A computer-based multi-platform informationization construction system and method

Abstract

The application provides a kind of computer-based multi-platform informationization construction system and method, solve the problem of existing multi-platform data synchronization not timely, format incompatible etc.System contains real-time data capture module, through database transaction log listens to real-time generation data change event;Intelligent routing decision module combines rules and machine learning optimization transmission path;Data conversion adaptation module utilizes dynamic template matching to realize format and interface automatic adaptation;Conflict detection and resolution module based on version control and priority determination guarantee data consistency;Data synchronization execution module transmits data according to optimized path, monitoring and management module real-time monitoring whole process.The method includes data capture, routing decision, format conversion, synchronization execution and monitoring feedback.The scheme improves data synchronization efficiency and accuracy, reduces multi-platform integration operation and maintenance cost, applicable to enterprise, government affairs and other multi-scene informationization construction.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer informationization, and particularly to a computer-based multi-platform informationization construction system and method. BACKGROUND

[0002] With the rapid development of information technology, enterprises and institutions usually build multiple business platforms with different functions to meet the diversified business needs, such as sales management platform, customer relationship management platform, financial management platform, etc. However, the existing multi-platform informationization construction faces serious data synchronization problems. For example, in a large enterprise, the sales management platform records customer order information, and the customer relationship management platform saves customer basic information and communication records. When the sales personnel updates the customer order status on the sales management platform, the customer relationship management platform cannot obtain the information in time, resulting in that the customer service personnel still communicate with the customer according to the old order status, causing poor customer experience. This is because the existing multi-platform informationization system lacks an efficient data synchronization mechanism, and the data update between platforms often depends on manual triggering or timed batch synchronization. The manual triggering method is low in efficiency and easy to miss, and the timed batch synchronization has the problem of inconsistent data within the time interval, which is difficult to meet the real-time requirement of business scenarios, seriously affecting the business collaboration efficiency and the accuracy of decision-making. SUMMARY

[0003] The present application aims to provide a computer-based multi-platform informationization construction system and method to solve the problems raised in the background.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solution: a computer-based multi-platform informationization construction system, comprising a real-time data capture module, an intelligent routing decision module, a data conversion and adaptation module, a conflict detection and resolution module, a data synchronization execution module and a monitoring and management module. The real-time data capture module is used to monitor the data changes of each business platform in real time, and the real-time data capture module sets data change listening points in the transaction logs of each platform database. Once the data insertion, update or deletion operation is detected, the relevant data change information is captured immediately, and a data change event containing the key information of operation type, data object and timestamp is generated.

[0005] The intelligent routing decision module uses a routing decision algorithm based on the combination of rules and machine learning to pre-set the basic rules of data flow direction, and dynamically optimizes the routing decision by analyzing the historical data synchronization path and efficiency through machine learning, and selects the optimal data transmission path.

[0006] The data conversion adaptation module adopts a dynamic template matching data conversion algorithm for data formats and interface specifications of different platforms, and constructs a rich data format template library. When receiving a data change event, the data conversion adaptation module automatically matches corresponding templates for data format conversion and interface adaptation according to data formats of a source platform and a target platform, so that data is accurately transmitted between different platforms.

[0007] The conflict detection and resolution module applies a conflict detection and resolution algorithm based on version control and priority determination to assign a version number to each data object. When multiple platforms operate on the same data object, the conflict detection and resolution algorithm automatically resolves data conflicts by comparing the version number and a preset operation priority, thereby ensuring data consistency.

[0008] The data synchronization execution module synchronizes data to a target platform according to the path determined by the intelligent routing decision module and the data processed by the data conversion adaptation module, and records the synchronization result.

[0009] The monitoring management module monitors the entire data synchronization process in real time, monitors and warns the running state, data synchronization progress, and abnormal conditions of each module, and is used for management and maintenance by an administrator.

[0010] Preferably, the intelligent routing decision module specifically implements the following logic:

[0011] Data change event receiving and analysis: The intelligent routing decision module receives a data change event from the real-time data capture module. The data change event includes, but is not limited to, operation type, data object, and timestamp key attribute information. These information is analyzed to extract key elements for routing decision, and the data change event is recorded as E={e1,e2,…,e n}, where e i represents the i-th attribute of the event E.

[0012] Basic rule matching: A basic rule library R={r1,r2,…,r m} of data flow direction is preset. Each rule r j is represented as a condition-result pair, that is, r j : IF C j THEN P j ; where C j is a condition expression composed of data change event attributes; P j is a corresponding transmission path decision result; the analyzed data change event attributes are matched with the rules in the rule library. If there is a rule that satisfies the condition C j , the corresponding transmission path P j is directly obtained, and routing decision is completed.

[0013] Machine learning prediction assisted decision making: if no matching rule is found in the basic rule base, a machine learning model is started to assist decision making; a path prediction model trained by historical data is used, the attributes of the data change event E are taken as the model input, and the input vector is F=[f1, f2, …, f n ], where f i corresponds to the attribute e i of the data change event E after feature engineering processing, the model outputs the probability distribution P=[p1, p2, … p k ] of each transmission path through internal calculation and weight parameters, where p s represents the probability of data transmission to the s-th path, and

[0014] Path optimization and adjustment: after determining the preliminary transmission path, the path is optimized in combination with the path selection, transmission time, and success rate data in the historical data synchronization process; the comprehensive evaluation index S of the current path is calculated, and the calculation formula is S=α×T+β×Q+γ×Z, where T is the average transmission time of the path in the historical data, Q is the transmission success rate, where Q is the number of successful transmissions / the total number of transmissions of the path in the past 24 hours, Z is the complexity of the path, α, β, γ are weight coefficients, which are set according to business requirements, and α+β+γ=1; if there is another path with a better comprehensive evaluation index, the transmission path is adjusted, and the optimal path is selected as the final data transmission path.

[0015] Route information generation and sending: after determining the final data transmission path, the intelligent routing decision module generates route information containing source platform information, target platform information, transmission path, etc., and sends the route information to the data conversion and adaptation module together with the data change event, so as to perform data format conversion and transmission subsequently.

[0016] Preferably, the path prediction model trained based on historical data has the following specific implementation steps:

[0017] Step 1, data preprocessing and feature engineering:

[0018] Features are extracted from historical data synchronization logs to build a sample set D={(X1, y1), (X2, y2), …, (X n , y n )}, where each input sample X i is a time series consisting of t time steps: X i =[x i1 , x i2 , …, x it ], and each time step feature vector x it contains:

[0019] Data change event attributes: operation type o t ; data object type d t ; timestamp feature ts t ; source platform ID st and target platform ID candidate set T t = {t t1 , t t2, …}; historical synchronization performance indicators: average delay lat t , success rate sr t and throughput thr t ; target variable y i is the best transmission path ID, where the best transmission path ID is selected from the candidate set T t ;

[0020] Step 2, improve the LSTM model architecture design:

[0021] Basic LSTM layer: process time series input, capture long-term dependencies: h t = LSTM(x t , h t―1 ), where h t is the hidden state, containing the comprehensive features of the data change event at this time step (such as operation type, data object, transmission delay, etc.), x t is the current input, and h t―1 is the hidden state at the previous time step;

[0022] Attention mechanism layer: weight the LSTM output, highlight key time steps: e t = v T tanh(Wh t +b), where a t is the attention weight, v, W, b are trainable parameters, where v is the "attention preference vector", the weight matrix W is the feature weighting of h t , and b is used to adjust the offset of the activation function to avoid all e t falling into the saturation region of the activation function; e t is the "attention score" calculated for each time step t;

[0023] Context vector:

[0024] Path probability output layer: p = softmax(W p c + b p ), where p is the probability distribution of each candidate path, W p , b p are output layer parameters;

[0025] Step 3, model training optimization

[0026] Loss function design: according to the characteristics of multi-platform data synchronization, the path success rate and delay index are integrated into the loss function, specifically, the loss function adopts category cross entropy: Where y ij is the one-hot encoding of the true path label of sample i, p ij is the predicted probability, N is the number of training samples, and K is the number of different categories of data flow paths, wherein the optimizer adopts Adam algorithm;

[0027] Step 4, training process with rule constraints:

[0028] Introduce rule consistency loss term: L rule = λ∑ i∈S ||p i ―s i || 2 , wherein S is the rule matching sample set, s i is the path probability distribution specified by the rule, and λ is the balance coefficient;

[0029] The total loss function is: L total =L+L rule ;

[0030] Step 5, prediction and decision process:

[0031] Input the current data change event feature sequence X current ; the path prediction model outputs the path probability distribution P = [p1, p2, … p k ]; select the path with the highest probability as the prediction result path pred = argmaxx j p j ; wherein x j is the feature value of the input vector.

[0032] Preferably, the data conversion adaptation module has the following working logic:

[0033] Data reception and information extraction: when the real-time data capture module monitors the data change of the business platform, and the intelligent routing decision module determines the transmission path, the data conversion adaptation module receives the data change event containing operation type, data object, and timestamp key information; at the same time, the source platform and target platform identifiers are extracted from the routing information to clarify the source and destination of the data, providing basic information for subsequent operations;

[0034] Template matching: Based on the extracted source and target platform data format information, a search and matching process is performed in a pre-built data format template library. The template library stores data conversion templates for different platform combinations, and each template defines the mapping relationship between the source format and the target format. By calculating the feature similarity between the source data format and the template, the most matching template is selected.

[0035] Data format conversion and interface adaptation: Using the matched template, the data in the data change event is converted into a new format; according to the mapping rules defined in the template, the structure, field names, and data types of the source data are converted into a format that the target platform can recognize; while converting the format, the data is adapted to the interface according to the interface specifications of the target platform, including but not limited to adjusting the data transmission protocol and request parameter format, to ensure that the data can be successfully connected to the interface of the target platform.

[0036] Post-processing data output and transmission: After completing data format conversion and interface adaptation, the processed data is sent to the data synchronization execution module; the data synchronization execution module synchronizes the adapted data to the target platform according to the path determined by the intelligent routing decision module, realizing accurate data transmission between multiple platforms and providing reliable data support for subsequent business processing.

[0037] Preferably, the specific implementation steps of the conflict detection and resolution module are as follows:

[0038] a. Version marking before data synchronization:

[0039] Version number allocation: When the real-time data capture module detects a data change, the conflict detection and resolution module generates a unique version number V for each data object, in the format of node ID + timestamp + sequence number; Metadata attachment: The version number is attached as metadata to the data change event and transmitted along with the data object;

[0040] b. Conflict pre-detection:

[0041] Version comparison: Before data synchronization is executed, the module queries the current version number V of the data object on the target platform. current Conflict prediction: If the version number of the data to be synchronized is V new Less than or equal to V current If V is deemed outdated, synchronization is terminated; new >V current If so, the synchronization process will begin and conflict monitoring will continue.

[0042] c. Concurrent operation conflict detection:

[0043] Multi-source write monitoring: When multiple platforms simultaneously initiate change requests for the same data object, the module captures concurrent operations through a distributed lock mechanism; Version number collision detection: Compare the version numbers of each change request. If there are version numbers that are the same but the contents are different, it is determined to be a conflict;

[0044] d. Priority-based conflict resolution:

[0045] Operation type priority rules: Pre-set operation priority order. When a conflict occurs, the high-priority operation is executed first; Business rule priority: Define exclusive rules for specific data objects; Timestamp priority: When the operation types are the same, compare the timestamps in the version numbers, and prefer the latest operation;

[0046] e. Version merging and data integration:

[0047] Mergeable change processing: For non-mutually exclusive changes, the module automatically merges the change content to generate a new version number V merge ; Data integration algorithm: Use graph database technology to analyze the relationship between data to ensure logical consistency of the merged data;

[0048] f. Conflict log recording and analysis:

[0049] Log storage: Record detailed information of all conflict events, including conflict data, version number, and resolution method; Then, based on historical conflict data, optimize priority rules.

[0050] Preferably, a computer-based multi-platform information construction method includes the following steps:

[0051] Step S1, the real-time data capture module uses an event-driven data capture algorithm to monitor the transaction logs of each business platform database in real time. When a data change is detected, a data change event is generated and sent to the intelligent routing decision module;

[0052] Step S2, the intelligent routing decision module receives the data change event, determines the data transmission path based on a routing decision algorithm that combines rules and machine learning, and sends the data change event and routing information to the data conversion adaptation module:

[0053] Step S3, the data conversion adaptation module uses a dynamic template matching data conversion algorithm to perform format conversion and interface adaptation on the data based on the received source platform and target platform information, and then sends the processed data to the data synchronization execution module;

[0054] Step S4, the data synchronization execution module synchronizes data to the target platform according to the path determined by the intelligent routing decision module, and the conflict detection and resolution module detects and handles data conflicts by using a conflict detection and resolution algorithm based on version control and priority determination;

[0055] Step S5, the data synchronization execution module records the data synchronization result and feeds back the synchronization result to the monitoring management module, and the monitoring management module monitors and manages the entire data synchronization process in real time.

[0056] Compared with the prior art, the beneficial effects of the present application are: the intelligent routing decision module of the present application combines the rule base and the improved LSTM model to realize intelligent optimization of the transmission path, effectively solving the problems of chaotic multi-platform data transmission path and low efficiency; by comprehensively evaluating the indicators, the transmission time, success rate and complexity of the path are calculated in real time, and high-delay paths are automatically avoided;

[0057] The dynamic template matching algorithm of the data conversion adaptation module of the present application supports real-time conversion of several data formats such as XML, JSON and CSV, and the template library covers most of the common business fields of enterprises, solving the industry pain point of incompatible data formats of different manufacturer platforms; and for API interface specifications (such as RESTful and SOAP) of different platforms, the module automatically completes the request parameter format adjustment and protocol conversion (such as HTTP and TCP / IP), greatly reducing the system integration cost.

[0058] The conflict detection and resolution module realizes the unique identification of data changes in a distributed environment through the version number mechanism of "node ID + timestamp + sequence number". In the multi-platform concurrent operation scene, the consistency of key data such as financial data and inventory information is ensured; the preset operation type priority (such as delete > update > insert) and business rule priority (such as financial data priority) are combined with the latest timestamp judgment to realize intelligent adjudication of conflicts. For example, when the sales platform and the inventory platform update the price of a commodity at the same time, the system processes according to the "latest update time" priority principle, avoids price data confusion, and ensures the accuracy of the business process. BRIEF DESCRIPTION OF DRAWINGS

[0059] Fig. 1 It is a schematic diagram of the overall structure of the present application;

[0060] Fig. 2 It is a specific implementation flowchart of the intelligent routing decision module of the present application. DETAILED DESCRIPTION

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1

[0063] Please see Figs. 1-2 This invention provides a technical solution: a computer-based multi-platform information construction system, including a real-time data capture module, an intelligent routing decision module, a data conversion and adaptation module, a conflict detection and resolution module, a data synchronization execution module, and a monitoring and management module. The real-time data capture module is used to monitor data changes of each business platform in real time. The real-time data capture module sets data change listening points in the transaction logs of each platform's database. Once a data insertion, update, or deletion operation is detected, the relevant data change information is immediately captured, and a data change event containing key information such as operation type, data object, and timestamp is generated.

[0064] The intelligent routing decision module, based on the attributes of data change events and the characteristics of the target platform, employs a routing decision algorithm combining rule-based and machine learning approaches. It pre-defines basic rules for data flow and simultaneously analyzes historical data synchronization paths and efficiency through machine learning to dynamically optimize routing decisions and select the optimal data transmission path. The specific implementation logic is as follows:

[0065] Data Change Event Reception and Parsing: The intelligent routing decision module receives data change events from the real-time data capture module. These events include, but are not limited to, operation types (insert, update, delete), data objects (such as orders, customer information), and timestamp-based key attribute information. This information is parsed to extract key elements for routing decisions. The data change event is denoted as E = {e1, e2, ..., e...}. n}, where e i This represents the i-th attribute of event E, for example, e1 is the operation type, e2 is the data object, etc.

[0066] Basic rule matching: A pre-defined basic rule base R = {r1, r2, ..., r...} for data flow. m}, each rule r j Represented as a condition-result pair, i.e., r j :IF C j THEN P j ; where C j It is a conditional expression composed of data change event attributes; P jcorresponding transmission path decision result; the parsed data change event attributes are matched with the rules in the rule library, if there is a rule that meets the condition C j , the corresponding transmission path P j is directly obtained, and the routing decision is completed; for example, when e1 (operation type) is "insert" and e2 (data object) is "order", the condition of a certain rule is met; P j is the corresponding transmission path decision result, such as transmitting the data to the financial management platform. The parsed data change event attributes are matched with the rules in the rule library, if there is a rule that meets the condition C j , the corresponding transmission path P j is directly obtained, and the routing decision is completed.

[0067] Machine learning prediction assisted decision: if no matching rule is found in the basic rule library, a machine learning model is started for assisted decision; a path prediction model trained with historical data is used, the attributes of the data change event E are taken as the model input, and the input vector is F = [f1, f2, …, f n ], where f i corresponds to the value of the attribute e i of the data change event E after feature engineering processing; the model outputs the probability distribution P = [p1, p2, … p k ] of each transmission path through internal calculation and weight parameters, where p s represents the probability of data transmission to the s-th path, and

[0068] Path optimization and adjustment: after determining the preliminary transmission path, the path is optimized in combination with the path selection, transmission time, and success rate data in the historical data synchronization process; the comprehensive evaluation index S of the current path is calculated, and the calculation formula is S = α × T + β × Q + γ × Z, where T is the average transmission time of the path in the historical data, Q is the transmission success rate, Q is the number of successful transmissions / the total number of transmissions in the past 24 hours, Z is the complexity of the path (such as the number of intermediate nodes passed, etc.), α, β, γ are weight coefficients, which are set according to business requirements, and α + β + γ = 1; if there is another path with a better comprehensive evaluation index, the transmission path is adjusted, and the optimal path is selected as the final data transmission path.

[0069] Routing information generation and sending: after determining the final data transmission path, the intelligent routing decision module generates routing information containing source platform information, target platform information, transmission path, etc., and sends the routing information together with the data change event to the data conversion and adaptation module for subsequent data format conversion and transmission.

[0070] The specific implementation steps of the path prediction model trained based on historical data are as follows:

[0071] Step 1: Data Preprocessing and Feature Engineering

[0072] In multi-platform data synchronization scenarios, historical synchronization logs contain rich decision-making information. For example, when an order change event occurs on a sales platform, the model needs to consider not only the event's inherent attributes (such as operation type and data object), but also time characteristics (such as 10 AM on weekdays being a typical peak business period) and historical synchronization performance (such as a path experiencing significantly increased latency during peak hours). By extracting these multi-dimensional features and constructing a sample set, the model can learn complex patterns such as "order creation events on weekday mornings should prioritize dedicated network paths." Specifically, features are extracted from historical data synchronization logs to construct a sample set D = {(X1,y1),(X2,y2),…,(X...}. n ,y n )}, where each input sample X i It is a time series consisting of t time steps: X i =[x i1 ,x i2 ,…,x it The time step t is dynamically adjusted based on the time interval of business data changes; for example, t = 5 minutes for high-frequency transaction data and t = 1 hour for low-frequency data. Each time step has a feature vector x. it Include:

[0073] Data change event attributes: Operation type o t Data object type d t Timestamp feature ts t Source platform IDst and target platform ID candidate set T t ={t t1 ,t t2 Historical synchronization performance metrics: average latency (lat) t Success rate sr t and throughput thr t ; Target variable y i It is the optimal transmission path ID, where the optimal transmission path ID is selected from the candidate set T. t Select from;

[0074] Step 2: Improve the LSTM model architecture design:

[0075] Base LSTM layer: processes sequential inputs and captures long-term dependencies: h t =LSTM(x t ,h t―1 ), where h t It is in a hidden state, xt is the current input, h t―1 is the hidden state at the previous time step; for example, consecutive order update operations can cause the target platform cache to be invalidated, affecting subsequent synchronization performance. LSTM can capture this long-term dependency through memory cells, avoiding the short-term memory defect of traditional models.

[0076] Attention mechanism layer: weights the LSTM output, highlighting key time steps: e t = v T tanh(Wh t +b), where a t is the attention weight, v, W, and b are trainable parameters, where v is the "attention preference vector", the weight matrix W is to feature-weight h t , and b is used to adjust the offset of the activation function to avoid all e t falling into the saturation zone of the activation function; e t is the "attention score" calculated for each time step t; the importance of features at different time steps to routing decisions is different. For example, when synchronizing customer sensitive information, the weight of security indicators should be significantly higher than that of ordinary business data. The attention mechanism automatically calculates the weights, allowing the model to focus on key features, such as when processing payment data, focusing more on features related to encrypted channels.

[0077] Context vector:

[0078] Path probability output layer: p = softmax(W p c+b p ), where p is the probability distribution of each candidate path, W p , b p are output layer parameters; that is, the routing decision is converted into a probability distribution, quantifying uncertainty. For example, when there are multiple optional paths, the model not only gives the optimal path, but also provides the confidence of each path, providing a basis for subsequent rule fusion.

[0079] Step 3, model training and optimization

[0080] Loss function design: considering the characteristics of multi-platform data synchronization, the success rate and delay indicators of the path are integrated into the loss function. Specifically, the loss function uses categorical cross-entropy: where y ij is the one-hot encoding of the true path label for sample i, and p ijis the predicted probability, N is the number of training samples, K is the number of different categories of data flow paths, K is the dynamically scalable path category number, the number of model output layer neurons is automatically adjusted with K, and the model quickly adapts to new paths through transfer learning, wherein the optimizer adopts the Adam algorithm; In view of the characteristics of multi-platform data synchronization, the success rate and delay indicators of the path are integrated into the loss function;

[0081] Step 4, training process with rule constraints:

[0082] Introduce a rule consistency loss term: L rule =λ∑ i∈S ||p i ―s i || 2 , wherein S is a rule matching sample set, s i is a rule specified path probability distribution, specifically, s i is a one-hot vector with the jth dimension being 1, λ is a balance coefficient, and λ dynamically adjusts the weight of machine learning and rules; In the early stage of new platform access, the rule weight is higher; With the accumulation of historical data, the model weight gradually increases, realizing the smooth transition from "expert dominance" to "data-driven"; In some scenarios, expert experience or safety policy requires that certain types of data must be transmitted through a specific path. For example, financial data must pass through an encrypted channel. Through the rule consistency loss term, it is ensured that the model prediction result does not violate these hard constraints; The total loss function is: L total =L+L rule ;

[0083] Step 5, prediction and decision process:

[0084] Input the current data change event feature sequence X current ; the path prediction model outputs the path probability distribution P = [p1, p2, … p k ]; select the path with the highest probability as the prediction result path pred =argmaxx j p j ; wherein x j is the feature value of the input vector.

[0085] The data conversion adaptation module adopts a dynamic template matching data conversion algorithm to construct a rich data format template library. When receiving a data change event, it automatically matches the corresponding template for data format conversion and interface adaptation according to the data format of the source platform and the target platform, so that the data can be accurately transmitted between different platforms; The specific working logic is as follows:

[0086] Data reception and information extraction: When the real-time data capture module detects data changes in the business platform and the intelligent routing decision module determines the transmission path, the data conversion and adaptation module receives data change events containing operation type, data object, and timestamp key information. At the same time, the source platform and target platform identifiers are extracted from the routing information, clarifying the data source and destination, and providing basic information for subsequent operations;

[0087] Template library matching: Based on the extracted source platform and target platform data format information, search and match in the pre-constructed data format template library. The template library stores data conversion templates for different platform combinations, each template defines the mapping relationship between the source format and the target format. By calculating the feature similarity between the source data format and the template, the most matching template is selected. For example, if the source platform is a sales platform that stores order data in XML format, and the target platform is a stock management platform that uses JSON format, the module will find the corresponding template in the template library that converts XML to JSON and is suitable for order data.

[0088] Data format conversion and interface adaptation: Use the matched template to convert the data in the data change event. According to the mapping rules defined in the template, convert the structure, field name, and data type of the source data into the format recognized by the target platform. At the same time of format conversion, perform interface adaptation processing on the data according to the interface specifications of the target platform, including but not limited to adjusting the transmission protocol, request parameter format of the data, to ensure that the data can be smoothly connected to the interface of the target platform;

[0089] Processed data output and transmission: After completing data format conversion and interface adaptation, send the processed data to the data synchronization execution module. The data synchronization execution module synchronizes the adapted data to the target platform according to the path determined by the intelligent routing decision module, realizing accurate transmission of data between multiple platforms and providing reliable data support for subsequent business processing.

[0090] The conflict detection and resolution module uses a conflict detection and resolution algorithm based on version control and priority determination to assign a version number to each data object during data synchronization. When multiple platforms operate on the same data object, the conflict is automatically resolved by comparing the version number and the preset operation priority, ensuring data consistency. The specific implementation steps are as follows:

[0091] a. Version marking before data synchronization:

[0092] Version number allocation: When the real-time data capture module detects a data change, the conflict detection and resolution module generates a unique version number V for each data object, in the format of node ID + timestamp + sequence number; Metadata attachment: The version number is attached as metadata to the data change event and transmitted along with the data object; for example, when the sales platform updates the order status, the order data will carry the latest version number.

[0093] b. Conflict pre-detection (before synchronization):

[0094] Version comparison: Before data synchronization is executed, the module queries the current version number V of the data object on the target platform. current Conflict prediction: If the version number of the data to be synchronized is V new Less than or equal to V current If V is deemed outdated, synchronization is terminated; new >V current If so, the synchronization process will begin and conflict monitoring will continue.

[0095] c. Concurrent operation conflict detection (in synchronization):

[0096] Multi-source write monitoring: When multiple platforms simultaneously initiate change requests for the same data object (such as customer information), the module captures concurrent operations through a distributed lock mechanism; Version number collision detection: Compare the version numbers of each change request. If there are cases where the version numbers are the same but the content is different, it is determined to be a conflict; for example, the inventory management platform and the sales platform update the inventory of a certain product at the same time.

[0097] d. Priority-based conflict resolution:

[0098] Operation type priority rules: A preset operation priority order is used (e.g., delete > update > insert; in financial data, delete operations require manual review and have lower priority than validated update operations, making the rules more flexible). When conflicts occur, higher-priority operations are executed first. For example, if both delete and update requests exist simultaneously, deletion is processed first. Business rule priority: Specific rules are defined for particular data objects. For example, changes to financial data have higher priority than ordinary business data, ensuring the accuracy of cash flow. Timestamp priority: When operation types are the same, the timestamps in the version numbers are compared, and the latest operation is used first.

[0099] e. Version merging and data integration:

[0100] Mergeable Change Handling: For non-mutually exclusive changes (such as one platform updating customer phone numbers and another updating email addresses), the module automatically merges the changes and generates a new version number V. merge Data integration algorithm: Utilize graph database technology to analyze the relationships between data and ensure the logical consistency of the merged data. For example, when merging order and inventory changes, verify the inventory deduction logic.

[0101] f. Conflict log recording and analysis:

[0102] Log storage: Records detailed information on all conflict events, including conflict data, version number, and resolution method; then, based on historical conflict data, optimizes priority rules. For example, if analysis reveals that a certain type of order frequently conflicts, its processing priority is adjusted.

[0103] The data synchronization execution module is used to synchronize data to the target platform according to the path determined by the intelligent routing decision module and the data processed by the data conversion and adaptation module, and to record the synchronization results.

[0104] The monitoring and management module monitors the entire data synchronization process in real time, and monitors and issues warnings about the operating status of each module, the progress of data synchronization, and any abnormal situations, which is used by administrators for management and maintenance.

[0105] Example 2

[0106] A computer-based multi-platform information technology construction method includes the following steps:

[0107] Step S1: The real-time data capture module uses an event-driven data capture algorithm to monitor the transaction logs of the databases of each business platform in real time. When a data change is detected, a data change event is generated and sent to the intelligent routing decision module.

[0108] Step S2: The intelligent routing decision module receives the data change event, determines the data transmission path based on a routing decision algorithm that combines rules and machine learning, and sends the data change event and routing information to the data conversion and adaptation module.

[0109] Step S3: The data conversion and adaptation module uses a dynamic template matching data conversion algorithm to convert the data format and adapt the interface based on the received source platform and target platform information, and then sends the processed data to the data synchronization execution module.

[0110] Step S4: The data synchronization execution module synchronizes the data to the target platform according to the path determined by the intelligent routing decision module. The conflict detection and resolution module uses a conflict detection and resolution algorithm based on version control and priority determination to detect and process data conflicts.

[0111] Step S5: The data synchronization execution module records the data synchronization results and feeds them back to the monitoring and management module. The monitoring and management module monitors and manages the entire data synchronization process in real time.

[0112] For example, deploy real-time data capture modules, intelligent routing decision modules, data transformation and adaptation modules, conflict detection and resolution modules, data synchronization execution modules, and monitoring and management modules on enterprise data center servers. Allocate server resources rationally based on the data traffic and business needs of each platform to ensure stable system operation. Simultaneously, configure data capture interfaces in the databases of each business platform so that the real-time data capture module can obtain data change information.

[0113] When a customer places an order on the sales platform, the sales platform's database inserts order data. The real-time data capture module immediately captures this data change event, generates a data change event containing information such as the order number, product information, customer information, and order time, and sends it to the intelligent routing decision module. Upon receiving the data change event, the intelligent routing decision module, based on pre-set rules and a machine learning-optimized routing decision algorithm, determines that the order data should be synchronized to the inventory management platform and the financial management platform, and generates corresponding routing information to send to the data conversion and adaptation module. The data conversion and adaptation module, based on the data format differences between the sales platform and the inventory management and financial management platforms, uses a dynamic template matching data conversion algorithm to convert the order data into a format recognizable by the two target platforms. For example, it converts the order data format from the sales platform to the inventory update data format required by the inventory management platform, and the order payment data format required by the financial management platform. The data synchronization execution module then synchronizes the converted data to the inventory management platform and the financial management platform respectively, following the path determined by the intelligent routing decision module. During the synchronization process, the conflict detection and resolution module monitors in real time for any data conflicts. Assuming that during the synchronization process, the inventory management platform simultaneously performs an inventory count, causing inventory data updates, the conflict detection and resolution module automatically resolves the conflict by comparing version numbers and preset priorities (order data updates have higher priority than inventory count data updates), ensuring inventory data consistency. The data synchronization execution module records the data synchronization results and feeds back the synchronization status information to the monitoring and management module. The monitoring and management module displays the data synchronization progress in real time, and if any abnormalities occur, such as data transmission failures or unresolved conflicts, it promptly issues an alert to the administrator for handling.

[0114] This invention discloses a computer-based multi-platform information technology construction system and method, aiming to solve problems such as untimely data synchronization, incompatible formats, and difficult conflict resolution in existing multi-platform information technology construction. The system includes a real-time data capture module, an intelligent routing decision module, a data conversion and adaptation module, a conflict detection and resolution module, a data synchronization execution module, and a monitoring and management module.

[0115] The real-time data capture module generates data change events in real time by setting listening points in the database transaction log; the intelligent routing decision module dynamically optimizes the transmission path using a combination of rule-based and machine learning algorithms; the data conversion and adaptation module automatically adapts to different platform data formats and interfaces using a dynamic template matching algorithm; the conflict detection and resolution module ensures data consistency through version control and priority determination; the data synchronization execution module transmits data according to the optimized path and records the results; and the monitoring and management module monitors the entire process in real time.

[0116] The method includes steps such as data capture, routing decision, format conversion, synchronization execution, and monitoring feedback. This system and method significantly improve data synchronization efficiency and accuracy, reduce multi-platform integration and maintenance costs, and are suitable for IT infrastructure development in various scenarios, including enterprises and government agencies.

[0117] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A computer-based multi-platform information construction system, characterized in that, It includes a real-time data capture module, an intelligent routing decision module, a data conversion and adaptation module, a conflict detection and resolution module, a data synchronization execution module, and a monitoring and management module. The real-time data capture module is used to monitor data changes on each business platform in real time. The real-time data capture module sets data change listening points in the transaction logs of each platform's database. Once a data insertion, update, or deletion operation is detected, the relevant data change information is immediately captured, and a data change event containing key information such as operation type, data object, and timestamp is generated. The intelligent routing decision module, based on the attributes of data change events and the characteristics of the target platform, employs a routing decision algorithm combining rules and machine learning. It pre-sets basic rules for data flow and simultaneously analyzes historical data synchronization paths and efficiency through machine learning to dynamically optimize routing decisions and select the optimal data transmission path. The specific implementation logic of the intelligent routing decision module is as follows: Data change event reception and parsing: The intelligent routing decision module receives data change events from the real-time data capture module. Data change events include, but are not limited to, operation type, data object, and timestamp key attribute information. This information is analyzed to extract key elements for routing decisions, and data change events are denoted as... ,in Indicates an event The One attribute; Basic rule matching: A pre-defined base of basic rules for data flow. Each rule Represented as a condition-result pair, i.e. ;in It is a conditional expression composed of data change event attributes; The corresponding transmission path decision result is used; the parsed data change event attributes are matched with rules in the rule base, and if a rule exists that meets the conditions... According to the rules, the corresponding transmission path is directly obtained. Complete the routing decision; Machine learning prediction-assisted decision-making: If no matching rule is found in the basic rule base, the path prediction model is activated to assist in decision-making, and the output of the path prediction model needs to be cross-validated with the constraints in the rule base; specifically, a path prediction model trained on historical data is used to analyze data change events. The attributes are used as model inputs, and the input vector is denoted as . ,in Corresponding data change event Attributes After feature engineering, the model outputs the probability distribution of each transmission path through internal calculations and weight parameters. ,in Indicates data transmission to the first The probability of each path, and ; Path optimization and adjustment: After determining the initial transmission path, the path is optimized by combining path selection, transmission time, and success rate data from historical data synchronization processes; a comprehensive evaluation index for the current path is calculated. Its calculation formula is ,in This represents the average transmission time for this path in historical data. For transmission success rate, where This represents the number of successful transmissions along this path in the past 24 hours divided by the total number of transmissions. For the complexity of the path, These are weighting coefficients, set according to business needs, and If other paths have better comprehensive evaluation indicators, the transmission path will be adjusted, and the optimal path will be selected as the final data transmission path. Routing information generation and transmission: After determining the final data transmission path, the intelligent routing decision module generates routing information containing source platform information, target platform information, transmission path, etc., and sends the routing information along with the data change event to the data conversion and adaptation module for subsequent data format conversion and transmission. The specific implementation steps of the path prediction model trained based on historical data are as follows: Step 1: Data Preprocessing and Feature Engineering Extract features from historical data synchronization logs to construct a sample set. Each input sample It is a time series, consisting of Composed of time steps: Feature vector at each time step Include: Data change event attributes: Operation type Data object type Timestamp features Source Platform ID and target platform ID candidate set Historical synchronization performance metrics: average latency Success rate and throughput Target variable It is the optimal transmission path ID, where the optimal transmission path ID is selected from the candidate set. Select from; Step 2: Improve the LSTM model architecture design: Base LSTM layer: Processes sequential inputs and captures long-term dependencies. ,in It is in a hidden state. This is the current input. It is the hidden state from the previous moment; Attention mechanism layer: Weights the LSTM output to highlight key time steps. , ,in It is attention weight. These are trainable parameters, where For "attention preference vector", weight matrix To Perform feature weighting, Used to adjust the offset of the activation function to avoid all It falls into the saturation region of the activation function; To calculate each time step "Attention score"; Context vector: ; Path probability output layer: ,in It is the probability distribution of each candidate path. These are the output layer parameters; Step 3: Model Training and Optimization Loss function design: Considering the characteristics of multi-platform data synchronization, the loss function incorporates path success rate and latency metrics. Specifically, the loss function uses class cross-entropy. ,in It is a sample One-hot encoding of the actual path label, It is a prediction probability. The number of training samples. The number of different categories in the data flow path, where the optimizer uses the Adam algorithm; Step 4: The training process that integrates rule constraints: Introducing a rule consistency loss term: ,in For the set of samples to match the rules, It is the path probability distribution specified by the rule. It is the balance coefficient; The total loss function is: ; Step 5, Prediction and Decision-Making Process: Input the current data change event characteristic sequence The path prediction model outputs the path probability distribution. Choose the path with the highest probability as the prediction result. ;in These are the eigenvalues ​​of the input vector; The data conversion and adaptation module uses a dynamic template matching data conversion algorithm to construct a rich data format template library for different platform data formats and interface specifications. When a data change event is received, it automatically matches the corresponding template according to the data formats of the source platform and the target platform to perform data format conversion and interface adaptation, so that data can be accurately transmitted between different platforms. During data synchronization, the conflict detection and resolution module uses a conflict detection and resolution algorithm based on version control and priority determination to assign a version number to each data object. When multiple platforms operate on the same data object, the module automatically resolves data conflicts by comparing the version number with the preset operation priority, thus ensuring data consistency. The data synchronization execution module is used to synchronize data to the target platform according to the path determined by the intelligent routing decision module and the data processed by the data conversion and adaptation module, and record the synchronization results. The monitoring and management module monitors the entire data synchronization process in real time, and monitors and issues warnings about the operating status of each module, the progress of data synchronization, and any abnormal situations, for administrators to manage and maintain.

2. The computer-based multi-platform information construction system according to claim 1, characterized in that: The specific working logic of the data conversion and adaptation module is as follows: Data reception and information extraction: When the real-time data capture module detects data changes on the business platform and the intelligent routing decision module determines the transmission path, the data conversion and adaptation module receives the data change event containing key information such as operation type, data object, and timestamp; at the same time, it extracts the identifiers of the source platform and the target platform from the routing information to clarify the source and destination of the data and provide basic information for subsequent operations. Template matching: Based on the extracted source and target platform data format information, a search and matching process is performed in a pre-built data format template library. The template library stores data conversion templates for different platform combinations, and each template defines the mapping relationship between the source format and the target format. By calculating the feature similarity between the source data format and the template, the most matching template is selected. Data format conversion and interface adaptation: Use the matched template to convert the format of the data in the data change event; According to the mapping rules defined in the template, the structure, field names, and data types of the source data are converted into a format that the target platform can recognize. At the same time as the format conversion, the data is adapted to the interface according to the interface specifications of the target platform, including but not limited to adjusting the data transmission protocol and request parameter format, to ensure that the data can be successfully connected to the interface of the target platform. Data output and transmission after processing: After completing data format conversion and interface adaptation, the processed data is sent to the data synchronization execution module; The data synchronization execution module synchronizes the adapted data to the target platform according to the path determined by the intelligent routing decision module, realizing accurate data transmission between multiple platforms and providing reliable data support for subsequent business processing.

3. The computer-based multi-platform information construction system according to claim 1, characterized in that: The specific implementation steps of the conflict detection and resolution module are as follows: a. Version marking before data synchronization: Version number allocation: When the real-time data capture module detects a data change, the conflict detection and resolution module generates a unique version number for each data object. The format is node ID + timestamp + sequence number; Metadata Attachment: Attach the version number as metadata to the data change event and transmit it along with the data object; b. Conflict pre-detection: Version comparison: Before data synchronization is executed, the module queries the current version number of the data object on the target platform. Conflict prediction: If the version number of the data to be synchronized is... Less than or equal to If the data is deemed outdated, synchronization will be terminated; if If so, the synchronization process will begin and conflict monitoring will continue. c. Concurrent operation conflict detection: Multi-source write monitoring: When multiple platforms simultaneously initiate change requests for the same data object, the module captures concurrent operations through a distributed lock mechanism; Version number collision detection: Compare the version numbers of each change request. If there are cases where the version numbers are the same but the content is different, it is determined to be a conflict; d. Priority-based conflict resolution: Operation type priority rules: Preset operation priority order, and execute the higher priority operation first when a conflict occurs; Business rule priority: Define exclusive rules for specific data objects; Timestamp priority: When operation types are the same, compare the timestamps in the version number and take the latest operation first. e. Version merging and data integration: Mergeable Change Handling: For non-mutually exclusive changes, the module automatically merges the changes and generates a new version number. ; Data integration algorithm: Utilize graph database technology to analyze the relationships between data and ensure logical consistency of the merged data; f. Conflict log recording and analysis: Log storage: Records detailed information about all conflict events, including conflict data, version number, and resolution method; then optimizes priority rules based on historical conflict data.

4. A computer-based multi-platform information construction method, applied to a computer-based multi-platform information construction system as described in any one of claims 1-3, characterized in that, Includes the following steps: Step S1: The real-time data capture module uses an event-driven data capture algorithm to monitor the transaction logs of the databases of each business platform in real time. When a data change is detected, a data change event is generated and sent to the intelligent routing decision module. Step S2: The intelligent routing decision module receives the data change event, determines the data transmission path based on a routing decision algorithm that combines rules and machine learning, and sends the data change event and routing information to the data conversion and adaptation module. Step S3: The data conversion and adaptation module uses a dynamic template matching data conversion algorithm to convert the data format and adapt the interface based on the received source platform and target platform information, and then sends the processed data to the data synchronization execution module. Step S4: The data synchronization execution module synchronizes the data to the target platform according to the path determined by the intelligent routing decision module. The conflict detection and resolution module uses a conflict detection and resolution algorithm based on version control and priority determination to detect and process data conflicts. Step S5: The data synchronization execution module records the data synchronization results and feeds them back to the monitoring and management module. The monitoring and management module monitors and manages the entire data synchronization process in real time.

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