Edge heterogeneous Internet of Things operation and maintenance chassis data processing method and system

Abstract

The invention provides an edge heterogeneous Internet of Things operation and maintenance chassis data processing method and system. The method comprises the following steps: firstly, according to the types of the multi-source heterogeneous terminals, formulating a unified data acquisition scheme, defining a unified acquired data format, respectively managing and executing a data acquisition task of each multi-source heterogeneous terminal by adopting an asynchronous multi-thread scheduling mechanism, and acquiring and generating corresponding acquired original data; and performing standardization processing on the collected original data in the normal sampling state to generate a multi-source collected data set. And then, according to the type of each multi-source heterogeneous terminal, for the corresponding multi-source acquisition data set, calculating a standardized anomaly score, performing weighted comprehensive processing, generating a comprehensive anomaly score, and determining an anomaly level according to the comprehensive anomaly score. And finally, correspondingly matching the exception level of the multi-source heterogeneous terminal with a preset local protection strategy, executing a local exception protection action based on a matching result, recording exception information and reporting exception alarm information to a remote center platform.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a method and system for processing data of an edge heterogeneous IoT operation and maintenance chassis. Background Art

[0002] As a key carrier for edge computing and front-end sensing, IoT operation and maintenance chassis are widely used in industries such as petroleum and petrochemicals, electricity, mining, smart cities, water services, highways, and rail transit. They primarily handle tasks such as power supply and distribution for front-end equipment, sensor data collection, and edge communication management. Currently, various IoT terminals deployed on-site are characterized by multi-source heterogeneity, diverse communication protocols, and non-uniform data structures. Existing solutions struggle to efficiently access and accurately identify operational anomalies within a unified architecture. In particular, when terminal devices experience communication interruptions, status fluctuations, or sensor anomalies, the lack of fine-grained anomaly identification mechanisms and differentiated local response strategies can easily lead to a backlog of hidden dangers or malfunctions, impacting system stability and remote operation and maintenance efficiency. Summary of the Invention

[0003] The present application provides a method and system for processing edge heterogeneous IoT operation and maintenance chassis data to solve the problems of inconsistent multi-source heterogeneous terminal status monitoring, low anomaly recognition accuracy, and lack of local rapid response capabilities.

[0004] In a first aspect, the present application provides a method for processing edge heterogeneous IoT operation and maintenance chassis data, comprising: Data collection and preprocessing for multi-source heterogeneous terminals: Develop a unified data collection plan based on the type of multi-source heterogeneous terminals, define a unified data collection format, and use an asynchronous multi-threaded scheduling mechanism to manage and execute data collection tasks for each multi-source heterogeneous terminal. Collect and generate corresponding raw data. Standardize the raw data with normal sampling status to generate a multi-source collection data set with a unified structure. Comprehensive anomaly assessment: based on each of the multi-source heterogeneous terminal types, for the corresponding multi-source collected data set, calculate a standardized anomaly score, perform weighted comprehensive processing, generate a comprehensive anomaly score, and determine the anomaly level based on the comprehensive anomaly score; Abnormal response and local protection decision-making, matching the abnormal level of the multi-source heterogeneous terminal with the preset local protection strategy, executing local abnormal protection actions including early warning mechanism, local protection measures and mandatory local protection measures based on the matching results, recording abnormal information and reporting abnormal alarm information to the remote center platform.

[0005] In one possible design, the unified data collection solution includes: Setting a sampling period, communication protocol parameters, and communication timeout threshold for each of the multi-source heterogeneous terminal types; Based on the sampling period, communication protocol parameters and communication timeout threshold, a unified collection task list is configured.

[0006] In one possible design, the unified acquisition data format includes a terminal unique identifier, a multi-source heterogeneous terminal type, a sampling value set, a timestamp, and an acquisition status; The sampling value set includes sampling values ​​and corresponding sampling value unique identifiers.

[0007] In one possible design, the comprehensive abnormality assessment includes: Determining a static normalized anomaly score for each of the sample values ​​in the multi-source acquisition data set; For each of the sample values ​​in the multi-source acquisition data set, determining a dynamic fluctuation normalized anomaly score of a continuous sample value and an enumerated sample value based on a dynamic fluctuation sliding window; For each sampling value, according to its static normalized anomaly score and dynamic fluctuation normalized anomaly score, the larger value of the two is selected as the single sampling item anomaly score of the sampling value; Based on the anomaly score of each single sampling item and in combination with the importance weight of each sampling value, a weighted comprehensive processing is performed to generate a weighted comprehensive anomaly score of each multi-source heterogeneous terminal; According to the weighted comprehensive anomaly score, the anomaly level of the multi-source heterogeneous terminal is determined based on an anomaly level classification rule.

[0008] In one possible design, the rule for determining the static normalized anomaly score is: When the sample value is within the corresponding static normal threshold range, the static normalized anomaly score of the sample value is 0; When the sample value exceeds the corresponding static normal threshold interval, the static normalized anomaly score of the sample value is calculated; The calculation formula of the static normalized anomaly score is:

[0009] Among them, i is the unique identifier of the sampling value, is the static normalized anomaly score of the sample value uniquely identified as i, is the sampling value with the unique identification of sampling value i, The sampling value The most recent static normal threshold.

[0010] In one possible design, the rule for determining the dynamic fluctuation normalized anomaly score of the continuous sampling value is: When the sliding standard deviation is less than or equal to the upper limit of the sliding standard deviation, the dynamic fluctuation normalized anomaly score of the continuous sampling value is 0; When the sliding standard deviation is greater than the upper limit of the sliding standard deviation, the dynamic fluctuation normalized anomaly score of the continuous sampling value is calculated; The calculation formula of the dynamic fluctuation normalized anomaly score of the continuous sampling value is:

[0011] Among them, i is the unique identifier of the sampling value, is the dynamic fluctuation normalized anomaly score of the sample value uniquely identified as i, is the sliding standard deviation, is the upper limit of the sliding standard deviation.

[0012] In one possible design, the calculation formula for determining the dynamic fluctuation anomaly score of the enumerated sampling value is:

[0013] Among them, i is the unique identifier of the sampling value, is the dynamic fluctuation normalized anomaly score of the sample value uniquely identified as i, If two adjacent sampling values ​​are different, the value is 1, otherwise the value is 0.

[0014] In one possible design, the abnormality levels include normal, mildly abnormal, moderately abnormal, and severely abnormal; The local protection strategy is to trigger local abnormality protection measures and abnormality reporting mechanisms when the abnormality level is mild abnormality, moderate abnormality and severe abnormality; The local abnormality protection measures include: When the abnormality level is a mild abnormality, the frequency of the sampling period is adjusted and the early warning mechanism is activated; When the abnormality level is moderate, local protection measures are implemented; When the abnormality level is severe, mandatory local protection measures are implemented; The abnormality reporting mechanism includes: When the abnormality level is a mild abnormality, the abnormality information is recorded in the local abnormality log, and the abnormality information is only synchronized to the local edge server or regional management node, and is not immediately reported to the remote central platform; When the abnormality level is moderate, the abnormality information is recorded in the local abnormality log, and the abnormality alarm information is reported to the remote center platform within the set short-term abnormality reporting window, marked as a medium warning level; When the abnormality level is a severe abnormality, the abnormality information is recorded in the local abnormality log, and the abnormality alarm information is immediately reported to the remote center platform, marked as a high alarm level.

[0015] In a second aspect, the present application provides an edge heterogeneous IoT operation and maintenance chassis data processing system, comprising: The multi-source heterogeneous terminal data collection and preprocessing module formulates a unified data collection plan and defines a unified collection data format based on the type of multi-source heterogeneous terminal. It adopts an asynchronous multi-threaded scheduling mechanism to manage and execute the data collection tasks of each multi-source heterogeneous terminal, collect and generate the corresponding collection raw data, and perform standardization processing on the collection raw data with normal sampling status to generate a multi-source collection data set with a unified structure. a comprehensive anomaly assessment module, which calculates a standardized anomaly score for each of the multi-source heterogeneous terminal types and the corresponding multi-source collected data set, performs weighted comprehensive processing to generate a comprehensive anomaly score, and determines an anomaly level based on the comprehensive anomaly score; The abnormal response and local protection decision module matches the abnormal level of the multi-source heterogeneous terminal with the preset local protection strategy, executes local abnormal protection actions including early warning mechanism, local protection measures and mandatory local protection measures based on the matching results, records abnormal information and reports abnormal alarm information to the remote center platform.

[0016] The present application provides a method and system for processing edge heterogeneous IoT operation and maintenance chassis data. By introducing a dual normalized scoring mechanism of static normalized anomaly scores of sampling values ​​and dynamic fluctuation normalized anomaly scores, and combining the importance weights of sampling values ​​to calculate the comprehensive anomaly scores of each multi-source heterogeneous terminal, it achieves accurate assessment of the operating status of each multi-source heterogeneous terminal and significantly improves the accuracy and robustness of anomaly identification.

[0017] At the same time, the present invention adopts differentiated local protection strategies based on the matching of multi-source heterogeneous terminal types and abnormality levels, and has the capabilities of hierarchical response, dynamic adjustment and traceable reporting. Without the intervention of a remote central platform, it can automatically complete lightweight operation and maintenance response and fault isolation on the edge side, thereby enhancing the ability to respond quickly to abnormalities and improving the stability of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] Figure 1 This is a flow chart of a method for processing edge heterogeneous IoT operation and maintenance chassis data according to an example embodiment of the present application; Figure 2 This is a schematic diagram of a process for multi-source heterogeneous terminal data collection and preprocessing according to an exemplary embodiment of the present application; Figure 3is a flowchart of a comprehensive anomaly assessment according to an exemplary embodiment of the present application; Figure 4 is a flowchart illustrating an abnormal response and local protection decision-making process according to an exemplary embodiment of the present application; Figure 5 This is a structural diagram of an edge heterogeneous Internet of Things operation and maintenance chassis data processing system shown in this application according to an example embodiment.

[0020] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0021] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0022] Figure 1 This is a flow chart of a method for processing edge heterogeneous IoT operation and maintenance chassis data according to an exemplary embodiment of the present application. Figure 1 As shown, the method provided in this embodiment includes: Step S101: Multi-source heterogeneous terminal data collection and preprocessing. According to the type of multi-source heterogeneous terminals, a unified data collection plan is formulated, a unified collection data format is defined, and an asynchronous multi-threaded scheduling mechanism is adopted to manage and execute the data collection tasks of each multi-source heterogeneous terminal separately, collect and generate corresponding collection raw data, and standardize the collection raw data with normal sampling status to generate a multi-source collection data set with a unified structure.

[0023] In this step, the multi-source heterogeneous terminal types include wireless communication link terminals, wired serial port terminals, digital input and output terminals, analog input and output terminals, and power distribution terminals. Wireless communication link terminals, including long-distance microwave terminals, 4G wireless communication terminals, CDMA wireless communication terminals and GPRS wireless communication terminals.

[0024] Wired serial port terminals include 4-port RS485 terminals, 8-port RS484 terminals and 4-port RS232 terminals.

[0025] Digital input and output terminals, including 8-channel digital input terminals (8DI terminals), 8-channel digital output terminals (8DO terminals), and 4-channel digital input + 4-channel digital output terminals (4DI4DO terminals).

[0026] Analog input and output terminals, including 8-channel analog input terminal (8AI terminal), 8-channel analog output terminal (8AO terminal), and 4-channel analog input + 4-channel analog output terminal (4AI4AO terminal).

[0027] Power distribution terminals, including AC220V power distribution terminals, AC24V power distribution terminals, DC48V power distribution terminals, DC36V power distribution terminals, DC24V power distribution terminals and DC12V power distribution terminals.

[0028] Step S102: Comprehensive anomaly assessment: based on each of the multi-source heterogeneous terminal types, for the corresponding multi-source acquisition data set, a standardized anomaly score is calculated, and weighted comprehensive processing is performed to generate a comprehensive anomaly score, and the anomaly level is determined based on the comprehensive anomaly score.

[0029] Step S103: Abnormal response and local protection decision, matching the abnormal level of the multi-source heterogeneous terminal with the preset local protection strategy, executing local abnormal protection actions including early warning mechanism, local protection measures and mandatory local protection measures based on the matching results, recording abnormal information and reporting abnormal alarm information to the remote center platform.

[0030] Figure 2 This is a flow chart of multi-source heterogeneous terminal data collection and preprocessing according to an exemplary embodiment of the present application. Figure 2 As shown, the method provided in this embodiment includes: Step S1011: Develop a unified data collection plan based on the multi-source heterogeneous terminal types and collection requirements.

[0031] In this step, the unified data collection solution includes: setting a sampling period, communication protocol parameters and communication timeout threshold for each of the multi-source heterogeneous terminal types; and configuring a unified collection task list based on the sampling period, communication protocol parameters and communication timeout threshold.

[0032] By configuring the unified acquisition solution, the scheduling standardization of multi-source heterogeneous terminal acquisition tasks is ensured, providing a unified basis for subsequent concurrent acquisition and standardized processing.

[0033] The settings of sampling period and communication timeout threshold are as follows: For wireless communication link terminals, the sampling period ranges from 10 seconds to 60 seconds, and the communication timeout threshold ranges from 1 second to 3 seconds.

[0034] For wired serial terminals, the sampling period ranges from 30 seconds to 120 seconds, and the communication timeout threshold ranges from 1 second to 5 seconds.

[0035] For digital input and output terminals, the sampling period ranges from 1 second to 10 seconds, and the communication timeout threshold ranges from 0.5 second to 1 second.

[0036] For analog input and output terminals, the sampling period ranges from 5 seconds to 30 seconds, and the communication timeout threshold ranges from 0.5 seconds to 2 seconds.

[0037] For power distribution terminals, the sampling period ranges from 10 seconds to 60 seconds, and the communication timeout threshold ranges from 1 second to 5 seconds.

[0038] The setting of communication protocol parameters is used to ensure that the IoT operation and maintenance chassis can correctly establish communication connections with each type of multi-source heterogeneous terminal and smoothly collect data. Therefore, the content of communication protocol parameters varies depending on the type of multi-source heterogeneous terminal, including: Wireless communication link terminal, the communication protocol parameters include: Wireless communication protocol type, including but not limited to LTE, CDMA or GPRS wireless communication protocols.

[0039] Authentication information, including but not limited to Access Point Name (APN) and SIM card identification number (IMSI).

[0040] For a wired serial terminal, the communication protocol parameters include: Serial port protocol type, including but not limited to Modbus RTU.

[0041] Baud Rate, measured in bps (bits per second).

[0042] Frame structure parameters, including but not limited to data bits, parity bits, and stop bits.

[0043] The register address mapping table is used to define the read or write location relationship of the device register.

[0044] Digital input and output terminal, the communication protocol parameters include: The GPIO Channel Mapping Table is used to identify the functions and addresses of each input and output channel.

[0045] Digital communication methods include but are not limited to GPIO (General Purpose Input Output) encoding, or fixed-length data frame structure protocols based on the RS485 interface.

[0046] Analog input and output terminal, the communication protocol parameters include: Analog-to-digital conversion interface protocols, including but not limited to ADC (Analog-to-Digital Converter) or DAC (Digital-to-Analog Converter).

[0047] Sampling resolution (Resolution), in bits.

[0048] Reference voltage configuration (Reference Voltage) is used to determine the sampling voltage range.

[0049] Power distribution terminal, the communication protocol parameters include: Communication protocol type, including but not limited to Modbus TCP or SNMP (Simple Network Management Protocol).

[0050] The IP address and communication port number of the distribution terminal are used to define the IP address and communication port number of the distribution terminal in the local area network or wide area network.

[0051] The object identifier mapping table (OID) is used to identify the access paths and data reading items of each functional module of the power distribution terminal.

[0052] Step S1012: define a unified acquisition data format.

[0053] In this step, a unified data collection format is defined according to the types of multi-source heterogeneous terminals and collection requirements, so that different types of multi-source heterogeneous terminal data have a unified field composition and structural order.

[0054] The unified acquisition data format includes a terminal unique identifier, a multi-source heterogeneous terminal type, a sampling value set, a timestamp, and an acquisition status.

[0055] The sampling value set includes sampling values ​​and corresponding sampling value unique identifiers.

[0056] The explanation of each field in the unified collection data format is as follows: Terminal unique identification: An identifier that uniquely identifies each multi-source heterogeneous terminal, used to ensure that the specific multi-source heterogeneous terminal can be accurately located during data collection, processing and reporting of abnormal information.

[0057] Multi-source heterogeneous terminal types: including wireless communication link terminals, wired serial port terminals, digital input and output terminals, analog input and output terminals, and power distribution terminals.

[0058] The sampling value set is a two-dimensional array set, including sampling values ​​and corresponding unique identifiers. The sampling values ​​are different depending on the type of multi-source heterogeneous terminal. The sampling values ​​of each multi-source heterogeneous terminal type are as follows: Wireless communication link endpoints, including RTT, packet loss rate, bandwidth utilization, link status, and signal quality.

[0059] Wired serial terminal, including operating voltage, operating power, operating status and communication status.

[0060] Digital input and output terminals, including switch status and output status.

[0061] Analog input and output terminals include continuous physical quantity data, including temperature, water level, and gas concentration in this embodiment.

[0062] Power distribution terminal, including output voltage, output current and over-limit abnormal alarm.

[0063] In this embodiment, core sampling values ​​that directly reflect the operating status of each type of multi-source heterogeneous terminal are selected based on the functional characteristics and operational status monitoring requirements of each type of multi-source heterogeneous terminal. This selected set of sampling values ​​serves as an important indicator for determining whether each multi-source heterogeneous terminal is in normal operation, ensuring the accuracy and efficiency of anomaly detection.

[0064] The expression for the set of sampled values ​​is:

[0065] in, It is the unique identifier of the sampling value; The sample value is uniquely identified as The sampling value of .

[0066] to They are the unique identifiers of the sampled values ​​of RTT, packet loss rate, bandwidth utilization, link status, signal quality, operating voltage, operating power, operating status, communication status, switch status, output status, continuous physical quantity data, voltage, output current and over-limit abnormal alarm.

[0067] to They are RTT, packet loss rate, bandwidth utilization, link status, signal quality, operating voltage, operating power, operating status, communication status, switch status, output status, continuous physical quantity data, voltage, output current and sampling values ​​of over-limit abnormal alarm.

[0068] Timestamp: records the standard timestamp corresponding to multi-source heterogeneous terminal data collection.

[0069] Collection status: indicates the status of data collection from each multi-source heterogeneous terminal, including normal sampling and abnormal sampling.

[0070] Step S1013: Based on the asynchronous multi-thread scheduling mechanism, periodically collect multi-source heterogeneous terminal data to generate collected raw data.

[0071] In this step, a thread pool is initialized based on the configured unified collection task list. Data collection tasks for each heterogeneous multi-source terminal are assigned to independent threads based on the terminal type. Within each independent thread, data collection tasks are asynchronously initiated using a timer based on the configured sampling period. Data exchange is completed according to the corresponding communication protocol parameters. The thread waits for a response within the configured communication timeout threshold, completing the periodic data collection task and generating raw data.

[0072] Step S1014: verify the validity of each piece of collected original data and mark the collection status.

[0073] In this step, each piece of collected raw data is tested for validity. The validity test includes: Communication test: Check whether the IoT operation and maintenance chassis communicates successfully with multiple heterogeneous terminals. If successful, the communication test is successful; otherwise, it fails.

[0074] Valid value test: Checks whether each sampled value in the sampled value set is a valid value. If all sampled values ​​are valid, the valid value test succeeds; otherwise, it fails.

[0075] Timestamp detection compares the timestamp of the collected value with the timestamp of the previous collected value. If the interval is consistent with the set sampling period and the timestamp does not regress, the timestamp is considered normal, otherwise it is considered a failure.

[0076] During the test, if any of the communication test, effective value test, and timestamp test fails, the acquisition status of the raw data is marked as sampling abnormal. Otherwise, the acquisition status of the raw data is marked as sampling normal.

[0077] Step S1015: performing standardization processing on the collected raw data marked as being sampled normally to generate a multi-source collected data set.

[0078] In this step, for each piece of collected raw data marked as being sampled normally, field encapsulation is performed according to a uniformly defined encapsulation template to generate standardized collected data.

[0079] The field encapsulation includes: encapsulating the terminal unique identifier, multi-source heterogeneous terminal type, sampling value set, time stamp and collection status information in sequence according to a unified field order.

[0080] After the field encapsulation is completed, the standardized collected data is uniformly organized according to the preset data output format to form a multi-source collected data set with a consistent structure. The multi-source collected data set is output in a standardized JSON format to ensure the standardization of the data structure and the consistency of the analysis.

[0081] Figure 3 FIG. 1 is a flow chart of a comprehensive abnormality assessment process according to an exemplary embodiment of the present application. Figure 3 As shown, the method provided in this embodiment includes: Step S1021: determining a static normalized anomaly score for each of the sample values ​​in the multi-source acquisition data set.

[0082] In this step, the rule for determining the static normalized anomaly score is: When the sample value is within the corresponding static normal threshold range, the static normalized anomaly score of the sample value is 0; When the sampling value exceeds the corresponding static normal threshold interval, a static normalized anomaly score of the sampling value is calculated.

[0083] The calculation formula of the static normalized anomaly score is:

[0084] Among them, the static normal threshold range is , is the static normal lower limit value of the sampling value uniquely identified as i, is the static normal upper limit value of the sampling value uniquely identified as i, and It is a preset value, and its value varies according to the type of multi-source heterogeneous terminals; and The value of is also different; i is the unique identifier of the sampling value; is the static normalized anomaly score of the sample value uniquely identified as i; The sampling value uniquely identified as i; The sampling value The nearest static normal threshold, i.e. The value of or value.

[0085] The settings of the static normal lower limit and the static normal upper limit are as follows: 1. Wireless communication link terminal RTT: The static normal lower limit is 0ms, and the static normal upper limit is 500ms. For a wireless communication link terminal that is operating normally, the link delay is usually less than 500ms.

[0086] Packet loss rate: The static normal lower limit is 0%, and the static normal upper limit is 5%. For a wireless communication link terminal operating normally, the packet loss rate should not exceed 5%.

[0087] Bandwidth utilization: The lower limit for static bandwidth utilization is 10%, and the upper limit for static bandwidth utilization is 90%. Bandwidth utilization that is too low or too high is considered abnormal.

[0088] Link status: This value is an enumeration type. Valid values ​​are 0 or 1, with 1 indicating normal and 0 indicating abnormal. The static normal lower limit and static normal upper limit are both 1, and only 1 is allowed.

[0089] Signal Quality: The static normal lower limit is -80dBm, and the static normal upper limit is 0dBm. Within the range of -80dBm to 0dBm, the signal strength is in a stable operating range.

[0090] 2. Wired serial terminal Working voltage: The static normal lower limit is 198 V, and the static normal upper limit is 242 V. The normal range is ±10% around the rated voltage of 220 V.

[0091] Operating Power: The lower limit for static normality is 5% of rated power, and the upper limit for static normality is 100% of rated power. Operating power is considered normal within the range of no-load to full-load and can be set according to the actual power of the specific equipment.

[0092] Running status: This value is an enumeration type. Legal values ​​are 0 or 1, with 1 indicating normal and 0 indicating abnormal. The static normal lower limit and static normal upper limit are both 1, and only 1 is allowed.

[0093] Communication status: This value is an enumeration type. Legal values ​​are 0 or 1, with 1 indicating normal and 0 indicating abnormal. The static normal lower limit and static normal upper limit are both 1, and only 1 is allowed.

[0094] 3. Digital input and output terminals Switch state: Its value is an enumeration type. The legal value is 0 or 1, 1 is on, 0 is off, the static normal lower limit value is 0, and the static normal upper limit value is 1.

[0095] Output status: Its value is an enumeration type. The legal value is 0 or 1, 1 is on, 0 is off, the static normal lower limit value is 0, and the static normal upper limit value is 1.

[0096] 4. Analog input and output terminals Continuous physical quantity data: preset according to actual business scenarios.

[0097] In this embodiment, it includes: Temperature: The static normal lower limit is -20℃, and the static normal upper limit is 80℃.

[0098] Water level: The static normal lower limit is 0m, and the static normal upper limit is 5m.

[0099] Gas concentration: The static normal lower limit is 0ppm, and the static normal upper limit is the preset safety concentration upper limit ppm.

[0100] 5. Power distribution terminal Output voltage: The static normal lower limit is 90% of the rated voltage, and the static normal upper limit is 110% of the rated voltage.

[0101] Output current: The static normal lower limit is 0A, and the static normal upper limit is the rated current value of the distribution terminal.

[0102] Over-limit abnormal alarm: This value is an enumeration type. The legal value is 0 or 1, with 1 indicating normal and 0 indicating abnormal. The static normal lower limit and static normal upper limit are both 1, and only 1 is allowed.

[0103] Step S1022: for each of the sample values ​​in the multi-source acquisition data set, determine the dynamic fluctuation normalized anomaly score of the continuous sample value and the enumeration sample value based on the dynamic fluctuation sliding window.

[0104] The continuous sampling values ​​include RTT, packet loss rate, bandwidth utilization, signal quality, operating voltage, operating power, continuous physical quantity data, output voltage, and output current.

[0105] The enumerated sampling values ​​include link status, operation status, communication status, switch status, output status, and over-limit abnormality alarm.

[0106] This step specifically includes: 1. Set the dynamic fluctuation sliding window length k, where k is a positive integer.

[0107] Based on the historical sampling values, combined with the timestamp and the dynamic fluctuation sliding window length k, a dynamic fluctuation sliding window is constructed. That is, the historical sampling values ​​with the unique identification of the sampling value i are sorted in ascending order by the timestamp, the tth sampling value in the sequence is selected as the current sample, and the most recent k sampling values ​​are extracted forward. Construct a dynamic fluctuation sliding window. In this embodiment, the value of k is set to 5.

[0108] 2. Determine the dynamic fluctuation normalized anomaly score of continuous sampling values, including: For each of the continuous sampling values, a corresponding sliding standard deviation upper limit is set to limit the short-term fluctuation amplitude and prevent occasional disturbances from being misjudged as abnormalities.

[0109] Upper limit of sliding standard deviation of continuous sampling values The settings are as follows: Wireless communication link terminal RTT: The upper limit of the sliding standard deviation is 50 seconds.

[0110] Packet loss rate: The upper limit of the sliding standard deviation is 2%.

[0111] Bandwidth utilization: The upper limit of the sliding standard deviation is 20%.

[0112] Signal quality: The upper limit of the sliding standard deviation is 5dB.

[0113] Wired serial terminal Working voltage: The upper limit of sliding standard deviation is 2V.

[0114] Operating power: The upper limit of sliding standard deviation is 10% of rated power.

[0115] Analog input and output terminals Continuous physical quantity data: preset according to actual business scenarios.

[0116] In this embodiment, it includes: Temperature: The upper limit of the sliding standard deviation is 3°C.

[0117] Water level: The upper limit of sliding standard deviation is 0.2m.

[0118] Gas concentration: The upper limit of the sliding standard deviation is 5% of the upper limit of the safe concentration.

[0119] Power distribution terminals Output voltage: The upper limit of sliding standard deviation is 2% of rated voltage.

[0120] Output current: The upper limit of the sliding standard deviation is 10% of the rated current.

[0121] Determine the dynamic fluctuation normalized anomaly score of the continuous sampling value. The rule for determining the dynamic fluctuation normalized anomaly score of the continuous sampling value is: When the sliding standard deviation is less than or equal to the upper limit of the sliding standard deviation, the dynamic fluctuation normalized anomaly score of the continuous sampling value is 0; When the sliding standard deviation is greater than the upper limit of the sliding standard deviation, the dynamic fluctuation normalized anomaly score of the continuous sampling value is calculated.

[0122] The calculation formula of the dynamic fluctuation normalized anomaly score of the continuous sampling value is:

[0123] Where i is the unique identifier of the sampling value; is the normalized anomaly score of the dynamic fluctuation of the sample value uniquely identified as i; is the sliding standard deviation; is the upper limit of the sliding standard deviation.

[0124] The calculation formula of the sliding standard deviation is:

[0125] in, is the sliding standard deviation of the sample value uniquely identified as i; k is the length of the dynamic fluctuation sliding window; The tjth sampling value whose unique identifier is i; It is the average value of the sampling values ​​uniquely identified as i in the dynamic fluctuation sliding window.

[0126] 3. Determine the dynamic fluctuation anomaly score of the enumerated sampling value.

[0127] The dynamic fluctuation anomaly score of the enumerated sampling value is calculated based on the state jump frequency to reflect its short-term volatility. The dynamic fluctuation anomaly score calculation formula of the enumerated sampling value is:

[0128] Where i is the unique identifier of the sampling value; is the normalized anomaly score of the dynamic fluctuation of the sample value uniquely identified as i; If two adjacent sampling values ​​are different, the value is 1, otherwise the value is 0. It is used to count the number of transition events and can reflect state instability.

[0129] Step S1023: for each sampling value, according to its static normalized anomaly score and dynamic fluctuation normalized anomaly score, select the larger value of the two as the single sampling item anomaly score of the sampling value.

[0130] The expression for determining the anomaly score of a single sample item is:

[0131] Where i is the unique identifier of the sampling value; is the abnormal score of a single sample item whose sample value is uniquely identified as i, is the static normalized anomaly score of the sample value uniquely identified as i, is the normalized anomaly score of the dynamic fluctuation of the sample value uniquely identified as i.

[0132] It should be noted that the static normalized anomaly score is used to identify whether a sample value exceeds the preset normal range, reflecting the absolute anomaly of the sample value itself. The dynamic fluctuation normalized anomaly score is used to identify sharp fluctuations in the sample value over a short period of time, reflecting issues with the sample value stability. This dual mechanism of static normalized anomaly scores and dynamic fluctuation normalized anomaly scores enables comprehensive identification of sampling anomalies across heterogeneous terminals, balancing accuracy and sensitivity, avoiding missed detections and false alarms, and improving the robustness and reliability of anomaly detection for heterogeneous terminals.

[0133] Step S1024: Based on the anomaly score of each single sampling item and in combination with the importance weight of each sampling value, a weighted comprehensive processing is performed to generate a weighted comprehensive anomaly score of each multi-source heterogeneous terminal.

[0134] In this step, when the multi-source heterogeneous terminal type is a wireless communication link terminal, the calculation formula for the weighted comprehensive anomaly score is:

[0135] in, is the weighted comprehensive anomaly score of the wireless communication link terminal; 、 、 、 and All are importance weights; is the single sample item anomaly score of RTT; is the single-sample anomaly score of the packet loss rate; is the single-sample anomaly score of bandwidth utilization; is the abnormal score of a single sample item of the link status; is the single-sample anomaly score of signal quality.

[0136] The value of is 0.25; The value of is 0.25; The value of is 0.2; The value of is 0.15; The value of is 0.15.

[0137] When the multi-source heterogeneous terminal type is a wired serial terminal, the weighted comprehensive anomaly score is:

[0138] in, The weighted comprehensive anomaly score of the wired serial port terminal; 、 、 and All are importance weights; is the single sampling item anomaly score of the working voltage; is the single sample item anomaly score of the operating power; is the abnormal score of a single sampling item in the running state; is the abnormal score of a single sample item in the communication status.

[0139] The value of is 0.3; The value of is 0.25; The value of is 0.25; The value of is 0.2.

[0140] When the multi-source heterogeneous terminal type is a digital input / output terminal, the weighted comprehensive anomaly score is:

[0141] in, The weighted comprehensive abnormality score of the digital input and output terminals; 、 All are importance weights; is the abnormal score of a single sample item in the switch state; is the single-sample abnormality score of the output state.

[0142] The value of is 0.5; The value of is 0.5.

[0143] When the multi-source heterogeneous terminal type is an analog input / output terminal, the weighted comprehensive anomaly score is:

[0144] in, The weighted comprehensive abnormality score of the analog input and output terminals; It is the anomaly score of a single sample item of continuous physical quantity data.

[0145] When the multi-source heterogeneous terminal type is a distribution terminal, the weighted comprehensive anomaly score is:

[0146] in, is the weighted comprehensive abnormality score of the distribution terminal; 、 and All are importance weights; is the single sample item anomaly score of the output voltage; is the single sample item anomaly score of the output current; The single sampling item anomaly score for the out-of-limit anomaly alarm.

[0147] The value of is 0.4; The value of is 0.4; The value of is 0.2.

[0148] Step S1025: determining the abnormality level of the multi-source heterogeneous terminal according to the weighted comprehensive abnormality score and based on an abnormality level classification rule.

[0149] In this step, the abnormal level classification rules include: When the weighted comprehensive abnormality score is less than 0.05, the abnormality level is normal; When the weighted comprehensive abnormality score is greater than or equal to 0.05 and less than 0.15, the abnormality level is mild abnormality; When the weighted comprehensive abnormality score is greater than or equal to 0.15 and less than 0.35, the abnormality level is moderate abnormality; When the weighted comprehensive abnormality score is greater than or equal to 0.35, the abnormality level is severe abnormality.

[0150] Figure 4 FIG. 1 is a flow chart of abnormal response and local protection decision-making according to an exemplary embodiment of the present application. Figure 4 As shown, the method provided in this embodiment includes: Step S1031: matching the abnormality levels of the multi-source heterogeneous terminals with the preset local protection strategies.

[0151] In this step, the local protection strategy is to trigger local abnormality protection measures and abnormality reporting mechanisms when the abnormality level is mild abnormality, moderate abnormality and severe abnormality.

[0152] The local abnormality protection measures include: When the abnormality level is a mild abnormality, the frequency of the sampling period is adjusted and the early warning mechanism is activated.

[0153] When the abnormality level is moderate, local protection measures are executed.

[0154] When the abnormality level is a severe abnormality, mandatory local protection measures are executed.

[0155] In this embodiment, the frequency of the sampling period, the warning mechanism, the local protection measures, and the mandatory local protection measures are different according to the different types of multi-source heterogeneous terminals, as shown below: 1. Wireless communication link terminal: When the abnormality level is mild, the sampling period is adjusted to 6 to 40 seconds. The early warning mechanism is to start local link quality detection. The IoT operation and maintenance chassis periodically performs ping tests and calculates the packet loss rate to evaluate the current link quality status.

[0156] When the abnormality level is moderate, the local protection measure is to soft restart the wireless module. The IoT operation and maintenance chassis issues a control instruction to drive the wireless communication link terminal to perform a module restart operation. The module restart includes re-registering the base station and rebuilding the link connection.

[0157] When the abnormality level is a severe abnormality, the mandatory local protection measure is to execute the primary-backup link switching. The IoT operation and maintenance chassis issues a control instruction to trigger the wireless communication link terminal to switch to the backup communication module or configure a backup access point name (APN) to ensure communication recovery.

[0158] 2. Wired serial terminal: When the abnormality level is mild, the sampling period is adjusted to 20 to 80 seconds. The early warning mechanism strengthens the detection of serial port communication status, and the IoT operation and maintenance chassis counts the number of retransmissions, timeout counts, and CRC check failures for evaluation.

[0159] When the abnormality level is moderate, the local protection measure is a soft restart of the serial port module. The IoT operation and maintenance chassis sends a control instruction to trigger the wired serial port terminal to reinitialize the serial port communication stack.

[0160] When the abnormality level is a severe abnormality, the mandatory local protection measure is to disconnect the serial port communication connection. The IoT operation and maintenance chassis issues a control instruction to control the wired serial port terminal to disconnect the communication by lowering the hardware interface level or triggering the interrupt pin.

[0161] 3. Digital input and output terminals: When the abnormality level is mild, the sampling period is adjusted to 0.6 seconds to 6 seconds. The early warning mechanism is fast polling. Status changes are read regularly by the IoT operation and maintenance chassis by shortening the sampling cycle The port status is recorded and the number of jumps is realized.

[0162] When the abnormality level is moderate, the local protection measure is to restart the digital input and output terminals. The acquisition module is triggered by the control instructions sent by the IoT operation and maintenance chassis to trigger the digital input and output terminal to execute the module restart operation.

[0163] If the anomaly level is severe, a mandatory local protection measure is to shut down the digital output driver circuits of the digital I / O terminals to prevent the propagation of the erroneous action. The IoT operation and maintenance chassis issues a control command, triggering the digital I / O terminals to shut down their internal drivers to block the output of erroneous control signals.

[0164] 4. Analog input and output terminals: When the abnormality level is mild, the sampling period is adjusted to 6 to 40 seconds. The early warning mechanism filters the sampled values, and the IoT operation and maintenance chassis uses a sliding average method to smooth and correct the sampled values, reducing the impact of sudden changes or jitter and more accurately reflecting the terminal's operating status. Specifically, this includes: based on the filtering sliding window length h, calculating the average of the current sampled value and the previous h-1 sampled values, and replacing the current value for subsequent analysis. Where h is a positive integer.

[0165] When the abnormality level is moderate, the local protection measure is to reset the analog interface module. The IoT operation and maintenance chassis sends a control instruction to trigger the analog input and output terminal to initialize its internal ADC module to restore the sampling function.

[0166] If the abnormality level is severe, a mandatory local protection measure is to disconnect the analog signal path to prevent abnormal output from causing a fault. The IoT operation and maintenance chassis issues a command, triggering the analog input and output terminals to perform a circuit breaker operation, preventing the abnormal signal from continuing to be output.

[0167] 5. Power distribution terminal: When the abnormality level is mild, the sampling period is adjusted to 20 to 80 seconds. The early warning mechanism monitors the output voltage and output current changes in real time. The IoT operation and maintenance chassis continuously collects the corresponding data based on the updated sampling period and performs fluctuation analysis.

[0168] If the abnormality level is moderate, the local protection measure is local current limiting, automatically reducing the output power. The IoT operation and maintenance chassis issues a control command, triggering the power distribution terminal to reduce the output power.

[0169] When the abnormality level is a severe abnormality, the mandatory local protection measure is rapid power-off protection. The IoT operation and maintenance chassis issues a control instruction to trigger the power distribution terminal to automatically reduce the output power to the set safety threshold.

[0170] The abnormality reporting mechanism includes: When the abnormality level is a mild abnormality, the abnormal information is recorded in the local abnormality log, and the abnormal information is only synchronized to the local edge server or regional management node, and is not immediately reported to the remote central platform. The abnormal information is summarized in batches or at a scheduled time and then reported to reduce the use of communication resources.

[0171] If the anomaly level is moderate, the anomaly information is recorded in the local anomaly log and reported to the remote central platform within the set short-term anomaly reporting window, marked as a medium warning level, to ensure that the remote central platform can promptly detect potential risks. In this example, the short-term anomaly reporting window is 5 minutes.

[0172] When the abnormality level is a severe abnormality, the abnormality information is recorded in the local abnormality log, and the abnormality alarm information is immediately reported to the remote center platform, marked as a high alarm level.

[0173] It should be noted that the exception information includes the terminal unique identifier, multi-source heterogeneous terminal type and exception level.

[0174] The abnormal alarm information further includes the local protection measures that have been implemented or the mandatory local protection measures based on the abnormal information.

[0175] When the abnormality level is normal, local abnormality protection measures and abnormality reporting mechanisms are not triggered. Only the periodic data collection task of multi-source heterogeneous terminals is executed, and the collected raw data is standardized to generate a multi-source collection data set, which is output in a standardized JSON format and saved to the local sampling log.

[0176] Step S1032: Based on the execution results of the early warning mechanism, local protection measures and mandatory local protection measures, the execution results are monitored.

[0177] Since the corresponding early warning mechanisms, local protection measures and mandatory local protection measures are different according to the different categories of multi-source heterogeneous terminals, the methods of monitoring the execution results based on the execution results of the early warning mechanisms, local protection measures and mandatory local protection measures are also different.

[0178] 1. Wireless communication link terminal: After the early warning mechanism is executed, monitor whether the Ping delay is reduced and the packet loss rate returns to the normal range.

[0179] After executing the local protection measures, check whether the base station registration status is successful and whether the link is re-established.

[0180] After executing the mandatory local protection measures, check whether the backup link is switched successfully and whether the communication is restored.

[0181] 2. Wired serial terminal: After the early warning mechanism is executed, monitor whether the number of retransmissions is reduced, whether the communication timeout ratio is reduced, and whether the CRC check pass rate is restored.

[0182] After executing the local protection measures, check whether the serial port communication is reconnected successfully and the communication data is transmitted normally.

[0183] After executing the mandatory local protection measures, check whether communication is re-established without abnormal interruption after the physical interface is reset.

[0184] 3. Digital input and output terminals: After the early warning mechanism is executed, check whether the IO status changes are stable and there is no frequent jump or abnormal jitter.

[0185] After executing the local protection measures, check whether the digital cache is refreshed successfully and whether the digital input and output resume normal communication.

[0186] After executing mandatory local protection measures, check whether the terminal is physically isolated after the control line is disconnected to avoid erroneous output.

[0187] 4. Analog input and output terminals: After the early warning mechanism is executed, check whether the fluctuation of the sampling value has decreased and whether the data has stabilized.

[0188] After executing the local protection measures, check whether the sampling values ​​of the analog input and output terminals are stable within the normal range.

[0189] After executing the mandatory local protection measures, check whether the analog output is reset to zero or the path is disconnected to prevent abnormal control signal output.

[0190] 5. Power distribution terminal: After the early warning mechanism is executed, check whether the output voltage and current are within the normal range and whether the fluctuation is stable.

[0191] After executing local protection measures, check whether the current limiting measures are effective and whether the output power drops to a safe range.

[0192] After executing the mandatory local protection measures, check whether the output terminal is successfully de-energized to prevent further electrical faults.

[0193] Figure 5 This is a structural diagram of an edge heterogeneous IoT operation and maintenance chassis data processing system according to an exemplary embodiment of the present application. Figure 5 As shown, the edge heterogeneous IoT operation and maintenance chassis data processing system 500 provided in this embodiment includes: The multi-source heterogeneous terminal data collection and preprocessing module 510 formulates a unified data collection plan based on the type of multi-source heterogeneous terminal, defines a unified collection data format, and uses an asynchronous multi-threaded scheduling mechanism to manage and execute the data collection tasks for each multi-source heterogeneous terminal, collect and generate the corresponding collected raw data. It also performs standardization processing on the collected raw data with normal sampling status to generate a multi-source collected data set with a unified structure. The comprehensive anomaly assessment module 520 calculates a standardized anomaly score for each of the multi-source heterogeneous terminal types and the corresponding multi-source collected data set, performs weighted comprehensive processing to generate a comprehensive anomaly score, and determines an anomaly level based on the comprehensive anomaly score; The abnormal response and local protection decision module 530 matches the abnormal level of the multi-source heterogeneous terminal with the preset local protection strategy, executes local abnormal protection actions including early warning mechanism, local protection measures and mandatory local protection measures based on the matching results, records abnormal information and reports abnormal alarm information to the remote center platform.

[0194] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0195] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for processing edge heterogeneous IoT operation and maintenance chassis data, characterized in that: include: Data collection and preprocessing for multi-source heterogeneous terminals: Develop a unified data collection plan based on the type of multi-source heterogeneous terminals, define a unified data collection format, and use an asynchronous multi-threaded scheduling mechanism to manage and execute data collection tasks for each multi-source heterogeneous terminal. Collect and generate corresponding raw data. Standardize the raw data with normal sampling status to generate a multi-source collection data set with a unified structure. Comprehensive anomaly assessment: based on each of the multi-source heterogeneous terminal types, for the corresponding multi-source collected data set, calculate a standardized anomaly score, perform weighted comprehensive processing, generate a comprehensive anomaly score, and determine the anomaly level based on the comprehensive anomaly score; Abnormal response and local protection decision-making, matching the abnormal level of the multi-source heterogeneous terminal with the preset local protection strategy, executing local abnormal protection actions including early warning mechanism, local protection measures and mandatory local protection measures based on the matching results, recording abnormal information and reporting abnormal alarm information to the remote center platform.

2. The edge heterogeneous IoT operation and maintenance chassis data processing method according to claim 1 is characterized in that: The unified data collection solution includes: Setting a sampling period, communication protocol parameters, and communication timeout threshold for each of the multi-source heterogeneous terminal types; Based on the sampling period, communication protocol parameters and communication timeout threshold, a unified collection task list is configured.

3. The edge heterogeneous IoT operation and maintenance chassis data processing method according to claim 1 is characterized in that: The unified data collection format includes a unique terminal identifier, a multi-source heterogeneous terminal type, a sampling value set, a timestamp, and a collection status; The sampling value set includes sampling values ​​and corresponding sampling value unique identifiers.

4. The edge heterogeneous IoT operation and maintenance chassis data processing method according to claim 3 is characterized in that: The comprehensive abnormality assessment includes: Determining a static normalized anomaly score for each of the sample values ​​in the multi-source acquisition data set; For each of the sample values ​​in the multi-source acquisition data set, determining a dynamic fluctuation normalized anomaly score of a continuous sample value and an enumerated sample value based on a dynamic fluctuation sliding window; For each sampling value, according to its static normalized anomaly score and dynamic fluctuation normalized anomaly score, the larger value of the two is selected as the single sampling item anomaly score of the sampling value; Based on the anomaly score of each single sampling item and in combination with the importance weight of each sampling value, a weighted comprehensive processing is performed to generate a weighted comprehensive anomaly score of each multi-source heterogeneous terminal; According to the weighted comprehensive anomaly score, the anomaly level of the multi-source heterogeneous terminal is determined based on an anomaly level classification rule.

5. The edge heterogeneous IoT operation and maintenance chassis data processing method according to claim 4 is characterized in that: The rule for determining the static normalized anomaly score is: When the sample value is within the corresponding static normal threshold range, the static normalized anomaly score of the sample value is 0; When the sample value exceeds the corresponding static normal threshold interval, the static normalized anomaly score of the sample value is calculated; The calculation formula of the static normalized anomaly score is: Among them, i is the unique identifier of the sampling value, is the static normalized anomaly score of the sample value uniquely identified as i, is the sampling value uniquely identified as i, The sampling value The most recent static normal threshold.

6. The edge heterogeneous IoT operation and maintenance chassis data processing method according to claim 4 is characterized in that: The rule for determining the dynamic fluctuation normalized anomaly score of the continuous sampling value is: When the sliding standard deviation is less than or equal to the upper limit of the sliding standard deviation, the dynamic fluctuation normalized anomaly score of the continuous sampling value is 0; When the sliding standard deviation is greater than the upper limit of the sliding standard deviation, the dynamic fluctuation normalized anomaly score of the continuous sampling value is calculated; The calculation formula of the dynamic fluctuation normalized anomaly score of the continuous sampling value is: Among them, i is the unique identifier of the sampling value, is the dynamic fluctuation normalized anomaly score of the sample value uniquely identified as i, is the sliding standard deviation, is the upper limit of the sliding standard deviation.

7. The edge heterogeneous IoT operation and maintenance chassis data processing method according to claim 4 is characterized in that: The calculation formula for determining the dynamic fluctuation anomaly score of the enumerated sampling value is: Among them, i is the unique identifier of the sampling value, is the dynamic fluctuation normalized anomaly score of the sample value uniquely identified as i, If two adjacent sampling values ​​are different, the value is 1, otherwise the value is 0.

8. The edge heterogeneous IoT operation and maintenance chassis data processing method according to claim 1 is characterized in that: The abnormality levels include normal, mild abnormality, moderate abnormality and severe abnormality; The local protection strategy is to trigger local abnormality protection measures and abnormality reporting mechanisms when the abnormality level is mild abnormality, moderate abnormality and severe abnormality; The local abnormality protection measures include: When the abnormality level is a mild abnormality, the frequency of the sampling period is adjusted and the early warning mechanism is activated; When the abnormality level is moderate, local protection measures are implemented; When the abnormality level is severe, mandatory local protection measures are implemented; The abnormality reporting mechanism includes: When the abnormality level is a mild abnormality, the abnormality information is recorded in the local abnormality log, and the abnormality information is only synchronized to the local edge server or regional management node, and is not immediately reported to the remote central platform; When the abnormality level is moderate, the abnormality information is recorded in the local abnormality log, and the abnormality alarm information is reported to the remote center platform within the set short-term abnormality reporting window, marked as a medium warning level; When the abnormality level is a severe abnormality, the abnormality information is recorded in the local abnormality log, and the abnormality alarm information is immediately reported to the remote center platform, marked as a high alarm level.

9. An edge heterogeneous IoT operation and maintenance chassis data processing system, characterized in that: include: The multi-source heterogeneous terminal data collection and preprocessing module formulates a unified data collection plan and defines a unified collection data format based on the type of multi-source heterogeneous terminal. It adopts an asynchronous multi-threaded scheduling mechanism to manage and execute the data collection tasks of each multi-source heterogeneous terminal, collect and generate the corresponding collection raw data, and perform standardization processing on the collection raw data with normal sampling status to generate a multi-source collection data set with a unified structure. a comprehensive anomaly assessment module, which calculates a standardized anomaly score for each of the multi-source heterogeneous terminal types and the corresponding multi-source collected data set, performs weighted comprehensive processing to generate a comprehensive anomaly score, and determines an anomaly level based on the comprehensive anomaly score; The abnormal response and local protection decision module matches the abnormal level of the multi-source heterogeneous terminal with the preset local protection strategy, executes local abnormal protection actions including early warning mechanism, local protection measures and mandatory local protection measures based on the matching results, records abnormal information and reports abnormal alarm information to the remote center platform.

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