Computer data communication method and system for multi-source data structure

Abstract

The invention discloses a computer data communication method and system for a multi-source data structure, and the method comprises the steps: obtaining communication types under different data structures based on the data structure format matching result of each piece of communication data, and classifying the communication data to be transmitted based on the communication types; based on the channel capacity of each communication channel, distributing and combining each communication type data, and obtaining a combination mode with the most balanced load; constructing a load rate change trend model based on the historical communication data volume, and predicting the communication path load rate of each communication type in the future; the communication type data distribution combination is adjusted, the predicted load rate is achieved with the minimum communication type data adjustment variable quantity, and an emergency path is established to relieve the situation that the load rate is too high. The method has the advantages that through intelligent classification and optimal distribution of communication data, historical data prediction and dynamic path adjustment are combined, the channel capacity utilization rate is improved, and efficient and stable data transmission is ensured.

Description

Technical Field

[0001] The present invention relates to data communication, and particularly to a computer data communication method and system for multi-source data structures. Background Art

[0002] With the rapid development of information technology, especially its wide application in the fields of Internet of Things, cloud computing, big data, etc., the types of devices and data sources involved in the system are numerous and the data volume is huge. When dealing with these multi-source data, traditional data communication methods often face problems such as inconsistent data structures, low transmission efficiency, and poor real-time performance. Therefore, researching efficient multi-source data communication methods has become the key to improving the performance and stability of the system.

[0003] Currently, the computer data communication methods for multi-source data structures on the market usually rely on preset fixed data protocols and static data transmission methods. Most of these methods communicate based on standardized data formats, and the sending end and the receiving end must reach a consistent format protocol before communication. However, this traditional method has certain limitations when facing complex multi-source data and heterogeneous systems. For example, when the data source formats are inconsistent, the sending end and the receiving end must rely on additional format conversion mechanisms, which usually increase latency, reduce efficiency, and cause data mismatch or loss problems in large-scale systems. At the same time, the traditional method has a weak ability to adjust network load and cannot dynamically optimize the communication path according to real-time load changes, resulting in some paths being overloaded while other paths are idle. Summary of the Invention

[0004] In order to improve the existing computer data communication methods, a computer data communication method and system for multi-source data structures are provided. This method can support efficient communication of multiple data structure formats, and at the same time, through dynamic adjustment and format conversion strategies, it maximally improves the scalability, robustness, and load balancing ability of the system, thereby achieving efficient and stable data transmission in a complex multi-source data environment.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A computer data communication method for multi-source data structures, comprising:

[0007] Before communication, the sending end and the receiving end respectively store information of multiple data structure formats;

[0008] When the sending end initiates a communication request, a format identifier is embedded in the packet header;

[0009] Based on the quantity correspondence relationship between the sending end and the receiving end, a communication path is constructed through a traversal matching mechanism;

[0010] Based on the data structure format matching results of each communication data, obtain the communication types under different data structures, and classify the communication data to be transmitted based on the communication types;

[0011] Based on the channel capacities of each communication path, allocate and combine the communication type data with different data sizes, obtain the channel load rates in different combination cases, and obtain the combination method with the most balanced load;

[0012] Build a load rate change trend model based on historical communication data volumes to predict the communication path load rates of each communication type in the future;

[0013] Based on the predicted communication path load rates of each communication type in the future, adjust the allocation and combination of the communication type data, obtain the least amount of adjustment and change of the communication type data to reach the predicted load rate, and create an emergency path to relieve the situation of too high load rate.

[0014] Preferably, the establishment of the communication path through the traversal matching mechanism based on the quantity correspondence relationship between the sending end and the receiving end specifically includes:

[0015] The quantity correspondence relationship between the sending end and the receiving end specifically includes: one-to-one, one-to-many, many-to-one, many-to-many;

[0016] The traversal matching mechanism preferably selects nodes with the same format specifically as: converting the structure of each data source into a hash value through a hash algorithm and storing it in a hash table. When traversing other data sources, calculate the hash value of the current structure and search in the hash table for format matching.

[0017] Preferably, the obtaining of the communication types under different data structures based on the data structure format matching results of each communication data and the classification of the communication data to be transmitted based on the communication types specifically includes:

[0018] Based on the format identifier embedded in the communication data packet header, match the communication data structure formats of the sending end and the receiving end;

[0019] Based on the matching result of each communication data structure format, if the match is successful, obtain a direct communication type;

[0020] If the match fails, convert the communication data sent by the sending end into a standard intermediate format, and then convert the standard intermediate format into the receiving end format to obtain a conversion communication type;

[0021] Based on each direct communication type and conversion communication type, obtain their data volume sizes.

[0022] Preferably, the method for allocating and combining communication type data with different data sizes based on the channel capacity of each communication path, obtaining the channel load rate in different combination cases, and obtaining the combination method with the most balanced load specifically includes:

[0023] Based on the data volume sizes of each communication type, construct a data set D = {d1, d2,..., d n}, and the size of each data d i is s i . Based on the channel capacity sizes of each communication path, construct a communication path set C = {c1, c2,..., c m}, and the size of each path c j is k j ;

[0024] Determine the allocable path set C i ={C j |k j ≥s i};

[0025] Traverse all possible combination methods through the depth-first algorithm;

[0026] Based on each allocation method, calculate the load rate of each path;

[0027] Based on the load rates of each path, calculate the mean load rate and load balance metrics, including variance and maximum-minimum load difference;

[0028] Based on the load balance metrics, attach weights to the variance and maximum-minimum load difference, and perform weighted average calculation to obtain the combination method with the smallest value as the combination method with the most balanced load.

[0029] Preferably, the method for constructing a load rate change trend model based on historical communication data volume and predicting the communication path load rate of each future communication type specifically includes:

[0030] Based on the processing delay and resource occupancy rate of each communication path in historical data, obtain the historical state parameters of the communication path;

[0031] Based on the obtained historical state parameters, calculate the load rate in each time period, construct a load rate change trend model, and obtain a predicted load rate sequence;

[0032] Based on the real-time state parameter data of each communication path, substitute it into the load rate change trend model to obtain the load rate change trend of each communication path in the future time period.

[0033] Preferably, adjusting the communication type data allocation combination based on the predicted communication path load rates of future communication types, obtaining the predicted load rate with the least amount of communication type data adjustment changes, and creating an emergency path to relieve the situation of excessive load rate specifically includes:

[0034] Calculating the difference between the current communication path load rates and the predicted communication path load rates;

[0035] Based on the load rate difference, determining the adjustment direction of communication type data, including addition and deletion;

[0036] Defining a binary variable x ij ∈ {0, 1}, indicating whether the communication type data d i is adjusted from the current path to path c j ;

[0037] Based on the predicted communication path load rates, allocating communication data with different data volumes to each communication path to obtain all combination methods;

[0038] Based on all possible combination methods, calculating the combination method with the smallest communication type data adjustment change amount among them, and obtaining the adjustment plan by minimizing the total adjustment amount ;

[0039] Based on the predicted load rate sequence, extracting key indicators to obtain the peak load and the duration of continuous high load;

[0040] Based on the extracted key indicators, conducting a risk assessment to obtain the overload ratio;

[0041] Based on the overload ratio, registering an emergency path to relieve the path with excessive load rate.

[0042] Furthermore, a computer data communication system for multi-source data structures is proposed, including:

[0043] Format matching module: The format matching module is mainly used to compare whether the data format sent by the sending end is consistent with the data format of the receiving end;

[0044] Data allocation module: The data allocation module is mainly used to allocate data to each communication type according to the format matching result;

[0045] Format conversion module: The format conversion module is mainly used to first convert the data with different formats at the sending end and the receiving end into a standard intermediate format, and then convert it into the target format of the receiving end;

[0046] Path registration module: The path registration module is mainly used to open up a new path space for data transmission, and only the registered paths can transmit data;

[0047] Load balancing combination module: The load balancing combination module is mainly used to allocate and combine communication type data with different data sizes, obtain the channel load rates in different combination cases, and obtain the combination method with the most balanced load among them;

[0048] Model construction module: The model construction module is mainly used to construct a load rate change trend model based on historical communication data volume and predict the communication path load rates of future communication types;

[0049] Path data adjustment module: The path data adjustment module is mainly used to adjust the allocation and combination of communication type data to obtain the predicted load rate with the least change in the amount of communication type data adjustment;

[0050] Database module: The database module is mainly used to store information in various data structure formats and parameter data of each communication path;

[0051] Processor: The processor is mainly used for the calculation process of each formula and the construction and calculation process of each model.

[0052] Compared with the prior art, the advantages of the present invention are as follows:

[0053] Through the reasonable allocation of channel capacity, it is possible to dynamically adjust the transmission combination of data, minimize the imbalance of communication load to the greatest extent, and ensure the efficient operation of the system. At the same time, through the analysis of historical data and the prediction of the load rate change trend model, it is possible to foresee the change of communication path load in advance and make timely adjustments to avoid potential overload problems. In addition, the introduction of emergency paths enables the system to flexibly respond when the load is too high, avoiding communication interruption or delay caused by sudden traffic surges, and further improving the stability and response speed of the system. This method realizes intelligent and dynamic communication traffic scheduling, not only improving the overall performance of the system, but also enhancing the adaptability of the system in complex environments. Brief Description of the Drawings

[0054] Figure 1 It is a schematic diagram of the method proposed by the present invention;

[0055] Figure 2 It is a schematic diagram of the quantity correspondence relationship and traversal matching mechanism proposed by the present invention;

[0056] Figure 3 It is a schematic diagram of the communication type classification proposed by the present invention;

[0057] Figure 4 It is a schematic diagram of obtaining the load balancing combination proposed by the present invention;

[0058] Figure 5 It is a schematic diagram of constructing the load rate prediction model proposed by the present invention;

[0059] Figure 6 Schematic diagram of communication type data adjustment proposed by the present invention;

[0060] Figure 7 Architecture diagram of the electronic device in this solution;

[0061] Figure 8 Schematic diagram of the structure of the computer-readable storage medium in this solution. Detailed implementation manners

[0062] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0063] A computer data communication system for multi-source data structures, comprising:

[0064] Format matching module: The format matching module is mainly used to compare whether the data format sent by the sending end is consistent with the data format of the receiving end;

[0065] Data distribution module: The data distribution module is mainly used to distribute data to each communication type according to the format matching result;

[0066] Format conversion module: The format conversion module is mainly used to first convert the data with different formats between the sending end and the receiving end into a standard intermediate format, and then convert it into the target format of the receiving end;

[0067] Path registration module: The path registration module is mainly used to open up a new path space for data transmission, and only the registered paths can transmit data;

[0068] Load balancing combination module: The load balancing combination module is mainly used to allocate and combine communication type data with different data sizes, obtain the channel load rates in different combination cases, and obtain the combination method with the most balanced load;

[0069] Model construction module: The model construction module is mainly used to construct a load rate change trend model based on historical communication data volume and predict the communication path load rates of each communication type in the future;

[0070] Path data adjustment module: The path data adjustment module is mainly used to adjust the distribution and combination of communication type data to obtain the predicted load rate with the least change in the amount of communication type data adjustment;

[0071] Database module: The database module is mainly used to store various data structure format information and parameter data of each communication path;

[0072] Processor: The processor is mainly used for the calculation process of each formula and the construction and calculation process of each model.

[0073] Refer to Figure 1 As shown, a computer data communication method for a multi-source data structure includes:

[0074] Step 1: The sending end and the receiving end respectively store various data structure format information before communication;

[0075] Step 2: When the sending end initiates a communication request, embed a format identifier in the packet header;

[0076] Step 3: Based on the quantity correspondence between the sending end and the receiving end, build a communication path through a traversal matching mechanism;

[0077] Step 4: Based on the data structure format matching results of each communication data, obtain the communication types under different data structures, and classify the communication data to be transmitted based on the communication types;

[0078] Step 5: Based on the channel capacity of each communication path, allocate and combine the communication type data with different data sizes, obtain the channel load rates in different combination cases, and obtain the combination method with the most balanced load;

[0079] Step 6: Build a load rate change trend model based on the historical communication data volume, and predict the communication path load rates of each future communication type;

[0080] Step 7: Based on the predicted communication path load rates of each future communication type, adjust the allocation and combination of the communication type data, obtain the predicted load rate with the least adjustment change amount of the communication type data, and create an emergency path to relieve the situation of too high load rate.

[0081] Refer to Figure 2 As shown, the establishment of a communication path through a traversal matching mechanism based on the quantity correspondence between the sending end and the receiving end specifically includes:

[0082] The quantity correspondence between the sending end and the receiving end specifically includes: one-to-one, one-to-many, many-to-one, many-to-many;

[0083] The traversal matching mechanism preferentially selects nodes with the same format specifically as: converting the structure of each data source into a hash value through a hash algorithm and storing it in a hash table. When traversing other data sources, calculate the hash value of the current structure and search in the hash table for format matching.

[0084] It is understandable that for very complex multi-layer nested structures, calculating hash values can be extremely complex, and factors such as hierarchical order, duplicate fields, and differences in data types need to be considered. For complex data structures, the calculation of the hash value of the data structure can be carried out layer by layer, and independent hash value generation rules can be designed for different nested levels to avoid the accumulation of the complexity of the entire structure. At the same time, consider storing the meta-information of the structure in the hash table, such as field relationships, nested levels, etc., so that it can be quickly verified during matching.

[0085] One-to-one, one-to-many, many-to-one, and many-to-many specifically include:

[0086] One-to-one includes:

[0087] The receiving end identifies the format identifier embedded in the packet header of the sending end and performs matching;

[0088] If the matching is successful, communication is carried out through the direct communication path;

[0089] If the matching fails, the format conversion module is activated to convert the data of the sending end into the standard intermediate format, and then into the target format of the receiving end, and communication is carried out through the conversion communication path;

[0090] One-to-many includes:

[0091] Multiple receiving ends identify the format identifier embedded in the packet header of the sending end and perform matching;

[0092] Based on the receiving ends with successful format matching, communication is carried out through the direct communication path;

[0093] Based on the receiving ends with failed format matching, the format conversion module is activated to convert the data of the sending end into the standard intermediate format, and then into the target format of the receiving end, and communication is carried out through the conversion communication path;

[0094] Merge the intermediate format conversion process for receiving ends with the same target format;

[0095] Many-to-one includes:

[0096] The receiving end identifies the format identifiers embedded in the packet headers of multiple sending ends and performs matching;

[0097] Based on the sending ends with successful format matching, communication is carried out through the direct communication path;

[0098] Based on the sending ends with failed format matching, the format conversion module is activated to convert the data of the sending end into the standard intermediate format, and then into the target format of the receiving end, and communication is carried out through the conversion communication path;

[0099] Many-to-many includes:

[0100] Based on a many-to-many communication structure, multiple nodes are selected as core nodes, and other nodes are connected to the core nodes to form multiple independent one-to-many and many-to-one sub-communication structures. Each sub-communication structure is processed according to the one-to-many and many-to-one situations.

[0101] Converting the data at the sending end to a standard intermediate format and then to the target format at the receiving end specifically includes:

[0102] Converting the data at the sending end to a standard intermediate format. The standard intermediate format is described structurally using JSON Schema and includes a meta-attribute definition layer for data fields, a data type mapping rule layer, and a data relationship constraint condition layer. Lossless format conversion is achieved through these three layers of structure.

[0103] Based on the three-layer structure in the standard intermediate format, perform field mapping to the target data structure at the receiving end to obtain the data structure data in the target format.

[0104] It can be understood that the relationships between data may be different at the sending end and the receiving end. For example, the data at the sending end may not fully follow the standard constraint rules, resulting in inconsistent data relationships during conversion. Therefore, in the data relationship constraint condition layer, constraints such as data integrity and uniqueness are clearly defined, and data verification is performed during the conversion process to ensure that the data still complies with the constraint conditions after conversion. For data that does not meet the constraint conditions, a verification mechanism can be used to filter it or prompt the user to make corrections.

[0105] Refer to Figure 3 As shown, based on the data structure format matching results of each communication data, obtain the communication types under different data structures, and classify the communication data to be transmitted based on the communication types. Specifically includes:

[0106] Based on the format identifier embedded in the communication data packet header, match the communication data structure formats at the sending end and the receiving end;

[0107] Based on the matching result of each communication data structure format, if the match is successful, obtain a direct communication type;

[0108] If the match fails, convert the communication data sent by the sending end to the standard intermediate format, and then convert the standard intermediate format to the receiving end format to obtain a conversion communication type;

[0109] Based on each direct communication type and conversion communication type, obtain their data volume sizes.

[0110] Specifically, the sending end generates a data part for each data packet with a size of D, embeds the format identifier F in the data packet header s , compare F s with the format identifier F at the receiving end r, enter the direct communication path and the conversion communication path respectively. In the conversion communication path, the sender format F s is converted to the standard intermediate format M, and the data volume after conversion is:

[0111]

[0112] Convert the data in the intermediate format M to the receiver format F r , and the data volume after conversion is:

[0113]

[0114] Among them, is the proportionality coefficient, is the fixed overhead, such as header metadata.

[0115] Refer to Figure 4 As shown, based on the channel capacity of each communication path, the data of different communication types with different data sizes are allocated and combined, and the channel load rates in different combination cases are obtained, and the combination method with the most balanced load is obtained, which specifically includes:

[0116] Based on the data volume sizes of each communication type, construct a data set D = {d1, d2,..., d n}}, the size of each data d i is s i , and based on the channel capacity sizes of each communication path, construct a communication path set C = {c1, c2,..., c m}}, the size of each path c j is k j ;

[0117] Based on the data volume size of each communication type, determine the allocable path set C i = {C j | k j ≥ s i};

[0118] Traverse all possible combination methods through the depth-first algorithm;

[0119] Based on each allocation method, calculate the load rate of each path;

[0120] Based on the load rates of each path, calculate the load rate mean and load balance indicators, including variance and maximum-minimum load difference;

[0121] Based on the load balance indicators, attach weights to the variance and maximum-minimum load difference, and perform weighted average calculation to obtain the combination method with the smallest value as the combination method with the most balanced load.

[0122] Specifically, for the path c j, whose load factor L j is the ratio of the sum of all data volumes allocated to this path to its capacity, and the formula is:

[0123]

[0124] where α k is the k-th allocation method.

[0125] The formula for calculating the average load factor is:

[0126]

[0127] where μ L is the average load factor, m is the number of load factors of each path,

[0128] The load factor variance is used to measure the load fluctuation, and the formula is:

[0129]

[0130] The maximum-minimum load is used to measure the extreme difference, and the formula is:

[0131] ΔL = max(L j ) - min(L j )

[0132] Refer to Figure 5 shown. Building a load factor change trend model based on historical communication data volumes to predict the load factors of communication paths for future communication types specifically includes:

[0133] Based on the processing delays and resource occupancy rates of each communication path in historical data, obtain the historical state parameters of the communication paths;

[0134] Based on the obtained historical state parameters, calculate the load factors within each time period, build a load factor change trend model, and obtain a predicted load factor sequence;

[0135] Based on the real-time state parameter data of each communication path, substitute it into the load factor change trend model to obtain the load factor change trend of each communication path within the future time period.

[0136] Specifically, based on a dynamic window that adaptively adjusts according to load fluctuations, obtain the average load factor and maximum value within the time period for load factor aggregation to obtain the load factors within each time period. Build a load factor change trend model through an LSTM neural network to avoid problems of gradient disappearance or gradient explosion when dealing with long time series, and obtain the load factor change trend within the next 1 hour.

[0137] Based on the obtained load rate change trend model, design the model architecture, including the core layer: stack 2 - 3 LSTM layers to capture long-term dependencies, with the number of neurons being 256 and ReLU as the activation function; the input layer: calculate the correlation coefficient between the request to be processed and the load rate, construct an input sequence containing the correlation coefficient, historical load rate, real-time request number, request change rate, and path type label, add the path type to the input features, train through a unified model, use the trained model to output the load rate within the next 1 hour, and distinguish the prediction results of direct communication paths and conversion communication paths according to the model output labels.

[0138] Among them, perform smoothing processing on the historical load rate sequence {L j (t1), L j (t2),..., L j (t n )} to reduce noise interference. The formula is:

[0139]

[0140] Among them, w is the sliding window size. For example, w = 5 means taking the average of 5 time points.

[0141] Refer to Figure 6 As shown, based on predicting the communication path load rates of each communication type in the future, adjust the allocation combination of communication type data, obtain the combination that can achieve the predicted load rate with the least amount of adjustment of communication type data changes, and create an emergency path to relieve the situation of too high load rate. Specifically include:

[0142] Based on the current load rates of each communication path and the predicted load rates of the communication paths, calculate the difference between the two;

[0143] Based on the load rate difference, judge the adjustment direction of communication type data, including addition and deletion;

[0144] Define a binary variable x ij ∈{0, 1}, indicating whether the communication type data d i is adjusted from the current path to path c j ;

[0145] Based on the predicted load rates of each communication path, allocate communication data with different data volumes to each communication path to obtain all combination methods;

[0146] Based on all possible combination methods, calculate the combination method with the smallest adjustment change amount of communication type data among them compared to the current combination method, and obtain the adjustment plan by minimizing the total adjustment amount ;

[0147] Based on the predicted load rate sequence, extract key indicators to obtain the peak load and the duration of continuous high load;

[0148] Based on the extracted key metrics, perform risk assessment to obtain the overload ratio;

[0149] Based on the overload ratio, register emergency channels to relieve the channels with too high load rate.

[0150] Specifically, use the greedy algorithm to obtain the combination method with the smallest change in communication type data adjustment, sort the data items of the channels that need to reduce the load from largest to smallest according to the data volume. Remove the data items one by one until the predicted load rate is reached. Allocate the removed data to the channels that need to increase the load as needed, and give priority to filling the channels with high demand.

[0151] The specific duration of continuous high load is the time window length exceeding 80% of the threshold. The risk assessment is specifically as follows: red warning: the predicted peak value of any channel > 90% or continuous high load > 10 minutes. Yellow warning: peak value > 80% and continuous high load > 5 minutes. Green status: all predicted loads < 70%. Among them, the red warning forces the registration of at least 20% of the emergency channels of resources, the yellow warning forces the registration of at least 10% of the emergency channels of resources, and the green status is dynamically adjusted based on the load. Calculate the load increase slope of the direct channel and the conversion channel based on the load rate change trend for weight allocation, and check the communication channel capacity in real time for reserved feasibility judgment.

[0152] Add constraint conditions during channel adjustment: each communication data can only be adjusted once:

[0153]

[0154] Communication data d i Adjust from the current channel to channel c j , and its adjustment amount is:

[0155] Δd ij = d i ·|j - i|

[0156] Furthermore, the method according to the embodiment of the present application can also be implemented by means of Figure 7 the architecture of the electronic device shown. As Figure 7 shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to the network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, can store the computer data communication method and system for multi-source data structure provided by the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 7The architecture shown is only exemplary. When implementing different devices, one or more components in the Figure 7 Figure 7 shown electronic device may be omitted according to actual needs.

[0157] Figure 7 is a schematic structural diagram of a computer-readable storage medium provided by an embodiment of the present application. As Figure 7 shown, it is a computer-readable storage medium 600 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, the computer data communication method and system for a multi-source data structure according to the embodiment of the present application described with reference to the above drawings can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0158] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0159] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

[0160] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A computer data communication method for a multi-source data structure, characterized in that: include: The sender and receiver store information in various data structure formats before communication; When the sender initiates a communication request, it embeds a format identifier in the packet header; Based on the corresponding relationship between the number of senders and receivers, a communication path is constructed through a traversal matching mechanism; Based on the data structure format matching results of each communication data, the communication type under different data structures is obtained, and the communication data to be transmitted is classified based on the communication type; Based on the channel capacity of each communication channel, the communication type data with different data sizes are allocated and combined, the load rate of each channel under different combinations is obtained, and the combination with the most balanced load is obtained; Build a load rate change trend model based on historical communication data volume to predict the communication channel load rate of each communication type in the future; Based on the predicted communication path load rate of each communication type in the future, the communication type data allocation combination is adjusted to obtain the predicted load rate with the least communication type data adjustment change, and an emergency path is created to alleviate the situation where the load rate is too high.

2. The computer data communication method for multi-source data structure according to claim 1, characterized in that: The establishing of the communication path through the traversal matching mechanism based on the corresponding relationship between the number of the sending end and the receiving end specifically includes: The specific correspondence between the number of sending ends and receiving ends includes: one-to-one, one-to-many, many-to-one, and many-to-many; The traversal matching mechanism preferentially selects nodes with the same format as follows: the structure of each data source is converted into a hash value through a hash algorithm and stored in a hash table. When traversing other data sources, the hash value of the current structure is calculated and searched in the hash table for format matching.

3. The computer data communication method for multi-source data structure according to claim 1, characterized in that: The obtaining of communication types under different data structures based on the data structure format matching results of each communication data, and the classification of the communication data to be transmitted based on the communication type specifically include: Matching the communication data structure formats of the sending end and the receiving end based on the format identifier embedded in the communication data packet header; Based on the matching result of each communication data structure format, if the match is successful, a direct communication type is obtained; If the match fails, the communication data sent by the sender is converted into a standard intermediate format, and then the standard intermediate format is converted into a receiving end format to obtain a converted communication type; Based on each direct communication type and converted communication type, the data volume size is obtained.

4. The computer data communication method for multi-source data structure according to claim 1, characterized in that: The method of allocating and combining communication type data of different sizes based on the channel capacity of each communication path, obtaining the load rate of each channel under different combinations, and obtaining the combination with the most balanced load specifically includes: Based on the data size of each communication type, a data set D = {d1, d2, ..., d n }, each data d i The size is s i , based on the channel capacity of each communication path, construct a communication path set C = {c1, c2, ..., c m }, each channel c j The size is k j ; Determine the allocable path set C based on the data size of each communication type i ={C j |k j ≥s i }; Traverse all possible combinations using a depth-first algorithm; Based on each allocation method, calculate the load rate of each channel; Calculate the load factor mean and load balancing indicators based on the load factor of each path, including variance and maximum-minimum load difference; Based on the load balancing index, weights are added to the variance and the maximum-minimum load difference, and weighted average calculation is performed to obtain the combination with the smallest value as the most load-balanced combination.

5. The computer data communication method for multi-source data structure according to claim 1, characterized in that: The load rate change trend model is constructed based on the historical communication data volume to predict the communication path load rate of each communication type in the future, specifically including: Based on the processing delay and resource occupancy rate of each communication path in the historical data, the historical state parameters of the communication path are obtained; Based on the acquired historical state parameters, the load rate in each time period is calculated, and a load rate change trend model is constructed to obtain a predicted load rate sequence; Based on the real-time status parameter data of each communication channel, it is substituted into the load rate change trend model to obtain the load rate change trend of each communication channel in the future time period.

6. The computer data communication method for multi-source data structure according to claim 1, characterized in that: The method of adjusting the communication type data allocation combination based on the predicted communication path load rate of each communication type in the future, obtaining a method to achieve the predicted load rate with the least communication type data adjustment change, and creating an emergency path to alleviate the situation where the load rate is too high specifically includes: Based on the current load rate of each communication channel and the predicted load rate of each communication channel, calculating the difference between the two; Based on the load rate difference, determine the communication type data adjustment direction, including adding and deleting; Define a binary variable x ij ∈{0, 1}, indicating the communication type data d i Whether to adjust from the current path to path c j ; Based on the predicted load rates of each communication channel, communication data with different data volumes are allocated to each communication channel to obtain all combinations; Based on all possible combinations, calculate the combination with the smallest change in communication type data adjustment compared to the current combination, and minimize the total adjustment. Obtain adjustment plans; Based on the predicted load rate sequence, key indicators are extracted to obtain peak load and continuous high load periods; Conduct risk assessment based on the extracted key indicators and obtain the excess load ratio; Based on the excess load ratio, emergency channels are registered to alleviate channels with excessively high load rates.

7. A computer data communication method for a multi-source data structure, used to implement a computer data communication system for a multi-source data structure as claimed in any one of claims 1 to 6, characterized in that: include: Format matching module: The format matching module is mainly used to compare whether the data format sent by the sending end is consistent with the data format of the receiving end; Data allocation module: The data allocation module is mainly used to allocate data to each communication type according to the format matching result; Format conversion module: The format conversion module is mainly used to convert the data with different formats at the sending end and the receiving end into a standard intermediate format first, and then into the target format of the receiving end; Path registration module: The path registration module is mainly used to open up new path space for data transmission. Only registered paths can transmit data. Load balancing combination module: The load balancing combination module is mainly used to distribute and combine communication type data with different data sizes, obtain the load rate of each channel under different combinations, and obtain the combination mode with the most balanced load; Model building module: The model building module is mainly used to build a load rate change trend model based on the historical communication data volume to predict the communication channel load rate of each communication type in the future; Path data adjustment module: The path data adjustment module is mainly used to adjust the communication type data allocation combination to obtain the predicted load rate with the least communication type data adjustment change; Database module: The database module is mainly used to store various data structure format information and parameter data of each communication channel; Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

8. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the computer data communication method for a multi-source data structure as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the computer data communication method for a multi-source data structure according to any one of claims 1 to 6 is implemented.

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