A communication path matching method and system

Abstract

The present invention provides a communication path matching method and system, which relate to the field of data processing technology. In the present invention, a key information mining operation is performed on the target data description information to output a multi-dimensional key information feature representation; an importance mining operation is performed on the target path description information and the target data description information to output a path importance parameter distribution and a data importance parameter distribution; based on the path importance parameter distribution, the multi-dimensional key information feature representation is aggregated to output the corresponding aggregated key information feature representation; the aggregated key information feature representation is matched and analyzed from multiple angles to output the corresponding multi-angle matching analysis data; based on the data importance parameter distribution, the multi-angle matching analysis data is aggregated to output the corresponding aggregated matching analysis data. Based on the above method, the reliability of path matching can be improved to a certain extent.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a communication path matching method and system. Background Art

[0002] The important basis for selecting a communication path is the matching process of the communication path, that is, calculating the matching degree between the data to be transmitted and the communication path. However, in the prior art, generally, a simple comparison is made between the type dimension of the data generating device of the data to be transmitted or the data type of the data to be transmitted and the communication path, which results in a low reliability of the path matching. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a communication path matching method and system to improve the reliability of path matching to a certain extent.

[0004] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0005] A communication path matching method, comprising:

[0006] Determine target path description information for reflecting the target communication path, and determine target data description information for reflecting the data to be transmitted, wherein the target path description information is used to reflect the data transmitted historically by the target communication path, and the target data description information is used to reflect the data to be transmitted;

[0007] Performing a multi-dimensional key information mining operation on the target data description information to output a corresponding multi-dimensional key information feature representation;

[0008] Performing importance mining operations on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution;

[0009] According to the path importance parameter distribution, the multi-dimensional key information feature representation is aggregated to output a corresponding aggregated key information feature representation;

[0010] Performing a multi-angle matching analysis operation on the aggregated key information feature representation to output corresponding multi-angle matching analysis data;

[0011] According to the data importance parameter distribution, the multi-angle matching analysis data are aggregated to output corresponding aggregated matching analysis data, where the aggregated matching analysis data is used to reflect the degree of matching between the data to be transmitted and the target communication path.

[0012] In some preferred embodiments, in the above communication path matching method, the step of determining target path description information reflecting the target communication path and determining target data description information reflecting the data to be transmitted includes:

[0013] Determine each piece of historical data that has been transmitted historically by the target communication path, and determine the historical transmission time information corresponding to each piece of the historical data;

[0014] Based on the corresponding historical transmission time information, respectively determine the data sampling parameters corresponding to each piece of the historical data; and based on the corresponding data sampling parameters, respectively perform sampling processing on each piece of the historical data to output the corresponding historical sampling data, wherein the data sampling parameters corresponding to the historical data with earlier corresponding historical transmission time information are smaller than the data sampling parameters corresponding to the historical data with later corresponding historical transmission time information;

[0015] Sorting and combining the historical sampling data corresponding to each piece of the historical data according to the order relationship between the corresponding historical transmission time information to obtain target path description information reflecting the target communication path;

[0016] The data sampling parameters corresponding to each piece of the historical data are averaged to output the corresponding average data sampling parameters, and based on the average data sampling parameters, the data to be transmitted are sampled to output the corresponding target data description information.

[0017] In some preferred embodiments, in the above-mentioned communication path matching method, the step of performing importance mining operation on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution includes:

[0018] Performing a key information mining operation on the target data description information to output a corresponding first key information feature representation, and performing a parameter extraction operation on the first key information feature representation to output a corresponding extracted key information feature representation;

[0019] Performing a key information mining operation on the target path description information to output a corresponding second key information feature representation;

[0020] A difference minimization operation is performed on the second key information feature representation and the selected key information feature representation to output a corresponding path importance parameter distribution and a corresponding data importance parameter distribution.

[0021] In some preferred embodiments, in the above-mentioned communication path matching method, the step of performing a difference minimization operation on the second key information feature representation and the selected key information feature representation to output a corresponding path importance parameter distribution and a corresponding data importance parameter distribution includes:

[0022] Perform a difference minimization operation on the second key information feature representation and the selected key information feature representation, and output first minimized output data and second minimized output data;

[0023] The first minimization output data and the second minimization output data are respectively subjected to parameter mapping operations to output corresponding path importance parameter distributions and corresponding data importance parameter distributions.

[0024] In some preferred embodiments, in the above communication path matching method, the multi-dimensional key information feature representation includes multiple data key information feature representations, and the parameters in the path importance parameter distribution are used to reflect the importance of the corresponding data key information feature representations;

[0025] The step of aggregating the multi-dimensional key information feature representations according to the path importance parameter distribution to output corresponding aggregated key information feature representations includes:

[0026] According to the path importance parameter distribution, the plurality of data key information feature representations are aggregated according to importance to output corresponding aggregated key information feature representations.

[0027] In some preferred embodiments, in the above communication path matching method, the multi-angle matching analysis data includes a plurality of original matching analysis data, and the parameters in the data importance parameter distribution are used to reflect the importance of the corresponding original matching analysis data;

[0028] The step of aggregating the multi-angle matching analysis data according to the data importance parameter distribution to output corresponding aggregated matching analysis data includes:

[0029] Performing an excitation mapping operation on the plurality of original matching analysis data to output a corresponding plurality of excitation mapping output data;

[0030] According to the data importance parameter distribution, the plurality of stimulus mapping output data are aggregated according to importance to output corresponding aggregate matching analysis data.

[0031] In some preferred embodiments, in the above communication path matching method, the communication path matching method further includes:

[0032] extracting a primary exemplary information combination having primary identification information, the primary exemplary information combination comprising primary exemplary path description information for reflecting a primary exemplary communication path and primary exemplary data description information for reflecting primary exemplary transmission data, the primary identification information being used to reflect an actual matching degree between the primary exemplary transmission data and the primary exemplary communication path;

[0033] Performing a loading process on the primary exemplary information combination so as to load it into the constructed primary path matching model, analyzing primary matching analysis data corresponding to the primary exemplary information combination, wherein the primary matching analysis data is used to reflect the predicted matching degree between the primary exemplary transmission data and the primary exemplary communication path;

[0034] Based on the primary matching analysis data and the primary identification information of the primary exemplary information combination, performing a model update operation on the primary path matching model to output a corresponding intermediate path matching model;

[0035] Extracting a high-level exemplary information combination with high-level identification information, the high-level exemplary information combination including the target path description information and high-level exemplary data description information for reflecting high-level exemplary transmission data, the high-level identification information for reflecting an actual matching degree between the high-level exemplary transmission data and the target communication path;

[0036] Loading the high-level exemplary information combination to load it into the intermediate path matching model, analyzing high-level matching analysis data corresponding to the high-level exemplary information combination, wherein the high-level matching analysis data is used to reflect the predicted matching degree between the high-level exemplary transmission data and the target communication path;

[0037] Based on the high-level matching analysis data and the high-level identification information possessed by the high-level exemplary information combination, a model update operation is performed on the intermediate path matching model to output a corresponding target path matching model, wherein the target path matching model is used to analyze the corresponding aggregate matching analysis data corresponding to the data to be transmitted and the corresponding target communication path based on the target path description information and the target data description information.

[0038] The embodiment of the present invention further provides a communication path matching system, including:

[0039] a description information determination module, used to determine target path description information reflecting the target communication path, and to determine target data description information reflecting the data to be transmitted, wherein the target path description information is used to reflect the data transmitted historically by the target communication path, and the target data description information is used to reflect the data to be transmitted;

[0040] A key information mining module, used to perform a multi-dimensional key information mining operation on the target data description information to output a corresponding multi-dimensional key information feature representation;

[0041] An importance mining module, used for performing importance mining operations on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution;

[0042] A feature representation aggregation module, used to aggregate the multi-dimensional key information feature representation according to the path importance parameter distribution, and output the corresponding aggregated key information feature representation;

[0043] A matching analysis module, used for performing a multi-angle matching analysis operation on the aggregated key information feature representation to output corresponding multi-angle matching analysis data;

[0044] The aggregation analysis matching module is used to aggregate the multi-angle matching analysis data according to the data importance parameter distribution to output corresponding aggregate matching analysis data, and the aggregate matching analysis data is used to reflect the matching degree between the data to be transmitted and the target communication path.

[0045] In some preferred embodiments, in the above communication path matching system, the importance mining module is specifically used for:

[0046] Performing a key information mining operation on the target data description information to output a corresponding first key information feature representation, and performing a parameter extraction operation on the first key information feature representation to output a corresponding extracted key information feature representation;

[0047] Performing a key information mining operation on the target path description information to output a corresponding second key information feature representation;

[0048] A difference minimization operation is performed on the second key information feature representation and the selected key information feature representation to output a corresponding path importance parameter distribution and a corresponding data importance parameter distribution.

[0049] In some preferred embodiments, in the above communication path matching system, the communication path matching system further includes at least one other functional module for:

[0050] A primary exemplary information combination with primary identification information is extracted, the primary exemplary information combination includes primary exemplary path description information for reflecting a primary exemplary communication path and primary exemplary data description information for reflecting primary exemplary transmission data, the primary identification information is used to reflect the actual matching degree between the primary exemplary transmission data and the primary exemplary communication path; the primary exemplary information combination is loaded to be loaded into the constructed primary path matching model, and primary matching analysis data corresponding to the primary exemplary information combination is analyzed, the primary matching analysis data is used to reflect the predicted matching degree between the primary exemplary transmission data and the primary exemplary communication path; based on the primary matching analysis data and the primary identification information of the primary exemplary information combination, a model updating operation is performed on the primary path matching model to output a corresponding intermediate path matching model; a high-level exemplary information combination with high-level identification information is extracted, the high-level exemplary information combination The high-level exemplary information combination includes the target path description information and the high-level exemplary data description information used to reflect the high-level exemplary transmission data, and the high-level identification information is used to reflect the actual matching degree between the high-level exemplary transmission data and the target communication path; the high-level exemplary information combination is loaded to be loaded into the intermediate path matching model, and the high-level matching analysis data corresponding to the high-level exemplary information combination is analyzed, and the high-level matching analysis data is used to reflect the predicted matching degree between the high-level exemplary transmission data and the target communication path; based on the high-level matching analysis data and the high-level identification information of the high-level exemplary information combination, the intermediate path matching model is updated to output the corresponding target path matching model, and the target path matching model is used to analyze the corresponding aggregate matching analysis data corresponding to the data to be transmitted and the corresponding target communication path based on the target path description information and the target data description information.

[0051] A communication path matching method and system provided by an embodiment of the present invention can perform key information mining operations on target data description information to output multi-dimensional key information feature representations; perform importance mining operations on target path description information and target data description information to output path importance parameter distribution and data importance parameter distribution; perform aggregation operations on multi-dimensional key information feature representations based on path importance parameter distribution to output corresponding aggregated key information feature representations; perform multi-angle matching analysis operations on aggregated key information feature representations to output corresponding multi-angle matching analysis data; perform aggregation operations on multi-angle matching analysis data based on data importance parameter distribution to output corresponding aggregated matching analysis data. Based on the above content, since multi-dimensional key information mining operations and multi-angle matching analysis operations are performed, a combined analysis is finally performed based on path importance parameter distribution and data importance parameter distribution, so that the reliability of the determined matching degree is higher, thereby improving the reliability of path matching to a certain extent, thereby improving the problem of poor reliability existing in the prior art.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A structural block diagram of a communication path matching platform provided in an embodiment of the present invention.

[0054] Figure 2 A flow chart of the steps of the communication path matching method provided in an embodiment of the present invention.

[0055] Figure 3 A schematic diagram of various modules included in the communication path matching system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0057] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] like Figure 1 As shown, an embodiment of the present invention provides a communication path matching platform, wherein the communication path matching platform may include a memory and a processor.

[0059] In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they can be electrically connected to each other through one or more communication buses or signal lines. The memory may store at least one software function module (computer program) that can exist in the form of software or firmware. The processor can be used to execute the executable computer program stored in the memory, thereby realizing the communication path matching method provided by the embodiment of the present invention (as described later).

[0060] Optionally, in some embodiments, the memory may be, but is not limited to, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0061] Optionally, in some embodiments, the processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0062] Optionally, in some implementations, the matching platform of the communication path may be a server with data processing capabilities.

[0063] Combination Figure 2 The embodiment of the present invention further provides a communication path matching method, which can be applied to the communication path matching platform. The method steps defined in the process related to the communication path matching method can be implemented by the communication path matching platform.

[0064] The following will Figure 2 The specific process shown is explained in detail.

[0065] Step S110, determining target path description information for reflecting the target communication path, and determining target data description information for reflecting the data to be transmitted.

[0066] In an embodiment of the present invention, the matching platform of the communication path may determine target path description information for reflecting the target communication path, and determine target data description information for reflecting the data to be transmitted. The target path description information is used to reflect the data that has been transmitted historically by the target communication path, and the target data description information is used to reflect the data to be transmitted. Exemplarily, the target communication path may include at least one communication device.

[0067] Step S120, performing a multi-dimensional key information mining operation on the target data description information to output a corresponding multi-dimensional key information feature representation.

[0068] In an embodiment of the present invention, the matching platform of the communication path may perform a multi-dimensional key information mining operation on the target data description information to output a corresponding multi-dimensional key information feature representation. Exemplarily, the target data description information may be subjected to a key information mining operation by multiple key information mining units to output multiple key information feature representations, so that the multiple key information feature representations may be used as the multi-dimensional key information feature representations. In addition, the key information mining operation may refer to mapping the target data description information to a feature space, and then performing a convolution operation or filtering process to obtain a key information feature representation.

[0069] Step S130, performing importance mining operation on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution.

[0070] In an embodiment of the present invention, the communication path matching platform may perform importance mining operations on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution.

[0071] Step S140 , performing an aggregation operation on the multi-dimensional key information feature representation according to the path importance parameter distribution, so as to output a corresponding aggregated key information feature representation.

[0072] In an embodiment of the present invention, the communication path matching platform can aggregate the multi-dimensional key information feature representations according to the path importance parameter distribution to output a corresponding aggregated key information feature representation. In other words, multiple key information feature representations corresponding to the multi-dimensional key information feature representations are aggregated.

[0073] Step S150 , performing a multi-angle matching analysis operation on the aggregated key information feature representation to output corresponding multi-angle matching analysis data.

[0074] In an embodiment of the present invention, the matching platform of the communication path may perform a multi-angle matching analysis operation on the aggregated key information feature representation to output corresponding multi-angle matching analysis data. Exemplarily, the aggregated key information feature representation may be respectively subjected to a matching analysis operation by a plurality of matching analysis units to output a plurality of matching analysis data. In other words, the plurality of matching analysis data may be used as the multi-angle matching analysis data.

[0075] Step S160 , aggregating the multi-angle matching analysis data according to the data importance parameter distribution to output corresponding aggregated matching analysis data.

[0076] In an embodiment of the present invention, the communication path matching platform can aggregate the multi-angle matching analysis data according to the data importance parameter distribution to output corresponding aggregated matching analysis data. The aggregated matching analysis data is used to reflect the degree of matching between the data to be transmitted and the target communication path. In other words, the multiple matching analysis data corresponding to the multi-angle matching analysis data are aggregated.

[0077] Based on the above content, such as the above steps S110-S160, since multi-dimensional key information mining operations and multi-angle matching analysis operations are performed, a combined analysis is finally performed based on the path importance parameter distribution and the data importance parameter distribution, so that the reliability of the determined matching degree is higher, thereby improving the reliability of path matching to a certain extent, thereby improving the problem of poor reliability existing in the prior art.

[0078] Alternatively, in some implementations, the above step S110, the step of determining the target path description information reflecting the target communication path and determining the target data description information reflecting the data to be transmitted, may further include the following contents:

[0079] Determine each piece of historical data that has been transmitted historically by the target communication path, and determine the historical transmission time information corresponding to each piece of the historical data;

[0080] Based on the corresponding historical transmission time information, respectively determine the data sampling parameters corresponding to each piece of the historical data, and based on the corresponding data sampling parameters, respectively perform sampling processing on each piece of the historical data to output the corresponding historical sampling data, the data sampling parameters corresponding to the historical data with earlier corresponding historical transmission time information are smaller than the data sampling parameters corresponding to the historical data with later corresponding historical transmission time information, that is, the data volume of the historical sampling data corresponding to the historical data with earlier corresponding historical transmission time information is smaller than the data volume of the historical sampling data corresponding to the historical data with later corresponding historical transmission time information;

[0081] Sorting and combining the historical sampling data corresponding to each piece of the historical data according to the order relationship between the corresponding historical transmission time information to obtain target path description information reflecting the target communication path;

[0082] Perform mean calculation on the data sampling parameters corresponding to each piece of the historical data to output the corresponding mean data sampling parameters, and, based on the mean data sampling parameters, perform sampling processing on the data to be transmitted to output the corresponding target data description information. Exemplarily, keywords can be extracted from the data to be transmitted to obtain a corresponding keyword set, and then, the keywords included in the keyword set are sampled based on the mean data sampling parameters.

[0083] Optionally, in some implementations, the above step S130, i.e., the step of performing importance mining operation on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution, may further include the following contents:

[0084] Performing a key information mining operation on the target data description information to output a corresponding first key information feature representation, and performing a parameter extraction operation on the first key information feature representation to output a corresponding extracted key information feature representation, wherein the parameter extraction operation may refer to performing a sliding window on the first key information feature representation, and then all feature representation parameters in the window may be represented by an average value of each feature representation parameter in the window;

[0085] Performing a key information mining operation on the target path description information to output a corresponding second key information feature representation;

[0086] The second key information feature representation and the selected key information feature representation are subjected to a difference minimization operation to output a corresponding path importance parameter distribution and a corresponding data importance parameter distribution. Exemplarily, the difference minimization operation may refer to, given a number of discrete function values ​​{f1, f2, …, fn} of a certain function, adjusting a number of unknown coefficients f (λ1, λ2, …, λn) in the function so that the difference (in the least squares sense) between the function and the known point set is minimized.

[0087] Optionally, in some implementations, the step of performing a difference minimization operation on the second key information feature representation and the selected key information feature representation to output a corresponding path importance parameter distribution and a corresponding data importance parameter distribution may further include the following content:

[0088] Perform a difference minimization operation on the second key information feature representation and the selected key information feature representation, and output first minimized output data and second minimized output data;

[0089] The first minimization output data and the second minimization output data are respectively subjected to parameter mapping operations to output corresponding path importance parameter distributions and corresponding data importance parameter distributions. Exemplarily, the parameter mapping operation may refer to mapping the included parameters to an interval of 0-1.

[0090] Optionally, in some implementations, the multi-dimensional key information feature representation includes multiple data key information feature representations, and the parameters in the path importance parameter distribution are used to reflect the importance of the corresponding data key information feature representations. Based on this, the above step S140, i.e., the step of aggregating the multi-dimensional key information feature representations according to the path importance parameter distribution to output the corresponding aggregated key information feature representation, may further include the following content:

[0091] According to the path importance parameter distribution, the multiple data key information feature representations are aggregated according to importance to output the corresponding aggregated key information feature representation. That is to say, the path importance parameters included in the path importance parameter distribution can be used as weighting coefficients to perform weighted superposition on the multiple data key information feature representations. In this way, the multi-dimensional key information feature representation can be aggregated to output the corresponding aggregated key information feature representation.

[0092] Optionally, in some implementations, the multi-angle matching analysis data includes a plurality of original matching analysis data, and the parameters in the data importance parameter distribution are used to reflect the importance of the corresponding original matching analysis data. Based on this, the above step S160, i.e., the step of aggregating the multi-angle matching analysis data according to the data importance parameter distribution to output the corresponding aggregated matching analysis data, may further include the following contents:

[0093] Performing an excitation mapping operation on the plurality of original matching analysis data to output a plurality of corresponding excitation mapping output data. Exemplarily, the excitation mapping operation may be implemented by an excitation function;

[0094] According to the data importance parameter distribution, the multiple incentive mapping output data are aggregated according to importance to output corresponding aggregate matching analysis data. That is to say, the data importance parameters included in the data importance parameter distribution can be used as weighting coefficients to perform weighted superposition on the multiple incentive mapping output data. In this way, the multiple incentive mapping output data can be aggregated according to importance to output corresponding aggregate matching analysis data.

[0095] Optionally, in some implementations, the communication path matching method may further include the following content:

[0096] extracting a primary exemplary information combination having primary identification information, the primary exemplary information combination comprising primary exemplary path description information for reflecting a primary exemplary communication path and primary exemplary data description information for reflecting primary exemplary transmission data, the primary identification information being used to reflect an actual matching degree between the primary exemplary transmission data and the primary exemplary communication path;

[0097] Performing a loading process on the primary exemplary information combination so as to load it into the constructed primary path matching model, analyzing primary matching analysis data corresponding to the primary exemplary information combination, wherein the primary matching analysis data is used to reflect the predicted matching degree between the primary exemplary transmission data and the primary exemplary communication path;

[0098] Based on the primary matching analysis data and the primary identification information of the primary exemplary information combination, performing a model update operation on the primary path matching model to output a corresponding intermediate path matching model;

[0099] Extracting a high-level exemplary information combination with high-level identification information, the high-level exemplary information combination including the target path description information and high-level exemplary data description information for reflecting high-level exemplary transmission data, the high-level identification information for reflecting an actual matching degree between the high-level exemplary transmission data and the target communication path;

[0100] Loading the high-level exemplary information combination to load it into the intermediate path matching model, analyzing high-level matching analysis data corresponding to the high-level exemplary information combination, wherein the high-level matching analysis data is used to reflect the predicted matching degree between the high-level exemplary transmission data and the target communication path;

[0101] Based on the advanced matching analysis data and the advanced identification information possessed by the advanced exemplary information combination, a model update operation is performed on the intermediate path matching model, that is, the model parameters included are updated to output a corresponding target path matching model, and the target path matching model is used to analyze the corresponding aggregate matching analysis data corresponding to the data to be transmitted and the corresponding target communication path based on the target path description information and the target data description information. That is to say, the above-mentioned steps S120-S160 can be executed through the target path matching model.

[0102] Based on the above content, the intermediate path matching model is obtained by training based on a combination of most primary exemplary information. During the training process of the intermediate path matching model, the parameters of different key information mining units will gradually become different, so that different key information mining units focus on mining key information of different data and obtain corresponding data key information feature representations. In addition, the parameters of different matching analysis units will also gradually become different.

[0103] Combination Figure 3 The embodiment of the present invention further provides a communication path matching system, which can be applied to the above communication path matching platform. The communication path matching system may include:

[0104] a description information determination module, used to determine target path description information reflecting the target communication path, and to determine target data description information reflecting the data to be transmitted, wherein the target path description information is used to reflect the data transmitted historically by the target communication path, and the target data description information is used to reflect the data to be transmitted;

[0105] A key information mining module, used to perform a multi-dimensional key information mining operation on the target data description information to output a corresponding multi-dimensional key information feature representation;

[0106] An importance mining module, used for performing importance mining operations on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution;

[0107] A feature representation aggregation module, used to aggregate the multi-dimensional key information feature representation according to the path importance parameter distribution, and output the corresponding aggregated key information feature representation;

[0108] A matching analysis module, used for performing a multi-angle matching analysis operation on the aggregated key information feature representation to output corresponding multi-angle matching analysis data;

[0109] The aggregation analysis matching module is used to aggregate the multi-angle matching analysis data according to the data importance parameter distribution to output corresponding aggregate matching analysis data, and the aggregate matching analysis data is used to reflect the matching degree between the data to be transmitted and the target communication path.

[0110] Optionally, in some implementations, the description information determination module is specifically configured to:

[0111] Determine each piece of historical data that has been transmitted historically by the target communication path, and determine the historical transmission time information corresponding to each piece of the historical data;

[0112] Based on the corresponding historical transmission time information, respectively determine the data sampling parameters corresponding to each piece of the historical data; and based on the corresponding data sampling parameters, respectively perform sampling processing on each piece of the historical data to output the corresponding historical sampling data, wherein the data sampling parameters corresponding to the historical data with earlier corresponding historical transmission time information are smaller than the data sampling parameters corresponding to the historical data with later corresponding historical transmission time information;

[0113] Sorting and combining the historical sampling data corresponding to each piece of the historical data according to the order relationship between the corresponding historical transmission time information to obtain target path description information reflecting the target communication path;

[0114] The data sampling parameters corresponding to each piece of the historical data are averaged to output the corresponding average data sampling parameters, and based on the average data sampling parameters, the data to be transmitted are sampled to output the corresponding target data description information.

[0115] Optionally, in some implementations, the importance mining module is specifically used to:

[0116] Performing a key information mining operation on the target data description information to output a corresponding first key information feature representation, and performing a parameter extraction operation on the first key information feature representation to output a corresponding extracted key information feature representation;

[0117] Performing a key information mining operation on the target path description information to output a corresponding second key information feature representation;

[0118] A difference minimization operation is performed on the second key information feature representation and the selected key information feature representation to output a corresponding path importance parameter distribution and a corresponding data importance parameter distribution.

[0119] Optionally, in some implementations, the multi-dimensional key information feature representation includes multiple data key information feature representations, and the parameters in the path importance parameter distribution are used to reflect the importance of the corresponding data key information feature representations. Based on this, the feature representation aggregation module is specifically used to:

[0120] According to the path importance parameter distribution, the plurality of data key information feature representations are aggregated according to importance to output corresponding aggregated key information feature representations.

[0121] Optionally, in some implementations, the multi-angle matching analysis data includes a plurality of original matching analysis data, and the parameters in the data importance parameter distribution are used to reflect the importance of the corresponding original matching analysis data. Based on this, the aggregate analysis matching module is specifically used to:

[0122] Performing an excitation mapping operation on the plurality of original matching analysis data to output a corresponding plurality of excitation mapping output data;

[0123] According to the data importance parameter distribution, the plurality of stimulus mapping output data are aggregated according to importance to output corresponding aggregate matching analysis data.

[0124] Optionally, in some embodiments, the communication path matching system further includes at least one other functional module for:

[0125] A primary exemplary information combination with primary identification information is extracted, the primary exemplary information combination includes primary exemplary path description information for reflecting a primary exemplary communication path and primary exemplary data description information for reflecting primary exemplary transmission data, the primary identification information is used to reflect the actual matching degree between the primary exemplary transmission data and the primary exemplary communication path; the primary exemplary information combination is loaded to be loaded into the constructed primary path matching model, and primary matching analysis data corresponding to the primary exemplary information combination is analyzed, the primary matching analysis data is used to reflect the predicted matching degree between the primary exemplary transmission data and the primary exemplary communication path; based on the primary matching analysis data and the primary identification information of the primary exemplary information combination, a model updating operation is performed on the primary path matching model to output a corresponding intermediate path matching model; a high-level exemplary information combination with high-level identification information is extracted, the high-level exemplary information combination The high-level exemplary information combination includes the target path description information and the high-level exemplary data description information used to reflect the high-level exemplary transmission data, and the high-level identification information is used to reflect the actual matching degree between the high-level exemplary transmission data and the target communication path; the high-level exemplary information combination is loaded to be loaded into the intermediate path matching model, and the high-level matching analysis data corresponding to the high-level exemplary information combination is analyzed, and the high-level matching analysis data is used to reflect the predicted matching degree between the high-level exemplary transmission data and the target communication path; based on the high-level matching analysis data and the high-level identification information of the high-level exemplary information combination, the intermediate path matching model is updated to output the corresponding target path matching model, and the target path matching model is used to analyze the corresponding aggregate matching analysis data corresponding to the data to be transmitted and the corresponding target communication path based on the target path description information and the target data description information.

[0126] In summary, the present invention provides a communication path matching method and system, which can perform key information mining operations on target data description information to output multi-dimensional key information feature representations; perform importance mining operations on target path description information and target data description information to output path importance parameter distribution and data importance parameter distribution; perform aggregation operations on multi-dimensional key information feature representations based on path importance parameter distribution to output corresponding aggregated key information feature representations; perform multi-angle matching analysis operations on aggregated key information feature representations to output corresponding multi-angle matching analysis data; perform aggregation operations on multi-angle matching analysis data based on data importance parameter distribution to output corresponding aggregated matching analysis data. Based on the above content, since multi-dimensional key information mining operations and multi-angle matching analysis operations are performed, a combined analysis is finally performed based on path importance parameter distribution and data importance parameter distribution, so that the reliability of the determined matching degree is higher, thereby improving the reliability of path matching to a certain extent, thereby improving the problem of poor reliability existing in the prior art.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A communication path matching method, characterized in that: include: Determine target path description information for reflecting the target communication path, and determine target data description information for reflecting the data to be transmitted, wherein the target path description information is used to reflect the data transmitted historically by the target communication path, and the target data description information is used to reflect the data to be transmitted; Performing a multi-dimensional key information mining operation on the target data description information to output a corresponding multi-dimensional key information feature representation; Performing importance mining operations on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution; According to the path importance parameter distribution, the multi-dimensional key information feature representation is aggregated to output a corresponding aggregated key information feature representation; Performing a multi-angle matching analysis operation on the aggregated key information feature representation to output corresponding multi-angle matching analysis data; According to the data importance parameter distribution, the multi-angle matching analysis data are aggregated to output corresponding aggregated matching analysis data, wherein the aggregated matching analysis data is used to reflect the matching degree between the data to be transmitted and the target communication path; The step of performing importance mining operation on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution includes: Performing a key information mining operation on the target data description information to output a corresponding first key information feature representation, and performing a parameter extraction operation on the first key information feature representation to output a corresponding extracted key information feature representation; Performing a key information mining operation on the target path description information to output a corresponding second key information feature representation; Performing a difference minimization operation on the second key information feature representation and the selected key information feature representation to output a corresponding path importance parameter distribution and a corresponding data importance parameter distribution; Wherein, the multi-angle matching analysis data includes a plurality of original matching analysis data, and the parameters in the data importance parameter distribution are used to reflect the importance of the corresponding original matching analysis data; The step of aggregating the multi-angle matching analysis data according to the data importance parameter distribution to output corresponding aggregated matching analysis data includes: Performing an excitation mapping operation on the plurality of original matching analysis data to output a corresponding plurality of excitation mapping output data; According to the data importance parameter distribution, the plurality of stimulus mapping output data are aggregated according to importance to output corresponding aggregate matching analysis data.

2. The communication path matching method according to claim 1, characterized in that: The step of determining target path description information reflecting the target communication path and determining target data description information reflecting the data to be transmitted includes: Determine each piece of historical data that has been transmitted historically by the target communication path, and determine the historical transmission time information corresponding to each piece of the historical data; Based on the corresponding historical transmission time information, respectively determine the data sampling parameters corresponding to each piece of the historical data; and based on the corresponding data sampling parameters, respectively perform sampling processing on each piece of the historical data to output the corresponding historical sampling data, wherein the data sampling parameters corresponding to the historical data with earlier corresponding historical transmission time information are smaller than the data sampling parameters corresponding to the historical data with later corresponding historical transmission time information; Sorting and combining the historical sampling data corresponding to each piece of the historical data according to the order relationship between the corresponding historical transmission time information to obtain target path description information reflecting the target communication path; The data sampling parameters corresponding to each piece of the historical data are averaged to output the corresponding average data sampling parameters, and based on the average data sampling parameters, the data to be transmitted are sampled to output the corresponding target data description information.

3. The communication path matching method according to claim 1, characterized in that: The step of performing a difference minimization operation on the second key information feature representation and the selected key information feature representation to output a corresponding path importance parameter distribution and a corresponding data importance parameter distribution includes: Perform a difference minimization operation on the second key information feature representation and the selected key information feature representation, and output first minimized output data and second minimized output data; The first minimization output data and the second minimization output data are respectively subjected to parameter mapping operations to output corresponding path importance parameter distributions and corresponding data importance parameter distributions.

4. The communication path matching method according to claim 1, characterized in that: The multi-dimensional key information feature representation includes multiple data key information feature representations, and the parameters in the path importance parameter distribution are used to reflect the importance of the corresponding data key information feature representations; The step of aggregating the multi-dimensional key information feature representations according to the path importance parameter distribution to output corresponding aggregated key information feature representations includes: According to the path importance parameter distribution, the plurality of data key information feature representations are aggregated according to importance to output corresponding aggregated key information feature representations.

5. The communication path matching method according to any one of claims 1 to 4, characterized in that: The communication path matching method further includes: extracting a primary exemplary information combination having primary identification information, the primary exemplary information combination comprising primary exemplary path description information for reflecting a primary exemplary communication path and primary exemplary data description information for reflecting primary exemplary transmission data, the primary identification information being used to reflect an actual matching degree between the primary exemplary transmission data and the primary exemplary communication path; Performing a loading process on the primary exemplary information combination so as to load it into the constructed primary path matching model, analyzing primary matching analysis data corresponding to the primary exemplary information combination, wherein the primary matching analysis data is used to reflect the predicted matching degree between the primary exemplary transmission data and the primary exemplary communication path; Based on the primary matching analysis data corresponding to the primary exemplary information combination and the primary identification information, performing a model update operation on the primary path matching model to output a corresponding intermediate path matching model; Extracting a high-level exemplary information combination with high-level identification information, the high-level exemplary information combination including the target path description information and high-level exemplary data description information for reflecting high-level exemplary transmission data, the high-level identification information for reflecting an actual matching degree between the high-level exemplary transmission data and the target communication path; Loading the high-level exemplary information combination to load it into the intermediate path matching model, analyzing high-level matching analysis data corresponding to the high-level exemplary information combination, wherein the high-level matching analysis data is used to reflect the predicted matching degree between the high-level exemplary transmission data and the target communication path; Based on the high-level matching analysis data corresponding to the high-level exemplary information combination and the high-level identification information, a model update operation is performed on the intermediate path matching model to output a corresponding target path matching model, wherein the target path matching model is used to analyze the corresponding aggregate matching analysis data corresponding to the data to be transmitted and the corresponding target communication path based on the target path description information and the target data description information.

6. A communication path matching system, characterized in that: include: a description information determination module, used to determine target path description information reflecting the target communication path, and to determine target data description information reflecting the data to be transmitted, wherein the target path description information is used to reflect the data transmitted historically by the target communication path, and the target data description information is used to reflect the data to be transmitted; A key information mining module, used to perform a multi-dimensional key information mining operation on the target data description information to output a corresponding multi-dimensional key information feature representation; An importance mining module, used for performing importance mining operations on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution; A feature representation aggregation module, used to aggregate the multi-dimensional key information feature representation according to the path importance parameter distribution, and output the corresponding aggregated key information feature representation; A matching analysis module, used for performing a multi-angle matching analysis operation on the aggregated key information feature representation to output corresponding multi-angle matching analysis data; An aggregation analysis matching module, used for aggregating the matching analysis data from multiple angles according to the data importance parameter distribution to output corresponding aggregate matching analysis data, wherein the aggregate matching analysis data is used to reflect the matching degree between the data to be transmitted and the target communication path; The step of performing importance mining operation on the target path description information and the target data description information to output corresponding path importance parameter distribution and data importance parameter distribution includes: Performing a key information mining operation on the target data description information to output a corresponding first key information feature representation, and performing a parameter extraction operation on the first key information feature representation to output a corresponding extracted key information feature representation; Performing a key information mining operation on the target path description information to output a corresponding second key information feature representation; Performing a difference minimization operation on the second key information feature representation and the selected key information feature representation to output a corresponding path importance parameter distribution and a corresponding data importance parameter distribution; Wherein, the multi-angle matching analysis data includes a plurality of original matching analysis data, and the parameters in the data importance parameter distribution are used to reflect the importance of the corresponding original matching analysis data; The step of aggregating the multi-angle matching analysis data according to the data importance parameter distribution to output corresponding aggregated matching analysis data includes: Performing an excitation mapping operation on the plurality of original matching analysis data to output a corresponding plurality of excitation mapping output data; According to the data importance parameter distribution, the plurality of stimulus mapping output data are aggregated according to importance to output corresponding aggregate matching analysis data.

7. The communication path matching system according to claim 6, characterized in that: The communication path matching system further includes at least one other functional module, which is used for: Extracting a primary exemplary information combination with primary identification information, the primary exemplary information combination includes primary exemplary path description information for reflecting a primary exemplary communication path and primary exemplary data description information for reflecting primary exemplary transmission data, the primary identification information is used to reflect the actual matching degree between the primary exemplary transmission data and the primary exemplary communication path; performing a loading process on the primary exemplary information combination to load it into the constructed primary path matching model, analyzing primary matching analysis data corresponding to the primary exemplary information combination, the primary matching analysis data is used to reflect the predicted matching degree between the primary exemplary transmission data and the primary exemplary communication path; based on the primary matching analysis data corresponding to the primary exemplary information combination According to the primary identification information and the primary identification information, a model update operation is performed on the primary path matching model to output a corresponding intermediate path matching model; a high-level exemplary information combination with high-level identification information is extracted, the high-level exemplary information combination includes the target path description information and high-level exemplary data description information for reflecting the high-level exemplary transmission data, and the high-level identification information is used to reflect the actual matching degree between the high-level exemplary transmission data and the target communication path; the high-level exemplary information combination is loaded to load into the intermediate path matching model, and the high-level matching analysis data corresponding to the high-level exemplary information combination is analyzed, and the high-level matching analysis data is used to reflect the predicted matching degree between the high-level exemplary transmission data and the target communication path; Based on the high-level matching analysis data corresponding to the high-level exemplary information combination and the high-level identification information, a model update operation is performed on the intermediate path matching model to output a corresponding target path matching model, wherein the target path matching model is used to analyze the corresponding aggregate matching analysis data corresponding to the data to be transmitted and the corresponding target communication path based on the target path description information and the target data description information.

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