An intelligent switch cabinet integrated monitoring system and method

Abstract

The application discloses an intelligent switch cabinet integrated monitoring system and method, and relates to the technical field of big data analysis.The application forms historical normal data records by collecting state data, updating times and time stamps of normal operation of switches, and arranging and adding unique identification.The application generates switch state sequences by counting updating times, and constructs a reference state library.The application extracts sequences to calculate state probabilities of other switches in different states of the switch, and constructs a judgment standard library.After detecting the change of the switch state, the application collects actual data, matches the actual data with the standard library, calculates the matching degree, and performs early warning and manual checking when the matching degree is lower than a threshold value.The system comprises a data collection module, a state sequence module, a probability standard module and a matching early warning module.The application improves the specification and query efficiency of monitoring data, improves the accuracy of fault early warning, avoids chain faults, ensures the safe operation of the switch cabinet, and improves the operation and maintenance efficiency.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, specifically to an intelligent integrated monitoring system and method for switchgear. Background Technology

[0002] Intelligent switchgear represents an upgrade from traditional switchgear as power systems evolve towards digitalization and automation. Its core essence lies in integrating new technologies to overcome the limitations of traditional equipment in areas such as status monitoring, remote control, and fault early warning, thereby meeting the core requirements of modern power systems for operational efficiency, operational safety, and intelligent management. However, this type of equipment relies on electrical signals to transmit data, which may pose a data delay risk: if a switch that should have completed its status update in the previous cycle is forced to postpone it due to a delay, it could further trigger a cascading failure in the power system. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent integrated monitoring system and method for switchgear, so as to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent switchgear integrated monitoring method, the integrated monitoring method comprising the following steps: Step S1: Collect the status data, update count, and timestamp of each status update for each switch during normal operation, and attach a unique identifier to form a historical normal data record; Step S2: From the historical normal data records, count the number of updates for each switch individually, generate a switch state sequence, and build a reference state library; Step S3: Extract all switch state sequences corresponding to a certain number of updates of a certain switch from the reference state library, calculate the probability of other switch states corresponding to the switch in the open state and the probability of other switch states corresponding to the switch in the closed state, and construct a standard library for switch state judgment. Step S4: When a change in switch state is detected, collect the actual state data of all other switches currently associated with the switch whose state has changed, and match them in the switch state judgment standard library. If the matching degree is lower than the preset threshold, issue an early warning and conduct manual verification.

[0005] Furthermore, the specific steps of step S1 are as follows: The system collects the status data, update count, and timestamp of each status update for each switch during normal operation. The status data includes the number of times the switch is turned on and off. The update count is refreshed daily. The system uses a unified data format to organize the collected status data, update count, and update timestamp of each switch and adds a unique identifier. The unique identifier is randomly generated by the system to form a historical record of normal data.

[0006] Furthermore, the specific steps of step S2 are as follows: Step S2-1: From the historical normal data records, count the number of updates for each switch individually. Based on the switch status, construct a switch status sequence each time the status is updated. The switch status sequence includes the status data of each switch, the corresponding number of updates, and the unique identifier of the switch. Record all switch status sequences to construct a status set. Step S2-2: Integrate the state sets of all switches, classify them according to switch identifier and state data, and build a reference state library with the unique switch identifier as the first-level index and the update number as the second-level index.

[0007] Furthermore, the specific steps of step S3 are as follows: Step S3-1: Extract all switch state sequences for a certain number of updates of a certain switch from the reference state library. Based on the switch state, count the state data of other switches, summarize the number of times the switch is turned on and the number of times it is turned off, and attach a unique identifier. Generate the probability of other switch states corresponding to the switch when it is turned on and the probability of other switch states corresponding to the switch when it is turned off. The switch state probability is the proportion of the number of times the switch is turned on in the total number of times and the proportion of the number of times the switch is turned off in the total number of times. The total number of times is the sum of the number of times the switch is turned on and the number of times the switch is turned off. Step S3-2: Perform the above calculations on all switches sequentially to generate a standard library for judging switch status, including the unique identifier of the switch and the number of updates.

[0008] Furthermore, the specific steps of step S4 are as follows: Step S4-1: When a change in switch state is detected, collect the unique identifier of the switch, the current state data, and the timestamp of the current state update. Organize this information according to the unified data format set in step S1, match the corresponding switch state sequence in the reference state library, and extract the state probabilities of all other switches corresponding to the open and closed states for the corresponding number of updates in the switch state judgment standard library. Step S4-2: Collect the actual state data of all other switches currently associated with the state change switch, count the actual number of times each other switch is turned on and off, calculate the percentage of each other switch's actual on count in its total actual count, and the percentage of its actual off count in its total actual count. The total actual count is the sum of the actual on count and the actual off count. Calculate the matching degree between the current state and the standard state using the following formula: ; in Indicates the degree of matching. This indicates a summation operation performed on all other associated switches. This represents the percentage of the actual state of the i-th other switch in its current state. The value represents the state probability of the i-th other switch in the switch state judgment standard library, and k represents the total number of other switches associated with the state change switch. If the matching degree is lower than the preset threshold, an early warning will be issued and manual verification will be carried out.

[0009] Furthermore, the integrated monitoring system includes a data acquisition module, a status sequence module, a probability standard module, and a matching early warning module; The data acquisition module is used to collect key data of each switch during normal operation and organize it into historical normal data records; the state sequence module is used to count the number of switch updates from the historical normal data records and generate a state sequence, and then integrate the data to build a reference state library; the probability standard module is used to extract data from the reference state library to calculate the probability of switch states, and then repeat the operation on all switches to build a switch state judgment standard library; the matching early warning module is used to collect relevant data and calculate the matching degree after the switch state changes, and decide whether to issue an early warning and trigger manual verification based on the matching degree result. The output of the data acquisition module is electrically connected to the input of the state sequence module; the output of the state sequence module is electrically connected to the input of the probability standard module; and the output of the probability standard module is electrically connected to the input of the matching early warning module.

[0010] Furthermore, the data acquisition module includes a status data acquisition unit and a historical record generation unit; The status data acquisition unit is used to collect the status data of each switch during normal operation, the number of daily updates, and the timestamp of each status update. The historical record generation unit is used to organize the collected status data, update count, and update timestamp of each switch in a unified data format, and add a unique identifier randomly generated by the system to form a historical normal data record.

[0011] Furthermore, the state sequence module includes an update count counting unit and a reference library construction unit; The update count unit is used to count the update count of each switch individually from historical normal data records, construct a switch state sequence based on the switch state and record all the formed state sets; the reference library construction unit is used to integrate the state sets of all switches, classify them according to switch identifier and state data, and construct a reference state library with the unique switch identifier as the first-level index and the update count as the second-level index.

[0012] Furthermore, the probability standard module includes a state probability calculation unit and a standard library construction unit; The state probability calculation unit is used to extract all switch state sequences corresponding to a certain number of updates of a certain switch from the reference state library, and to count the number of times other switches are turned on and off based on the switch state, and to calculate the state probability of other switches under different states of the switch; the standard library construction unit is used to perform state probability calculation operations on all switches in sequence to generate a switch state judgment standard library containing the unique identifier of the switch and the number of updates.

[0013] Furthermore, the matching early warning module includes an actual data acquisition unit and a matching early warning triggering unit; The actual data acquisition unit is used to collect the unique identifier of the switch, the current status data, the current update timestamp, and the actual status data of other related switches after detecting a change in the switch status. The matching warning triggering unit is used to count the actual number of times other related switches are opened and closed and calculate the actual percentage. It calculates the matching degree between the current status and the standard status using a formula. If the matching degree is lower than a preset threshold, a warning is issued and manual verification is performed.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention collects the status data of each switch during normal operation, the number of daily updates, and the timestamp of each status update. It organizes these data in a unified format and adds a unique identifier to form a historical normal data record. It also counts the number of updates to generate a status sequence and builds a reference status library with multi-level indexes. This provides accurate and traceable historical basis for subsequent status judgment, greatly improving the availability and query efficiency of monitoring data.

[0015] 2. This invention extracts the state sequence of a specific switch with a specific number of updates from a reference state library, calculates the state probability of other associated switches in its open and closed states, constructs a judgment standard library containing switch identifiers and update counts, and calculates the matching degree by combining the actual data after state changes with the standard probabilities. This achieves anomaly identification based on historical patterns, overcomes the problems of traditional monitoring relying on single data and having a high rate of false positives and false negatives, and improves the accuracy and pertinence of fault warnings.

[0016] 3. This invention establishes a complete system encompassing "data acquisition, state sequence generation, probability standard construction, and matching early warning." After a switch state changes, it quickly completes actual data acquisition and matching degree calculation. When the data falls below a preset threshold, an early warning is immediately triggered, and a manual verification instruction is pushed, forming a closed-loop monitoring mechanism. This avoids cascading power system failures caused by data delays, ensuring the safety and stability of switchgear operation, while reducing ineffective maintenance work and improving maintenance efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an intelligent switchgear integrated monitoring method according to the present invention. Figure 2 This is a schematic diagram of the structure of an intelligent switchgear integrated monitoring system according to the present invention. Detailed Implementation

[0018] 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.

[0019] Example 1: As Figure 1 As shown, the present invention provides a technical solution, an intelligent switchgear integrated monitoring method, the integrated monitoring method comprising the following steps: Step S1: Collect the status data, update count, and timestamp of each status update for each switch during normal operation, and attach a unique identifier to form a historical normal data record; The specific steps of step S1 are as follows: The system collects the status data, update count, and timestamp of each status update for each switch during normal operation. The status data includes the number of times the switch is turned on and off. The update count is refreshed daily. The system uses a unified data format to organize the collected status data, update count, and update timestamp of each switch and adds a unique identifier. The unique identifier is randomly generated by the system to form a historical normal data record. Step S2: From the historical normal data records, count the number of updates for each switch individually, generate a switch state sequence, and build a reference state library; The specific steps of step S2 are as follows: Step S2-1: From the historical normal data records, count the number of updates for each switch individually. Based on the switch status, construct a switch status sequence each time the status is updated. The switch status sequence includes the status data of each switch, the corresponding number of updates, and the unique identifier of the switch. Record all switch status sequences to construct a status set. Step S2-2: Integrate the state sets of all switches, classify them according to switch identifier and state data, and build a reference state library with the unique switch identifier as the first-level index and the update number as the second-level index; Step S3: Extract all switch state sequences corresponding to a certain number of updates of a certain switch from the reference state library, calculate the probability of other switch states corresponding to the switch in the open state and the probability of other switch states corresponding to the switch in the closed state, and construct a standard library for switch state judgment. The specific steps of step S3 are as follows: Step S3-1: Extract all switch state sequences for a certain number of updates of a certain switch from the reference state library. Based on the switch state, count the state data of other switches, summarize the number of times the switch is turned on and the number of times it is turned off, and attach a unique identifier. Generate the probability of other switch states corresponding to the switch when it is turned on and the probability of other switch states corresponding to the switch when it is turned off. The switch state probability is the proportion of the number of times the switch is turned on in the total number of times and the proportion of the number of times the switch is turned off in the total number of times. The total number of times is the sum of the number of times the switch is turned on and the number of times the switch is turned off. Step S3-2: Perform the above calculations on all switches sequentially to generate a standard library for judging switch status, including the unique identifier of the switch and the number of updates. Step S4: When a change in switch state is detected, collect the actual state data of all other switches currently associated with the switch whose state has changed, and match them in the switch state judgment standard library. If the matching degree is lower than the preset threshold, issue an early warning and conduct manual verification. The specific steps of step S4 are as follows: Step S4-1: When a change in switch state is detected, collect the unique identifier of the switch, the current state data, and the timestamp of the current state update. Organize this information according to the unified data format set in step S1, match the corresponding switch state sequence in the reference state library, and extract the state probabilities of all other switches corresponding to the open and closed states for the corresponding number of updates in the switch state judgment standard library. Step S4-2: Collect the actual state data of all other switches currently associated with the state change switch, count the actual number of times each other switch is turned on and off, calculate the percentage of each other switch's actual on count in its total actual count, and the percentage of its actual off count in its total actual count. The total actual count is the sum of the actual on count and the actual off count. Calculate the matching degree between the current state and the standard state using the following formula: ; in Indicates the degree of matching. This indicates a summation operation performed on all other associated switches. This represents the percentage of the actual state of the i-th other switch in its current state. The value represents the state probability of the i-th other switch in the switch state judgment standard library, and k represents the total number of other switches associated with the state change switch. If the matching degree is lower than the preset threshold, an early warning will be issued and manual verification will be carried out.

[0020] For example, the status data acquisition unit collects key data within one month of normal operation: status data, the number of daily updates, and the timestamp of each status update.

[0021] The status data of switch S1 on May 1, 2024 is "12 times on, 8 times off", with 3 updates and the update time recorded. The historical record generation unit organizes the above data in a unified format, adds a unique identifier randomly generated by the system, and forms and stores historical normal data records.

[0022] The update count unit counts the update counts for each of S1-S5 from one month of historical normal data records.

[0023] Based on the switch state update, a state sequence is constructed for each update: for example, the state sequence of S1 at update number 3 is "switch identifier S1 - state data, update number 3, unique identifier". Record the state sequences of all switches to form a state set.

[0024] The reference library construction unit integrates the state set, classifies it according to switch identifier and state data, and uses "S1", "S2", etc. as first-level indexes and update counts such as "3", "4", etc. as second-level indexes to construct the reference state library. The index "S1-3" corresponds to all state sequences when S1 is updated 3 times.

[0025] The state probability calculation unit extracts all state sequences corresponding to the 3rd update of S1 from the reference state library, and statistically analyzes the state data of S2-S5 based on the S1 state: when S1 is open, S2 is open 28 times and closed 7 times, for a total of 35 times, so the probability of S2 being open is 80% and the probability of being closed is 20%; when S1 is closed, S2 is open 9 times and closed 26 times, with an open probability of 25.7% and a closed probability of 74.3%.

[0026] Calculate the state probabilities of other switches under all update counts of S1 and under all update counts of S2-S5 respectively using the above method, and generate a standard library containing "switch identifier, update count, probability of other switches corresponding to open state, and probability of other switches corresponding to closed state".

[0027] When the matching warning module detects that the S1 state changes from closed to open, the actual data acquisition unit immediately collects the unique identifier, current state data, and timestamp of S1, and also collects the actual state data of S2-S5.

[0028] Statistics on the actual percentage of states S2-S5: S2 was actually opened 1 time and closed 4 times, with an opening percentage of 20%; S3 was actually opened 3 times and closed 2 times, with an opening percentage of 60%; S4 was actually opened 0 times and closed 5 times, with an opening percentage of 0%; S5 was actually opened 4 times and closed 1 time, with an opening percentage of 80%.

[0029] The matching degree is calculated using the formula: k=4. The probabilities of other switch states under the 42th update of S1 in the standard library are extracted: S2 open probability 82%, S3 open probability 75%, S4 open probability 18%, S5 open probability 85%. P=0.25 is calculated.

[0030] Because the matching degree of 0.25 is lower than the preset threshold of 0.85, the system immediately triggers a local audible and visual alarm and pushes a remote warning message to the maintenance personnel, prompting them to manually check S1 and the associated switch.

[0031] Example 2, as Figure 2 As shown, the present invention provides an intelligent switchgear integrated monitoring system, which includes a data acquisition module, a status sequence module, a probability standard module, and a matching early warning module. The data acquisition module is used to collect key data of each switch during normal operation and organize it into historical normal data records; the state sequence module is used to count the number of switch updates from the historical normal data records and generate a state sequence, and then integrate the data to build a reference state library; the probability standard module is used to extract data from the reference state library to calculate the probability of switch states, and then repeat the operation on all switches to build a switch state judgment standard library; the matching early warning module is used to collect relevant data and calculate the matching degree after the switch state changes, and decide whether to issue an early warning and trigger manual verification based on the matching degree result. The output of the data acquisition module is electrically connected to the input of the state sequence module; the output of the state sequence module is electrically connected to the input of the probability standard module; and the output of the probability standard module is electrically connected to the input of the matching early warning module.

[0032] The data acquisition module includes a status data acquisition unit and a historical record generation unit; The status data acquisition unit is used to collect the status data of each switch during normal operation, the number of daily updates, and the timestamp of each status update. The historical record generation unit is used to organize the collected status data, update count, and update timestamp of each switch in a unified data format, and add a unique identifier randomly generated by the system to form a historical normal data record.

[0033] The state sequence module includes an update count unit and a reference library construction unit; The update count unit is used to count the update count of each switch individually from historical normal data records, construct a switch state sequence based on the switch state and record all the formed state sets; the reference library construction unit is used to integrate the state sets of all switches, classify them according to switch identifier and state data, and construct a reference state library with the unique switch identifier as the first-level index and the update count as the second-level index.

[0034] The probability standard module includes a state probability calculation unit and a standard library construction unit; The state probability calculation unit is used to extract all switch state sequences corresponding to a certain number of updates of a certain switch from the reference state library, and to count the number of times other switches are turned on and off based on the switch state, and to calculate the state probability of other switches under different states of the switch; the standard library construction unit is used to perform state probability calculation operations on all switches in sequence to generate a switch state judgment standard library containing the unique identifier of the switch and the number of updates.

[0035] The matching early warning module includes an actual data acquisition unit and a matching early warning triggering unit; The actual data acquisition unit is used to collect the unique identifier of the switch, the current status data, the current update timestamp, and the actual status data of other related switches after detecting a change in the switch status. The matching warning triggering unit is used to count the actual number of times other related switches are opened and closed and calculate the actual percentage. It calculates the matching degree between the current status and the standard status using a formula. If the matching degree is lower than a preset threshold, a warning is issued and manual verification is performed.

[0036] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An intelligent switch cabinet integrated monitoring method, characterized in that: The integrated monitoring method includes the following steps: Step S1: Collect the status data, update count, and timestamp of each status update for each switch during normal operation, and attach a unique identifier to form a historical normal data record; Step S2: From the historical normal data records, count the number of updates for each switch individually, generate a switch state sequence, and build a reference state library; The specific steps of step S2 are as follows: Step S2-1: From the historical normal data records, count the number of updates for each switch individually. Based on the switch status, construct a switch status sequence each time the status is updated. The switch status sequence includes the status data of each switch, the corresponding number of updates, and the unique identifier of the switch. Record all switch status sequences to construct a status set. Step S2-2: Integrate the state sets of all switches, classify them according to switch identifier and state data, and build a reference state library with the unique switch identifier as the first-level index and the update number as the second-level index; Step S3: Extract all switch state sequences corresponding to a certain number of updates of a certain switch from the reference state library, calculate the probability of other switch states corresponding to the switch in the open state and the probability of other switch states corresponding to the switch in the closed state, and construct a standard library for switch state judgment. Step S4: When a change in switch state is detected, collect the actual state data of all other switches currently associated with the switch whose state has changed, and match them in the switch state judgment standard library. If the matching degree is lower than the preset threshold, issue an early warning and conduct manual verification. The specific steps of step S4 are as follows: Step S4-1: When a change in switch state is detected, collect the unique identifier of the switch, the current state data, and the timestamp of the current state update. Organize this information according to the unified data format set in step S1, match the corresponding switch state sequence in the reference state library, and extract the state probabilities of all other switches corresponding to the open and closed states for the corresponding number of updates in the switch state judgment standard library. Step S4-2: Collect the actual state data of all other switches currently associated with the state change switch, count the actual number of times each other switch is turned on and off, calculate the percentage of each other switch's actual on count in its total actual count, and the percentage of its actual off count in its total actual count. The total actual count is the sum of the actual on count and the actual off count. Calculate the matching degree between the current state and the standard state using the following formula: ； in Indicates the degree of matching. This indicates a summation operation performed on all other associated switches. This represents the percentage of the actual state of the i-th other switch in its current state. The value represents the state probability of the i-th other switch in the switch state judgment standard library, and k represents the total number of other switches associated with the state change switch. If the matching degree is lower than the preset threshold, an early warning will be issued and manual verification will be carried out.

2. The integrated monitoring method for the intelligent switch cabinet according to claim 1, characterized in that: The specific steps of step S1 are as follows: The system collects the status data, update count, and timestamp of each status update for each switch during normal operation. The status data includes the number of times the switch is turned on and off. The update count is refreshed daily. The system uses a unified data format to organize the collected status data, update count, and update timestamp of each switch and adds a unique identifier. The unique identifier is randomly generated by the system to form a historical record of normal data.

3. The intelligent switchgear integrated monitoring method according to claim 1, characterized in that: The specific steps of step S3 are as follows: Step S3-1: Extract all switch state sequences for a certain update count of a certain switch from the reference state library. Based on the switch state, statistically analyze the state data of other switches, summarize the number of times the switch is turned on and the number of times it is turned off, and attach a unique identifier. Generate the probability of other switch states corresponding to the switch when it is turned on and the probability of other switch states corresponding to the switch when it is turned off. The switch state probability is the proportion of the number of times the switch is turned on in the total number of times and the proportion of the number of times the switch is turned off in the total number of times. The total number of times is the sum of the number of times the switch is turned on and the number of times the switch is turned off. Step S3-2: Perform the above calculations on all switches sequentially to generate a standard library for judging switch status, including the unique identifier of the switch and the number of updates.

4. An intelligent switch cabinet integrated monitoring system applied to the intelligent switch cabinet integrated monitoring method of any one of claims 1-3, characterized in that: The integrated monitoring system includes a data acquisition module, a status sequence module, a probability standard module, and a matching early warning module; The data acquisition module is used to collect key data of each switch during normal operation and organize it to form a historical normal data record; the state sequence module is used to count the number of switch updates from the historical normal data record and generate a state sequence, and then integrate the data to build a reference state library. The probability standard module is used to extract data from the reference state library to calculate the probability of the switch state, and then repeat the operation on all switches to build a standard library for judging the switch state; the matching early warning module is used to collect relevant data and calculate the matching degree after the switch state changes, and decide whether to issue an early warning and trigger manual verification based on the matching degree result. The output of the data acquisition module is electrically connected to the input of the state sequence module; the output of the state sequence module is electrically connected to the input of the probability standard module; and the output of the probability standard module is electrically connected to the input of the matching early warning module.

5. The intelligent switchgear integrated monitoring system according to claim 4, characterized in that: The data acquisition module includes a status data acquisition unit and a historical record generation unit; The status data acquisition unit is used to collect the status data of each switch during normal operation, the number of daily updates, and the timestamp of each status update. The historical record generation unit is used to organize the collected status data, update count, and update timestamp of each switch in a unified data format, and add a unique identifier randomly generated by the system to form a historical normal data record.

6. The intelligent switchgear integrated monitoring system according to claim 4, characterized in that: The state sequence module includes an update count unit and a reference library construction unit; The update count unit is used to count the update count of each switch individually from historical normal data records, construct a switch state sequence based on the switch state and record all the formed state sets. The reference library construction unit is used to integrate the state sets of all switches, classify them according to switch identifier and state data, and construct a reference state library with the unique switch identifier as the first-level index and the update number as the second-level index.

7. The intelligent switchgear integrated monitoring system according to claim 4, characterized in that: The probability standard module includes a state probability calculation unit and a standard library construction unit; The state probability calculation unit is used to extract all switch state sequences corresponding to a certain number of updates of a certain switch from the reference state library, and to count the number of times other switches are turned on and off based on the switch state, and to calculate the state probability of other switches under different states of the switch; the standard library construction unit is used to perform state probability calculation operations on all switches in sequence to generate a switch state judgment standard library containing the unique identifier of the switch and the number of updates.

8. The intelligent switchgear integrated monitoring system according to claim 4, characterized in that: The matching early warning module includes an actual data acquisition unit and a matching early warning triggering unit; The actual data acquisition unit is used to collect the unique identifier of the switch, the current status data, the update timestamp, and the actual status data of other associated switches after detecting a change in the switch status. The matching early warning triggering unit is used to count the actual number of times other related switches are turned on and off and calculate the actual percentage. It calculates the matching degree between the current state and the standard state using a formula. If the matching degree is lower than a preset threshold, an early warning is issued and manual verification is performed.

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