A simulation optimization method and system based on TCP protocol hybrid CAN communication parameters

Abstract

The application relates to the technical field of communication parameter simulation optimization, and provides a TCP protocol mixed CAN communication parameter simulation optimization method and system, which comprises the following steps: obtaining service data before and after protocol conversion; establishing a data semantic mapping rule; checking the data semantic consistency of the service data based on the data semantic mapping rule; when the field relationship checking result, the range and unit checking result or the logic integrity checking result of the data semantic consistency indicates inconsistency, outputting an abnormal report, and performing simulation optimization based on the corresponding checking result and the abnormal report. The application has the effects of improving the communication reliability, data processing capacity and overall stability of a chemical composition and capacity power supply system.

Description

Technical Field

[0001] This invention relates to the technical field of communication parameter simulation and optimization, specifically to a method and system for simulating and optimizing hybrid CAN communication parameters based on the TCP protocol. Background Technology

[0002] In the development and deployment of modern industrial control systems, especially in fields like modular power supply systems where high reliability of communication and data processing capabilities are crucial, ensuring the stability of equipment operation is paramount. These systems typically employ a multi-tiered architecture, such as a three-tier structure consisting of a host computer, a mid-level computer, and a slave computer, using a hybrid communication method combining TCP and CAN buses for data interaction. To simulate actual operating conditions as realistically as possible in a laboratory environment, engineers often use external data sources, such as MongoDB databases or Excel spreadsheets, to inject simulation parameters. These parameters are then sent to the embedded slave computer unit via TCP protocol, and then transmitted to the mid-level and host computers via CAN communication for logic processing and status monitoring. This complex communication mechanism places high demands on joint system debugging, protocol compatibility verification, and overall performance evaluation.

[0003] However, despite these meticulous simulations and tests, significant technical bottlenecks remain in actual system testing and field deployment. For example, due to the complexity of system layers and the diversity of communication protocols, unverified code logic or communication parameter configurations can easily lead to subtle faults, especially in production environments. If a system outage occurs due to an unexpected program error, the window for repair is extremely limited. Traditional testing methods often struggle to simulate real-world scenarios where multiple nodes are simultaneously under pressure and experiencing abnormal data flows, and they lack the ability to deeply simulate and optimize the TCP packet assembly and CAN frame parsing processes. This results in potential system risks not being effectively identified and eliminated before equipment deployment, severely limiting improvements in system reliability and maintenance efficiency.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] This application discloses a simulation optimization method and system for hybrid CAN communication parameters based on the TCP protocol. It aims to solve the technical problem that traditional simulation testing methods are difficult to fully verify the compatibility of multi-level communication protocols, data semantic consistency, and system stability under abnormal data flow in complex industrial control systems, especially in batch power supply systems. This results in the inability to effectively identify and eliminate potential system risks, thereby limiting the improvement of system reliability and maintenance efficiency.

[0006] The technical solution of this application is as follows:

[0007] Firstly, this application discloses a simulation optimization method for hybrid CAN communication parameters based on the TCP protocol, including:

[0008] Activate the monitoring unit preset at the data protocol conversion point to obtain business data before and after the protocol conversion;

[0009] Establish data semantic mapping rules;

[0010] Based on data semantic mapping rules, the data semantic consistency of business data is verified in real time. The steps for real-time verification of the data semantic consistency of business data include:

[0011] Based on the data semantic mapping rules, the data field mapping relationship between the business data before and after the protocol conversion is verified to obtain the field relationship verification result.

[0012] Based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the business data after protocol conversion, and obtain the range and unit verification results;

[0013] Based on the data semantic mapping rules, the logical meaning integrity of the business data after protocol conversion is checked to obtain the logical integrity check result;

[0014] When the field relationship verification results, range verification results, or logical integrity verification results are inconsistent, an anomaly report is output so that simulation optimization can be performed based on the corresponding verification results and anomaly reports.

[0015] This technical solution enables comprehensive semantic consistency verification of business data during the TCP and CAN protocol conversion process, effectively identifying issues related to data field mapping, numerical range and units, and the integrity of logical meaning. This allows for the discovery and resolution of potential communication parameter anomalies during the simulation phase, significantly improving system reliability.

[0016] Furthermore, according to the above method, the steps for verifying the logical integrity of the business data after protocol conversion and obtaining the logical integrity verification result include:

[0017] Define the operational procedures for the DCDC module;

[0018] Maintain the current operating status of the DC-DC module in real time and perform state transitions based on received CAN commands;

[0019] According to the operation procedure specifications and the current operation status, perform instruction timing legality verification, check whether the currently received CAN instruction conforms to the next instruction corresponding to the DCDC module in the current operation status, and obtain the timing legality verification result;

[0020] According to the operation procedure specifications and the current operation status, perform state transition consistency verification to verify whether the received CAN command causes the DC-DC module to normally transition from the current operation status to the next expected status, and obtain the state transition verification result.

[0021] When the sequence validity verification result indicates that the CAN command arrival order does not conform to the order defined in the operation procedure specification, or when the state transition verification result indicates that the state transition caused by the CAN command does not conform to the operation procedure specification, an exception report is output.

[0022] This technical solution enables in-depth logical semantic verification of the CAN commands of the DC-DC module, ensuring the timing validity of the commands and the correctness of state transitions, effectively avoiding system anomalies caused by command logic errors, and further enhancing the accuracy of simulation and the stability of the system.

[0023] In some preferred embodiments, according to the above method, the steps for verifying the consistency of the numerical range and unit of the business data after protocol conversion, and obtaining the range and unit verification result, include:

[0024] Real-time acquisition of operating parameters of the DC-DC module;

[0025] Determine the operating range of the DC-DC module under the current operating conditions based on the operating parameters;

[0026] The converted business data is compared with the operational scope under the current working conditions to obtain the operational comparison results;

[0027] Based on the operation comparison results, an anomaly report is output.

[0028] This technical solution enables dynamic adjustment of numerical ranges and unit verification standards based on the actual operating conditions of the DC-DC module, making data consistency verification more closely resemble actual operating scenarios and improving the accuracy and sensitivity of anomaly detection.

[0029] Based on the above, this application further proposes that the steps for real-time verification of the semantic consistency of business data include:

[0030] Define the operation procedure specification for the DCDC module; this operation procedure specification stipulates the logical order and timing dependencies between the operation instructions of the DCDC module.

[0031] Maintain the current operating status of the DC-DC module in real time and perform state transitions based on received CAN commands;

[0032] Based on the data semantic mapping rules, the data field mapping relationship between the business data before and after the protocol conversion is verified to obtain the field relationship verification result.

[0033] Based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the business data after protocol conversion, and obtain the range and unit verification results;

[0034] Once the field relationship verification result and the range and unit verification result both indicate that the verification has passed, according to the operation process specifications and the current operation status, the instruction timing legality verification is performed. This checks whether the currently received CAN instruction conforms to the next instruction corresponding to the DCDC module in the current operation status, and obtains the timing legality verification result.

[0035] According to the operation procedure specifications and the current operation status, perform state transition consistency verification to verify whether the received CAN command causes the DC-DC module to normally transition from the current operation status to the next expected status, and obtain the state transition verification result.

[0036] An exception report is output when the instruction timing validity verification result indicates that the arrival order of CAN instructions does not conform to the order defined in the operation procedure specification, or when the state transition verification result indicates that the state transition caused by the CAN instructions does not conform to the operation procedure specification.

[0037] This technical solution combines the verification of data fields, numerical ranges, and units with the logical verification of instruction timing and state transitions, forming a more comprehensive and rigorous data semantic consistency verification process, ensuring the integrity and correctness of data during protocol conversion and logical processing.

[0038] Furthermore, based on the above method, the steps for defining the operational procedure specification of the DCDC module include:

[0039] Real-time acquisition of operating parameters of the DC-DC module;

[0040] Based on the operating parameters, adjust the logical order and timing dependencies of CAN instructions in the DCDC module's operation procedure specification;

[0041] During real-time operation process semantic verification, the legality verification of instruction timing is performed according to the adjusted operation process specification.

[0042] During real-time operation process semantic verification, state transition consistency verification is performed according to the adjusted operation process specification.

[0043] This technical solution enables dynamic adjustment of the operational procedures, allowing optimization based on the real-time operating conditions of the DC-DC module. This makes the simulation optimization method more adaptable and robust, enabling it to cope with complex and ever-changing real-world operating environments.

[0044] In one implementation, the steps for obtaining the operating condition parameters of the DC-DC module in real time according to the above method include:

[0045] Activate several pre-selected sensors to collect the operating parameters of the DC-DC module in real time and obtain raw data;

[0046] Perform multi-point sampling on the original data to obtain multi-point sampled data;

[0047] Perform a consistency comparison on the multi-point sampled data to obtain the multi-point sampled consistency comparison results;

[0048] When the multi-point sampling consistency comparison result indicates that there are transient fluctuations or anomalies in the multi-point sampling data, data smoothing processing is initiated to smooth the multi-point sampling data within a preset continuous time window.

[0049] The smoothed multi-point sampling data is compared with the preset physical boundary values ​​to obtain the boundary comparison results.

[0050] When the boundary comparison result indicates that the smoothed multi-point sampled data exceeds the physical boundary value, the smoothed multi-point sampled data is marked as erroneous data and corrected to obtain the corrected operating condition parameters.

[0051] This technical solution ensures the accuracy and reliability of the acquired operating parameters through a series of processes, including multi-point sampling, consistency comparison, data smoothing, and boundary value verification, providing high-quality input data for subsequent simulation optimization.

[0052] As an optional approach, the steps for performing a consistency comparison on multi-point sampled data and obtaining the multi-point sampled consistency comparison result according to the above method include:

[0053] Select the corresponding alignment rule set based on the type of DCDC module;

[0054] Based on the comparison rule set, a consistency comparison is performed on the multi-point sampled data to obtain the multi-point sampled consistency comparison result.

[0055] This technical solution enables the selection of customized comparison rules based on the specific type of DC-DC module, making consistency comparison more accurate and effective, and improving the ability to identify abnormal operating parameters of different types of DC-DC modules.

[0056] To enhance functionality, according to the above method, when the multi-point sampling consistency comparison result indicates that there are transient fluctuations or anomalies in the multi-point sampling data, data smoothing processing is initiated. The steps for smoothing the multi-point sampling data within a preset continuous time window include:

[0057] Noise type identification is performed on multi-point sampled data to determine whether transient fluctuations or anomalies belong to one or more of spike pulses, step changes, or periodic interference, and the noise type identification result is obtained.

[0058] Based on the noise type identification results, select the corresponding smoothing algorithm;

[0059] Based on the selected smoothing algorithm, the multi-point sampled data within the preset continuous time window is smoothed.

[0060] This technical solution allows for the selection of the most suitable smoothing algorithm based on the identified noise type, thereby more effectively removing transient fluctuations or anomalies in the data and improving the targeting and effectiveness of data smoothing.

[0061] To improve the solution, based on the above method, the steps for noise type identification of multi-point sampled data include:

[0062] Real-time acquisition of the type of DCDC module and the category of currently monitored operating parameters;

[0063] Select the corresponding noise feature identification rule set based on the type of DC-DC module and the category of operating parameters;

[0064] Based on the selected noise feature recognition rule set, feature extraction is performed on the multi-point sampled data to identify whether the transient fluctuations or anomalies in the multi-point sampled data conform to the feature patterns of spike pulses, step changes or periodic interference, and to obtain the feature extraction results and feature pattern recognition results.

[0065] Based on the feature extraction results and feature pattern recognition results, determine the type of transient fluctuations or abnormal noise.

[0066] This technical solution enables accurate identification of the type of transient fluctuations or anomalies through refined noise feature recognition, providing a reliable basis for selecting appropriate smoothing algorithms and further enhancing the intelligence level of data processing.

[0067] Secondly, this application also discloses a simulation optimization system for hybrid CAN communication parameters based on the TCP protocol, used to perform simulation optimization of hybrid CAN communication parameters based on the TCP protocol, including:

[0068] The business data acquisition module is used to activate the monitoring unit preset at the data protocol conversion point to acquire business data before and after the protocol conversion.

[0069] The mapping rule establishment module is used to establish data semantic mapping rules;

[0070] The data semantic verification module is used to verify the data semantic consistency of business data in real time based on data semantic mapping rules. The steps for real-time verification of the data semantic consistency of business data include:

[0071] Based on the data semantic mapping rules, the data field mapping relationship between the business data before and after the protocol conversion is verified to obtain the field relationship verification result.

[0072] Based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the business data after protocol conversion, and obtain the range and unit verification results;

[0073] Based on the data semantic mapping rules, the logical meaning integrity of the business data after protocol conversion is checked to obtain the logical integrity check result;

[0074] When the field relationship verification results, range verification results, or logical integrity verification results are inconsistent, an anomaly report is output so that simulation optimization can be performed based on the corresponding verification results and anomaly reports.

[0075] This technical solution provides an integrated system that automates the semantic consistency check of business data during the TCP and CAN protocol conversion process, effectively identifies and reports data anomalies, provides system-level support for simulation optimization, and significantly improves the efficiency and accuracy of simulation testing.

[0076] Beneficial Effects: This application discloses a simulation optimization method for hybrid CAN communication parameters based on the TCP protocol. By activating a monitoring unit at the data protocol conversion point, it acquires business data before and after the protocol conversion and establishes data semantic mapping rules. Based on these mapping rules, it verifies the semantic consistency of the business data in real time, specifically checking the mapping relationships of data fields, the consistency of numerical ranges and units, and the completeness of logical meaning. When any verification result is inconsistent, the system outputs an anomaly report, thereby performing simulation optimization based on the corresponding verification result and anomaly report. This method effectively solves the problems in existing technologies where complex system layers and diverse communication protocols make it difficult for traditional testing methods to comprehensively simulate data flow anomalies in real-world scenarios, and lacks the ability to deeply simulate and optimize the TCP packet assembly and CAN frame parsing processes. By comprehensively verifying the semantics of the data, this application can promptly detect and locate various data anomalies that may occur during the protocol conversion process, such as field misalignment, numerical out-of-bounds errors, unit mismatches, or logical errors. This allows for the identification and elimination of potential system risks during the simulation phase, significantly improving the communication reliability, data processing capabilities, and overall stability of complex industrial control systems such as modularized capacity power supply systems. It also avoids system interruptions caused by unexpected program errors in actual deployments, thereby improving maintenance efficiency. Attached Figure Description

[0077] Figure 1 This is a flowchart of a method for simulating and optimizing hybrid CAN communication parameters based on the TCP protocol in one embodiment of the present invention;

[0078] Figure 2 This is a flowchart of a method for simulating and optimizing hybrid CAN communication parameters based on the TCP protocol, according to another embodiment of the present invention.

[0079] Figure 3 This is a system block diagram of a simulation optimization system for hybrid CAN communication parameters based on the TCP protocol, according to another embodiment of the present invention.

[0080] Explanation of reference numerals in the attached figures:

[0081] 1. Simulation and optimization system for hybrid CAN communication parameters based on TCP protocol; 11. Business data acquisition module; 12. Mapping rule establishment module; 13. Data semantic verification module. Detailed Implementation

[0082] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0083] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0084] This application proposes a simulation optimization method based on TCP protocol and hybrid CAN communication parameters, combined with... Figure 1 As shown, it includes:

[0085] S1, activate the monitoring unit preset at the data protocol conversion point to obtain business data before and after the protocol conversion;

[0086] S2, Establish data semantic mapping rules;

[0087] S3, based on data semantic mapping rules, verifies the data semantic consistency of business data in real time; the steps for real-time verification of the data semantic consistency of business data include:

[0088] Based on the data semantic mapping rules, the data field mapping relationship between the business data before and after the protocol conversion is verified to obtain the field relationship verification result.

[0089] Based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the business data after protocol conversion, and obtain the range and unit verification results;

[0090] Based on the data semantic mapping rules, the logical meaning integrity of the business data after protocol conversion is checked to obtain the logical integrity check result;

[0091] When the field relationship verification results, range verification results, or logical meaning integrity verification results are inconsistent, an anomaly report is output so that simulation optimization can be performed based on the corresponding verification results and anomaly reports.

[0092] To facilitate a clearer understanding of the technical solution presented in this application, some key terms and implementation environments will first be explained. The "TCP protocol hybrid CAN communication" mentioned in this application refers to the simultaneous use of TCP / IP and CAN (Controller Area Network) bus protocols for data transmission within a system. TCP is typically used for data transmission between a host computer and a secondary computer, or for remote data transmission, providing reliable, connection-oriented data stream services. CAN, on the other hand, is commonly used for real-time control between a secondary computer and a lower-level computer, or for internal device control, offering high real-time performance and high reliability. In a batch-capacity power supply system, this hybrid communication mode can meet the differentiated communication performance requirements of devices at different levels.

[0093] The "data protocol conversion point" refers to the node or module in the system responsible for converting data formats from one protocol to another, such as parsing TCP packets and converting them into CAN frames. This conversion process is a critical link in data flow and a key area for data semantic consistency verification. "Business data" refers to data transmitted between different devices during system operation, carrying specific business logic or control instructions, such as battery charging and discharging parameters, device status information, and control commands. "Data semantic mapping rules" are a predefined set of rules used to describe the correspondence between business data before and after protocol conversion in terms of fields, values, units, and logical meaning. These rules form the basis for data semantic consistency verification.

[0094] The implementation environment of this application typically includes a simulation platform capable of simulating the multi-level architecture of a split-capacity power supply system and supporting hybrid communication via TCP and CAN buses. In this environment, external simulation parameters can be injected to simulate data flow under actual operating conditions, and the semantic consistency of data during the data protocol conversion process can be monitored and verified in real time using the method described in this application.

[0095] This application proposes a simulation optimization method for hybrid CAN communication parameters based on TCP protocol. Its core lies in the real-time verification of the semantic consistency of business data before and after protocol conversion, thereby discovering potential communication problems.

[0096] Specifically, this method first activates a monitoring unit pre-installed at the data protocol conversion point to acquire business data before and after the protocol conversion. This monitoring unit can be a software module deployed at the input and output ends of the protocol conversion module, capturing the flowing data in real time. For example, during the conversion of TCP data into CAN frames, the monitoring unit can capture the original TCP packets and the converted CAN frame data. Alternatively, the monitoring unit can be a hardware probe, accessing the communication link in a bypass manner to acquire data non-intrusively.

[0097] Next, data semantic mapping rules need to be established. These rules define the correspondence between data fields before and after protocol conversion, the expected range and unit of values, and the logical meaning of business data. For example, an XML file or database table can be manually configured, which details which byte in the CAN frame a certain field in the TCP packet (such as "voltage value") should map to, the value range should be between 0-5V, the unit is volts, and the logical conditions that the voltage value should meet in a specific operating mode.

[0098] Based on established data semantic mapping rules, the system verifies the data semantic consistency of business data in real time. This verification process includes three main aspects:

[0099] First, based on the data semantic mapping rules, verify the data field mapping relationships between the business data before and after the protocol conversion to obtain the field relationship verification results. For example, if the "battery temperature" field in a TCP packet is incorrectly mapped to the "battery current" field in a CAN frame, the field relationship verification results will indicate inconsistency. This can be achieved by comparing the preset mapping table with the actual converted data field positions.

[0100] Second, based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the converted business data to obtain the range and unit verification result. For example, if the "voltage value" field in the CAN frame exceeds the preset range of 0-5V after conversion, or if its unit is incorrectly interpreted as millivolts instead of volts, the range and unit verification result will indicate inconsistency. This can be achieved by comparing the converted data value with the upper and lower limits defined in the mapping rules and verifying the unit identifier.

[0101] Third, based on the data semantic mapping rules, verify the logical meaning integrity of the business data after protocol conversion to obtain the logical integrity verification result. For example, if the CAN command sequence loses a critical start command after conversion, or if the order of the commands does not conform to the operation process specification of the DC-DC module, the logical integrity verification result will indicate inconsistency. This can be achieved by comparing the actual received CAN command sequence with the expected logical flow using a preset state machine model or business process diagram.

[0102] When any of the above verification results (field relationship verification result, range and unit verification result, or logical meaning integrity verification result) are inconsistent, the system will output an anomaly report. This anomaly report will detail the type of inconsistency, the time of occurrence, the data fields involved, and the specific error value or error logic, so that engineers can perform simulation optimization based on the corresponding verification results and anomaly report. For example, the anomaly report might indicate that "the 3rd byte (voltage value) of CAN frame ID 0x123 is out of range; the actual value is 6.2V, and the expected range is 0-5V," or "the CAN command sequence lacks the initialization command for the DC-DC module."

[0103] Optional, combined Figure 2 As shown, the steps to verify the logical integrity of the business data after protocol conversion, based on the data semantic mapping rules, and to obtain the logical integrity verification result, include:

[0104] A1 defines the operational procedures for the DC-CDC module;

[0105] A2, maintains the current operating status of the DC-DC module in real time and performs state transitions based on received CAN commands;

[0106] A3, according to the operation procedure specifications and the current operation status, perform instruction timing legality verification, check whether the currently received CAN instruction conforms to the next instruction corresponding to the DCDC module in the current operation status, and obtain the timing legality verification result;

[0107] A4. Based on the operation procedure specifications and the current operation status, perform state transition consistency verification to verify whether the received CAN command causes the DC-DC module to normally transition from the current operation status to the next expected status, and obtain the state transition verification result.

[0108] A5, when the sequence validity verification result indicates that the CAN command arrival order does not conform to the order defined in the operation procedure specification, or when the state transition verification result indicates that the state transition caused by the CAN command does not conform to the operation procedure specification, an exception report is output.

[0109] Defining the operational procedure specification for the DCDC module involves pre-establishing a detailed set of rules. This set of rules clearly defines the instruction sequence, state transition conditions, logical dependencies, and timing requirements between instructions under different operating modes. For example, this specification can take the form of a state machine diagram, flowchart, or text description, ensuring that every operation of the DCDC module follows a predetermined, safe, and efficient path. Its purpose is to provide an authoritative reference benchmark for subsequent instruction verification and state transition verification.

[0110] Furthermore, maintaining the current operating state of the DC-DC module in real time and performing state transitions based on received CAN commands can be understood as the system continuously tracking the real-time operating status of the DC-DC module. Whenever a CAN command is received, the system determines whether the DC-DC module should transition from its current state to another expected state based on the command's type and content, as well as the preset operating procedure specifications. For example, a DC-DC module may exist in multiple states such as "initialization," "running," "standby," and "fault." Specific CAN commands (such as "start command") will cause it to transition from the "standby" state to the "running" state. The purpose is to ensure that the actual operating state of the DC-DC module is consistent with the state recorded internally by the system, providing accurate contextual information for subsequent legality verification.

[0111] Specifically, instruction timing validity verification refers to checking the received CAN instruction according to a predefined operating procedure specification while the DC-DC module is in its current operating state. This checks whether the instruction matches the next instruction expected by the DC-DC module in its current state. For example, if the specification stipulates that in the "running" state, the "voltage setting instruction" must be received before the "current setting instruction," then if the system receives the "current setting instruction" before receiving the "voltage setting instruction," it will be marked as having invalid timing. The purpose is to prevent malfunctions or abnormal behavior of the DC-DC module due to incorrect instruction sequence.

[0112] Furthermore, performing state transition consistency verification refers to verifying, upon receiving a CAN command, whether the command, based on the operational procedure specifications and the current operating state of the DC-DC module, can cause the DC-DC module to transition normally and reasonably from its current state to the next expected state. For example, if the specification stipulates that the transition from the "running" state to the "standby" state can only be achieved through a "power-off command," but a received command attempts to directly transition it to the "fault" state (and this command is not a fault-triggered command), then this state transition will be deemed inconsistent. The purpose is to ensure that the state transition logic of the DC-DC module conforms to design expectations and to avoid system instability caused by illegal state transitions.

[0113] In some embodiments of this application, when verifying the consistency of the numerical range and units of the protocol-converted business data, only a static comparison of the numerical range and units may be performed, failing to fully consider the dynamic characteristics and actual operational limitations of the DC-DC module under different operating conditions. This may lead to misjudgment as normal under certain specific operating conditions, even if the data is within a preset general range, because it exceeds the actual operational limitations of the current operating condition, or failure to detect data anomalies caused by changes in operating conditions in a timely manner. If the above problems are not resolved, the accuracy and reliability of the simulation optimization results may be affected. To address this, this application further proposes to optimize the steps for verifying the consistency of the numerical range and units of the protocol-converted business data by dynamically determining the operating range through real-time acquisition of the operating condition parameters of the DC-DC module, thereby improving the accuracy of the verification.

[0114] Optionally, according to the data semantic mapping rules, the steps to verify the consistency of the numerical range and unit of the business data after protocol conversion, and to obtain the range and unit verification results, include:

[0115] Real-time acquisition of operating parameters of the DC-DC module;

[0116] Determine the operating range of the DC-DC module under the current operating conditions based on the operating parameters;

[0117] The converted business data is compared with the operational scope under the current working conditions to obtain the operational comparison results;

[0118] Based on the operation comparison results, an anomaly report is output.

[0119] Real-time acquisition of the operating parameters of the DC-DC module refers to continuously collecting various physical quantities and status information of the DC-DC module during actual operation through sensors or other data interfaces, such as input voltage, output current, temperature, and power. The purpose is to provide real-time and accurate contextual information for subsequent dynamic range verification.

[0120] Furthermore, determining the operating range of the DC-DC module under current operating conditions based on operating parameters means dynamically calculating or finding the reasonable numerical ranges and units allowed for various business data (such as voltage, current, etc.) of the DC-DC module under the current specific operating state, based on real-time acquired operating parameters and in conjunction with the module's design specifications, performance curves, or a pre-set operating condition-range mapping table. For example, under high-temperature, high-load conditions, the allowable range of some parameters may differ from that under low-temperature, low-load conditions. The purpose is to ensure the accuracy of the verification and avoid misjudgments caused by changes in operating conditions.

[0121] Specifically, comparing the protocol-converted business data with the operational range under the current operating conditions to obtain the operational comparison result means comparing each value in the protocol-converted business data with the operational range dynamically determined based on the current operating conditions. This comparison includes not only whether the value falls within the allowable range, but may also include whether the units match. Its purpose is to identify abnormal data that exceeds the current actual operational limits.

[0122] Therefore, based on the operation comparison results, the output of an anomaly report means that when the comparison results show that the business data after protocol conversion exceeds the operational range under the current working conditions, the system generates a detailed anomaly report. This report can include the specific value of the anomaly data, the parameters that exceed the range, the corresponding operating conditions, the time of occurrence, and other information. Its purpose is to provide clear anomaly evidence and location information for subsequent simulation optimization.

[0123] In some preferred embodiments, a specific example is given below. Assume that the theoretical range of the output voltage of a DC-DC module during normal operation is 48V ± 1V. However, under specific operating conditions of extreme high temperature (e.g., 80°C) and high load (e.g., 90% of rated power), the actual stable range of its output voltage may drift slightly due to internal losses and thermal effects, for example, becoming 47.5V to 48.5V. If a traditional static verification method is used, judging solely based on the general range of 48V ± 1V, then an output voltage of 47.5V might be misjudged as normal, when in fact, under the current high temperature and high load conditions, this may already be an abnormally low voltage.

[0124] The solution proposed in this application effectively addresses this problem. Specifically, the system acquires the operating parameters of the DC-DC module in real time, including the current temperature of 80℃ and load of 90%. Based on these parameters, the system dynamically determines the operating range of the DC-DC module under the current conditions, for example, adjusting the allowable range of the output voltage to 47.5V to 48.5V. Subsequently, when the output voltage in the protocol-converted service data is checked, it is compared with this dynamically adjusted range. If the output voltage is 47.4V, even though it is within the general range of 48V±1V, it exceeds the 47.5V to 48.5V range under the current operating conditions, and the system will immediately output an anomaly report. This allows for more accurate identification of abnormal data that does not conform to the current operating conditions, providing a more reliable basis for subsequent simulation optimization.

[0125] Optionally, the steps for real-time verification of the semantic consistency of business data include:

[0126] Define the operation procedure specification for the DCDC module; the operation procedure specification stipulates the logical order and timing dependencies between the operation instructions of the DCDC module.

[0127] Maintain the current operating status of the DC-DC module in real time and perform state transitions based on received CAN commands;

[0128] Based on the data semantic mapping rules, the data field mapping relationship between the business data before and after the protocol conversion is verified to obtain the field relationship verification result.

[0129] Based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the business data after protocol conversion, and obtain the range and unit verification results;

[0130] Once the field relationship verification result and the range and unit verification result both indicate that the verification has passed, according to the operation process specifications and the current operation status, the instruction timing legality verification is performed. This checks whether the currently received CAN instruction conforms to the next instruction corresponding to the DCDC module in the current operation status, and obtains the timing legality verification result.

[0131] According to the operation procedure specifications and the current operation status, perform state transition consistency verification to verify whether the received CAN command causes the DC-DC module to normally transition from the current operation status to the next expected status, and obtain the state transition verification result.

[0132] When the sequence validity verification result indicates that the CAN command arrival order does not conform to the order defined in the operation procedure specification, or when the state transition verification result indicates that the state transition caused by the CAN command does not conform to the operation procedure specification, an exception report is output.

[0133] Specifically, defining the operational procedure specification for a DC-DC module refers to pre-setting a standardized set of instruction sequences and state transition rules for each operation of the DC-DC module. This specification details the logical order and time dependencies that the CAN instructions received by the DC-DC module should follow in different operating modes. For example, a DC-DC module may need to receive a "pre-charge" instruction before it can receive a "main contactor closed" instruction. The purpose of this specification is to provide a benchmark for subsequent verification of instruction timing validity and state transition consistency.

[0134] The real-time maintenance of the DC-DC module's current operating state refers to the system continuously tracking the module's real-time operating status, such as "standby," "pre-charging," "charging," and "discharging." Whenever the DC-DC module receives and executes a new CAN command, its operating state transitions according to a pre-defined state machine model. This step ensures that subsequent verification processes can be performed based on the DC-DC module's actual operating context.

[0135] In practical applications, instruction timing validity verification and state transition consistency verification are only performed after the field relationship verification results and range and unit verification results both indicate that the verification has passed. This means that only after confirming the basic structure and numerical validity of the business data will a deeper level of logical semantic verification be conducted. Instruction timing validity verification aims to check whether the currently received CAN instruction conforms to the next instruction allowed by the DCDC module in its current operating state. For example, if the DCDC module is in the "pre-charging" state and receives a "stop charging" instruction, the instruction timing is invalid. State transition consistency verification further verifies whether the received CAN instruction can cause the DCDC module to normally transition from the current operating state to the next expected state. For example, if the "start charging" instruction fails to successfully transition the DCDC module from the "standby" state to the "charging" state, the state transition is considered inconsistent.

[0136] In some preferred embodiments, a specific example is given below. Assume an electric vehicle's battery management system (BMS) communicates with an external diagnostic tool via TCP protocol, while the BMS's internal DC-DC module receives commands via a CAN bus. When the diagnostic tool sends a "start charging" command, this command first passes through a protocol conversion point. The monitoring unit acquires the operational data before and after the protocol conversion.

[0137] First, the system checks the data field mapping relationship after the TCP command is converted into the CAN command according to the data semantic mapping rules. For example, it checks whether the "charging mode" field in the TCP message is correctly mapped to a specific data bit in the CAN message. If the mapping relationship is incorrect, an exception report is immediately output.

[0138] Secondly, if the field mapping is correct, the system will further verify the range and unit consistency of the values ​​in the CAN command. For example, it checks whether the set charging current value is within the physical range allowed by the DCDC module (e.g., 0-100A) and whether the unit is correct. If the value is out of range or the unit is inconsistent, an error report will be output.

[0139] Only after the above field relationship verification and range and unit verification have passed will the system perform a logical meaning integrity verification. At this time, the system will, according to the predefined DC-DC module operation procedure specifications (for example, stipulating that the DC-DC module must first be in a "standby" state before it can receive the "start charging" command), and in conjunction with the current real-time operation status of the DC-DC module, perform command timing legality verification, checking whether the "start charging" command conforms to the next legal command corresponding to the current "standby" state of the DC-DC module. If the DC-DC module is currently in a "fault" state and receives the "start charging" command, it will output a timing illegality exception report. At the same time, the system will also perform state transition consistency verification, verifying whether the "start charging" command successfully causes the DC-DC module to transition normally from the "standby" state to the "charging" state. If the DC-DC module fails to successfully enter the "charging" state, it will output a state transition inconsistency exception report. Through this hierarchical and progressive verification mechanism, the consistency of the basic data can be guaranteed before performing complex logical semantic verification, thereby improving the efficiency and accuracy of simulation optimization.

[0140] Optionally, the steps for defining the operational procedures of the DCDC module include:

[0141] Real-time acquisition of operating parameters of the DC-DC module;

[0142] Based on the operating parameters, adjust the logical order and timing dependencies of CAN instructions in the DCDC module's operation procedure specification;

[0143] During real-time operation process semantic verification, the legality verification of instruction timing is performed according to the adjusted operation process specification.

[0144] During real-time operation process semantic verification, state transition consistency verification is performed according to the adjusted operation process specification.

[0145] Specifically, real-time acquisition of the operating parameters of a DC-DC module refers to continuously collecting various environmental and performance indicators of the DC-DC module during actual operation through sensors or other monitoring devices. These indicators include input voltage, output current, operating temperature, load status, heat dissipation, and the health status of internal components. These parameters reflect the current operating environment and performance of the DC-DC module.

[0146] Adjusting the logical order and timing dependencies of CAN instructions in the DC-DC module's operation procedure specification based on operating parameters can be understood as dynamically modifying or selecting the operation procedure suitable for the current operating conditions based on the real-time operating status of the DC-DC module. For example, when the DC-DC module is in low-power mode, some high-power instructions may be disabled or their execution order adjusted; when the temperature is too high, the timing of some operations may need to be extended to avoid overheating. The purpose of this adjustment is to ensure that the operation procedure specification is consistent with the actual operating state of the DC-DC module, thereby improving the accuracy of subsequent verification.

[0147] In practical applications, during real-time operation procedure semantic verification, the execution of instruction timing legality verification according to the adjusted operation procedure specification means that when checking the legality of CAN instruction sequences, a fixed specification is no longer used; instead, an operation procedure specification adjusted based on real-time operating parameters is adopted. This ensures that the verification process can adapt to the dynamic behavior of the DC-DC module and accurately determine whether the currently received CAN instruction conforms to the next instruction corresponding to the DC-DC module in the current operating state.

[0148] Furthermore, during real-time operation procedure semantic verification, state transition consistency verification is performed according to the adjusted operation procedure specification. This means that when verifying whether the CAN command causes the DC-DC module to normally transition from the current operating state to the next expected state, the operation procedure specification adjusted by real-time operating condition parameters is also followed. This helps ensure that the verification logic for state transitions matches the actual operating conditions of the DC-DC module, avoiding misjudgments caused by changes in operating conditions.

[0149] In some preferred embodiments, a specific example is given below. Suppose a DC-DC module is operating in an electric vehicle, and its operating parameters include battery voltage, output power, module temperature, and vehicle driving mode (e.g., Eco mode, Sport mode).

[0150] When the vehicle is in Eco mode and the DC-DC module temperature is low, its operating procedures may allow certain CAN commands to be executed in a faster sequence for quick response. However, when the vehicle switches to Sport mode, causing a significant increase in the DC-DC module's output power and a rapid rise in module temperature, the system will acquire these changing operating parameters in real time.

[0151] Based on these new operating parameters, the operating procedure specifications for the DC-DC module will be dynamically adjusted. For example, to prevent overheating or overload, the execution timing of some high-power CAN commands may be extended, or the conditions for certain state transitions may be tightened. During real-time operating procedure semantic verification, command timing validity verification and state transition consistency verification will be performed according to this adjusted, more stringent, or more adapted to the current high-temperature, high-load operating conditions.

[0152] Therefore, even under extreme or dynamically changing operating conditions, the method of this application can accurately identify operating instructions or state transitions that do not conform to the current operating conditions, thereby avoiding misjudgments that may occur under static specifications and ensuring the accuracy and safety of DC-DC module communication parameter simulation optimization.

[0153] Optionally, the steps for obtaining the operating condition parameters of the DC-DC module in real time include:

[0154] Activate several pre-selected sensors to collect the operating parameters of the DC-DC module in real time and obtain raw data;

[0155] Perform multi-point sampling on the original data to obtain multi-point sampled data;

[0156] Perform a consistency comparison on the multi-point sampled data to obtain the multi-point sampled consistency comparison results;

[0157] When the multi-point sampling consistency comparison result indicates that there are transient fluctuations or anomalies in the multi-point sampling data, data smoothing processing is initiated to smooth the multi-point sampling data within a preset continuous time window.

[0158] The smoothed multi-point sampling data is compared with the preset physical boundary values ​​to obtain the boundary comparison results.

[0159] When the boundary comparison result indicates that the smoothed multi-point sampled data exceeds the physical boundary value, the smoothed multi-point sampled data is marked as erroneous data and corrected to obtain the corrected operating condition parameters.

[0160] The process involves activating several pre-selected sensors to collect real-time operating parameters of the DC-DC module, obtaining raw data. This involves deploying various sensors, such as voltage, current, and temperature sensors, at key locations within the DC-DC module to monitor its operational indicators in real time. These sensors are pre-selected and deployed to ensure their measurement accuracy and response speed meet the requirements for collecting operating parameters from the DC-DC module. The collected, unprocessed, direct measurement data constitutes the raw data.

[0161] Furthermore, multi-point sampling of the raw data yields multi-point sampled data. This refers to sampling the raw data collected by the same or different sensors multiple times within a certain time period. Multi-point sampling can be performed redundantly in space using multiple sensors of the same type, or it can be performed at high frequency in time using a single sensor. The purpose is to improve the reliability and accuracy of the data by increasing the number of sampling points, and to provide a basis for subsequent data consistency comparison.

[0162] Furthermore, performing consistency comparison on multi-point sampled data to obtain multi-point sampled consistency comparison results refers to comparing multi-point sampled data with preset consistency rules. These rules may include threshold comparison, statistical analysis (such as mean, variance), trend analysis, etc., to determine whether there are significant differences, transient fluctuations, or outliers among the multi-point sampled data. For example, when multiple sensors measure the same physical quantity, if their readings differ beyond the allowable range, inconsistency is considered to exist.

[0163] When the multi-point sampling consistency comparison result indicates that there are transient fluctuations or anomalies in the multi-point sampled data, data smoothing processing is initiated. This smoothing process is applied to the multi-point sampled data within a preset continuous time window. Essentially, once a consistency comparison detects data quality issues, the system automatically triggers the data smoothing mechanism. This mechanism applies specific smoothing algorithms, such as moving average, exponential smoothing, and Kalman filtering, to the affected multi-point sampled data within a preset continuous time window to filter out noise, eliminate transient fluctuations, or correct outliers, thereby restoring the true trend of the data.

[0164] Furthermore, the smoothed multi-point sampling data is compared with preset physical boundary values ​​to obtain the boundary comparison result. This involves comparing the smoothed data with the legal operating range determined by the physical characteristics of the DC-DC module. These physical boundary values ​​are preset according to the design specifications, safety standards, and actual operating limitations of the DC-DC module, such as upper and lower limits for voltage, current, and temperature. This step aims to ensure the physical validity of the data and prevent values ​​from exceeding their actual physical meaning.

[0165] Finally, when the boundary comparison results indicate that the smoothed multi-point sampled data exceeds the physical boundary value, the smoothed multi-point sampled data is marked as erroneous data and corrected to obtain the corrected operating condition parameters. This means that once data is found to exceed the physical boundary value, the system will mark it as erroneous data and take corrective measures. Correction methods may include clamping the data to the nearest boundary value, using interpolation algorithms to estimate based on preceding and following normal data, or replacing it with a default safe value. In this way, it is ensured that the final output operating condition parameters are accurate, reliable, and conform to physical reality.

[0166] In some preferred embodiments, a specific example is given below. Assume the DC-DC module is a high-voltage DC converter in an electric vehicle, and its operating parameters include input voltage, output current, and module temperature.

[0167] First, multiple pre-selected voltage, current, and temperature sensors are activated to collect raw data such as the input voltage, output current, and module temperature of the DC-DC module in real time. For example, two voltage sensors are deployed at the input of the DC-DC module, two current sensors are deployed at the output, and three temperature sensors are deployed at different locations inside the module.

[0168] Next, multi-point sampling is performed on this raw data. For example, data is collected from each sensor every 10 milliseconds and accumulated within a continuous time window of 100 milliseconds to form multi-point sampled data.

[0169] Then, these multi-point sampled data are compared for consistency. For example, for data collected by two input voltage sensors, if their readings at the same sampling time differ by more than a preset threshold (e.g., 0.5V), a transient fluctuation or anomaly is considered to exist. For three temperature sensors, if the reading of one sensor deviates too much from the average of the other two readings, it is also considered an anomaly.

[0170] When transient fluctuations or anomalies are detected, data smoothing is initiated. For example, if spikes appear in the input voltage data, median filtering or moving average filtering algorithms can be used to smooth the voltage data within that time window to eliminate the spike effect.

[0171] Subsequently, the smoothed multi-point sampled data is compared with preset physical boundary values. For example, the physical boundary value of the input voltage of a DC-DC module may be 200V to 450V, the physical boundary value of the output current may be 0A to 100A, and the physical boundary value of the module temperature may be -40℃ to 120℃. If the smoothed input voltage data is 460V, it indicates that it exceeds the physical boundary value.

[0172] Finally, when the boundary comparison results indicate that the data exceeds the physical boundary value, the data is marked as erroneous and corrected. For example, the 460V input voltage data exceeding the physical boundary value is corrected to 450V (the upper limit), or interpolation is performed based on historical data trends to obtain corrected and reliable operating parameters. These corrected operating parameters will be used to adjust the operating procedure specifications of the DCDC module to ensure the accuracy of subsequent semantic verification.

[0173] Optionally, the step of performing a consistency comparison on the multi-point sampled data to obtain the multi-point sampled consistency comparison result can be further refined as follows:

[0174] Select the corresponding alignment rule set based on the type of DCDC module;

[0175] Based on the comparison rule set, a consistency comparison is performed on the multi-point sampled data to obtain the multi-point sampled consistency comparison result.

[0176] The type of DC-DC module can refer to its model, manufacturer, design specifications, power rating, or application scenario. Different DC-DC modules may have different electrical characteristics, communication protocol details, data formats, or performance indicators. For example, a high-power DC-DC module and a low-power DC-DC module may have different data sampling consistency requirements, or DC-DC modules from different manufacturers may have different data encoding methods.

[0177] A set of comparison rules can be understood as a predefined set of data consistency verification standards and algorithms for a specific type of DC-DC module. This set of rules may include, but is not limited to: expected ranges for data fields, verification rules for data units, temporal relationship constraints between data points, data fluctuation thresholds under specific operating conditions, and data pattern recognition rules. For example, for a specific model of DC-DC module, its voltage sample values ​​may be specified within a certain range, and under steady-state conditions, the difference between consecutive sample values ​​should not exceed a certain small threshold.

[0178] Specifically, when performing consistency comparisons on multi-point sampled data, the system first retrieves and loads a matching set of comparison rules from a pre-defined rule base based on the actual type of the DC-DC module currently being monitored. Then, according to the selected set of comparison rules, the multi-point sampled data collected from the DC-DC module is verified item-by-item or comprehensively. For example, it can compare whether the differences between consecutive sample points are within the allowable fluctuation range, or check whether the sampled values ​​of a specific parameter conform to the physical or logical limitations of that type of DC-DC module.

[0179] Optionally, when the multi-point sampling consistency comparison result indicates that there are transient fluctuations or anomalies in the multi-point sampling data, data smoothing processing is initiated. The steps for smoothing the multi-point sampling data within a preset continuous time window include:

[0180] Noise type identification is performed on multi-point sampled data to determine whether transient fluctuations or anomalies belong to one or more of spike pulses, step changes, or periodic interference, and the noise type identification result is obtained.

[0181] Based on the noise type identification results, select the corresponding smoothing algorithm;

[0182] Based on the selected smoothing algorithm, the multi-point sampled data within the preset continuous time window is smoothed.

[0183] Specifically, noise type identification for multi-point sampled data means that when transient fluctuations or anomalies are detected in the multi-point sampled data, the system no longer simply performs general smoothing processing, but first analyzes the intrinsic characteristics of these fluctuations or anomalies. The purpose is to distinguish different types of noise, such as spikes, step changes, or periodic interference. Spikes typically manifest as data values ​​rising or falling sharply in a short period of time, then quickly returning to normal; step changes manifest as data values ​​suddenly jumping from one stable state to another; periodic interference presents a regular repetitive pattern in the data. By identifying these characteristics, noise type identification results can be obtained, providing a basis for subsequently selecting an appropriate smoothing algorithm.

[0184] The selection of a smoothing algorithm based on noise type identification can be understood as follows: once the type of transient fluctuation or abnormal noise is determined, the system will select the most suitable smoothing algorithm for handling that type of noise according to preset rules or an algorithm library. For example, if the identification result is a spike pulse, algorithms such as median filtering and amplitude limiting filtering, which have good suppression effects on impulse noise, can be selected; if the identification result is a step change, algorithms such as Kalman filtering and adaptive filtering, which can track signal changes and reduce overshoot, can be considered; if the identification result is periodic interference, algorithms based on frequency analysis, such as Fourier transform and notch filtering, can be used. The goal is to ensure that the selected algorithm can remove noise to the greatest extent possible while minimizing damage to the effective signal.

[0185] In practical applications, smoothing is performed on multi-point sampled data within a preset continuous time window, based on the selected smoothing algorithm. Specifically, the chosen smoothing algorithm is applied to the multi-point sampled data collected within the preset continuous time window. The length of this time window can be dynamically adjusted according to the operating characteristics of the DC-DC module and the data sampling frequency to ensure that the true changes in the DC-DC module's operating parameters are captured while processing noise. For example, if a median filtering algorithm is selected, the data within each time window is sorted, and the median is taken as the output value of that window, thereby effectively eliminating spikes. The aim is to obtain more accurate and reliable DC-DC module operating parameters through targeted smoothing.

[0186] In some preferred embodiments, a specific example is given below. Suppose that during the acquisition of operating parameters of the DC-DC module, an anomaly occurs in the multi-point sampling data collected by the monitoring unit within a certain continuous time window. First, the system identifies the noise type of the multi-point sampling data within this time window. If the identification result shows obvious spikes in the data, such as short-term high-amplitude fluctuations caused by momentary sensor malfunctions or electromagnetic interference, the system will select a median filtering algorithm for smoothing based on the noise type identification result. The median filtering algorithm can effectively eliminate these spikes while maintaining the overall trend of the data.

[0187] Furthermore, if the identification results show periodic interference in the data, such as regular fluctuations caused by power supply ripple or vibrations at a specific frequency, the system will select a Fourier transform-based filtering algorithm or a notch filter algorithm. By identifying and suppressing noise components at specific frequencies in the frequency domain, periodic interference can be accurately removed without affecting the valid signals at other frequencies.

[0188] For example, if the recognition results show a step change in the data, such as a sudden jump in parameter values ​​from one stable level to another due to a switching of the DC-DC module's operating mode, the system might choose a Kalman filter or an adaptive moving average algorithm. These algorithms can better track the true changes in the signal, respond quickly when a step occurs, and provide good smoothing in a steady state, avoiding the hysteresis or overshoot that may be caused by traditional smoothing algorithms.

[0189] Optionally, the steps for noise type identification of multi-point sampled data include:

[0190] Real-time acquisition of the type of DCDC module and the category of currently monitored operating parameters;

[0191] Select the corresponding noise feature identification rule set based on the type of DC-DC module and the category of operating parameters;

[0192] Based on the selected noise feature recognition rule set, feature extraction is performed on the multi-point sampled data to identify whether the transient fluctuations or anomalies in the multi-point sampled data conform to the feature patterns of spike pulses, step changes or periodic interference, and to obtain the feature extraction results and feature pattern recognition results.

[0193] Based on the feature extraction results and feature pattern recognition results, determine the type of transient fluctuations or abnormal noise.

[0194] The real-time acquisition of the DCDC module type and the category of currently monitored operating parameters aims to provide contextual information for subsequent noise feature identification. The type of DCDC module may include different power levels, topologies, or application scenarios, all of which affect the inherent noise characteristics of its operating parameters. The categories of operating parameters, such as voltage, current, temperature, or frequency, also have their own typical noise patterns. Acquiring this information provides targeted guidance for the noise identification process.

[0195] Furthermore, based on the type of DC-DC module and the category of operating parameters, a corresponding noise feature identification rule set is selected. Different DC-DC module types and operating parameter categories may correspond to different noise characteristics. For example, the current ripple of a high-frequency switching DC-DC module may exhibit periodic interference, while the voltage drop of a low-voltage, high-current DC-DC module may exhibit step changes. Therefore, it is necessary to predefine and store noise feature identification rule sets for different scenarios. This rule set can include mathematical models, threshold conditions, or pattern matching algorithms for various noise patterns.

[0196] Based on this, and according to the selected noise feature identification rule set, feature extraction is performed on the multi-point sampled data. This identifies whether transient fluctuations or anomalies in the multi-point sampled data conform to the characteristic patterns of spike pulses, step changes, or periodic interference, yielding feature extraction results and feature pattern identification results. Feature extraction can employ various signal processing techniques, such as Fourier transform for identifying periodic components, wavelet analysis for capturing transient features, or statistical analysis for detecting outliers. By comparing the extracted features with the feature patterns in the rule set, it can be determined whether the fluctuations or anomalies in the multi-point sampled data belong to a specific noise type.

[0197] Finally, based on the feature extraction and feature pattern recognition results, the type of transient fluctuations or abnormal noise is determined. For example, if the feature extraction results show that the data has high-frequency, narrow-amplitude instantaneous peaks that highly match the characteristic pattern of spike pulses, it is identified as spike pulse noise. If the data undergoes significant amplitude changes within a short period and then tends to stabilize, it may be identified as a step change. If the data exhibits regular, repetitive fluctuations, it may be identified as periodic interference. Accurately determining the noise type is a crucial prerequisite for subsequently selecting a suitable smoothing algorithm.

[0198] This application also discloses a simulation and optimization system for hybrid CAN communication parameters based on the TCP protocol, used to perform simulation and optimization of hybrid CAN communication parameters based on the TCP protocol, combined with... Figure 3 As shown, the TCP protocol-based hybrid CAN communication parameter simulation optimization system 1 includes:

[0199] The business data acquisition module 11 is used to activate the monitoring unit preset at the data protocol conversion point to acquire business data before and after the protocol conversion;

[0200] Mapping rule establishment module 12 is used to establish data semantic mapping rules;

[0201] Data semantic verification module 13 is used to verify the data semantic consistency of business data in real time based on data semantic mapping rules; the steps for real-time verification of the data semantic consistency of business data include:

[0202] Based on the data semantic mapping rules, the data field mapping relationship between the business data before and after the protocol conversion is verified to obtain the field relationship verification result.

[0203] Based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the business data after protocol conversion, and obtain the range and unit verification results;

[0204] Based on the data semantic mapping rules, the logical meaning integrity of the business data after protocol conversion is checked to obtain the logical integrity check result;

[0205] When the field relationship verification results, range verification results, or logical integrity verification results are inconsistent, an anomaly report is output so that simulation optimization can be performed based on the corresponding verification results and anomaly reports.

[0206] To facilitate a clearer understanding of the technical solution presented in this application, some key terms and implementation environments will first be explained. The "TCP protocol hybrid CAN communication" mentioned in this application refers to the simultaneous use of TCP / IP and CAN (Controller Area Network) bus protocols for data transmission within a system. TCP is typically used for data transmission between a host computer and a secondary computer, or for remote data transmission, providing reliable, connection-oriented data stream services. CAN, on the other hand, is commonly used for real-time control between a secondary computer and a lower-level computer, or for internal device control, offering high real-time performance and high reliability. In a batch-capacity power supply system, this hybrid communication mode can meet the differentiated communication performance requirements of devices at different levels.

[0207] "Data protocol conversion" refers to the node or module in the system responsible for converting data formats from one protocol to another, such as parsing TCP packets and converting them into CAN frames, or vice versa. This conversion process is a critical link in data flow and a key area for data semantic consistency verification. "Business data" refers to data transmitted between different devices during system operation, carrying specific business logic or control instructions, such as battery charging and discharging parameters, device status information, and control commands. "Data semantic mapping rules" are a predefined set of rules used to describe the correspondence between business data before and after protocol conversion in terms of fields, values, units, and logical meaning. These rules form the basis for data semantic consistency verification.

[0208] The implementation environment of this application typically includes a simulation platform capable of simulating the multi-level architecture of a split-capacity power supply system and supporting hybrid communication via TCP and CAN buses. In this environment, external simulation parameters can be injected to simulate data flow under actual operating conditions, and the system of this application can be used to monitor and verify the semantic consistency of data during the data protocol conversion process in real time.

[0209] This application proposes a simulation and optimization system for hybrid CAN communication parameters based on the TCP protocol. Its core lies in the modular design to realize real-time verification of the semantic consistency of business data before and after protocol conversion, thereby discovering potential communication problems.

[0210] Specifically, the system includes a business data acquisition module, a mapping rule establishment module, and a data semantic verification module.

[0211] The business data acquisition module is used to activate a monitoring unit pre-installed at the data protocol conversion point to acquire business data before and after the protocol conversion. This monitoring unit can be a software module deployed at the input and output ends of the protocol conversion module, capturing the flowing data in real time. For example, during the conversion of TCP data into CAN frames, the monitoring unit can capture the original TCP packets and the converted CAN frame data. Alternatively, the monitoring unit can be a hardware probe, accessing the communication link non-intrusively to acquire data. This module can be configured to capture all data streams or, based on preset filtering conditions, only capture specific types of business data. The acquired data can be temporarily stored in a memory buffer or directly streamed to subsequent processing modules.

[0212] The mapping rule creation module is used to establish data semantic mapping rules. These rules define the correspondence between data fields before and after protocol conversion, the expected range and unit of values, and the logical meaning of business data. For example, an XML file or database table can be manually configured, detailing which byte in the CAN frame a field in a TCP packet (such as "voltage value") should map to, its value range should be between 0-5V (in volts), and the logical conditions that the voltage value should meet under a specific operating mode. This module provides a user-friendly interface, allowing engineers to define, modify, and manage these mapping rules, and supports persistent storage of rules for reuse and version control.

[0213] The data semantic verification module is used to verify the data semantic consistency of business data in real time based on established data semantic mapping rules. This module receives data from the business data acquisition module and applies the rules defined by the mapping rule establishment module for verification. This verification process includes three main aspects:

[0214] First, based on the data semantic mapping rules, verify the data field mapping relationships between the business data before and after the protocol conversion to obtain the field relationship verification results. For example, if the "battery temperature" field in a TCP packet is incorrectly mapped to the "battery current" field in a CAN frame, the field relationship verification results will indicate inconsistency. This can be achieved by comparing the preset mapping table with the actual converted data field positions.

[0215] Second, based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the converted business data to obtain the range and unit verification result. For example, if the "voltage value" field in the CAN frame exceeds the preset range of 0-5V after conversion, or if its unit is incorrectly interpreted as millivolts instead of volts, the range and unit verification result will indicate inconsistency. This can be achieved by comparing the converted data value with the upper and lower limits defined in the mapping rules and verifying the unit identifier.

[0216] Third, based on the data semantic mapping rules, verify the logical meaning integrity of the business data after protocol conversion to obtain the logical integrity verification result. For example, if the CAN command sequence loses a critical start command after conversion, or if the order of the commands does not conform to the operation process specification of the DC-DC module, the logical integrity verification result will indicate inconsistency. This can be achieved by comparing the actual received CAN command sequence with the expected logical flow using a preset state machine model or business process diagram.

[0217] When any of the above verification results (field relationship verification result, range and unit verification result, or logical integrity verification result) are inconsistent, the data semantic verification module will output an anomaly report. This anomaly report will detail the type of inconsistency, the time of occurrence, the data fields involved, and the specific error value or error logic, so that engineers can perform simulation optimization based on the corresponding verification results and anomaly report. For example, the anomaly report might indicate that "the 3rd byte (voltage value) of CAN frame ID 0x123 is out of range; the actual value is 6.2V, and the expected range is 0-5V," or "the CAN command sequence lacks the initialization command for the DCDC module."

[0218] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A simulation optimization method for hybrid CAN communication parameters based on TCP protocol, characterized in that, include: Activate the monitoring unit preset at the data protocol conversion point to obtain business data before and after the protocol conversion; Establish data semantic mapping rules; Based on the data semantic mapping rules, the data semantic consistency of the business data is checked in real time. The step of real-time verification of the semantic consistency of the business data includes: Based on the data semantic mapping rules, the data field mapping relationship between the business data before and after the protocol conversion is verified to obtain the field relationship verification result. Based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the business data after protocol conversion to obtain the range and unit verification result; According to the data semantic mapping rules, the logical meaning integrity of the business data after protocol conversion is verified to obtain a logical integrity verification result. The steps of verifying the logical meaning integrity of the business data after protocol conversion according to the data semantic mapping rules to obtain a logical integrity verification result include: defining the operation process specification of the DC-DC module; maintaining the current operation state of the DC-DC module in real time and performing state transitions according to received CAN commands; performing command timing validity verification according to the operation process specification and the current operation state, checking whether the currently received CAN command conforms to the next command corresponding to the DC-DC module in the current operation state, and obtaining a timing validity verification result; performing state transition consistency verification according to the operation process specification and the current operation state, verifying whether the received CAN command causes the DC-DC module to normally transition from the current operation state to the next expected state, and obtaining a state transition verification result; when the timing validity verification result indicates that the arrival order of CAN commands does not conform to the order defined in the operation process specification, or the state transition verification result indicates that the state transition caused by the CAN command does not conform to the operation process specification, an exception report is output. When the field relationship verification result, the range and unit verification result, or the logical integrity verification result are inconsistent, an anomaly report is output so that simulation optimization can be performed based on the corresponding verification result and anomaly report.

2. The simulation optimization method for hybrid CAN communication parameters based on TCP protocol according to claim 1, characterized in that, The step of verifying the consistency of the numerical range and unit of the protocol-converted business data according to the data semantic mapping rules, and obtaining the range and unit verification result, includes: Real-time acquisition of operating parameters of the DC-DC module; Based on the operating parameters, determine the operating range of the DC-DC module under the current operating conditions; The converted business data is compared with the operational scope under the current working conditions to obtain the operational comparison results; Based on the comparison results of the operation, an anomaly report is output.

3. The simulation optimization method for hybrid CAN communication parameters based on TCP protocol according to claim 1, characterized in that, The step of real-time verification of the semantic consistency of the business data includes: Define the operation procedure specification for the DC-CDC module; the operation procedure specification stipulates the logical order and timing dependencies between the operation instructions of the DC-CDC module. Maintain the current operating status of the DC-DC module in real time and perform state transitions based on received CAN commands; Based on the data semantic mapping rules, the data field mapping relationship between the business data before and after the protocol conversion is verified to obtain the field relationship verification result. Based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the business data after protocol conversion to obtain the range and unit verification result; When both the field relationship verification result and the range and unit verification result indicate that the verification has passed, according to the operation process specification and the current operation status, the instruction timing legality verification is performed to check whether the currently received CAN instruction conforms to the next instruction corresponding to the DCDC module in the current operation status, and the timing legality verification result is obtained. According to the operation procedure specification and the current operation state, perform state transition consistency verification to verify whether the received CAN command causes the DC-DC module to normally transition from the current operation state to the next expected state, and obtain the state transition verification result. When the timing validity verification result indicates that the arrival order of CAN commands does not conform to the order defined in the operation procedure specification, or when the state transition verification result indicates that the state transition caused by the CAN commands does not conform to the operation procedure specification, an exception report is output.

4. The simulation optimization method for hybrid CAN communication parameters based on TCP protocol according to claim 3, characterized in that, The steps for defining the operational procedures for the DC-CDC module include: Real-time acquisition of operating parameters of the DC-DC module; Based on the operating parameters, adjust the logical order and timing dependencies of CAN instructions in the DCDC module's operation procedure specification; During real-time operation process semantic verification, the legality verification of instruction timing is performed according to the adjusted operation process specification. During real-time operation process semantic verification, state transition consistency verification is performed according to the adjusted operation process specification.

5. The simulation optimization method for hybrid CAN communication parameters based on TCP protocol according to claim 4, characterized in that, The steps for obtaining the operating parameters of the DC-DC module in real time include: Activate several pre-selected sensors to collect the operating parameters of the DC-DC module in real time and obtain raw data; Perform multi-point sampling on the original data to obtain multi-point sampled data; The consistency comparison of the multi-point sampled data is performed to obtain the multi-point sampled consistency comparison result; When the multi-point sampling consistency comparison result indicates that there are transient fluctuations or anomalies in the multi-point sampling data, data smoothing processing is initiated to smooth the multi-point sampling data within a preset continuous time window. The smoothed multi-point sampling data is compared with the preset physical boundary values ​​to obtain the boundary comparison results. When the boundary comparison result indicates that the smoothed multi-point sampled data exceeds the physical boundary value, the smoothed multi-point sampled data is marked as erroneous data and corrected to obtain the corrected operating condition parameters.

6. The simulation optimization method for hybrid CAN communication parameters based on TCP protocol according to claim 5, characterized in that, The step of performing a consistency comparison on the multi-point sampled data to obtain the multi-point sampled consistency comparison result includes: Select the corresponding alignment rule set based on the type of DCDC module; Based on the comparison rule set, the multi-point sampled data is compared for consistency to obtain the multi-point sampled consistency comparison result.

7. The simulation optimization method for hybrid CAN communication parameters based on TCP protocol according to claim 5, characterized in that, When the multi-point sampling consistency comparison result indicates that there are transient fluctuations or anomalies in the multi-point sampling data, the step of initiating data smoothing processing and smoothing the multi-point sampling data within a preset continuous time window includes: The noise type is identified by performing noise type identification on the multi-point sampled data to determine whether the transient fluctuation or anomaly belongs to one or more of the following: spike pulse, step change or periodic interference, and the noise type identification result is obtained. Based on the noise type identification results, select the corresponding smoothing algorithm; The multi-point sampled data within a preset continuous time window is smoothed according to the selected smoothing algorithm.

8. The simulation optimization method for hybrid CAN communication parameters based on TCP protocol according to claim 7, characterized in that, The step of identifying the noise type of the multi-point sampled data includes: Real-time acquisition of the type of DCDC module and the category of currently monitored operating parameters; Based on the type of the DC-DC module and the category of the operating condition parameters, select the corresponding noise feature identification rule set; Based on the selected noise feature recognition rule set, feature extraction is performed on the multi-point sampling data to identify whether the transient fluctuations or anomalies in the multi-point sampling data conform to the feature patterns of spike pulses, step changes or periodic interference, and to obtain feature extraction results and feature pattern recognition results. Based on the feature extraction results and feature pattern recognition results, the type of transient fluctuations or abnormal noise is determined.

9. A simulation and optimization system for hybrid CAN communication parameters based on TCP protocol, used to perform simulation and optimization of hybrid CAN communication parameters based on TCP protocol, characterized in that, include: The business data acquisition module is used to activate the monitoring unit preset at the data protocol conversion point to acquire business data before and after the protocol conversion. The mapping rule establishment module is used to establish data semantic mapping rules; The data semantic verification module is used to verify the data semantic consistency of the business data in real time based on the data semantic mapping rules; The step of real-time verification of the semantic consistency of the business data includes: Based on the data semantic mapping rules, the data field mapping relationship between the business data before and after the protocol conversion is verified to obtain the field relationship verification result. Based on the data semantic mapping rules, verify the consistency of the numerical range and unit of the business data after protocol conversion to obtain the range and unit verification result; According to the data semantic mapping rules, the logical meaning integrity of the business data after protocol conversion is verified to obtain a logical integrity verification result. The steps of verifying the logical meaning integrity of the business data after protocol conversion according to the data semantic mapping rules to obtain a logical integrity verification result include: defining the operation process specification of the DC-DC module; maintaining the current operation state of the DC-DC module in real time and performing state transitions according to received CAN commands; performing command timing validity verification according to the operation process specification and the current operation state, checking whether the currently received CAN command conforms to the next command corresponding to the DC-DC module in the current operation state, and obtaining a timing validity verification result; performing state transition consistency verification according to the operation process specification and the current operation state, verifying whether the received CAN command causes the DC-DC module to normally transition from the current operation state to the next expected state, and obtaining a state transition verification result; when the timing validity verification result indicates that the arrival order of CAN commands does not conform to the order defined in the operation process specification, or the state transition verification result indicates that the state transition caused by the CAN command does not conform to the operation process specification, an exception report is output. When the field relationship verification result, the range and unit verification result, or the logical integrity verification result are inconsistent, an anomaly report is output so that simulation optimization can be performed based on the corresponding verification result and anomaly report.

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