A method and system for remote monitoring of a frequency converter based on the Internet of Things

Abstract

The application relates to the technical field of industrial automation, and discloses a frequency converter remote monitoring method and system based on the Internet of Things, which comprises the following steps: acquiring initial instruction data sets and operation behavior data; evaluating each instruction according to the initial instruction data sets and outputting an abnormal marking result; dynamically analyzing the abnormal marking result and the operation behavior data, determining high-risk instructions, and generating a risk classification label; continuously tracking the high-risk instructions according to the risk classification label, monitoring state changes, and outputting a state abnormal signal; activating a response trigger point according to the state abnormal signal and generating a protection instruction sequence; performing secondary verification on the protection instruction sequence and generating a final execution instruction set; distributing the instructions according to the final execution instruction set, synchronously updating the operation behavior data, and outputting an execution state report. The method can realize full-process safety monitoring of frequency converter operation instructions and improves the safety of remote management.

Description

Technical Field

[0001] This invention relates to the field of industrial automation technology, and in particular to a method and system for remote monitoring of frequency converters based on the Internet of Things. Background Technology

[0002] Remote monitoring technology originates from the development of communication and sensing technologies. It possesses real-time, high-efficiency, and secure application value, and is widely used in industrial automation, security, medical monitoring, smart homes, and other fields. Its core foundation includes sensor technology, wireless communication (such as 4G / 5G, Wi-Fi, LoRa), data processing and analysis, and cloud or edge computing platforms. By collecting, transmitting, and analyzing equipment or environmental status information, it achieves all-weather, long-distance intelligent monitoring and control of target objects. Currently, in the field of modern industrial automation, remote monitoring technology is crucial for improving equipment management efficiency and safety, directly impacting the stable operation of production systems, enterprise economic benefits, and personnel safety.

[0003] One existing technology combines traditional video surveillance with equipment data acquisition (such as SCADA systems). Cameras record operational footage, and PLCs or gateways collect operating parameters of key equipment like frequency converters. The data is then stored locally or uploaded to a monitoring center for centralized viewing and analysis. However, current remote monitoring methods lack the ability to dynamically analyze operational behavior in complex environments, making it difficult to comprehensively capture multi-dimensional information such as operator identity, time, and specific operational content. Furthermore, existing methods lack a real-time evaluation mechanism for the rationality of operational instructions, making it impossible for the system to accurately determine whether operations comply with safety regulations, resulting in the difficulty of timely detection and prevention of abnormal operations. More seriously, even when anomalies are detected, the lack of automated safety protection response mechanisms prevents the system from taking effective measures immediately, potentially leading to equipment failure or safety accidents.

[0004] In summary, existing technologies have shortcomings in remote management security. Summary of the Invention

[0005] This invention provides a method and system for remote monitoring of frequency converters based on the Internet of Things (IoT) to address the shortcomings in the security of remote management.

[0006] In a first aspect, to address the aforementioned technical problems, the present invention provides a method and system for remote monitoring of frequency converters based on the Internet of Things, comprising: Acquire the initial instruction dataset and operation behavior data; Based on the initial instruction dataset, each instruction is evaluated for reasonableness using rules from a preset security specification library. When an instruction deviates from the preset compliance range, it is marked as a potential abnormal instruction, and the abnormal marking result is output. Based on the anomaly marking results and the operational behavior data, dynamic analysis is performed to analyze the deviation of instruction execution. When there is a significant deviation, the instruction is determined to be a high-risk instruction, and a risk classification label is generated. Based on the risk classification label, the high-risk instructions are continuously tracked, the state changes during the instruction execution process are monitored, and it is determined whether the state change triggers the preset abnormal detection item. If so, an abnormal state signal is output. Based on the abnormal status signal, activate the response trigger point and generate a protection instruction sequence; The protection instruction sequence is subjected to a second verification to determine whether it is consistent with the protection logic in the security specification library. If the verification passes, the final execution instruction set is generated. Based on the final execution instruction set, instructions are distributed, the operation behavior data is updated synchronously, and an execution status report is output.

[0007] Preferably, the step of evaluating the reasonableness of each instruction based on the initial instruction dataset using rules from a preset security specification library to obtain anomaly marking results includes: The initial instruction dataset is matched with the preset security specification library according to rules, and instructions whose parameters deviate from the preset rules are initially marked to obtain a set of initially marked instructions; Perform data integrity verification on the initially marked instruction set. If an instruction has missing data, it is classified as an instruction to be verified. For the instruction to be verified, a security standard comparison is performed in conjunction with a preset evaluation basis database to obtain an anomaly marking result.

[0008] Preferably, in an optional implementation, the step of performing dynamic analysis based on the anomaly marker results and the operational behavior data to analyze the deviation in instruction execution, and determining the instruction as a high-risk instruction when a significant deviation exists, and generating a risk classification label, includes: Based on the anomaly marking results, the corresponding operation behavior data and historical record data are extracted from the operation behavior data to obtain abnormal behavior data and abnormal record data. The abnormal behavior data is compared with the abnormal record data to obtain the execution deviation of the instruction. When the execution deviation exceeds a preset deviation threshold, the instruction is determined to be a high-risk instruction, and a high-risk instruction set is obtained. The high-risk instruction set is classified and labeled to obtain risk classification labels.

[0009] Preferably, the step of continuously tracking the high-risk instructions based on the risk classification label, monitoring state changes during instruction execution, and determining whether the state change triggers a preset anomaly detection item, and if so, outputting a state anomaly signal, includes: Acquire data on the status changes of high-risk instructions during execution; Based on the risk classification labels, the state change data is classified to obtain the classified state change data; When the classified state change data exceeds the corresponding threshold in the preset anomaly detection item, the anomaly detection item is triggered, the state is determined to be abnormal, and the corresponding state abnormality signal is output.

[0010] Preferably, the step of activating the response trigger point and generating a protection command sequence based on the abnormal status signal includes: The abnormal status signal is compared with the preset response triggering conditions. When the triggering conditions are met, the activated response triggering point is obtained. Based on the activated response trigger points, a protection instruction sequence is generated.

[0011] Preferably, the protection instruction sequence is subjected to secondary verification to determine whether it is consistent with the protection logic in the security specification library. If the verification passes, a final execution instruction set is generated, including: The protection instruction sequence is logically matched with the security specification library to obtain a list of logically conflicting instructions; The instructions in the logically conflicting instruction list are modified or replaced to obtain a modified instruction set. The corrected instruction set is subjected to a logical consistency check. Once the check passes, the final execution instruction set is generated.

[0012] Preferably, the step of distributing instructions according to the final execution instruction set, synchronously updating the operation behavior data, and outputting an execution status report includes: Based on the final execution instruction set, instructions are distributed to obtain a list of distributed instructions; Based on the final execution instruction set and the distributed instruction list, the operation behavior data is synchronously updated to obtain the updated operation behavior data; Based on the distributed instruction list, obtain the real-time feedback data after execution, compare it with the preset standard, and obtain the execution status evaluation result; An execution status report is generated and output based on the execution status assessment results and the updated operation behavior data.

[0013] Secondly, the present invention provides an Internet of Things-based remote monitoring device for frequency converters, comprising: The acquisition module is used to acquire the initial instruction dataset and operation behavior data; The evaluation module is used to evaluate the reasonableness of each instruction based on the initial instruction dataset and the rules in the preset security specification library. When an instruction deviates from the preset compliance range, it is marked as a potential abnormal instruction and the abnormal marking result is output. The analysis module is used to dynamically analyze the operation behavior data based on the anomaly marking results, analyze the deviation of the instruction execution, and when there is a significant deviation, determine that the instruction is a high-risk instruction and generate a risk classification label. The monitoring module is used to continuously track the high-risk instructions based on the risk classification labels, monitor the state changes during the execution of the instructions, and determine whether the state changes trigger preset abnormal detection items. If so, it outputs a state abnormality signal. The response module is used to activate the response trigger point and generate a protection instruction sequence based on the abnormal status signal. The verification module is used to perform secondary verification on the protection instruction sequence to determine whether it is consistent with the protection logic in the security specification library. If the verification passes, the final execution instruction set is generated. The execution module is used to distribute instructions according to the final execution instruction set, synchronously update the operation behavior data, and output an execution status report.

[0014] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the Internet of Things-based remote monitoring method for frequency converters as described in any one of the above.

[0015] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the IoT-based inverter remote monitoring method described above.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention significantly enhances the safety of inverter operation by introducing a multi-stage instruction evaluation and dynamic analysis mechanism. First, operating instructions are collected in real time and their rationality is evaluated in conjunction with a safety specification library to preliminarily identify potential abnormal instructions. For these potential abnormal instructions, this invention utilizes a dynamic analysis method to compare historical operating behavior data, accurately determine high-risk instructions, and generate detailed risk classification labels. This layered and dynamic evaluation and analysis process enables the system to identify and warn of unsafe or unauthorized instructions earlier and more accurately, reducing the risks caused by misoperation, malicious attacks, or system failures from the source, and greatly improving the safety of remote inverter control.

[0017] (2) This invention continuously tracks high-risk commands and monitors the state changes during command execution in real time. Once a state change triggers a preset abnormality detection item, the system will immediately activate an automatic protection sequence. This closed-loop management, from risk identification to real-time monitoring to automatic protection, ensures that when deviations or abnormalities occur in the operation of the frequency converter, the system can quickly and decisively take protective measures, minimizing equipment damage, production interruptions, or safety accidents, and significantly improving the reliability and resilience of the frequency converter operation.

[0018] (3) This invention ensures the rigor of the system's protection logic and the traceability of operations through secondary verification of protection measures and real-time updates of operation logs. After generating the automatic protection sequence, this invention also performs rigorous instruction verification to check whether the protection measures are consistent with the protection logic in the security specification library. Only after verification can the execution instruction set be finally generated and issued. At the same time, the system will update the operation log and output an execution status report after executing the instruction. This mechanism ensures that all issued protection instructions are fully verified and meet the highest security standards, and records all operations and responses in detail, providing comprehensive and reliable data support for subsequent fault investigation, responsibility identification, and system optimization, further enhancing the reliability of the entire remote monitoring system. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of a method for remote monitoring of frequency converters based on the Internet of Things provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a remote monitoring system for frequency converters based on the Internet of Things, provided in the second embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Reference Figure 1 The first embodiment of the present invention provides a method for remote monitoring of frequency converters based on the Internet of Things, including the following steps: S11, Obtain the initial instruction dataset and operation behavior data; S12, Based on the initial instruction dataset, each instruction is evaluated for reasonableness using rules from a preset security specification library. When an instruction deviates from the preset compliance range, it is marked as a potential abnormal instruction, and the abnormal marking result is output. S13. Based on the anomaly marking results and the operation behavior data, perform dynamic analysis to analyze the deviation of the instruction execution. When there is a significant deviation, determine that the instruction is a high-risk instruction and generate a risk classification label. S14. Based on the risk classification label, continuously track the high-risk instruction, monitor the state changes during the instruction execution process, and determine whether the state change triggers a preset abnormal detection item. If so, output a state abnormality signal. S15, based on the abnormal status signal, activate the response trigger point and generate a protection instruction sequence; S16, perform a second verification on the protection instruction sequence to determine whether it is consistent with the protection logic in the security specification library. If the verification passes, generate the final execution instruction set. S17, Distribute instructions according to the final execution instruction set, update the operation behavior data synchronously, and output an execution status report.

[0022] In step S11, it is necessary to obtain the initial instruction dataset and operation behavior data.

[0023] The initial instruction dataset refers to the set of inverter operation instructions acquired in real time through the data acquisition terminal, which records the operation time, source identifier, and specific content of each instruction. This step involves capturing inverter operation instructions in real time through the data acquisition terminal. During instruction capture, the operation time, source identifier, and specific instruction content are recorded, thus forming the initial instruction dataset.

[0024] It should be noted that the captured initial instruction dataset will be stored in a pre-established database to form a structured set of instruction records, which will serve as the basis for subsequent evaluation and analysis.

[0025] Based on the structured set of instruction records, the source identifier of each instruction is categorized. If the source identifier falls within a preset range of valid identifiers, the instruction is marked as valid, and its subsequent processing order is determined. Furthermore, the system sorts the operation times of a subset of valid instructions and calculates the frequency and interval of instruction execution. If the time interval exceeds a preset threshold, the instruction is marked as an instruction awaiting verification. For the list of instructions awaiting verification, the system retrieves the specific content of the instructions and historical operation records from the database and compares them to check for consistency to determine if any anomalies exist, ultimately determining the status of the instructions. The specific comparison rules include: Absolute threshold matching matches command parameters against absolute thresholds defined in the safety specification library. For example, the safety specification library may stipulate that the speed regulation frequency range of the frequency converter must be between 20Hz and 60Hz, and any command exceeding this range is considered abnormal; Relative deviation comparison compares the command with historical operation records to analyze their relative deviation. This comparison is limited to historical data within a historical time window (e.g., the past 24 hours) and is judged based on a preset relative deviation threshold. For example, if the historical average speed regulation frequency is 50Hz under current operating conditions and the set deviation threshold is 5Hz, then a command with a frequency of 58Hz will be marked as abnormal because its deviation (8Hz) exceeds the threshold.

[0026] When an instruction fails to meet any of the above rules, the system will identify it as an abnormal or high-risk instruction and trigger subsequent processing. Of course, the range of legal identifiers, absolute thresholds, relative deviation thresholds, and historical record time windows can all be adjusted according to different industrial scenarios and management needs; this invention does not impose any limitations on these.

[0027] It should be noted that the key parameters used to determine whether an instruction is abnormal mainly include a preset range of legal identifiers and preset thresholds in a series of comparison rules. In specific implementation, the system verifies the legitimacy of the instruction source through the preset range of legal identifiers (a set of authorized device IDs, such as containing "DEV_001").

[0028] It should be noted that operational behavior data refers to data that includes the inverter's historical operation records, command execution context, and environmental information. This data will be used for subsequent dynamic analysis.

[0029] For example, an IoT-based data acquisition terminal device can be deployed. This device communicates with the frequency converter via the Modbus protocol and automatically collects operation command data every 0.5 seconds. The collected data packets typically contain a timestamp, device ID, and command content, such as "adjust speed to 50Hz". The acquisition system transmits this data to a cloud server in real time via the MQTT protocol and uses the AES-128 encryption algorithm to ensure data security. After receiving the data, the server parses it, uses the timestamp to chronologically sequence the commands, and associates them with the specific frequency converter device through the device ID, forming a structured data record. In addition, the system also verifies the content of each command, for example, by using the CRC32 algorithm to calculate the checksum and comparing it with the checksum in the data packet. If the error is less than 0.01%, the data is considered complete. At the same time, the system collects environmental information about the frequency converter's operation in real time, such as using a temperature sensor to monitor its operating temperature and obtaining the current load status of the device. This environmental data is also integrated for analysis. Subsequently, the valid data will be stored in the MySQL database, and the instruction data can be correlated with the inverter's operating status (such as current and voltage) for analysis. The effect of instruction execution can be judged by calculating the rate of change of current before and after instruction execution. If the rate of change is lower than the expected value, an alarm logic will be triggered, and optimization suggestions will be automatically generated.

[0030] In step S12, based on the initial instruction dataset, the reasonableness of each instruction is evaluated using rules from a preset security specification library. When an instruction deviates from a preset compliance range, it is marked as a potentially abnormal instruction, and the abnormal marking result is output, including: The initial instruction dataset is matched with the preset security specification library according to rules, and instructions whose parameters deviate from the preset rules are initially marked to obtain a set of initially marked instructions; Perform data integrity verification on the initially marked instruction set. If an instruction has missing data, it is classified as an instruction to be verified. For the instruction to be verified, a security standard comparison is performed in conjunction with a preset evaluation basis database to obtain an anomaly marking result.

[0031] For example, the safety specification library stipulates that the frequency range of speed control commands for frequency converters should be between 20Hz and 60Hz. When the collected command content is "speed up to 70Hz", the system will find that it exceeds the preset threshold range through comparison, initially judge it as a potential anomaly, and mark it as an abnormal state, forming a preliminary set of anomaly tags. If, during the data integrity check of the preliminary set of anomaly tags, a command marked as abnormal is found to lack key fields such as timestamps, the system will classify it as a command to be verified. A secondary comparison is performed on the command to be verified based on the evaluation database. For example, if the database stores the safety parameter range of the frequency converter under different operating conditions, the system will find that the command not only exceeds the frequency range but may also cause equipment overload under the current operating conditions, thus confirming it as an abnormal command and generating a tagging result. Subsequently, the system will save the classification details of these abnormal commands, forming a complete anomaly log file. For example, on a certain day, a total of 10 abnormal commands are marked, of which 5 are frequency over-limit, 3 are missing fields, and 2 are operating conditions incompatible. The log will record the anomaly cause, time, and equipment identifier of each command in detail. The system will also check whether the file covers all abnormal commands, for example by comparing the total number of abnormal commands with the total number of records to ensure that nothing is missed before outputting the final summary.

[0032] For example, suppose the safety specification library stipulates that the frequency range of speed control commands for frequency converters should be between 20Hz and 60Hz. When the collected command content is "speed up to 70Hz", the system will find that it exceeds the preset threshold range by comparison, initially judge it as a potential anomaly, and mark it. Subsequently, the system will perform a data integrity check on the initially marked command.

[0033] It should be noted that the purpose of data integrity checks is to verify whether an instruction contains all the key fields. These key fields are essential information for subsequent analysis and mainly include: operation timestamp, source identifier (i.e., device ID), and instruction type or specific content. If an instruction lacks any of these fields, it will be classified as an instruction awaiting verification.

[0034] The system will perform a secondary comparison with the verification instructions in the assessment basis database. It should be noted that the data in this assessment basis database may originate from equipment manuals, industry safety standards, or patterns summarized from historical operating data. Its data structure uses operating conditions as an index, defining specific parameter ranges for safe operation under different operating conditions. For example, the operating condition index fields of the database may include load level, ambient temperature, and running time, while the corresponding safety parameter fields include frequency upper limit and current threshold. During the secondary comparison, the system first acquires the current operating condition data of the frequency converter in real time through external sensors, such as obtaining the operating temperature through a temperature sensor or the equipment load rate through a monitoring module. Subsequently, the system uses these real-time operating condition data (e.g., "load rate higher than 80%)" as query conditions to retrieve and match the entry in the assessment basis database that best matches the current operating condition, in order to obtain the safety parameters for that specific operating condition (e.g., the frequency upper limit should be 55Hz under this high load condition). If the parameters of the command to be verified (e.g., "adjust speed to 60Hz") exceed the safety parameter range for that specific operating condition, it will still be identified as an abnormal command even if it does not exceed the global absolute threshold (20-60Hz). The system will then save the classification details of these abnormal commands, forming a complete abnormal log file. For example, if 10 abnormal commands are marked on a certain day, 5 of them are for frequency exceeding limits, 3 for missing fields, and 2 for incompatible operating conditions, the log will record the cause, time, and device identifier of each command in detail. The system will also check whether the file covers all abnormal commands, for example, by comparing the total number of abnormal commands with the total number of records to ensure no omissions before outputting the final summary.

[0035] In step S13, dynamic analysis is performed based on the anomaly marker results and the operation behavior data to analyze the deviation in instruction execution. When a significant deviation exists, the instruction is determined to be a high-risk instruction, and a risk classification label is generated, including: Based on the anomaly marking results, the corresponding operation behavior data and historical record data are extracted from the operation behavior data to obtain abnormal behavior data and abnormal record data. The abnormal behavior data is compared with the abnormal record data to obtain the execution deviation of the instruction. When the execution deviation exceeds a preset deviation threshold, the instruction is determined to be a high-risk instruction, and a high-risk instruction set is obtained. The high-risk instruction set is classified and labeled to obtain risk classification labels.

[0036] It should be noted that the purpose of comparing the abnormal behavior data and abnormal record data to ensure consistency between the current instruction and historical behavior patterns is to analyze the consistency between them. In practice, the system extracts key parameters (e.g., the frequency value in the "adjust speed to 65Hz" instruction) and the execution environment (e.g., equipment load) from the operation behavior log file. Subsequently, the system obtains the typical range of parameters for similar historical instructions under similar execution environments (e.g., historical data shows that under similar loads, the speed adjustment frequency is typically between 20Hz and 60Hz). The comparison algorithm calculates the deviation between the current instruction parameters and the historical typical range.

[0037] When the deviation exceeds a preset deviation threshold, it is considered a "significant deviation." It should be noted that deviation refers to the degree of difference between the actual execution of the instruction and its historical normal behavior pattern or a preset safety threshold. For example, a preset deviation threshold can be set as "frequency exceeding the normal range by 5Hz." If the current instruction's frequency is 65Hz, and its deviation is exactly 5Hz, reaching the threshold, it can be identified as a high-risk instruction.

[0038] Based on the identified high-risk instructions, the system will classify the risk according to preset risk classification criteria. These criteria can include multiple dimensions; for example, one criterion might specify that "a frequency deviation exceeding 8Hz and a load rate higher than 80%" are considered high-risk. After classification, the system will use a tag generation tool to generate corresponding risk classification tags for the instructions, such as generating a text tag for "frequency exceeding limits, high risk," and will associate and save this tag with the operational behavior data for subsequent tracking and processing.

[0039] In step S14, based on the risk classification label, the high-risk instruction is continuously tracked, the state changes during instruction execution are monitored, and it is determined whether the state change triggers a preset anomaly detection item. If so, an anomaly signal is output, including: Acquire data on the status changes of high-risk instructions during execution; Based on the risk classification labels, the state change data is classified to obtain the classified state change data; When the classified state change data exceeds the corresponding threshold in the preset anomaly detection item, the anomaly detection item is triggered, the state is determined to be abnormal, and the corresponding state abnormality signal is output.

[0040] First, the system monitors and continuously tracks high-risk commands with risk classification tags in real time, acquiring data on the status changes of the command during execution. For example, if a command's risk tag is "high-risk power anomaly," the system will focus on extracting relevant data such as its real-time power output and current changes from the monitoring stream.

[0041] The next step is to categorize the acquired state change data based on the aforementioned risk classification labels. It's important to note that categorization refers to the system determining which data dimensions to focus on analyzing based on the label type (e.g., "High Risk of Power Anomaly"). For example, for the "High Risk of Power Anomaly" label, the system will focus on processing state change data related to power and current, rather than other irrelevant data.

[0042] Finally, the system matches the state change data with preset anomaly detection items. It should be noted that an anomaly detection item can be a rule containing multiple conditions and corresponding thresholds. For example, a detection item could be triggered when "power fluctuation exceeds 250 watts and current deviation is greater than 2 amperes". If the real-time data meets all the conditions of this detection item, the system will determine that the state is abnormal and output the corresponding state abnormality signal.

[0043] For example, suppose the system has generated a "high-risk power anomaly" risk classification label for a certain instruction. The system will continuously track this instruction in conjunction with real-time monitoring streams. If the instruction requests a power output of 1800 watts, while the device's current operating power is 1500 watts, the system, through a power fluctuation detection algorithm, finds a difference of 300 watts, exceeding the preset anomaly threshold of 200 watts, and initially records it as a potential anomaly. Further analysis of current data reveals that if the current suddenly increases to 14.5 amps, exceeding the normal range, the system calculates a comprehensive anomaly index that is higher than the preset warning value, confirming an anomaly risk. Subsequently, the system matches the status change data with preset anomaly detection items (e.g., triggering an alarm when power fluctuation exceeds 250 watts and current deviation is greater than 2 amps). If the conditions are met, the system will automatically generate an "emergency level" status anomaly signal and associate it with the instruction execution timestamp and device number. The system will also compare the anomaly signal with the device's operating logs; for example, if the logs show that the device is currently under high load, this further corroborates the rationality of the anomaly signal.

[0044] In step S15, based on the abnormal status signal, a response trigger point is activated, and a protection command sequence is generated, including: The abnormal status signal is compared with the preset response triggering conditions. When the triggering conditions are met, the activated response triggering point is obtained. Based on the activated response trigger points, a protection instruction sequence is generated.

[0045] It should be noted that when the system receives a status anomaly signal, it compares it with preset response trigger conditions. These response trigger conditions are a set of predefined rules, which can be composite logic conditions. For example, a trigger condition could be set as "when a temperature anomaly signal is detected and the real-time monitored current fluctuation exceeds 10% of the rated value." If the received status anomaly signal meets this preset rule, the response trigger point is activated.

[0046] Subsequently, based on the activated response trigger points, the system invokes the corresponding automatic protection program to generate a protection instruction sequence. It should be noted that the protection program defines specific protection instruction types and parameters; for example, it may include instructions such as "reduce inverter output power" and "limit frequency upper limit." The parameters are also predefined in the protection sequence, for example, reducing power to "70% of rated power" or limiting the frequency upper limit to "40 Hz." Furthermore, the sequence specifies the execution order and priority of the instructions. For example, a three-step instruction sequence including power limiting, frequency locking, and alarm notification can be used, with the execution interval of each step set to ensure orderly execution of protection measures.

[0047] For example, when the sensor collects an abnormal status signal in real time, such as detecting that the operating temperature exceeds the set threshold of 85 degrees Celsius and rises by more than 2 degrees Celsius per minute, the system will determine it as a high-risk state and trigger the subsequent response mechanism. The system will activate the response trigger point based on a preset rule base. For example, when the temperature is abnormal and the current fluctuation exceeds 10% of the rated value, the system will immediately generate a response event with a priority of 1 and push it to the protection sequence control module. Subsequently, the system calls the automatic protection program to make restrictive adjustments to the operating environment through the built-in algorithm, such as reducing the inverter output power to 70% of the rated power and limiting the frequency upper limit to 40 Hz to prevent further deterioration of overheating. The system will calculate the execution time window of the protection command based on the severity of the abnormal signal and the equipment operating status. For example, if the temperature abnormality lasts for more than 5 minutes and there is no downward trend, a three-step command sequence including power limiting, frequency locking, and alarm notification will be generated, and the execution interval of each command will be marked with a timestamp of 30 seconds to ensure that the commands are executed in an orderly manner. Finally, the system will use a weighted scoring algorithm to determine the execution priority of protection measures. For example, it will comprehensively consider abnormal temperature (weight 0.5), current fluctuation (weight 0.3), and historical fault frequency (weight 0.2) to calculate the total risk score. If the score exceeds the preset threshold of 60 points, the power limiting measures will be executed first, and the execution status will be fed back to the monitoring system to form a closed-loop control.

[0048] In step S16, the protection instruction sequence is subjected to a second verification to determine whether it is consistent with the protection logic in the security specification library. If the verification passes, the final execution instruction set is generated.

[0049] The system checks whether each instruction matches the protection logic in the specification library. The key feature of this step is the secondary verification of the generated protection instruction sequence to check its consistency with the protection logic in the security specification library, thereby obtaining a preliminary verification result. It should be noted that the protection logic is a set of rules, which may include explicit security constraints (e.g., stipulating that the transaction frequency must not exceed 400 times per minute) or business compliance requirements (e.g., stipulating that the execution of a certain operation requires the account balance to be below a specific value). This secondary verification is not based on fuzzy matching, but on a strict logical judgment, with the preliminary verification result being a binary conclusion of "consistent" or "inconsistent".

[0050] If the initial verification result of the instruction is "inconsistent," the system will calculate the deviation data of the instruction parameters. This deviation data will be compared with a preset threshold range. For example, if a protection instruction is set to a frequency of 500 times, while the upper limit of the safety specification is 400 times, the deviation data will be 100 times. If the preset allowable deviation threshold is 50 times, then since 100 times exceeds the threshold range of 50 times, the instruction will be marked as needing adjustment. For marked instructions, the system will perform correction. It should be noted that the correction algorithm replaces or adjusts the instruction parameters based on the "standard value" in the safety specification. For example, the system will directly correct the erroneous parameter of 500 times in the above instruction to the standard value of 400 times required by the safety specification, thus obtaining the updated instruction. Finally, the system will integrate all updated instructions and check for logical conflicts between them. Only after ensuring that all instructions comply with the safety specification and are logically consistent will the final execution instruction set be generated and output.

[0051] In step S17, instructions are distributed according to the final execution instruction set, the operation behavior data is updated synchronously, and an execution status report is output.

[0052] During the instruction distribution phase, the system automatically sends the final set of execution instructions to the inverter control module. Upon distribution, the system simultaneously records the instruction's sending time, content summary, and other information in the operation behavior log file. Secondly, during the real-time feedback data acquisition phase, the system actively extracts real-time feedback data from the inverter control module after instruction execution. For example, if the issued instruction is a frequency adjustment, the data acquisition tool will extract the current actual operating frequency value.

[0053] Finally, the system compares the acquired feedback data with pre-established standards to determine whether the execution meets expectations. It should be noted that the pre-established standards are the target parameter values ​​in the issued commands (for example, if the command requires the frequency to be adjusted to 400Hz, then the standard value is 400Hz). The judgment standard is not based on a fixed quantization percentage, but on the significance of the deviation. For example, if the real-time feedback frequency is 380Hz, which is close to the standard value of 400Hz, it can be initially assessed as normal; however, if the feedback value is only 300Hz, showing a significant deviation from the standard value, it is assessed as abnormal.

[0054] If the preliminary status assessment shows an anomaly, the operation log in the behavior record file is supplemented to obtain the specific fields of the anomaly data and determine the identification information of the anomaly command. Based on the identification of the anomaly command, the preliminary status assessment and the updated log content are integrated to generate a detailed report, determine whether the final execution was completed, and output a report result containing anomaly information.

[0055] For example, suppose the system needs to send a set of frequency adjustment commands to the inverter control module. The commands are sent to the target module in batches, and the sending time, content summary, and other information of each command are simultaneously recorded in the behavior log file, forming a list of commands containing sending details. Subsequently, the system extracts real-time trading frequency data from the inverter control module; for example, the current frequency is 380 times / minute, while the preset standard is 400 times / minute. The system compares the two, and if the frequency is significantly lower than expected (e.g., only 300 times / minute), it is initially assessed as an anomaly. For this anomaly, relevant operation logs are automatically added to the behavior log file, recording the time of the anomaly, the frequency value of 300 times, and the identification information of the relevant commands. After confirming the anomaly command identification, a detailed report is generated based on the preliminary status assessment results and the updated log content, including the specific identification of the anomaly command and the frequency deviation value. Finally, the system marks the execution as incomplete and outputs a report containing anomaly information.

[0056] In summary, this invention discloses an IoT-based remote monitoring method for frequency converters, comprising: acquiring an initial instruction dataset and operational behavior data; evaluating the rationality of each instruction based on the initial instruction dataset using rules from a preset safety specification library; marking instructions as potentially abnormal instructions when they deviate from a preset compliance range and outputting the abnormal marking result; performing dynamic analysis based on the abnormal marking result and the operational behavior data to analyze the deviation in instruction execution; determining the instruction as a high-risk instruction and generating a risk classification label when significant deviations exist; continuously tracking the high-risk instructions based on the risk classification label, monitoring state changes during instruction execution, and determining whether the state changes trigger preset abnormal detection items; outputting a state abnormality signal if so; activating response trigger points and generating a protection instruction sequence based on the state abnormality signal; performing secondary verification on the protection instruction sequence to determine whether it is consistent with the protection logic in the safety specification library; generating a final execution instruction set if the verification passes; distributing instructions based on the final execution instruction set, synchronously updating the operational behavior data, and outputting an execution status report. This invention provides a remote monitoring method and system for frequency converters based on the Internet of Things, which solves the problem of insufficient security in the remote management of existing technologies.

[0057] Reference Figure 2 The second embodiment of the present invention provides an Internet of Things-based remote monitoring system for frequency converters, comprising: The acquisition module is used to acquire the initial instruction dataset and operation behavior data; The evaluation module is used to evaluate the reasonableness of each instruction based on the initial instruction dataset and the rules in the preset security specification library. When an instruction deviates from the preset compliance range, it is marked as a potential abnormal instruction and the abnormal marking result is output. The analysis module is used to dynamically analyze the operation behavior data based on the anomaly marking results, analyze the deviation of the instruction execution, and when there is a significant deviation, determine that the instruction is a high-risk instruction and generate a risk classification label. The monitoring module is used to continuously track the high-risk instructions based on the risk classification labels, monitor the state changes during the execution of the instructions, and determine whether the state changes trigger preset abnormal detection items. If so, it outputs a state abnormality signal. The response module is used to activate the response trigger point and generate a protection instruction sequence based on the abnormal status signal. The verification module is used to perform secondary verification on the protection instruction sequence to determine whether it is consistent with the protection logic in the security specification library. If the verification passes, the final execution instruction set is generated. The execution module is used to distribute instructions according to the final execution instruction set, synchronously update the operation behavior data, and output an execution status report.

[0058] It should be noted that the IoT-based inverter remote monitoring device provided in this embodiment of the invention is used to execute all the process steps of the IoT-based inverter remote monitoring method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0059] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an Internet of Things (IoT)-based inverter remote monitoring program. When the processor executes the computer program, it implements the steps described in the various IoT-based inverter remote monitoring method embodiments above, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the acquisition module.

[0060] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0061] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0062] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0063] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0064] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0065] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0066] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for remote monitoring of frequency converters based on the Internet of Things, characterized in that, Executed by a computer, including: Acquire the initial instruction dataset and operation behavior data; Based on the initial instruction dataset, each instruction is evaluated for reasonableness using rules from a preset security specification library. When an instruction deviates from the preset compliance range, it is marked as a potential abnormal instruction, and the abnormal marking result is output. Based on the anomaly marking results and the operational behavior data, dynamic analysis is performed to analyze the deviation of instruction execution. When there is a significant deviation, the instruction is determined to be a high-risk instruction, and a risk classification label is generated. Based on the risk classification label, the high-risk instructions are continuously tracked, the state changes during the instruction execution process are monitored, and it is determined whether the state change triggers the preset abnormal detection item. If so, an abnormal state signal is output. Based on the abnormal status signal, activate the response trigger point and generate a protection instruction sequence; The protection instruction sequence is subjected to a second verification to determine whether it is consistent with the protection logic in the security specification library. If the verification passes, the final execution instruction set is generated. Based on the final execution instruction set, instructions are distributed, the operation behavior data is updated synchronously, and an execution status report is output. The step of performing a secondary verification on the protection instruction sequence to determine whether it is consistent with the protection logic in the security specification library, and if the verification passes, generating a final execution instruction set, including: The protection instruction sequence is logically matched with the security specification library to obtain a list of logically conflicting instructions; The instructions in the logically conflicting instruction list are modified or replaced to obtain a modified instruction set. The corrected instruction set is subjected to a logical consistency check. Once the check passes, the final execution instruction set is generated.

2. The method for remote monitoring of frequency converters based on the Internet of Things according to claim 1, characterized in that, The step of evaluating the reasonableness of each instruction based on the initial instruction dataset using rules from a preset security specification library to obtain anomaly marking results includes: The initial instruction dataset is matched with the preset security specification library according to rules, and instructions whose parameters deviate from the preset rules are initially marked to obtain a set of initially marked instructions; Perform data integrity verification on the initially marked instruction set. If an instruction has missing data, it is classified as an instruction to be verified. For the instruction to be verified, a security standard comparison is performed in conjunction with a preset evaluation basis database to obtain an anomaly marking result.

3. The method for remote monitoring of frequency converters based on the Internet of Things according to claim 1, characterized in that, The process involves dynamic analysis based on the anomaly marker results and the operational behavior data to analyze the deviation in instruction execution. When significant deviations are found, the instruction is identified as a high-risk instruction, and a risk classification label is generated, including: Based on the anomaly marking results, the corresponding operation behavior data and historical record data are extracted from the operation behavior data to obtain abnormal behavior data and abnormal record data. The abnormal behavior data is compared with the abnormal record data to obtain the execution deviation of the instruction. When the execution deviation exceeds a preset deviation threshold, the instruction is determined to be a high-risk instruction, and a high-risk instruction set is obtained. The high-risk instruction set is classified and labeled to obtain risk classification labels.

4. The method for remote monitoring of frequency converters based on the Internet of Things according to claim 1, characterized in that, The step involves continuously tracking high-risk instructions based on the risk classification tags, monitoring state changes during instruction execution, and determining whether the state changes trigger preset anomaly detection items. If so, an anomaly signal is output, including: Acquire data on the status changes of high-risk instructions during execution; Based on the risk classification labels, the state change data is classified to obtain the classified state change data; When the classified state change data exceeds the corresponding threshold in the preset anomaly detection item, the anomaly detection item is triggered, the state is determined to be abnormal, and the corresponding state abnormality signal is output.

5. The method for remote monitoring of frequency converters based on the Internet of Things according to claim 1, characterized in that, The step of activating a response trigger point and generating a protection instruction sequence based on the abnormal status signal includes: The abnormal status signal is compared with the preset response triggering conditions. When the triggering conditions are met, the activated response triggering point is obtained. Based on the activated response trigger points, a protection instruction sequence is generated.

6. The method for remote monitoring of frequency converters based on the Internet of Things according to claim 1, characterized in that, The step of distributing instructions according to the final execution instruction set, synchronously updating the operation behavior data, and outputting an execution status report includes: Based on the final execution instruction set, instructions are distributed to obtain a list of distributed instructions; Based on the final execution instruction set and the distributed instruction list, the operation behavior data is synchronously updated to obtain the updated operation behavior data; Based on the distributed instruction list, obtain the real-time feedback data after execution, compare it with the preset standard, and obtain the execution status evaluation result; An execution status report is generated and output based on the execution status assessment results and the updated operation behavior data.

7. An Internet of Things (IoT)-based remote monitoring system for frequency converters, used to implement the IoT-based remote monitoring method for frequency converters as described in any one of claims 1-6, characterized in that, include: The acquisition module is used to acquire the initial instruction dataset and operation behavior data; The evaluation module is used to evaluate the reasonableness of each instruction based on the initial instruction dataset and the rules in the preset security specification library. When an instruction deviates from the preset compliance range, it is marked as a potential abnormal instruction and the abnormal marking result is output. The analysis module is used to dynamically analyze the operation behavior data based on the anomaly marking results, analyze the deviation of the instruction execution, and when there is a significant deviation, determine that the instruction is a high-risk instruction and generate a risk classification label. The monitoring module is used to continuously track the high-risk instructions based on the risk classification labels, monitor the state changes during the execution of the instructions, and determine whether the state changes trigger preset abnormal detection items. If so, it outputs a state abnormality signal. The response module is used to activate the response trigger point and generate a protection instruction sequence based on the abnormal status signal. The verification module is used to perform secondary verification on the protection instruction sequence to determine whether it is consistent with the protection logic in the security specification library. If the verification passes, the final execution instruction set is generated. The execution module is used to distribute instructions according to the final execution instruction set, synchronously update the operation behavior data, and output an execution status report.

8. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the Internet of Things-based inverter remote monitoring method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the Internet of Things-based inverter remote monitoring method as described in any one of claims 1 to 6.

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