A method and system for information docking between heterogeneous power trading systems

Abstract

This invention relates to the field of data interaction technology for heterogeneous power trading systems, specifically disclosing a method and system for information docking between heterogeneous power trading systems. This invention constructs a unified event semantic model for power trading, extracts semantic feature vectors from messages of source heterogeneous platforms for matching, generates semantic check codes to form standardized event records, and simultaneously converts messages according to the protocol parameters of the target heterogeneous platform. It also updates the synchronization status by binding incremental synchronization sequence numbers to a real-time interactive window. This solves the problems of semantic ambiguity and format conflicts caused by the lack of unified semantic standards among various business platforms, as well as the timeliness and interface compatibility issues of existing docking methods. It improves the accuracy and real-time performance of cross-platform power trading information flow, ensuring the smooth operation of high-frequency bidding and clearing operations in the spot market.

Description

Technical Field

[0001] This invention relates to the field of data interaction technology for heterogeneous power trading systems, and in particular to a method and system for information docking of heterogeneous power trading systems. Background Technology

[0002] Against the backdrop of the deepening power market reform, power trading involves multiple stages, including generation, transmission, distribution, user, and power trading centers. The information systems at each stage differ significantly in data formats, communication protocols, and interface standards, forming a typical heterogeneous system environment. Existing technologies for power trading information interfacing typically rely on manual configuration or simple data conversion tools, which are insufficient to meet the demands of real-time aggregation and dynamic interaction of multi-source heterogeneous data. On the one hand, the lack of a unified semantic standard in the data models of different business systems leads to semantic ambiguity and format conflicts during cross-system data flow, making it difficult to guarantee information consistency. On the other hand, existing interfacing methods have significant shortcomings in data synchronization timeliness and interface compatibility. When facing high-frequency bidding and real-time clearing scenarios in the power spot market, the risk of information transmission delays and interfacing failures is high, hindering the smooth and efficient operation of power trading.

[0003] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method and system for information exchange between heterogeneous power trading systems.

[0005] In a first aspect, the present invention provides a method for information docking in heterogeneous power trading systems, the technical solution of which is as follows:

[0006] A unified event semantic model for power trading is constructed, which includes the semantic feature vector definition, event correlation, and event semantic verification rules for various business events in power trading.

[0007] Upon receiving a transaction data message from a heterogeneous power trading system, the communication protocol features and service payload of the transaction data message are extracted. Based on the communication protocol features and service payload, the unified event semantic model for power trading is invoked to generate a semantic feature vector of the transaction data message.

[0008] The semantic feature vector is matched with the semantic feature vector definition in the unified event semantic model of power trading to determine the target business event identifier corresponding to the transaction data message, and a semantic verification code is generated according to the event semantic verification rules. The target business event identifier and the semantic verification code are combined with the key business fields corresponding to the transaction data message to form a standardized transaction event record.

[0009] Obtain the communication protocol parameters and real-time interactive window information of the target power trading heterogeneous system; convert the standardized transaction event record into a target data packet adapted to the target power trading heterogeneous system according to the communication protocol parameters; and bind an incremental synchronization sequence number to the target data packet according to the real-time interactive window information.

[0010] Send the target data message carrying the incremental synchronization sequence number to the target power trading heterogeneous system, and update the synchronization status of the standardized transaction event records between the source power trading heterogeneous system and the target power trading heterogeneous system according to the incremental synchronization sequence number and the target business event identifier.

[0011] Secondly, the present invention provides an information docking system for heterogeneous power trading systems, the technical solution of which is as follows:

[0012] The module is used to build a unified event semantic model for power trading. The unified event semantic model for power trading includes the semantic feature vector definition, event association relationship and event semantic verification rules for various business events in power trading.

[0013] The generation module is used to extract the communication protocol features and service payload of the transaction data message when it receives the transaction data message from the source power trading heterogeneous system, and to call the unified event semantic model of power trading to generate the semantic feature vector of the transaction data message based on the communication protocol features and the service payload.

[0014] The combination module is used to match the semantic feature vector with the semantic feature vector definition in the unified event semantic model of power trading, determine the target business event identifier corresponding to the transaction data message, generate a semantic verification code according to the event semantic verification rules, and combine the target business event identifier and the semantic verification code with the key business fields corresponding to the transaction data message into a standardized transaction event record.

[0015] The running module is used to obtain the communication protocol parameters and real-time interactive window information of the target power trading heterogeneous system, convert the standardized transaction event record into a target data packet adapted to the target power trading heterogeneous system according to the communication protocol parameters, and bind an incremental synchronization sequence number to the target data packet according to the real-time interactive window information.

[0016] The update module is used to send the target data message carrying the incremental synchronization sequence number to the target power trading heterogeneous system, and update the synchronization status of the standardized transaction event records between the source power trading heterogeneous system and the target power trading heterogeneous system according to the incremental synchronization sequence number and the target business event identifier.

[0017] The technical solution of this invention constructs a unified event semantic model for power trading, extracts semantic feature vectors from heterogeneous source platform messages for matching, generates semantic check codes to form standardized event records, and converts messages according to the protocol parameters of the target heterogeneous platform. It also updates the synchronization status by binding incremental synchronization sequence numbers in a real-time interactive window. This solves the problems of semantic ambiguity and format conflicts caused by the lack of unified semantic standards among various business platforms, as well as the timeliness and interface compatibility issues of existing docking methods. It improves the accuracy and real-time performance of cross-platform power trading information flow and ensures the smooth operation of high-frequency bidding and clearing business in the spot market.

[0018] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0020] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0021] Figure 1 This is a flowchart illustrating an embodiment of a method for information docking in heterogeneous power trading systems according to the present invention. Detailed Implementation

[0022] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0023] Figure 1 This diagram illustrates a flowchart of an embodiment of a method for information interoperability between heterogeneous power trading systems provided by the present invention, executed by a control terminal. Figure 1 As shown, the information docking method for heterogeneous power trading systems includes the following steps:

[0024] S1. Construct a unified event semantic model for power trading. The unified event semantic model for power trading includes the semantic feature vector definition, event association relationship and event semantic verification rules for various business events in power trading.

[0025] The unified event semantic model for power trading refers to a pre-constructed standardized representation framework for defining various events in power trading at the semantic level. This model specifies the semantic feature vector definitions for different business events, the event correlation relationships between events, and event semantic verification rules. For example, when power generation company A declares its power generation in the day-ahead market, the corresponding event semantic model defines the semantic feature vector of the "power generation side declaration" event as including components such as equipment identification dimension (e.g., generator unit number GF-001), transaction type dimension (e.g., day-ahead declaration), and price dimension (e.g., 0.35 yuan / kWh). The event correlation relationship indicates a causal relationship between the "power generation side declaration" event and the subsequent "trading center clearing" event. The event semantic verification rules require that the timestamps and transaction period identifiers in the declaration data conform to the day-ahead market time window requirements.

[0026] Electricity trading refers to the buying and selling of electricity and ancillary services by various participants in the electricity market under the constraints of trading rules. This encompasses various models, including bilateral negotiated transactions, centralized bidding transactions, and listed transactions. For example, in a provincial electricity market, power generation company A publishes its next day's power generation capacity through a trading platform, while user B submits its electricity demand. The trading platform matches supply and demand and forms a settlement price during the trading period. A business event refers to a business activity that occurs within the electricity trading process, possessing independent business meaning and a clear time boundary, distinct from system-level technical events. For example, power generation company A's submission of power generation quotation data in the trading system constitutes a "power generation quotation business event." The trading system's processing of "power generation quotation business events" includes receiving data, verifying formats, and storing records.

[0027] The semantic feature vector definition refers to the pre-defined feature vector template in the unified semantic model of power trading events for each type of business event. This template determines all dimensions required to describe the business event and the value range of each dimension. For example, for a "generation-side bidding business event," the semantic feature vector definition stipulates that the feature vector corresponding to this event must include at least the dimensions of the bidding entity identifier, the bidding amount, the bidding period, and the bidding timestamp. Event correlation refers to the rules defined in the unified semantic model of power trading events that reflect the logical connections between different business events, including temporal, causal, and dependency relationships. For example, a generation-side bidding event submitted by power generation company A has a temporal and causal relationship with the subsequent clearing event formed by the trading center. The trading system automatically traces the impact of the generation-side bidding event on the clearing price based on the event correlation. Event semantic verification rules refer to the set of rules set in the unified event semantic model for power trading, which are used to verify the semantic correctness and consistency of business events. These rules include field format verification, business logic verification, and cross-event consistency verification. For example, for the power generation side quotation event submitted by power generation company A, the event semantic verification rules require that the unit of the quotation quantity be consistent with the time span of the quotation period, and that the quotation price be within the price range specified in the declaration rules.

[0028] S2. Upon receiving a transaction data message from a heterogeneous power trading system, extract the communication protocol features and service payload of the transaction data message. Based on the communication protocol features and service payload, invoke the unified event semantic model for power trading to generate the semantic feature vector of the transaction data message.

[0029] Among them, the source power trading heterogeneous system refers to an information system that generates original transaction data in power trading operations and uses different data formats or communication protocols than the target power trading heterogeneous system; for example, the power generation quotation system used internally by power generation company A encapsulates data using the IEC 62325 standard and transmits messages through a custom TCP protocol, and the power generation quotation system is a source power trading heterogeneous system. A transaction data message refers to a data transmission unit containing power trading business data sent by the source power trading heterogeneous system through the network. A transaction data message consists of a message header and a business payload; for example, when power generation company A's power generation quotation system sends a transaction data message to the control system, the message header identifies the source address, destination address, and protocol type, and the business payload carries the quotation amount of 150 MW and the quotation price of 0.35 yuan / kWh for generator unit GF-001.

[0030] The communication protocol characteristics refer to the identification information extracted from the transaction data message that reflects the type of communication protocol used in the transaction data message, including the transport layer protocol identifier, application layer protocol identifier, and message format characteristics. For example, the control system extracts the application layer protocol from the transaction data message sent by power generation company A and finds that it uses the IEC 62325 message format and the transport layer protocol uses the TCP protocol. The net load refers to the content portion of the transaction data message that carries the actual power transaction data after removing the communication protocol header and trailer. For example, the net load obtained after stripping the protocol header from the transaction data message sent by power generation company A includes generator unit GF-001, a bid quantity of 150 MW, a bid price of 0.35 yuan / kWh, and a bid period of 14:00-15:00 on January 1, 2026.

[0031] The semantic feature vector refers to the vectorized representation of business semantics in a multi-dimensional numerical form, generated after processing the business load and communication protocol features of the transaction data message by calling the unified event semantic model for power trading. For example, after the control system inputs the business load of power generation company A into the unified event semantic model for power trading, it generates a semantic feature vector (GF-001 encoding: 0.82, quotation quantity normalization: 0.75, price normalization: 0.50, time period encoding: 0.33, protocol encoding: 0.90). Each dimension component represents the semantic encoding information of the transaction data message in the dimensions of entity object attributes and action event types.

[0032] S3. Match the semantic feature vector with the semantic feature vector definition in the unified event semantic model of power trading to determine the target business event identifier corresponding to the transaction data message, generate a semantic verification code according to the event semantic verification rules, and combine the target business event identifier and the semantic verification code with the key business fields corresponding to the transaction data message to form a standardized transaction event record.

[0033] The target business event identifier refers to the business event type number or name corresponding to the semantic feature vector with the highest semantic similarity to the semantic feature vector of the transaction data message during the semantic matching process. For example, after the control system matches the semantic feature vector of the power generation company A's message with the semantic feature vector definitions of each semantic feature vector in the unified event semantic model of power trading one by one, the business event identifier corresponding to the highest semantic similarity is "power generation side quotation event".

[0034] The semantic checksum refers to the code obtained by indexing a set of check parameters from the event semantic check rules based on the target business event identifier, and performing a hash operation on the check parameter set and the target business event identifier. This code is used to quickly verify data integrity and semantic consistency. For example, for the generation-side bidding event of power generation company A, the control system indexes a set of check parameters containing data type rules, numerical range rules, and time constraints. The check parameter set is then hashed together with the target business event identifier "generation-side bidding event" to obtain the semantic checksum "5A3F". Key business fields refer to data fields in the net load that are directly related to the semantics of power trading. Key business fields are distinct from auxiliary routing fields and formatting fields. For example, in the net load of power generation company A, the key business fields are the generator unit identifier field "GF-001", the bid quantity field "150", the bid price field "0.35", and the bid period field "202601011400-1500".

[0035] Standardized transaction event records refer to standardized data records with complete semantic information formed by combining target business event identifiers, semantic check codes, and key business fields according to a preset unified data structure. For example, the control system combines the "generation-side quotation event" identifier of power generation company A, the semantic check code "5A3F", and the key business fields GF-001, 150 MW, 0.35 yuan / kWh, and time period 202601011400-1500 into a standardized transaction event record. The standardized transaction event records are stored using a unified JSON structure.

[0036] S4. Obtain the communication protocol parameters and real-time interaction window information of the target power trading heterogeneous system, convert the standardized transaction event record into a target data message adapted to the target power trading heterogeneous system according to the communication protocol parameters, and bind an incremental synchronization sequence number to the target data message according to the real-time interaction window information.

[0037] Among them, the target heterogeneous power trading system refers to the destination information system that receives standardized transaction event records in power trading business and has different data formats or communication protocols from the source heterogeneous power trading system; for example, the unified clearing system of the power trading center adopts the industry standard format based on XML and receives data through the HTTP transmission protocol, and the unified clearing system is the target heterogeneous power trading system.

[0038] The communication protocol parameters refer to the specific protocol configuration parameters used by the target heterogeneous power trading system during communication, including message encapsulation format parameters, transmission method parameters, and interface address parameters. For example, the communication protocol parameters of the unified clearing system of the power trading center specify the message encapsulation format as XML, the transmission method as HTTP protocol, and the interface address as https: / / trade.example.com / api / clearing. The real-time interaction window information refers to the relevant parameters of the interaction time window that the target heterogeneous power trading system can currently receive data from. The real-time interaction window information includes the window opening time and window closing time. For example, the unified clearing system of the power trading center opens an interaction window during the operation of the spot market. The window opens at 14:00:00 on January 1, 2026, closes at 14:15:00 on January 1, 2026, and the window duration is 15 minutes.

[0039] It should be noted that the real-time interactive window information is obtained by the control system by listening to the interactive window broadcast messages of the target power trading heterogeneous system, and the window opening time and closing time are automatically updated each time the window is opened to adapt to the clearing period of the dynamic changes in the power spot market.

[0040] The target data message refers to a data message generated by re-encapsulating standardized transaction event records according to the communication protocol parameters of the target heterogeneous power trading system, and adapted to the format requirements of the target heterogeneous power trading system. For example, the control system re-encapsulates the standardized transaction event records of power generation company A according to the XML message encapsulation format of the unified clearing system, generating a target data message that conforms to the interface specification of the target heterogeneous power trading system. The target data message contains XML elements of event identifier, semantic check code, and key business fields. The incremental synchronization sequence number refers to a numerical identifier that is read from the synchronization counter and bound to the target data message in the current interaction window, and increments in the order of transmission. For example, when the control system sends the target data message of power generation company A to the unified clearing system in the current interaction window, it reads the current count value "42" from the synchronization counter as the incremental synchronization sequence number and binds it to the target data message. The synchronization counter then increments to "43".

[0041] S5. Send the target data message carrying the incremental synchronization sequence number to the target power trading heterogeneous system, and update the synchronization status of the standardized transaction event records between the source power trading heterogeneous system and the target power trading heterogeneous system according to the incremental synchronization sequence number and the target business event identifier.

[0042] Among them, the synchronization status refers to the marking information of the synchronization completion status of the source power trading heterogeneous system and the target power trading heterogeneous system for a specific standardized transaction event record; for example, after the control system receives the confirmation message returned by the unified clearing system and confirms that the received sequence number is consistent with the issued incremental synchronization sequence number, it marks the synchronization status of the standardized transaction event record of power generation enterprise A from "pending synchronization" to "synchronization completed".

[0043] The technical solution of this embodiment constructs a unified event semantic model for power trading, extracts semantic feature vectors from heterogeneous source platform messages for matching, generates semantic check codes to form standardized event records, and converts messages according to the protocol parameters of the target heterogeneous platform. It also updates the synchronization status by binding incremental synchronization sequence numbers in a real-time interactive window. This solves the problems of semantic ambiguity and format conflicts caused by the lack of unified semantic standards among various business platforms, as well as the timeliness and interface compatibility issues of existing docking methods. It improves the accuracy and real-time performance of cross-platform power trading information flow and ensures the smooth operation of high-frequency bidding and clearing business in the spot market.

[0044] In one alternative approach, constructing a unified event semantic model for electricity trading includes:

[0045] Historical message samples of power trading business, including generation-side bidding events, user-side declaration events, transmission-side maintenance events, distribution-side dispatch events, and trading center clearing events, are collected. The historical message samples are semantically annotated to obtain a semantically annotated dataset. Based on the semantically annotated dataset, the semantic feature vector definition, event association relationship, and event semantic verification rules for each business event in the unified event semantic model of power trading are obtained through multi-dimensional semantic feature clustering and event relationship graph construction.

[0046] Among them, generation-side bidding events refer to the business activities of power generation companies submitting power generation bidding data to the trading institution in the power trading market; for example, power generation company A submits a bid of 150 MW for generator unit GF-001 and a bid price of RMB 0.35 / kWh for the trading period of 14:00-15:00 on January 1, 2026, through the power generation bidding system in the spot market. User-side declaration events refer to the business activities of power users submitting electricity demand declaration data to the trading institution in the power trading market; for example, user B submits electricity demand declaration data for the period of 10:00-11:00 on January 2, 2026, through the user declaration system in the day-ahead market, with a declared volume of 200 MW. Transmission-side maintenance events refer to the business activities of transmission network operators submitting information on planned maintenance or fault maintenance of transmission equipment in the power trading system; for example, transmission company C submits an application in the trading system for planned maintenance of line L12 during the period of 0:00-6:00 on January 3, 2026. Distribution-side dispatch events refer to the business activities of distribution network operators issuing distribution network dispatch instructions or dispatch plan change information in the power trading system; for example, distribution company D issues a dispatch instruction in the trading system that requires adjusting the operation mode of the distribution network due to a sudden increase in load, involving the load adjustment of substation SV-08 to 60% of its rated capacity. Trading center clearing events refer to the business activities of the power trading center completing supply and demand matching and publishing transaction results based on the bids and declarations of market participants through a clearing calculation engine; for example, the unified clearing system of the power trading center calculates the transaction price of 0.38 yuan / kWh and the transaction volume based on the bid data of power generation company A and the declaration data of user-side B through a clearing algorithm, and publishes the clearing results through the unified clearing system.

[0047] Historical message samples refer to a collection of historical data messages containing various business event types, collected from historical operational data of power trading. For example, the control system collects nearly six months of bidding data messages from power generation company A's power generation bidding system, nearly six months of electricity demand data messages from user-side B's declaration system, and nearly six months of clearing result data messages from the trading center's clearing system. Semantic annotation datasets refer to a collection of labeled data used to train a unified event semantic model for power trading, formed by manually or automatically annotating each message sample in the historical message samples with semantic tags. For example, the semantic annotation dataset consists of all labeled data obtained after annotating the generator unit identification field in power generation company A's historical bidding messages with entity object tags, the bidding quantity field with action attribute tags, and the bidding price field with price attribute tags.

[0048] Among the above optional methods, historical samples from the power generation side, user side, transmission side, distribution side, and trading center are further collected and semantically labeled. Based on the semantically labeled dataset, multi-dimensional semantic feature clustering and event relationship graph construction are performed to obtain semantic feature vector definitions, event association relationships, and semantic verification rules, which enriches the model construction methods.

[0049] In one optional approach, the step of extracting the communication protocol features and service payload of the transaction data message, and generating a semantic feature vector of the transaction data message by invoking the unified event semantic model for power trading based on the communication protocol features and service payload, includes:

[0050] Identify the communication protocol type used in the transaction data message, and call the corresponding protocol parser to parse the transaction data message according to the communication protocol type to obtain the service payload.

[0051] The communication protocol type refers to the identifier of the communication protocol followed by the transaction data message. Different communication protocol types correspond to different message parsing rules. For example, the application layer protocol followed by the transaction data message sent by power generation company A is identified as IEC 62325 from the communication protocol characteristics, and the communication protocol type identifier is IEC62325. The protocol parser refers to a program component pre-built according to the communication protocol type, used to parse data messages of a specific protocol format. The input of the protocol parser is the transaction data message, and the output is the service payload. For example, the control system calls the protocol parser corresponding to the IEC62325 protocol type to parse the message header fields with byte offsets 0 to 4 in the transaction data message of power generation company A into source address, destination address, and message length, and to parse the fields with byte offsets 5 to 130 into the service payload.

[0052] Entity object attribute fields and action event fields are extracted from the business payload. The entity object attribute fields and action event fields are input into the unified event semantic model for power trading. The semantic feature extraction layer in the unified event semantic model for power trading performs embedding encoding and attention weighted fusion on the entity object attribute fields and action event fields to generate the semantic feature vector. The semantic feature vector contains encoded information of entity object attribute dimension and action event type dimension.

[0053] Among them, the entity object attribute field refers to the data field in the net load of the business that describes the static attribute information of the entity objects involved in the power trading business; for example, in the net load of power generation company A, the field describing the equipment number, rated capacity, and node information of generator unit GF-001 is an entity object attribute field. The action event field refers to the data field in the net load of the business that describes the dynamic information of the action or event type that occurs in the power trading business; for example, in the net load of power generation company A, the field describing the bidding action type as "day-ahead submission", the bidding quantity as "150", and the bidding price as "0.35" is an action event field.

[0054] The semantic feature extraction layer refers to the processing layer in the unified event semantic model for power transactions that is responsible for converting the input entity object attribute fields and action event fields into semantic feature vectors through embedding encoding and attention weighted fusion. For example, in the message processing of power generation company A, the semantic feature extraction layer converts the character sequence of the entity object attribute field into a 128-dimensional numerical vector and the character sequence of the action event field into a 128-dimensional numerical vector. Then, it calculates attention weights and weights the two sets of 128-dimensional numerical vectors to fuse them into a 128-dimensional semantic feature vector.

[0055] The entity object attribute dimension refers to the component positions in the semantic feature vector used to encode the entity objects and their attribute features involved in power trading. For example, in the semantic feature vector of power generation company A, the first 32 dimensions represent the equipment identifier, equipment type, and rated capacity attribute coding information of generator unit GF-001. The action event type dimension refers to the component positions in the semantic feature vector used to encode the action event type and its parameter features that occur in power trading. For example, in the semantic feature vector of power generation company A, the components from the 33rd to the 64th dimensions represent the coding information that the corresponding action event type is "generation-side quotation", the quotation quantity is "150 MW", and the quotation price is "0.35 yuan / kWh".

[0056] In the above optional methods, the communication protocol type is further identified and the protocol parser is called to extract the business payload, extract the entity object attribute field and action event field, and the semantic feature extraction layer performs embedding encoding and attention weighted fusion to generate a semantic feature vector that covers the encoded information of the entity object attribute dimension and the action event type dimension.

[0057] In one optional approach, the steps of matching the semantic feature vector with the semantic feature vector definition in the unified event semantic model for power transactions to determine the target business event identifier corresponding to the transaction data message, and generating a semantic checksum according to the event semantic verification rules, include:

[0058] Calculate the semantic similarity between the semantic feature vector and the semantic feature vector definitions in the unified event semantic model for power trading, and determine the business event identifier associated with the semantic feature vector definition corresponding to the maximum semantic similarity as the target business event identifier.

[0059] Semantic similarity refers to the degree of matching between the semantic feature vector of a transaction data message, calculated by a semantic matching algorithm, and the semantic feature vector definition in the unified event semantic model for power transactions. For example, the control system calculates the differences in each dimension component between the semantic feature vector of power generation company A's message and the semantic feature vector definition of "power generation side bidding event" in the unified event semantic model for power transactions, and then sums them by weight to obtain a semantic similarity value of 0.96. The semantic similarity value between the semantic feature vector of A's message and the semantic feature vector definition of "user side declaration event" is 0.12.

[0060] Based on the target business event identifier, the corresponding verification parameter group is indexed from the event semantic verification rules, and a hash operation is performed on the verification parameter group and the target business event identifier to obtain the semantic verification code.

[0061] Among them, the verification parameter group refers to a set of rule parameters indexed from the event semantic verification rules based on the target business event identifier, used to perform semantic verification. The verification parameter group includes data type verification parameters, numerical range verification parameters, and timing consistency verification parameters. For example, the control system indexes the verification parameter group from the event semantic verification rules based on the target business event identifier "generation side bidding event". The bidding amount must be a numerical type, the bidding price must be a floating point type within the range of 0.2 to 1.0 yuan / kWh, and the bidding period must be consistent with the format of the trading period.

[0062] In the above optional methods, the semantic similarity between the semantic feature vector and each definition is further calculated, the event identifier corresponding to the maximum value is determined as the target business event identifier, the verification parameter group is indexed according to the verification rules, and the semantic verification code is generated by hashing the verification parameter group and the event identifier, so as to realize the collaboration of event classification and integrity verification.

[0063] In one alternative approach, the step of calculating the semantic similarity between the semantic feature vector and the semantic feature vector definitions in the unified event semantic model for electricity trading includes:

[0064] Using the attention weighting parameters output by the semantic feature extraction layer, a nonlinear transformation is performed on the multidimensional weighted semantic distance between the semantic feature vector and the k-th semantic feature vector to obtain the semantic similarity. The calculation formula for the nonlinear transformation is as follows:

[0065]

[0066] in, This represents the semantic feature vector. Indicates the first A semantic feature vector is defined. and They represent and In the Dimensional components, This represents the total dimension of the semantic feature vector. The first value obtained from the attention weighting parameters Dimension importance coefficient Indicates the first Dimensional scaling factor This represents the attenuation adjustment factor related to the historical synchronization delay corresponding to the service payload. This represents a temperature coefficient that reflects the urgency of the events associated with the semantic feature vector definition. The balance coefficient represents the semantic orientation compensation term. Represents the linear rectified function. and They represent and The L2 norm.

[0067] It should be noted that the above formula uses the attention weighting parameters output by the semantic feature extraction layer as importance coefficients for each dimension. It normalizes the absolute differences between the semantic feature vector and its definition in each dimension by dividing the absolute difference by the dimension scaling factor. Then, an exponential term, composed of a decay adjustment factor related to historical synchronization delay and a temperature coefficient reflecting the urgency of the event, is introduced to perform a nonlinear power transformation on the normalized distance. This nonlinear power transformation can adaptively adjust the distance scaling according to the dynamic changes in the business scenario. Simultaneously, a semantic direction compensation term is added to the formula. This term is activated by calculating the cosine similarity between the semantic feature vector and its definition, combined with a linear rectified function, incorporating semantic direction consistency information into the similarity metric. Finally, an exponential function converts the comprehensive distance into a continuously valued semantic similarity value. During the matching process, the above formula quantifies the degree of matching between the current transaction data message and the semantic definition templates of each business event. Through a composite mechanism of multi-dimensional weighting, dynamic exponential adjustment, and direction compensation, it can more accurately distinguish semantically similar but different business meanings in high-frequency trading scenarios, improving the matching accuracy of target business event identifiers.

[0068] In the above-mentioned optional methods, attention weighting parameters are further used to perform nonlinear transformation on the multidimensional weighted semantic distance to calculate semantic similarity. Dimensional importance coefficient, scale factor, attenuation adjustment factor, temperature coefficient and semantic direction compensation term are introduced to make the similarity calculation more in line with the semantic characteristics of power trading business.

[0069] In one alternative approach, the steps of obtaining the communication protocol parameters and real-time interaction window information of the target heterogeneous power trading system, converting the standardized transaction event records into target data packets adapted to the target heterogeneous power trading system according to the communication protocol parameters, and binding incremental synchronization sequence numbers to the target data packets according to the real-time interaction window information include:

[0070] Obtain the window opening time and window closing time of the current interaction window of the target power trading heterogeneous system, and bind the target data packet to the current interaction window between the window opening time and the window closing time.

[0071] The current interaction window refers to the time interval during which the target heterogeneous power trading system is allowed to receive data at the current moment. The current interaction window is defined by the window's opening and closing times. For example, the current interaction window of the unified clearing system of the power trading center is marked as 14:00:00 to 14:15:00 on January 1, 2026. The control system will allocate the target data packet corresponding to the message from power generation company A to the current interaction window for transmission. The window opening time refers to the starting time of the current interaction window; for example, the window opening time of the unified clearing system of the power trading center is 14:00:00 on January 1, 2026. The window closing time refers to the ending time of the current interaction window; for example, the window closing time of the unified clearing system of the power trading center is 14:15:00 on January 1, 2026.

[0072] Based on the target heterogeneous power trading system and the business event type corresponding to the target business event identifier in the standardized transaction event record, the message encapsulation format of the target heterogeneous power trading system for the business event type is obtained from the communication protocol parameters, and the standardized transaction event record is converted into the target data message according to the message encapsulation format.

[0073] The message encapsulation format refers to the organization format specification of data packets used by the target heterogeneous power trading system during communication. The message encapsulation format specifies the order, length, and encoding method of each field in the message. For example, the unified clearing system of the power trading center requires target data packets to be organized in XML format, with the event identifier field placed in the tag.<event_id> Inside, the semantic checksum field is placed in the tag.<verify_code> Within the tags, key business fields are placed in their respective fields.

[0074] The current count value is read from the synchronization counter corresponding to the current interaction window, the current count value is bound to the target data packet as the incremental synchronization sequence number, and the synchronization counter is incremented.

[0075] The current count value refers to the number of messages sent as recorded by the synchronization counter within the current interaction window. For example, if the control system has sent 41 target data messages to the unified clearing system of the power trading center within the current interaction window, the current count value of the synchronization counter is 41. When sending a message from power generation company A, the current count value is read as a reference.

[0076] In the above optional methods, the current interaction window of the target platform is further obtained and the target data message is bound within the window period. The encapsulation format is obtained and converted according to the communication protocol parameters. The count value is read from the synchronization counter as the incremental synchronization sequence number and bound and the counter is incremented, thus realizing the adaptation of the message sending timing with the interaction rhythm of the target platform.

[0077] In one optional approach, the step of sending a target data packet carrying the incremental synchronization sequence number to the target heterogeneous power trading system, and updating the synchronization status of the standardized transaction event record between the source heterogeneous power trading system and the target heterogeneous power trading system based on the incremental synchronization sequence number and the target service event identifier, includes:

[0078] Obtain the confirmation message returned by the target heterogeneous power trading system, and parse the received sequence number from the confirmation message.

[0079] The "acknowledgment of receipt" message refers to the message returned by the target heterogeneous power trading system to the source end after successfully receiving the target data packet, confirming that it has been received. For example, after receiving the target data packet from power generation company A, the unified clearing system of the power trading center returns an acknowledgment of receipt message to the control system. This message includes a successful reception status code and the received incremental synchronization sequence number "42". The "received sequence number" refers to the incremental synchronization sequence number parsed from the acknowledgment of receipt message returned by the target heterogeneous power trading system, which the target heterogeneous power trading system confirms has been successfully received. For example, the control system parses the received sequence number "42" from the acknowledgment of receipt message returned by the unified clearing system, which is consistent with the incremental synchronization sequence number "42" sent locally.

[0080] The received sequence number is compared with the incremental synchronization sequence number sent locally. If they match, the synchronization status of the standardized transaction event record corresponding to the target business event identifier between the source power trading heterogeneous system and the target power trading heterogeneous system stored locally is marked as synchronization completed.

[0081] It should be noted that if the comparison is inconsistent, the control system will generate a retransmission request based on the incremental synchronization sequence number and the target service event identifier, and mark the synchronization status of the standardized transaction event record as pending retransmission. In the next interaction window, the sending of the target data packet and the synchronization status update will be re-executed.

[0082] In the above optional methods, the confirmation message returned by the target platform is further obtained and the received sequence number is parsed and compared with the incremental synchronization sequence number sent locally. When the comparison is consistent, the synchronization status of the corresponding standardized transaction event record is marked as synchronization completed, thus realizing the active confirmation of the receiving status and the traceability of the synchronization result.

[0083] In one optional approach, the historical message samples include IEC62325 standard message samples corresponding to power generation-side bidding events, custom JSON format message samples corresponding to user-side declaration events, and XML format message samples corresponding to trading center clearing events; the step of semantically annotating the historical message samples includes:

[0084] The same semantic tags are assigned to fields expressing the same business semantics in the IEC62325 standard message sample, the custom JSON format message sample, and the XML format message sample.

[0085] The IEC 62325 standard message sample refers to historical data messages in the historical message sample that conform to the International Electrotechnical Commission (IEC) 62325 standard format and are used for generation-side bidding events. For example, the generation-side bidding event messages stored in the historical bidding system of power generation company A adopt the IEC 62325 standard, with the message header containing the standard number and version identifier, and the business load organizing fields according to the message body format customized by IEC 62325. The custom JSON format message sample refers to historical data messages in the historical message sample that adopt the custom JSON format and are used for user-side reporting events. For example, the historical electricity demand reporting data messages stored in the user reporting system of user-side B adopt the custom JSON key-value pair format, with the message content being {“user_id”:“B”,“demand”:“200”,“unit”:“MW”,“period”:“202601021000-1100”}. XML format message samples refer to historical data messages in the Extensible Markup Language (XML) format used for clearing events in the power trading center; for example, the historical data messages of clearing results stored in the unified clearing system of the power trading center are in XML format, and the message content includes...<clearing_event> Root element <price>sub-elements and <volume>Child elements express business data through nested tags.

[0086] Semantic tags refer to the labels assigned to fields expressing the same business semantics when semantically annotating historical message samples. Semantic tags are used to unify the semantic expression of fields with the same business meaning in different message formats. For example, the semantic tag "ENTITY_ID" is assigned to the field representing the generator set identifier in the IEC 62325 standard message sample of power generation company A, the semantic tag "ENTITY_ID" is assigned to the field representing the user identifier in the custom JSON format message sample of user side B, and the semantic tag "ENTITY_ID" is assigned to the field representing the clearing subject identifier in the XML format message sample of the trading center.

[0087] Among the above optional methods, IEC62325 standard messages, custom JSON format messages, and XML format messages are further included in the historical message samples. Unified semantic tags are assigned to fields that express the same business semantics, which expands the data sources for semantic annotation and the compatibility of heterogeneous protocols.

[0088] In one optional approach, the semantic feature extraction layer in the unified event semantic model for power trading performs embedding encoding and attention-weighted fusion on the entity object attribute fields and the action event fields to generate the semantic feature vector, including:

[0089] Vector embedding is performed on the entity object attribute field and the action event field respectively to obtain entity embedding vector sequences and action embedding vector sequences. An interaction attention matrix between the entity embedding vector sequences and the action embedding vector sequences is calculated using a context-aware attention mechanism. A gating mechanism is then introduced to sparsify the interaction attention matrix. Based on the sparsified interaction attention matrix, the entity embedding vector sequences and the action embedding vector sequences are weighted and aggregated to obtain the semantic feature vector. The formula for calculating the weighted aggregation is:

[0090]

[0091] in, The first term extracted from the said service payload Embedding vectors of attribute fields of an entity object The first term extracted from the said service payload Embedsion vectors of each action event field Represents the element-wise product of vectors. This indicates calculating the product for each element. Hadamard exponentiation of powers. For the introduced nonlinear order factor, This indicates the entity field obtained based on the interaction attention matrix. With action field The original joint attention weights between them, This represents a gating function used to filter data that is less than a preset threshold. Set to zero. This indicates the total number of attribute fields of the extracted entity object. This indicates the total number of action event fields extracted. This represents the bias vector. This represents the activation function.

[0092] It should be noted that the above formula performs vector embedding on the entity object attribute fields and action event fields extracted from the business payload, respectively, to obtain entity embedding vector sequences and action embedding vector sequences. An interaction attention matrix between the two sets of embedding vector sequences is calculated using a context-aware attention mechanism. The interaction attention matrix reflects the association strength between entity objects and action events in each pair. A gating function is introduced to sparsify the interaction attention matrix. The gating function sets the portion of the original joint attention weights below a preset threshold to zero to filter out weakly associated noise information. Based on this, the element-wise product of the entity embedding vector and the action embedding vector is subjected to Hadamard exponentiation. By introducing a non-... Linear order factors enhance the expressive power of feature interactions. Finally, the nonlinear interaction vectors of all entity-action pairs are weighted and aggregated using sparsed joint attention weights. The final semantic feature vector is obtained by mapping transformation through bias vectors and activation functions. The above formula encodes the discrete entity attribute fields and action event fields in the power transaction data message into a unified low-dimensional dense semantic representation vector. Through attention interaction and gating sparsity mechanism, the key semantic combinations between entities and actions are automatically captured, and the interference of non-key information is suppressed. This enables the generated semantic feature vector to more accurately represent the business semantics of the original message, providing high-quality feature input for subsequent semantic matching and standardization processing.

[0093] In the above optional methods, the semantic feature vector generation process is further refined by embedding entity attributes and action event fields into vectors respectively, calculating the interaction attention matrix using a context-aware attention mechanism, introducing a gating mechanism for sparsification, and weighting and aggregating based on the sparsity matrix.

[0094] This invention provides an information docking system for heterogeneous power trading systems, which includes:

[0095] The module is used to build a unified event semantic model for power trading. The unified event semantic model for power trading includes the semantic feature vector definition, event association relationship and event semantic verification rules for various business events in power trading.

[0096] The generation module is used to extract the communication protocol features and service payload of the transaction data message when it receives the transaction data message from the source power trading heterogeneous system, and to call the unified event semantic model of power trading to generate the semantic feature vector of the transaction data message based on the communication protocol features and the service payload.

[0097] The combination module is used to match the semantic feature vector with the semantic feature vector definition in the unified event semantic model of power trading, determine the target business event identifier corresponding to the transaction data message, generate a semantic verification code according to the event semantic verification rules, and combine the target business event identifier and the semantic verification code with the key business fields corresponding to the transaction data message into a standardized transaction event record.

[0098] The running module is used to obtain the communication protocol parameters and real-time interactive window information of the target power trading heterogeneous system, convert the standardized transaction event record into a target data packet adapted to the target power trading heterogeneous system according to the communication protocol parameters, and bind an incremental synchronization sequence number to the target data packet according to the real-time interactive window information.

[0099] The update module is used to send the target data message carrying the incremental synchronization sequence number to the target power trading heterogeneous system, and update the synchronization status of the standardized transaction event records between the source power trading heterogeneous system and the target power trading heterogeneous system according to the incremental synchronization sequence number and the target business event identifier.

[0100] The technical solution of this embodiment constructs a unified event semantic model for power trading, extracts semantic feature vectors from heterogeneous source platform messages for matching, generates semantic check codes to form standardized event records, and converts messages according to the protocol parameters of the target heterogeneous platform. It also updates the synchronization status by binding incremental synchronization sequence numbers in a real-time interactive window. This solves the problems of semantic ambiguity and format conflicts caused by the lack of unified semantic standards among various business platforms, as well as the timeliness and interface compatibility issues of existing docking methods. It improves the accuracy and real-time performance of cross-platform power trading information flow and ensures the smooth operation of high-frequency bidding and clearing business in the spot market.

[0101] Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0102] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.< / volume> < / price>

Claims

1. A method for information docking between heterogeneous power trading systems, characterized in that, The method includes: A unified event semantic model for power trading is constructed, which includes the semantic feature vector definition, event correlation, and event semantic verification rules for various business events in power trading. Upon receiving a transaction data message from a heterogeneous power trading system, the communication protocol features and service payload of the transaction data message are extracted. Based on the communication protocol features and service payload, the unified event semantic model for power trading is invoked to generate a semantic feature vector of the transaction data message. The semantic feature vector is matched with the semantic feature vector definition in the unified event semantic model of power trading to determine the target business event identifier corresponding to the transaction data message, and a semantic verification code is generated according to the event semantic verification rules. The target business event identifier and the semantic verification code are combined with the key business fields corresponding to the transaction data message to form a standardized transaction event record. Obtain the communication protocol parameters and real-time interactive window information of the target power trading heterogeneous system; convert the standardized transaction event record into a target data packet adapted to the target power trading heterogeneous system according to the communication protocol parameters; and bind an incremental synchronization sequence number to the target data packet according to the real-time interactive window information. Send the target data message carrying the incremental synchronization sequence number to the target power trading heterogeneous system, and update the synchronization status of the standardized transaction event records between the source power trading heterogeneous system and the target power trading heterogeneous system according to the incremental synchronization sequence number and the target business event identifier.

2. The method for information docking of heterogeneous power trading systems according to claim 1, characterized in that, The construction of a unified event semantic model for power trading includes: Historical message samples of power trading business, including generation-side bidding events, user-side declaration events, transmission-side maintenance events, distribution-side dispatch events, and trading center clearing events, are collected. The historical message samples are semantically annotated to obtain a semantically annotated dataset. Based on the semantically annotated dataset, the semantic feature vector definition, event association relationship, and event semantic verification rules for each business event in the unified event semantic model of power trading are obtained through multi-dimensional semantic feature clustering and event relationship graph construction.

3. The method for information docking of heterogeneous power trading systems according to claim 1, characterized in that, The steps of extracting the communication protocol features and service payload of the transaction data message, and generating the semantic feature vector of the transaction data message by invoking the unified event semantic model for power trading based on the communication protocol features and service payload, include: Identify the communication protocol type used in the transaction data message, and call the corresponding protocol parser to parse the transaction data message according to the communication protocol type to obtain the service payload; Entity object attribute fields and action event fields are extracted from the business payload. The entity object attribute fields and action event fields are input into the unified event semantic model for power trading. The semantic feature extraction layer in the unified event semantic model for power trading performs embedding encoding and attention weighted fusion on the entity object attribute fields and action event fields to generate the semantic feature vector. The semantic feature vector contains encoded information of entity object attribute dimension and action event type dimension.

4. The method for information docking of heterogeneous power trading systems according to claim 3, characterized in that, The steps of matching the semantic feature vector with the semantic feature vector definition in the unified event semantic model for power transactions to determine the target business event identifier corresponding to the transaction data message, and generating a semantic check code according to the event semantic verification rules, include: Calculate the semantic similarity between the semantic feature vector and the semantic feature vector definitions in the unified event semantic model of power trading, and determine the business event identifier associated with the semantic feature vector definition corresponding to the maximum semantic similarity as the target business event identifier; Based on the target business event identifier, the corresponding verification parameter group is indexed from the event semantic verification rules, and a hash operation is performed on the verification parameter group and the target business event identifier to obtain the semantic verification code.

5. The method for information docking of heterogeneous power trading systems according to claim 4, characterized in that, The steps for calculating the semantic similarity between the semantic feature vector and the semantic feature vector definitions in the unified event semantic model for power trading include: Using the attention weighting parameters output by the semantic feature extraction layer, a nonlinear transformation is performed on the multidimensional weighted semantic distance between the semantic feature vector and the k-th semantic feature vector to obtain the semantic similarity. The calculation formula for the nonlinear transformation is as follows: in, This represents the semantic feature vector. Indicates the first A semantic feature vector is defined. and They represent and In the Dimensional components, This represents the total dimension of the semantic feature vector. The first value obtained from the attention weighting parameters Dimension importance coefficient Indicates the first Dimensional scaling factor This represents the attenuation adjustment factor related to the historical synchronization delay corresponding to the service payload. This represents a temperature coefficient that reflects the urgency of the events associated with the semantic feature vector definition. The balance coefficient represents the semantic orientation compensation term. Represents the linear rectified function. and They represent and The L2 norm.

6. The method for information docking of heterogeneous power trading systems according to claim 1, characterized in that, The steps of obtaining the communication protocol parameters and real-time interactive window information of the target power trading heterogeneous system, converting the standardized transaction event records into target data packets adapted to the target power trading heterogeneous system according to the communication protocol parameters, and binding incremental synchronization sequence numbers to the target data packets according to the real-time interactive window information include: Obtain the window opening time and window closing time of the current interaction window of the target power trading heterogeneous system, and bind the target data packet to the current interaction window between the window opening time and the window closing time; Based on the target power trading heterogeneous system and the business event type corresponding to the target business event identifier in the standardized transaction event record, the message encapsulation format of the target power trading heterogeneous system for the business event type is obtained from the communication protocol parameters, and the standardized transaction event record is converted into the target data message according to the message encapsulation format; The current count value is read from the synchronization counter corresponding to the current interaction window, the current count value is bound to the target data packet as the incremental synchronization sequence number, and the synchronization counter is incremented.

7. The method for information docking of heterogeneous power trading systems according to claim 6, characterized in that, The steps of sending a target data packet carrying the incremental synchronization sequence number to the target heterogeneous power trading system, and updating the synchronization status of the standardized transaction event record between the source heterogeneous power trading system and the target heterogeneous power trading system based on the incremental synchronization sequence number and the target service event identifier, include: Obtain the confirmation message returned by the target heterogeneous power trading system, and parse the received sequence number from the confirmation message; The received sequence number is compared with the incremental synchronization sequence number sent locally. If they match, the synchronization status of the standardized transaction event record corresponding to the target business event identifier between the source power trading heterogeneous system and the target power trading heterogeneous system stored locally is marked as synchronization completed.

8. The method for information docking of heterogeneous power trading systems according to claim 2, characterized in that, The historical message samples include IEC62325 standard message samples corresponding to power generation side bidding events, custom JSON format message samples corresponding to user side declaration events, and XML format message samples corresponding to trading center clearing events; The steps for semantically annotating the historical message samples include: The same semantic tags are assigned to fields expressing the same business semantics in the IEC62325 standard message sample, the custom JSON format message sample, and the XML format message sample.

9. The method for information docking of heterogeneous power trading systems according to claim 3, characterized in that, The semantic feature extraction layer in the unified event semantic model for power trading performs embedding encoding and attention-weighted fusion on the entity object attribute fields and the action event fields to generate the semantic feature vector, including: Vector embedding is performed on the entity object attribute field and the action event field respectively to obtain entity embedding vector sequences and action embedding vector sequences. An interaction attention matrix between the entity embedding vector sequences and the action embedding vector sequences is calculated using a context-aware attention mechanism. A gating mechanism is then introduced to sparsify the interaction attention matrix. Based on the sparsified interaction attention matrix, the entity embedding vector sequences and the action embedding vector sequences are weighted and aggregated to obtain the semantic feature vector. The formula for calculating the weighted aggregation is: in, The first term extracted from the said service payload Embedding vectors of attribute fields of an entity object The first term extracted from the said service payload Embedsion vectors of each action event field Represents the element-wise product of vectors. This indicates calculating the product for each element. Hadamard exponentiation of powers. For the introduced nonlinear order factor, This indicates the entity field obtained based on the interaction attention matrix. With action field The original joint attention weights between them, This represents a gating function used to filter data that is less than a preset threshold. Set to zero. This indicates the total number of attribute fields of the extracted entity object. This indicates the total number of action event fields extracted. This represents the bias vector. This represents the activation function.

10. An information docking system for heterogeneous power trading systems, characterized in that, The system includes: The module is used to build a unified event semantic model for power trading. The unified event semantic model for power trading includes the semantic feature vector definition, event association relationship and event semantic verification rules for various business events in power trading. The generation module is used to extract the communication protocol features and service payload of the transaction data message when it receives the transaction data message from the source power trading heterogeneous system, and to call the unified event semantic model of power trading to generate the semantic feature vector of the transaction data message based on the communication protocol features and the service payload. The combination module is used to match the semantic feature vector with the semantic feature vector definition in the unified event semantic model of power trading, determine the target business event identifier corresponding to the transaction data message, generate a semantic verification code according to the event semantic verification rules, and combine the target business event identifier and the semantic verification code with the key business fields corresponding to the transaction data message into a standardized transaction event record. The running module is used to obtain the communication protocol parameters and real-time interactive window information of the target power trading heterogeneous system, convert the standardized transaction event record into a target data packet adapted to the target power trading heterogeneous system according to the communication protocol parameters, and bind an incremental synchronization sequence number to the target data packet according to the real-time interactive window information. The update module is used to send the target data message carrying the incremental synchronization sequence number to the target power trading heterogeneous system, and update the synchronization status of the standardized transaction event records between the source power trading heterogeneous system and the target power trading heterogeneous system according to the incremental synchronization sequence number and the target business event identifier.

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