priority to transmitting data objects with high information utility and maximize the value of each transmitted data. Combining these two principles to dynamically calculate marginal utility and determine the transmission order can ensure efficient transmission of data information under limited transmission resources, so that the system can quickly and accurately obtain the most valuable data. It is especially suitable for Internet of Things scenarios with huge data volumes and limited resources, and improves the data processing capabilities and operating efficiency of the entire system.

[0084] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The method for extracting IoT data information from a funnel based on hierarchical naming prefixes is characterized by: include: Construct a hierarchical data naming system and measure data similarity by the prefix distance of data names. The longer the prefix distance of data names, the higher the semantic overlap of data objects. Dynamically screen data objects based on the information funnel mechanism, calculate the semantic overlap between samples by the longest common prefix length of the data name, and give priority to samples with the lowest overlap with the existing sample set and the smallest prefix data range to maximize information value and reduce information redundancy; According to the hierarchical similarity principle and the principle of diminishing returns of information, the marginal utility of data objects is dynamically calculated to determine the optimal transmission order.

2. The method for extracting IoT data information from a funnel based on hierarchical naming prefixes according to claim 1 is characterized in that: The steps for constructing the hierarchical data naming system include: Based on the named data network theory, the data name is constructed into a tree-like hierarchical structure consisting of three levels: region, sub-region, and node. The hierarchical path is formed by a unified separator between the levels. Based on the hierarchical path, the semantic overlap is measured by the longest common prefix length of the data name, and the tree structure nodes can be dynamically expanded, modified or merged according to data collection requirements or transmission feedback.

3. The method for extracting IoT data from a funnel based on hierarchical naming prefixes according to claim 1 is characterized in that: The marginal utility calculation method of the information funnel mechanism includes: Comparing the longest common prefix length of two data objects relative to a given data set, the data object with a shorter prefix has a greater marginal utility; If the longest common prefix lengths are equal, the data name subspace occupancy is compared, and the data object with smaller occupancy has greater marginal utility.

4. The method for extracting IoT data information from a funnel based on hierarchical naming prefixes according to claim 2 is characterized in that: The recursive merging algorithm based on tree hierarchy includes: Input the application root name prefix R determined based on the tree-like hierarchical structure, the sender data set I, and the receiver name tree T used to record the receiver namespace occupancy status; Traverse the receiver name tree T from the bottom up, calculate the priority of nodes at each level, merge the child nodes to determine the priority of internal nodes. The specific merging rules are as follows: Select the data object with the shortest common prefix length with the receiver's name tree T from the sender's data set I and give it the highest priority; Select the data object of the sender data set I corresponding to the branch with the least number of occupied numbers in the receiver name tree T to balance the tree hierarchy; After adding the new priority data object to the corresponding branch of the receiver name tree T, update the occupied numbers of each branch; Returns the priority order of data objects in the sender's dataset I as the basis for data transmission.

5. The method for extracting IoT data from a funnel based on hierarchical naming prefixes according to claim 4 is characterized in that: In the recursive merging algorithm, when the data objects have the same size and weight, the recursive merging algorithm generates an optimal transmission order based on the priority rule of the tree-like hierarchical structure.

6. The method for extracting IoT data information from a funnel based on hierarchical naming prefixes according to claim 1 is characterized in that: The system architecture of the hierarchical data naming system includes: The application layer is used to perform operations such as perceiving data objects and naming the perceived data objects; The information funnel layer is in communication with the application layer and is configured to maximize the transmission of application-independent information and select data objects to be transmitted from the data objects named by the application layer through a dynamic priority sorting mechanism; The named data network layer is in communication with the information funnel layer and transmits the data objects selected by the information funnel layer based on the hierarchical data naming system.

7. The method for extracting IoT data information from a funnel based on hierarchical naming prefixes according to any one of claims 1 to 6, characterized in that: It also includes a receiver feedback mechanism, which adjusts the transmission order of data objects in the sender's dataset I in real time based on the occupancy of the receiver's name tree T.

8. The method for extracting IoT data information from a funnel based on hierarchical naming prefixes according to claim 1 is characterized in that: The hierarchical similarity principle and the principle of diminishing information returns include: Hierarchical similarity rule, under the hierarchical data naming system, the similarity between data objects is measured by the longest common prefix length of the data names. The longer the longest common prefix length of two data objects, the higher their semantic overlap; The rule of diminishing utility is that for a set of data objects with the same name prefix in a hierarchical data naming system, as the number of transmitted data objects in the set increases, the information utility brought by each data object in the set tends to decrease.

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