A fast matching search method based on an addressing matrix

Abstract

The present invention belongs to the field of Internet of Things identification networks, and discloses a fast matching and searching method based on an addressing matrix. According to rules, various category attributes are mapped to coordinate values of corresponding dimensions in a multi-dimensional space to determine the position of a point in the multi-dimensional space. An addressing request is filled in a preset addressing matrix and sent to a multi-dimensional identification server at the edge layer. The server extracts the attribute content filled in the matrix and, according to the mapping rules, converts the extracted attribute values into coordinate values of corresponding dimensions in the multi-dimensional space. The point obtained by mapping according to the addressing matrix is used as the position of the addressing request in the multi-dimensional space, and distance calculations are performed with the space points corresponding to all objects that have been mapped and stored in the database. The communication object corresponding to the nearest point within a preset threshold range of the distance is selected as the best communication target. The present invention solves the problems that under the condition of large-scale identification data, the existing addressing mechanism has low searching efficiency and limits the instant response ability of the network, etc.

Description

Technical Field

[0001] The present invention belongs to the field of Internet of Things identification networks, and particularly relates to a fast matching and searching method based on an addressing matrix. Background Art

[0002] The traditional TCP / IP network is designed as a point-to-point communication protocol, focusing on the communication between two terminal nodes. With the emergence of new network scenarios such as mobile Internet, industrial Internet, Internet of Things, mobile edge computing network, vehicle-to-everything network, and space-ground integrated network, the essence of network users' Internet access needs has also changed, gradually shifting from simple network access to network communication needs centered on obtaining content and services. However, the existing IP network system has difficulty meeting the new requirements of current and future users for network communication.

[0003] In the existing Internet, the mismatch problem between the architecture function and its main use is becoming increasingly prominent. Many studies are thinking about the development direction of the future network architecture, and the network architecture urgently needs to evolve. The common problem of traditional IP networks and new network architectures emerging in recent years, including IPv6, content-centric network, etc., is that they all advocate using a single network protocol or communication entity as the primary communication method of the network and reconstructing a difficult-to-expand network layer to achieve the goal.

[0004] Based on the existing network architecture, IP as the only addressing method has difficulty adapting to diverse, pluralistic, and specialized application requirements. The homogeneous pattern of "thousands of troops crossing a single-log bridge of IP" weakens the inherent diversity of the network system, restricts technological innovation and ecological vitality, and leads to a slow network evolution process.

[0005] To solve the above problems, multi-dimensional identification networks have emerged. Among them, multi-dimensional identification is a network identification based on spatio-temporal benchmarks, divided into three types: object, connection, and application, and uses diverse attribute combinations to describe all nodes in the network. The multi-dimensional identification network is based on multi-dimensional identification, realizes diverse addressing and routing methods, and supports the efficient interconnection of each node in the network. However, in practical applications, there are still many challenges in fast matching under high-dimensional attribute combinations. Facing large-scale identification data, frequently changing attributes, and high real-time requirements, existing addressing mechanisms mostly rely on linear search or fixed rule matching, making it difficult to achieve an efficient and scalable multi-dimensional matching process, which limits the instant response ability and resource scheduling efficiency of the network. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides a fast matching and searching method based on an addressing matrix, which realizes the efficient positioning and approximate matching of multi-dimensional identification through attribute mapping and spatial distance calculation, and significantly improves the addressing processing performance in scenarios of large data volume, high concurrency, and multi-attribute combinations.

[0007] A fast matching search method based on an addressing matrix according to the present invention includes the following steps:

[0008] S1. Store the current multi-dimensional attributes for addressing in the edge layer database of the multi-dimensional identification network;

[0009] S2. Map various category attributes into coordinate values of corresponding dimensions in the multi-dimensional space according to different rules to determine the positions of each point in the multi-dimensional space; wherein, each point in the multi-dimensional space corresponds to a registered or existing communication object, and the communication object is jointly located by multiple attribute dimensions, and its position in the multi-dimensional space is jointly determined by the attribute values of each dimension;

[0010] S3. Fill in the addressing request in the preset addressing matrix, and send the addressing matrix filled with the addressing request to the edge layer multi-dimensional identification server;

[0011] S4. The edge layer multi-dimensional identification server extracts the attribute content filled in the matrix, and converts the extracted attribute values into coordinate values of corresponding dimensions in the multi-dimensional space according to the mapping rules in S2;

[0012] S5. Take the point obtained by mapping according to the addressing matrix as the position of the addressing request in the multi-dimensional space, calculate the distance from the space points corresponding to all the objects that have been mapped and stored in the database in S2, and select the communication object corresponding to the nearest point within the preset threshold range as the best communication target.

[0013] Further, in S1, the current multi-dimensional attributes for addressing include device type attributes, geographical location attributes, and service identification attributes; wherein, the device type attributes include smart home, smart door lock, security camera, and smart meter; the geographical location attribute refers to the specific geographical area where the task occurs; the service identification attribute is used to mark a specific type of service, and independent identifiers are assigned to different services.

[0014] Further, S2 is specifically as follows:

[0015] S21. Based on the idea of Word2Vector, map the device type attributes into the multi-dimensional space through vectorization to generate the multi-dimensional coordinates of the device type, that is, the coordinate values of the first dimension;

[0016] S22. Convert the longitude and latitude information of the geographical location attribute into a unique numerical representation as the coordinate value of the second dimension in the multi-dimensional space;

[0017] S23. Judge the traffic volume of the service identification attribute, perform modulo operation in different ways according to the amount of traffic volume, and map the value to a predefined coordinate interval to obtain the multi-dimensional coordinates of the service identification, that is, the coordinate values of the third dimension.

[0018] Further, S21, in the multidimensional identification network, the device type attribute as a string needs to be mapped to the multidimensional space through vectorization. Based on the idea of Word2Vector, the multidimensional coordinate generation of the device type is completed, which specifically includes the following steps:

[0019] S211. To ensure the standardization and consistency of the device type string, the original data is cleaned and standardized, including removing redundant characters, punctuation marks and irrelevant information;

[0020] Defines the device type string , i represents the index of the device type string, and its value range is , where n is the total number of device type strings;

[0021] Each device type string Represented as a sequence of characters, all of which come from the preset legal character set , ;in It is the complete set of all characters, including the valid character set and the redundant character set; A string indicating the device type The lth character in , m is the device type string The actual number of characters that satisfies , N is the maximum length defined by the system; define the valid character set , including English letters, numbers or other specific characters;

[0022] Traverse the device type string , retain the characters that belong to the valid character set, and remove other characters. The screening formula is:

[0023] ;

[0024] Define the set of punctuation marks to be removed and irrelevant character sets , traverse and match from valid characters Remove the Characters: ;

[0025] Convert the cleaned string to lowercase to complete the standardization process: ;

[0026] S212, encoding based on semantic similarity to capture semantic relationships between device types;

[0027] Defining the semantic similarity matrix ,in, Represents the device type string The device classes semantically related to it The semantic similarity of the type, which is calculated by cosine similarity:

[0028] ,

[0029] where and represent the device type strings and their related devices word vectors, and are and the vector norms of; d is the dimension of the word vector, is the set of real numbers;

[0030] S213. Use the word vector model to map the semantic feature vector to the coordinate values in the multi-dimensional space;

[0031] Construct a corpus based on the device type set and train the word vectors. The goal is to maximize the conditional probability, where represents the set of device types included in the context window centered on the device type string :

[0032] ,

[0033] where the conditional probability is calculated by the Softmax function:

[0034] ,

[0035] where represents the index of all candidate device types in the device type set, is the word vector representation of the k-th device type; after training, organize the word vectors of all device types into a matrix V, where :

[0036] ;

[0037] To eliminate the influence of different dimensional value ranges, normalize the matrix V column-wise, where and are the mean and standard deviation of the j-th dimension respectively, and the element in the matrix represents the value of the device type string on the j-th dimension of the word vector. The normalized matrix is :

[0038] ,

[0039] ,

[0040] ;

[0041] Calculate the covariance matrix of the standardized matrix , where the covariance matrix of the standardized matrix is denoted as :

[0042] ;

[0043] Perform eigenvalue decomposition on the covariance matrix C:

[0044] ,

[0045] ,

[0046] ;

[0047] where is the eigenvector matrix, is the transpose matrix of the eigenvector matrix , is the eigenvalue matrix, and the first principal component direction corresponds to the largest eigenvalue; select the first principal component direction , and project the standardized word vector of each device type string onto this direction to obtain the coordinate value :

[0048] ;

[0049] Finally, the high-dimensional word vector of each device type is reduced to a single value , and this value is used as the coordinate value of the first dimension of the point in the multi-dimensional space to participate in subsequent point distance calculation and matching.

[0050] Furthermore, in S22, in the multi-dimensional identification network, the geographical location is described by longitude and latitude data, and the longitude and latitude information is converted into a unique numerical representation as the coordinate value of the second dimension in the multi-dimensional space, which specifically includes the following steps:

[0051] S221. Let the longitude of the geographical location be x and the latitude be y, and their value ranges are respectively:

[0052] ;

[0053] Normalize the longitude and latitude and map them to the interval :

[0054] ;

[0055] Normalized longitude , normalized latitude ;

[0056] S222. According to the normalization result, convert into integer grid coordinates. Let the coding precision be b bits, and the grid coordinate calculation formula is:

[0057] ,

[0058] ,

[0059] where represents the position of the longitude and latitude in the grid, is the floor operation;

[0060] S223. Binary-encode the grid coordinates and respectively, and the encoding results are represented as and :

[0061] ,

[0062] ;

[0063] S224. Use the Z-shaped curve to alternately splice the binary bits to generate an overall binary string, representing the unique encoding of the geographical location:

[0064] ;

[0065] S225. Convert the spliced binary string into a decimal number as the unique mapping value of the geographical location:

[0066] ,

[0067] This value is used as the second-dimensional coordinate value of the point in the multi-dimensional space.

[0068] Furthermore, in S23, in the multi-dimensional identification network, the service identification h is a 128-bit digital code. When the type of service identification is less than three digits, directly take the modulus of the 128-bit number of the service identification by 10 to obtain a single-digit number as the mapping value, that is, the third-dimensional coordinate value; then ;

[0069] When the types of service identifiers gradually increase to three digits or more, to avoid uneven distribution caused by direct modulo operation, the service identifier h needs to be hashed first, denoted as , and then modulo 10 is taken on , and the final result is assigned to as the coordinate value of the third dimension in the multi-dimensional space.

[0070] Furthermore, in S4, the Euclidean distance is used as the standard to evaluate the best object. Let the point obtained by attribute mapping in the addressing matrix be , and any point in the multi-dimensional space be , then the distance between the two points is defined as: ;

[0071] wherein , , respectively represent the device type, geographical location, and service identifier coordinate value of the point obtained by attribute mapping in the addressing matrix; , , respectively represent the device type, geographical location, and service identifier coordinate value of any point in the multi-dimensional space; The point with the shortest distance from point A within the limited range is selected as the best communication object.

[0072] The beneficial effects of the present invention are as follows:

[0073] 1) The present invention performs node addressing through the combination of multi-dimensional attributes, maps the multi-dimensional attributes of the node into multi-dimensional coordinate points, which is different from the traditional single ID addressing method, ensures the accurate matching of task requirements, and thus improves the success rate of task execution and the overall performance of the system;

[0074] 2) The present invention constructs a dynamic matching mechanism by calculating the Euclidean distance between the node mapping point and the target point, accurately quantifies the adaptability between the node and the task, and optimizes the rationality of task scheduling; Compared with the traditional multi-condition database polling mechanism, the present invention has certain advantages in reducing communication delays and resource waste caused by matching errors;

[0075] 3) The present invention transforms the complex multi-attribute addressing problem into a point distance problem in geometric space through unified numerical mapping and a simple distance calculation model, avoids the computational overhead of complex rules and conditional judgments in traditional methods, significantly improves the operating efficiency of the system, and provides guarantee for real-time task processing at the same time;

[0076] In summary, through the hierarchical organization and matrix expression of the multi-dimensional attributes of nodes, the present invention effectively improves the information matching efficiency, realizes fast search in scenarios of large scale, high concurrency, and multi-attribute combination, ensures the efficient operation and wide applicability of the network system, and is applicable to fields with complex task requirements such as dynamic resource matching of Internet of Things devices and high requirements for real-time performance, having broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 is a flowchart of the method of the present invention;

[0078] Figure 2 is a schematic diagram of the mapping rule of device types in the method of the present invention;

[0079] Figure 3 is a schematic diagram of the mapping rule of geographical locations in the method of the present invention;

[0080] Figure 4 is a schematic diagram of the performance comparison between the present invention and other search algorithms under different database scales. DETAILED DESCRIPTION OF THE INVENTION

[0081] In order to make the content of the present invention more clearly understood, the following further describes the present invention in detail according to specific embodiments and in conjunction with the accompanying drawings.

[0082] As Figure 1 shown, a fast matching and searching method based on an addressing matrix according to the present invention includes the following steps:

[0083] S1. In the multi-dimensional identification network, the current multi-dimensional attributes available for addressing mainly include device type, geographical location, and service identification. Therefore, the edge layer database stores the above attributes and other related information to ensure the accuracy and efficiency of network addressing. The device types mainly include: various nodes such as smart home, smart door lock, security camera, smart electricity meter, etc.; the geographical location refers to the specific geographical area where the task occurs; the service identification is used to mark specific types of services, and different services are assigned independent identifications, which, as one of the three types of multi-dimensional identifications, are assigned by the multi-dimensional identification server to each node.

[0084] S2. Before searching and matching, different attributes in the edge layer database need to adopt corresponding mapping mechanisms according to their data types or their own characteristics, and finally be mapped to numerical values as the coordinate values of the corresponding dimensions in the multi-dimensional space. These coordinate values together determine the position of this point in the multi-dimensional space;

[0085] S3. When the addressing request in the addressing matrix is sent to the edge layer multi-dimensional identification server, the server extracts the attribute content filled in the matrix and, according to the mapping rules described in S2, maps the attributes to the coordinate values of different dimensions in the multi-dimensional space;

[0086] S4. Taking Euclidean distance as the criterion for evaluating the best object, let the point obtained by attribute mapping in the addressing matrix be , any point in the multidimensional space is , then the distance between two points is defined as: ;

[0087] The point with the shortest distance to point A within the limited range is selected as the best communication partner.

[0088] Among them, the specific implementation steps of S2 are as follows:

[0089] S21. In the multidimensional identification network, the device type attribute as a string needs to be mapped to the multidimensional space through vectorization; based on the idea of Word2Vector, the multidimensional coordinate generation of the device type is completed, such as Figure 2 As shown, the specific steps include:

[0090] S211. To ensure the standardization and consistency of the device type string, the original data needs to be cleaned and standardized, including removing redundant characters, punctuation marks and irrelevant information. Define the device type string , i represents the index of the device type string, and its value range is , where n is the total number of device type strings;

[0091] Each device type string Represented as a sequence of characters, all of which come from the preset legal character set , ;in It is the complete set of all characters, including the valid character set and the redundant character set; A string indicating the device type The lth character in , m is the device type string The actual number of characters that satisfies , N is the maximum length defined by the system; define the valid character set , including English letters, numbers or other specific characters;

[0092] Traverse the device type string , retain the characters that belong to the valid character set, and remove other characters. The screening formula is:

[0093] ;

[0094] Define the set of punctuation marks to be removed and irrelevant character sets , traverse and match from valid characters Remove the Characters: ;

[0095] Convert the cleaned string to lowercase to complete the standardization process: ;

[0096] S212. Define the semantic similarity matrix , where represents the device type string and its semantically related device class type's semantic similarity. The similarity is calculated by cosine similarity:

[0097] ,

[0098] where and represent the device type strings and their related devices 's word vectors. and are and 's vector norms; d is the dimension of the word vector;

[0099] S213. Use the word vector model to map the semantic feature vector to the coordinate values in the multi-dimensional space;

[0100] Construct a corpus based on the device type set and train the word vector. The goal is to maximize the conditional probability. Among them, represents the set of device types included in the context window centered on the device type string :

[0101] ,

[0102] where the conditional probability is calculated by the Softmax function:

[0103] ,

[0104] where represents the index of all candidate device types in the device type set. is the word vector representation of the k-th device type; after training, organize the word vectors of all device types into a matrix V, where :

[0105] ;

[0106] To eliminate the influence of different dimensional value ranges, standardize the matrix V column by column, where and They are the mean and standard deviation of the j-th dimension respectively, and the elements in the matrix represents the device type string The value on the j-th dimension of the word vector. The standardized matrix is :

[0107] ,

[0108] ,

[0109] ;

[0110] Calculate the covariance matrix of the standardized matrix , represents the standardized matrix of the transposed matrix:

[0111] ;

[0112] Perform eigenvalue decomposition on the covariance matrix C:

[0113] ,

[0114] ,

[0115] ;

[0116] Among them, is the eigenvector matrix, is the transposed matrix of the eigenvector matrix , is the eigenvalue matrix, and the first principal component direction corresponds to the largest eigenvalue; Select the first principal component direction , and project the standardized word vector of each device type string onto this direction to obtain the coordinate value :

[0117] ;

[0118] Finally, the high-dimensional word vector of each device type is reduced to a single value , and this value is used as the first dimension coordinate value of the point in the multi-dimensional space to participate in subsequent point distance calculation and matching.

[0119] S22. In the multi-dimensional identification network, the geographical location is described by longitude and latitude data, where the longitude range is , and the latitude range is , convert the longitude and latitude information into a unique numerical representation, which is used as the coordinate value of the second dimension in the multi-dimensional space, as Figure 3 shown, specifically including the following steps:

[0120] S221. Let the longitude of the geographical location be x and the latitude be y, and their value ranges are respectively:

[0121] ;

[0122] Perform normalization processing on the longitude and latitude, and map them to the interval :

[0123] ;

[0124] The normalized longitude , the normalized latitude ;

[0125] S222. According to the normalization result, convert into an integer grid coordinate. Let the coding precision be b bits, and the grid coordinate calculation formula is:

[0126] ,

[0127] ,

[0128] where represents the position of the longitude and latitude in the grid, and is the floor operation;

[0129] S223. Binary-encode the grid coordinates and respectively, and the encoding results are respectively represented as and :

[0130] ,

[0131] ;

[0132] S224. Use the Z-shaped curve to alternately splice the binary bits to generate an overall binary string, which represents the unique encoding of the geographical location:

[0133] ;

[0134] S225. Convert the spliced binary string into a decimal number, which is used as the unique mapping value of the geographical location:

[0135] ,

[0136] This value is used as the second dimension coordinate value of the point in the multidimensional space.

[0137] S23. In a multi-dimensional identification network, the service identification is a string of 128-bit digital codes. When the current number of services is small, the 128-bit number is directly modulo 10 to obtain a single digit as the mapping value. Let the service identification be h, then If the business identifier increases to three digits or more, in order to avoid uneven distribution caused by direct modulus, the business identifier h needs to be hashed first, recorded as , then Modulo 10, the final result is assigned to , as the coordinate value of the third dimension in multidimensional space.

[0138] To make the present invention easier to understand, the following description is given by taking a device addressing scenario in a smart industrial park as an example.

[0139] The edge node receives the following addressing request:

[0140] Device type: "Smart_Conveyor_V1"

[0141] Geographic location: longitude x=113.9356, latitude y=22.5460

[0142] Business ID: Hexadecimal code: 0xDAE345AF03BCD99012

[0143] The system performs mapping processing according to preset rules:

[0144] The string is standardized to "smartconveyorv1", and its high-dimensional word vector is obtained using the trained word vector model. After principal component dimensionality reduction, the projection value on the first principal component is: , as the first dimension coordinate of the device type in the multidimensional space.

[0145] In terms of geographic location, the system normalizes the original longitude and latitude and maps them into integer coordinate values using Z-curve encoding. Assuming the encoding precision is 10 bits, the geographic space is divided into The coordinates obtained after normalization are binary interleaved and converted to decimal, and the final geographic mapping value generated is 1039232, which is used as the second dimension coordinate of the multidimensional space.

[0146] For the business identification field, in order to improve discreteness and stability, the system first uses a hash algorithm to perform hash processing, and then modulo 10 on the result. The final generated coordinate value is 7, corresponding to the third dimension coordinate. At this point, the addressing request is mapped to a point (0.384, 1039232, 7) in the multidimensional space.

[0147] The system retrieves all registered communication objects from the edge layer database and calculates the Euclidean distance between each of them and the current request point one by one. Suppose the spatial coordinates of a candidate object are (0.390, 1039240, 6), and the calculated distance between the two is approximately 8.06. According to the maximum matching distance threshold set by the system (which can be set to 10 for this search), it is determined that this candidate object meets the matching condition and is finally identified as the best communication target node.

[0148] To verify the actual performance advantages of the method of the present invention in multi-dimensional matching scenarios, the method of the present invention is compared and evaluated with three typical retrieval algorithms (Euclidean distance algorithm, Manhattan distance algorithm, and K-means clustering algorithm) in terms of operation efficiency under different database scales. The experiment is implemented in the Python 3.8 environment, using NumPy for data generation and distance calculation, scikit-learn for implementing K-means clustering, and Matplotlib for result visualization. To fairly evaluate the response efficiency of different algorithms to addressing requests in multi-dimensional space, datasets of multiple scales are constructed in the experiment to simulate the multi-dimensional coordinate information of pre-registered communication objects in the edge database, and the matching time of each algorithm is statistically analyzed.

[0149] A three-dimensional random coordinate point set in the range of [0, 1000] is generated by np.random.rand(db_size, 3)*1000 to simulate the three-dimensional vector representation after device type mapping, geographical location coding, and service identification combination. The test point is fixed at [500, 500, 500] to simulate the spatial position of a typical addressing request. Six groups of database scales are selected for the experiment, which are 1000, 2000, 3000, 5000, 8000, and 10000 data points respectively. For each group of experiments, the data is randomly generated again, and np.random.seed(42) is used to ensure the reproducibility of the results.

[0150] The experimental results are as Figure 4 shown. The curve of the method of the present invention always closely approaches the horizontal axis. As the number of data points increases from 1000 to 10000, the execution time always remains at about 0.002 seconds, only showing an extremely slow linear growth trend. This indicates that this method has good scalability and high efficiency, and can still maintain fast response ability in the case of a sharp increase in data volume.

[0151] In contrast, the performance of the traditional Euclidean distance search method shows an obvious linear growth trend. The results corresponding to the orange curve show that when the data scale reaches 5000, the search time has exceeded 0.03 seconds, and when it reaches 10000 entries, it is almost approaching 0.05 seconds. The processing logic of this method is to traverse all data points to calculate the Euclidean distance and select the minimum value, and the complexity is , and when the data scale expands, the search time increases sharply, which is not suitable for business scenarios with high concurrency or high response time requirements. Similar to the Euclidean search, the running efficiency of the Manhattan distance search method also increases rapidly with the increase in the number of data points. Although the calculation of the Manhattan distance itself is theoretically simpler than the Euclidean distance, the actual experimental results show that the difference in execution time between the two is not obvious under large data scales, and the overall time consumption of the Manhattan method is even slightly higher. The K-means clustering search method shows certain disadvantages in the initial stage of the experiment. Its initial running time is higher than other methods and reaches about 0.01 seconds at the scale of 1000 data points, which is mainly due to the overhead brought by its clustering initialization and center point recalculation. However, as the data volume increases, the curve change of this method is relatively stable, indicating that it has a certain anti-scale ability. However, since this method only makes a rough judgment based on the distance between the clustering center and the query point and lacks a fine capture of the original data distribution, there are certain limitations in terms of matching accuracy and applicable scenarios, and it is mainly applicable to application scenarios with low accuracy requirements, focusing on approximate positioning or candidate screening.

[0152] The above experimental results illustrate the feasibility and practicality of the present invention. The method described in the present invention maintains the lowest execution time consumption under different data scales, far superior to the Euclidean and Manhattan methods, and significantly superior to the K-means clustering approximate search. The method described in the present invention has excellent scalability and real-time performance when dealing with high-dimensional, multi-attribute, large-scale addressing tasks, and is particularly suitable for being deployed in scenarios with high throughput and low latency requirements such as the Internet of Things, edge intelligence, and industrial device interconnection, and can effectively support the rapid response and accurate matching of high-frequency dynamic addressing requests.

[0153] The above is only the preferred solution of the present invention and is not intended to further limit the present invention. All equivalent changes made by using the content of the specification and drawings of the present invention are within the protection scope of the present invention.

Claims

1. A fast matching search method based on an addressing matrix, characterized in that, Including the following steps: S1. Store the multi-dimensional attributes currently used for addressing in the edge layer database of the multi-dimensional identification network; S2. Map various category attributes into coordinate values of corresponding dimensions in the multi-dimensional space according to different rules to determine the positions of each point in the multi-dimensional space; wherein, each point in the multi-dimensional space corresponds to a registered or existing communication object; S3. Fill in the addressing request in the preset addressing matrix and send the addressing matrix filled with the addressing request to the edge layer multi-dimensional identification server; S4. The edge layer multi-dimensional identification server extracts the attribute content filled in the matrix and converts the extracted attribute values into coordinate values of corresponding dimensions in the multi-dimensional space according to the mapping rules in S2; S5. Take the point obtained by mapping according to the addressing matrix as the position of the addressing request in the multi-dimensional space, calculate the distance from the space points corresponding to all objects that have been mapped and stored in the database in S2, and select the communication object corresponding to the nearest point within the preset threshold range as the best communication target.

2. The fast matching search method based on an addressing matrix according to claim 1, wherein, In S1, the multi-dimensional attributes currently used for addressing include device type attributes, geographical location attributes, and service identification attributes; wherein, the device type attributes include smart home, smart door lock, security camera, and smart meter; the geographical location attribute refers to the specific geographical area where the task occurs; the service identification attribute is used to mark specific types of services, and independent identifiers are assigned to different services.

3. A fast matching search method based on an addressing matrix according to claim 2, characterized in that S2 is specifically as follows: S21. Based on the idea of Word2Vector, map the device type attributes into the multi-dimensional space through vectorization to generate the multi-dimensional coordinates of the device type, that is, the coordinate values of the first dimension; S22. Convert the longitude and latitude information of the geographical location attribute into a unique numerical representation as the coordinate values of the second dimension in the multi-dimensional space; S23. Judge the traffic volume of the service identification attribute, take the modulus in different ways according to the amount of traffic volume, and map the value to the predefined coordinate interval to obtain the multi-dimensional coordinates of the service identification, that is, the coordinate values of the third dimension.

4. A fast matching search method based on an addressing matrix according to claim 3, characterized in that, In S21, in the multi-dimensional identification network, the device type attributes, as strings, need to be mapped into the multi-dimensional space through vectorization. Based on the idea of Word2Vector, the generation of the multi-dimensional coordinates of the device type is completed, specifically including the following steps: S211. To ensure the standardization and consistency of the device type strings, clean and standardize the original data, including removing redundant characters, punctuation marks, and irrelevant information; Define the device type string , where i represents the index of the device type string, and the value range is , where n is the total number of device type strings; Each device type string is represented as a character sequence, where each character comes from a preset legal character set , ; where is the complete set of all characters, including the valid character set and the redundant character set; represents the l-th character in the device type string , where m is the actual number of characters in the device type string , satisfying , and N is the maximum length defined by the system; define the valid character set , including English letters, numbers, or other specific characters; ​ Traverse the device type string , retain the characters that belong to the valid character set, and remove other characters. The screening formula is: ； Define the set of punctuation marks to be removed and the set of irrelevant characters Traverse and match to remove characters belonging to from the valid characters : ; Convert the cleaned string to lowercase to complete the standardization process: ; S212. Encode based on semantic similarity to capture the semantic relationship between device types; Define the semantic similarity matrix , where represents the device type string and its semantic similarity with the device class semantically related to it. The similarity is calculated by cosine similarity: ， Among them, and represent the device type string and its related devices word vectors, and are and vector norms of; d is the dimension of the word vector, is the set of real numbers; S213. Use the word vector model to map the semantic feature vectors to the coordinate values in the multi-dimensional space; Construct a corpus based on the set of device types and train word vectors with the goal of maximizing the conditional probability, where, represents the set of device types included in the context window centered on the device type string in the corpus: ， Wherein, the conditional probability is calculated by the Softmax function: ， Among them, represents the index of all candidate device types in the set of device types, is the word vector representation of the k-th device type; after training, the word vectors of all device types are organized into a matrix V, where : ； To eliminate the influence of the value ranges of different dimensions, the matrix V is column-normalized, where and are the mean and standard deviation of the j-th dimension respectively, and the element in the matrix represents the numerical value of the device type string on the j-th dimension of the word vector. The normalized matrix is : ， ， ； Calculate the covariance matrix of the standardized matrix , denotes the transpose matrix of the standardized matrix : ； Perform eigenvalue decomposition on the covariance matrix C: ， ， ； Among them, is the eigenvector matrix, is the eigenvector matrix is the transpose matrix of is the eigenvalue matrix, and the first principal component direction corresponds to the largest eigenvalue; select the first principal component direction and project the normalized word vector of each device type string onto this direction to obtain the coordinate value : ； The high-dimensional word vectors of each device type finally are reduced to a single value , which is used as the coordinate value of the first dimension of the point in the multi-dimensional space and participates in subsequent point distance calculation and matching.

5. A fast matching search method based on an addressing matrix according to claim 4, characterized in that, In S22, in the multi-dimensional identification network, the geographical location is described by longitude and latitude data. Convert the longitude and latitude information into a unique numerical representation as the coordinate values of the second dimension in the multi-dimensional space, specifically including the following steps: S221. Let the longitude of the geographical location be x and the latitude be y, and their value ranges are respectively: ； Normalize the longitude and latitude and map it to the interval : ； Normalized longitude , normalized latitude ; S222. According to the normalization result, convert into integer grid coordinates. Assume the coding precision is b bits, and the grid coordinate calculation formula is: ， ， Among them, represents the position in the grid of the longitude and latitude, is the floor operation; S223. Binary-encode the grid coordinates and respectively, and represent the encoding results as and : ， ； S224. Use a Z-shaped curve to alternately splice binary bits to generate an overall binary string, representing the unique encoding of the geographical location: ； S225. Convert the concatenated binary string into a decimal number, which serves as the unique mapping value for the geographical location: ， This value serves as the second-dimensional coordinate value of a point in the multi-dimensional space.

6. A fast matching search method based on an addressing matrix according to claim 5, characterized in that, In S23, in the multi-dimensional identification network, the service identifier h is a 128-bit digital code. When the type of the service identifier is less than three digits, the 128 bits of the service identifier are directly modulo 10 to obtain a single digit as the mapping value, that is, the third-dimensional coordinate value; then ; When the types of business identifiers gradually increase to three digits or more, to avoid uneven distribution caused by direct modulo operation, the business identifier h needs to be hashed first, denoted as , and then is modulo 10, and the final result is assigned to , as the coordinate value of the third dimension in the multi-dimensional space.

7. A fast matching search method based on an addressing matrix according to claim 6, characterized in that, In S4, the Euclidean distance is used as the standard for evaluating the best object. Let the point obtained by attribute mapping in the addressing matrix be , and any point in the multi-dimensional space be . Then the distance between the two points is defined as: ; Among them, , , respectively represent the device type, geographical location, and service identification coordinate values of the points obtained by attribute mapping within the addressing matrix; , , respectively represent the device type, geographical location, and service identification coordinate values of any point within the multi-dimensional space; Select the point with the shortest distance from point A within the limited range as the best communication object.

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