A multi-protocol intelligent data acquisition method, system, device and medium for an energy storage power station

Abstract

This application belongs to the field of data acquisition technology, and particularly relates to a multi-protocol intelligent data acquisition method, system, device, and medium for energy storage power stations. The method includes: acquiring actual message data from energy storage power station devices based on an initial acquisition frequency; identifying the protocol type of the actual message data based on a pre-stored protocol feature library and a hybrid matching algorithm; adjusting the initial acquisition frequency in real time according to the protocol type and the real-time data volume; generating parsing instructions based on the protocol type and its corresponding parsing strategy template; parsing the message data according to the parsing instructions to extract valid data; and preprocessing the parsed valid data to obtain target data. This application improves the efficiency of data acquisition in energy storage power stations.

Description

Technical Field

[0001] This application belongs to the field of data acquisition technology, and in particular relates to a multi-protocol intelligent data acquisition method, system, equipment and medium for energy storage power stations. Background Technology

[0002] During the data acquisition process of energy storage power stations, due to the large number of equipment manufacturers and the different industrial communication protocols used, traditional parsing equipment such as Modbus, CAN, IIC, and IEC 61850 requires manual configuration of the protocol type. This not only increases the complexity of operation, but also has extremely poor compatibility and is difficult to cope with scenarios where multiple protocols coexist.

[0003] Meanwhile, the protocol scalability of existing parsing devices is severely insufficient. When new protocols are required, the devices need to be reprogrammed, and dynamic loading is not possible, which greatly limits the use of the devices in scenarios with frequent protocol updates. In addition, data parsing efficiency is low, and in high-concurrency data transmission scenarios, packet loss or delays are prone to occur, affecting the real-time performance and integrity of the data.

[0004] Existing solutions also have obvious shortcomings: fixed protocol parsing libraries are difficult to cover complex industrial scenarios; they lack intelligent recognition mechanisms and rely too much on manually predefined parameters; and their data verification and fault tolerance capabilities are insufficient, resulting in data responsibility errors after parsing, which affects the accuracy of subsequent analysis. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a multi-protocol intelligent data acquisition method, system, equipment, and medium for energy storage power stations. By constructing a complete closed-loop process from data acquisition, intelligent protocol identification, dynamic adjustment of acquisition frequency to data parsing and preprocessing, the automation and intelligence of multi-protocol data acquisition for energy storage power stations are realized, thereby improving data acquisition efficiency.

[0006] Firstly, this application provides a multi-protocol intelligent data acquisition method for energy storage power stations, the method comprising, Collect actual message data from energy storage power station equipment based on the initial acquisition frequency; The protocol type of the actual message data is identified based on a pre-stored protocol feature library and a hybrid matching algorithm. The initial acquisition frequency is adjusted in real time according to the protocol type and the amount of real-time data. A parsing instruction is generated based on the protocol type and its corresponding parsing strategy template. The message data is then parsed according to the parsing instruction to extract valid data. The parsed valid data is preprocessed to obtain the target data.

[0007] Furthermore, Identifying the protocol type of the actual message data based on a pre-stored protocol feature library and a hybrid matching algorithm, specifically including: The first matching result is obtained by matching the first letter of the actual message data with the first letter of the preset message corresponding to each protocol in the protocol feature library. Identify the actual message length and actual check bits corresponding to the actual message data, and calculate the actual matching degree between the actual message length and the preset message length corresponding to each protocol in the protocol feature library, and between the actual check bits and the preset check bits corresponding to each protocol in the protocol feature library through a decision tree model; The actual matching degree is compared with a preset confidence threshold, and the protocol type is determined based on the comparison result.

[0008] Furthermore, The initial acquisition frequency is adjusted in real time according to the protocol type and the amount of real-time data, specifically including: The protocol type is matched with a preset acquisition mode database, and the acquisition mode and its corresponding matching acquisition frequency are determined based on the matching results. The system acquires real-time CPU load and real-time data inflow rate, compares the real-time system CPU load with the preset system CPU load and the real-time data inflow rate with the preset data inflow rate, obtains the comparison results, and adjusts the matching acquisition frequency based on the comparison results.

[0009] Furthermore, Based on the protocol type and its corresponding parsing strategy template, a parsing instruction is generated, specifically including: The protocol type is matched with a preset protocol policy mapping table, and the parsing policy template is called based on the matching result; Based on the parsing strategy template, a corresponding parsing instruction is generated, which includes data extraction rules, format conversion rules, and verification rules.

[0010] Furthermore, The message data is parsed according to the parsing instructions to extract valid data, specifically including: Extract the original data segments from the message data according to the data extraction rules specified by the parsing instruction; The original data segment is parsed according to the format conversion rules in the parsing instruction to obtain parsed data; According to the verification rules specified by the parsing instruction, the parsed data is verified, and valid data is obtained based on the verification results.

[0011] Furthermore, The parsed valid data is preprocessed to obtain the target data, specifically including: The effective data is scanned point by point based on a preset data threshold range. Values ​​exceeding the data threshold range are identified and marked as abnormal data points. The marked abnormal data points are removed from the effective data sequence to form preliminary cleaned data. Logical consistency verification is performed on the preliminary cleaned data based on the data association rules stored in the protocol feature library, and the data is repaired based on the verification results to obtain the target data.

[0012] Furthermore, Based on the verification results, the data is repaired to obtain the target data, specifically including: When a logical contradiction is detected between the associated parameters in the preliminary cleaning data, the baseline parameter and the parameter to be corrected are determined according to the preset parameter priority mapping table. The benchmark parameters are calibrated using a weighted average algorithm based on multi-source data fusion. Identify the data state of the parameter to be corrected; when the parameter to be corrected is continuous data, use a time-series-based Kalman filter algorithm for correction. When the parameter to be corrected is discrete state data, a moving average algorithm based on historical data is used for correction. Verify the corrected baseline parameters and the parameters to be corrected, and obtain the target data based on the verification results.

[0013] Secondly, based on the same inventive concept, this application also provides a multi-protocol intelligent data acquisition system for energy storage power stations, the system comprising: The data acquisition module is used to collect actual message data from energy storage power station equipment based on the initial acquisition frequency; The type identification module is used to identify the protocol type of the actual message data based on a pre-stored protocol feature library and a hybrid matching algorithm. An adjustment module is used to adjust the initial acquisition frequency in real time according to the protocol type and the amount of real-time data; The extraction module is used to generate parsing instructions based on the protocol type and its corresponding parsing strategy template, parse the message data according to the parsing instructions, and extract valid data. The target acquisition module is used to preprocess the parsed valid data to obtain the target data.

[0014] Thirdly, this application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of any of the multi-protocol intelligent data acquisition methods for energy storage power stations as described above.

[0015] Fourthly, this application also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described multi-protocol intelligent data acquisition methods for energy storage power stations.

[0016] Compared with the prior art, this application has the following advantages: 1. This application realizes the automation and intelligence of multi-protocol data acquisition in energy storage power stations by constructing a complete closed-loop process from data acquisition, intelligent protocol identification, dynamic adjustment of acquisition frequency to data parsing and preprocessing, which significantly reduces the cost of manual configuration and maintenance.

[0017] 2. This application identifies protocol types by using a hybrid matching algorithm (combining head matching with decision tree model matching degree calculation) and dynamically adjusts the acquisition frequency based on real-time system load and data inflow rate, thereby improving the system's adaptability to different device protocols and complex operating environments.

[0018] 3. This application generates parsing instructions containing extraction, transformation, and verification rules by calling the parsing strategy template according to the protocol type, and then performs in-depth preprocessing including anomaly removal and logical verification. In particular, it uses algorithms such as Kalman filtering and moving average to repair the data, which greatly improves the accuracy and reliability of the collected data.

[0019] 4. This application enhances the overall stability and robustness of the energy storage power station data acquisition system by using a dynamic frequency adjustment mechanism based on real-time data volume and system load, as well as by actively identifying and repairing abnormal data and logical contradictions during the data preprocessing stage.

[0020] 5. This application organically combines protocol recognition, adaptive acquisition, strategic parsing, and intelligent cleaning and repair to form an efficient and reliable data acquisition and processing solution, providing a high-quality data foundation for the monitoring, analysis, and optimization of upper-level energy storage power stations, and improving the overall operational efficiency of the power stations.

[0021] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating a multi-protocol intelligent data acquisition method for an energy storage power station according to an embodiment of this application is shown. Figure 2 A schematic diagram of an electronic device structure according to an embodiment of this application is shown. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Figure 1 A flowchart illustrating a multi-protocol intelligent data acquisition method for an energy storage power station according to an embodiment of this application is shown, as follows: Figure 1 As shown in the figure, an embodiment of this application provides a multi-protocol intelligent data acquisition method for an energy storage power station, comprising: S1, collects actual message data from energy storage power station equipment based on the initial acquisition frequency; In this embodiment, the initial acquisition frequency is preset based on the operating characteristics of the energy storage power station equipment and historical data flow patterns. This ensures basic data acquisition needs are met while avoiding excessive resource consumption in the initial stage. For example, for key parameters such as voltage and current of the battery management system (BMS), the initial acquisition frequency is set to 100ms / time; for auxiliary parameters such as ambient temperature and humidity, it is set to 1s / time. The acquisition process is implemented through a multi-threaded parallel mechanism to ensure independent acquisition of data from different protocol devices, avoiding the accumulation of delays caused by serial acquisition.

[0026] S2, Identify the protocol type of the actual message data based on the pre-stored protocol feature library and hybrid matching algorithm; In this embodiment of the application, step S2 specifically includes: S21, based on the first letter of the actual message data corresponding to the actual message data, match it with the first letter of the preset message corresponding to each protocol in the protocol feature library to obtain the first matching result; S22, identify the actual message length and actual check bit corresponding to the actual message data, and calculate the actual matching degree between the actual message length and the preset message length corresponding to each protocol in the protocol feature library, and between the actual check bit and the preset check bit corresponding to each protocol in the protocol feature library through the decision tree model; S23, compare the actual matching degree with the preset confidence threshold, and determine the protocol type based on the comparison result.

[0027] In this embodiment, the protocol feature library contains metadata such as message header identifiers, frame structure features, and verification rules for various protocols, such as the 0x01 function code of the Modbus protocol, the 11-bit identifier of the CAN protocol, and the 68H start character of the DL / T645 protocol. The hybrid matching algorithm adopts a two-level matching mechanism of "feature word priority + verification rule verification": first, the protocol category is quickly located by the message header or identifier, and then the specific protocol version is verified by rules such as CRC check and frame length check. For example, for a message containing 0x55, it is first determined whether it conforms to the ASDU header feature of IEC 61850, and then its APDU length is verified to conform to the standard; if the match fails, it is then verified whether it is an extended frame format of the DL / T645 protocol.

[0028] In this embodiment of the application, the first letter of the actual message is the first byte of the actual message data (i.e., the "first byte"). This "first byte of the actual message" is quickly compared with the "preset first letter of the message" of each protocol in the "protocol feature library" (such as the function code of Modbus, 0xAA of a custom protocol, etc.). If the first byte matches the feature of a certain protocol (for example, the first byte is in the range of 0x01-0x06, which conforms to the Modbus RTU function code feature), the identification range is quickly narrowed down to that protocol or a few candidate protocols.

[0029] In this embodiment of the application, the actual check bit is the part of the message used for verification (such as the check value of CRC16 or CRC32).

[0030] In this embodiment of the application, the preset message length and preset check bit are preset according to the specifications of each protocol and stored in the protocol feature library.

[0031] In this embodiment of the application, the preset confidence threshold is 90%.

[0032] S3, adjust the initial acquisition frequency in real time according to the protocol type and the amount of real-time data; In this embodiment of the application, step S3 specifically includes: S31, Match the protocol type with the preset acquisition mode database, and determine the acquisition mode and its corresponding matching acquisition frequency based on the matching result; S32, acquire the real-time system CPU load and the real-time data inflow rate, compare the real-time system CPU load with the preset system CPU load and the real-time data inflow rate with the preset data inflow rate, obtain the comparison results, and adjust the matching acquisition frequency based on the comparison results.

[0033] In this application embodiment, an example of determining the acquisition mode and its corresponding matching acquisition frequency based on the matching result is as follows: When the protocol type is identified as CAN protocol, it automatically switches to high frequency acquisition mode and adjusts the acquisition interval to the range of 10-100 milliseconds to meet its communication characteristics of small data volume and high real-time requirements. When the protocol type is identified as Modbus RTU / TCP, a periodic polling acquisition mode is adopted, with the acquisition interval set to the range of 100 milliseconds to 2 seconds, and the polling order is dynamically adjusted according to the function code priority. When the protocol type is identified as Manufacturing Message Specification (MMS) protocol such as IEC 61850, the event-driven on-demand acquisition mode is enabled, and data acquisition is initiated only when the data changes, the quality is not updated, or specific triggering conditions are met.

[0034] In this embodiment, the preset system CPU load threshold is 40% to 70%; The preset data inflow rate is set according to the system's processing capacity; in this embodiment, it can be set to 80Mbps.

[0035] In this embodiment, when the real-time system CPU load exceeds the preset system CPU load and the real-time data inflow rate exceeds the preset data inflow rate, a load reduction strategy is implemented to reduce the sampling frequency of non-critical protocols: for example, the sampling interval for the Modbus protocol is extended from 1 second to 3 seconds. Background tasks are paused: non-real-time tasks such as historical data backup and deep log analysis are paused. Slowly changing parameters are downsampled: for slowly changing data such as temperature, the sampling interval is adjusted from 1 second to 5 seconds.

[0036] When the real-time system CPU load is less than the preset system CPU load and the real-time data inflow rate is less than or equal to the preset data inflow rate, the previously reduced frequency protocol will be restored to its reference acquisition frequency.

[0037] S4, Generate a parsing instruction according to the protocol type and its corresponding parsing strategy template, and parse the message data according to the parsing instruction to extract valid data; In this embodiment of the application, step S4 specifically includes: S41, match the protocol type with the preset protocol policy mapping table, and call the parsing policy template based on the matching result; S42, Generate corresponding parsing instructions based on the parsing strategy template. The parsing instructions include data extraction rules, format conversion rules, and verification rules.

[0038] In this embodiment of the application, when the protocol type is CAN protocol, a first parsing strategy template based on identifier filtering is invoked to generate a first parsing instruction for filtering specific CAN identifiers and extracting the corresponding data field. The first parsing instruction includes: Data extraction rules: Extract matching data frames based on the CAN identifier filter table, and locate the start position and length of the data field; Format conversion rules: Convert the raw bytes of the data field into engineering physical values ​​according to big-endian / little-endian format; Verification rules: Verify the integrity of the CAN frame format and the CRC check value of the data field; When the protocol type is DL / T645, the second parsing strategy template based on the frame reassembly mechanism is invoked to generate a second parsing instruction for splicing multi-frame messages and verifying data integrity. The second parsing instruction includes: Data extraction rules: Identify the start and end symbols of the frame, extract the data fields, and reassemble them according to the frame sequence number; Format conversion rules: Convert the recombined BCD code data into decimal values, and process data identifiers to distinguish different measurements; Verification rules: Verify the checksum of each frame of data, and check the continuity of frame sequence numbers and data integrity; When the protocol type is a nested protocol, a third parsing strategy template based on a cascading parsing mechanism is invoked to generate a third parsing instruction for first parsing the outer protocol frame header and then extracting and parsing the inner protocol data. The third parsing instruction includes: Data extraction rules: Parse the outer Ethernet / TCP header and extract the inner protocol payload data; Format conversion rules: Convert the inner protocol data from network byte order to host byte order; Verification rules: Verify the outer protocol frame check sequence and the inner protocol dedicated check code respectively.

[0039] In this embodiment of the application, step S4 further includes: S43, extract the original data segment from the message data according to the data extraction rules specified by the parsing instruction; S44, according to the format conversion rules in the parsing instruction, the original data segment is parsed to obtain parsed data; S45, the parsed data is verified according to the verification rules specified by the parsing instruction, and valid data is obtained based on the verification results.

[0040] In this embodiment, a continuous segment of raw bytes is extracted from the original message as the raw data segment based on the starting position (e.g., byte offset) and length in the data extraction rules. For example, the data extraction rule for parsing instructions corresponding to the Modbus protocol might be "starting from the 6th byte of the message, extract 4 consecutive bytes as a floating-point temperature value". The data extraction rule for parsing instructions corresponding to the CAN protocol might be "only process the message with ID 0x100 and extract all 8 bytes of its data field".

[0041] In this embodiment, the format conversion rules include byte order conversion and data type parsing. Byte order conversion specifies the storage order of data in memory, such as "interpreting 4 bytes in big-endian" or "converting 2 bytes in little-endian". Data type parsing defines how to interpret bytes into a specific data type, such as "converting 2 bytes into a 16-bit signed integer", "converting 4 bytes into a single-precision floating-point number according to the IEEE 754 standard", or "parsing 2 bytes into BCD code and converting it into a decimal value".

[0042] In this embodiment, the verification rules include computational verification and logical verification. Computational verification uses an algorithm specified in the rules (such as CRC16, CRC32, or summation) to recalculate the message's checksum and compares it with the checksum embedded in the message. Logical verification checks whether the data value is within a reasonable physical range (e.g., voltage values ​​should not be negative), whether the data length meets expectations, and whether there are logical contradictions between multiple related data. Once the message data passes verification, the data is marked as valid.

[0043] S5 preprocesses the parsed valid data to obtain the target data.

[0044] In this embodiment of the application, step S5 specifically includes: S51, based on a preset data threshold range, the valid data is scanned point by point, values ​​exceeding the data threshold range are identified and marked as abnormal data points, and the marked abnormal data points are removed from the valid data sequence to form preliminary cleaned data; S52, perform logical consistency verification on the preliminary cleaned data based on the data association rules stored in the protocol feature library, and repair the data based on the verification results to obtain the target data.

[0045] In this embodiment, the preset data threshold range is set according to the normal operating parameter range of the energy storage power station equipment. For example, for battery voltage, its normal operating range may be between 2.5V and 3.65V. If the collected voltage data exceeds this range, it is marked as an abnormal data point. During the point-by-point scanning process, the system compares each data point with the preset threshold in real time. Once data exceeding the range is found, it is immediately marked and removed to ensure the quality of the initial data cleaning.

[0046] In this embodiment of the application, step S52 specifically includes: S521, when a logical contradiction is detected between the associated parameters in the preliminary cleaning data, the baseline parameter and the parameter to be corrected are determined according to the preset parameter priority mapping table; S522, The benchmark parameters are calibrated using a weighted average algorithm based on multi-source data fusion; S523, Identify the data state of the parameter to be corrected; when the parameter to be corrected is continuous data, use a time-series-based Kalman filter algorithm for correction. When the parameter to be corrected is discrete state data, a moving average algorithm based on historical data is used for correction. S524, verify the repaired baseline parameters and the parameters to be corrected, and obtain the target data based on the verification results.

[0047] In this embodiment of the application, the parameter priority mapping table assigns higher priority to the core parameters of the energy storage system and lower priority to the auxiliary monitoring parameters. When the ratio of DC side voltage to AC side voltage exceeds the reasonable range, the DC side voltage is set as the reference parameter and the AC side voltage is set as the parameter to be corrected. When there is a logical conflict between the battery charging / discharging power and the charging / discharging state, the charging / discharging state reported by the battery management system is set as the baseline parameter, and the power measurement value is set as the parameter to be corrected.

[0048] In this embodiment of the application, the comprehensive verification of the repaired parameters includes: Re-execute the association rule validation to ensure that the logical contradictions have been eliminated; Check whether the parameter values ​​after repair are within a reasonable physical range; Verify whether the mathematical relationships and physical constraints between the parameters are satisfied; The parameters that pass the verification are marked as the final target data.

[0049] In this embodiment of the application, the method further includes a protocol dynamic extension step: It receives user-submitted protocol feature definitions based on configuration files through a standardized protocol extension interface; The configuration file is automatically parsed to extract the message structure features, verification rules, and data parsing rules of the new protocol. Generate the corresponding protocol feature vectors and dynamically add them to the protocol feature library; Based on the updated protocol feature library, the decision tree model in the hybrid matching algorithm is reconstructed in real time. This enables the system to recognize and parse new protocols without requiring a reboot.

[0050] Based on the above method, this application also provides a multi-protocol intelligent data acquisition system for energy storage power stations corresponding to the above method, the system comprising: The data acquisition module is used to collect actual message data from energy storage power station equipment based on the initial acquisition frequency; The type identification module is used to identify the protocol type of the actual message data based on a pre-stored protocol feature library and a hybrid matching algorithm. An adjustment module is used to adjust the initial acquisition frequency in real time according to the protocol type and the amount of real-time data; The extraction module is used to generate parsing instructions based on the protocol type and its corresponding parsing strategy template, parse the message data according to the parsing instructions, and extract valid data. The target acquisition module is used to preprocess the parsed valid data to obtain the target data.

[0051] Based on the same inventive concept, this application also provides an electronic device. For example... Figure 2 As shown, the electronic device in this application embodiment includes at least one processor and at least one memory electrically connected to each other. The memory is electrically connected to the processor, wherein the memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to execute the multi-protocol intelligent data acquisition method for energy storage power stations as described above.

[0052] It should be noted that the electrical connections between the above-mentioned units do not necessarily represent the connections between lines. Indirect connections are applicable to the embodiments of this application as long as they achieve the purpose of this application.

[0053] Based on the same inventive concept, this application also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the above method.

[0054] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-protocol intelligent data acquisition method for an energy storage power station, characterized in that, The method includes, Collect actual message data from energy storage power station equipment based on the initial acquisition frequency; The protocol type of the actual message data is identified based on a pre-stored protocol feature library and a hybrid matching algorithm. The initial acquisition frequency is adjusted in real time according to the protocol type and the amount of real-time data. A parsing instruction is generated based on the protocol type and its corresponding parsing strategy template. The message data is then parsed according to the parsing instruction to extract valid data. The parsed valid data is preprocessed to obtain the target data.

2. The method according to claim 1, characterized in that, Identifying the protocol type of the actual message data based on a pre-stored protocol feature library and a hybrid matching algorithm, specifically including: The first matching result is obtained by matching the first letter of the actual message data with the first letter of the preset message corresponding to each protocol in the protocol feature library. Identify the actual message length and actual check bits corresponding to the actual message data, and calculate the actual matching degree between the actual message length and the preset message length corresponding to each protocol in the protocol feature library, and between the actual check bits and the preset check bits corresponding to each protocol in the protocol feature library through a decision tree model; The actual matching degree is compared with a preset confidence threshold, and the protocol type is determined based on the comparison result.

3. The method according to claim 1, characterized in that, The initial acquisition frequency is adjusted in real time according to the protocol type and the amount of real-time data, specifically including: The protocol type is matched with a preset acquisition mode database, and the acquisition mode and its corresponding matching acquisition frequency are determined based on the matching results. The system acquires real-time CPU load and real-time data inflow rate, compares the real-time system CPU load with the preset system CPU load and the real-time data inflow rate with the preset data inflow rate, obtains the comparison results, and adjusts the matching acquisition frequency based on the comparison results.

4. The method according to claim 1, characterized in that, Based on the protocol type and its corresponding parsing strategy template, a parsing instruction is generated, specifically including: The protocol type is matched with a preset protocol policy mapping table, and the parsing policy template is called based on the matching result; Based on the parsing strategy template, a corresponding parsing instruction is generated, which includes data extraction rules, format conversion rules, and verification rules.

5. The method according to claim 4, characterized in that, The message data is parsed according to the parsing instructions to extract valid data, specifically including: Extract the original data segments from the message data according to the data extraction rules specified by the parsing instruction; The original data segment is parsed according to the format conversion rules in the parsing instruction to obtain parsed data; According to the verification rules specified by the parsing instruction, the parsed data is verified, and valid data is obtained based on the verification results.

6. The method according to claim 1, characterized in that, The parsed valid data is preprocessed to obtain the target data, specifically including: The effective data is scanned point by point based on a preset data threshold range. Values ​​exceeding the data threshold range are identified and marked as abnormal data points. The marked abnormal data points are removed from the effective data sequence to form preliminary cleaned data. Logical consistency verification is performed on the preliminary cleaned data based on the data association rules stored in the protocol feature library, and the data is repaired based on the verification results to obtain the target data.

7. The method according to claim 6, characterized in that, Based on the verification results, the data is repaired to obtain the target data, specifically including: When a logical contradiction is detected between the associated parameters in the preliminary cleaning data, the baseline parameter and the parameter to be corrected are determined according to the preset parameter priority mapping table. The benchmark parameters are calibrated using a weighted average algorithm based on multi-source data fusion. Identify the data state of the parameter to be corrected; when the parameter to be corrected is continuous data, use a time-series-based Kalman filter algorithm for correction. When the parameter to be corrected is discrete state data, a moving average algorithm based on historical data is used for correction. Verify the corrected baseline parameters and the parameters to be corrected, and obtain the target data based on the verification results.

8. A multi-protocol intelligent data acquisition system for an energy storage power station, characterized in that, The system includes: The data acquisition module is used to collect actual message data from energy storage power station equipment based on the initial acquisition frequency; The type identification module is used to identify the protocol type of the actual message data based on a pre-stored protocol feature library and a hybrid matching algorithm. An adjustment module is used to adjust the initial acquisition frequency in real time according to the protocol type and the amount of real-time data. The extraction module is used to generate parsing instructions based on the protocol type and its corresponding parsing strategy template, parse the message data according to the parsing instructions, and extract valid data. The target acquisition module is used to preprocess the parsed valid data to obtain the target data.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in a memory, it implements the steps of the multi-protocol intelligent data acquisition method for energy storage power stations as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the multi-protocol intelligent data acquisition method for energy storage power stations according to any one of claims 1-7.

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