Intelligent switch cabinet integrated monitoring system and method

Abstract

The invention discloses an intelligent switch cabinet integrated monitoring system and method, and relates to the technical field of big data analysis, and the method comprises the steps: collecting state data, updating times and timestamps during the normal operation of a switch, carrying out the arrangement, and adding a unique identifier to form a historical normal data record; counting updating times to generate a switch state sequence, and constructing a reference state library; extracting a sequence, calculating the state probabilities of other switches in different states of the switch, and constructing a judgment standard library; after detecting that the switch state is changed, collecting actual data, matching the actual data with a standard library, calculating a matching degree, and if the matching degree is lower than a threshold value, performing early warning and manual checking; the system comprises a data acquisition module, a state sequence module, a probability standard module and a matching early warning module. The system improves the normativity and query efficiency of monitoring data, improves the accuracy of fault early warning, avoids cascading faults, guarantees the operation safety of the switch cabinet, and improves the operation and maintenance efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data analysis, and particularly relates to an intelligent switch cabinet integrated monitoring system and method. BACKGROUND

[0002] The intelligent switch cabinet is an upgraded iteration product of the traditional switch cabinet in the evolution process of the digital and automatic power system. The core essence is to break through the limitations of the traditional equipment in state monitoring, remote control, fault warning and the like by fusing new technologies, so as to meet the core needs of the modern power system for operation efficiency, operation safety and management intelligence. However, the device depends on the transmission of electric signals and data, and there is a risk of data delay. If a switch should complete state updating at the last time, but is forced to be executed in the next cycle due to delay, it may further cause a chain fault of the power system. SUMMARY

[0003] The present application aims to provide an intelligent switch cabinet integrated monitoring system and method to solve the problems in the prior art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an intelligent switch cabinet integrated monitoring method, the integrated monitoring method comprising the following steps: Step S1, collecting state data, updating times and time stamps of each switch in the normal operation process, and adding a unique identifier to form a historical normal data record; Step S2, from the historical normal data record, counting the updating times corresponding to each switch respectively, generating a switch state sequence, and constructing a reference state library; Step S3, extracting all switch state sequences corresponding to a certain updating time of a certain switch from the reference state library, calculating the state probability of other switches corresponding to the opening state of the switch and the state probability of other switches corresponding to the closing state of the switch, and constructing a switch state judgment standard library; Step S4, after detecting the change of the switch state, collecting the actual state data of all other switches associated with the switch, matching in the switch state judgment standard library, and if the matching degree is lower than a preset threshold, issuing a warning and performing manual verification.

[0005] Further, the specific steps of step S1 are as follows: Collecting state data, updating times and time stamps of each switch in the normal operation process, the state data including opening times and closing times, the updating times being refreshed daily, and using a unified data format to sort the collected state data, updating times and updating time stamps of each switch and adding a unique identifier, the unique identifier being randomly generated by the system, to form a historical normal data record.

[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 comprises the following steps: Step S1, collecting state data, update times and time stamps of each switch during normal operation, and attaching a unique identifier to form a historical normal data record; Step S2, from the historical normal data record, counting the update times corresponding to each switch individually, generating a switch state sequence, and constructing a reference state library; Step S3, extracting all switch state sequences corresponding to a certain update time of a certain switch from the reference state library, calculating the corresponding other switch state probability under the open state of the switch and the corresponding other switch state probability under the closed state of the switch, and constructing a switch state judgment standard library; Step S4, when detecting a change in switch state, collecting the actual state data of all other switches associated with the switch that changes, matching in the switch state judgment standard library, and if the matching degree is lower than a preset threshold, issuing a warning and performing manual verification.

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: Collecting state data, update times and time stamps of each switch during normal operation, the state data including the number of openings and the number of closings, the update times being refreshed daily, and using a unified data format to organize the collected state data, update times and update time stamps of each switch and attach a unique identifier, the unique identifier being randomly generated by the system, to form a historical normal data record.

3. The integrated monitoring method of the intelligent switch cabinet according to claim 2, characterized in that: The specific steps of step S2 are as follows: Step S2-1, from the historical normal data record, counting the update times corresponding to each switch individually, constructing a switch state sequence at each state update according to the switch state as the standard, the switch state sequence including the state data of each switch, the corresponding update times and the unique identifier of the switch, recording all switch state sequences to construct a state set; Step S2-2, integrating the state sets of all switches, classifying according to switch identifier and state data, taking the switch unique identifier as the first index and the update times as the second index to construct a reference state library.

4. The intelligent switch cabinet integrated monitoring method according to claim 3, characterized in that: The specific steps of step S3 are as follows: Step S3-1, extracting all switch state sequences of a certain update time of a certain switch from the reference state library, counting the state data of other switches based on the switch state, summarizing the number of openings and the number of closings and attaching a unique identifier, generating the corresponding other switch state probability under the open state of the switch and the corresponding other switch state probability under the closed state of the switch, the switch state probability being the proportion of the number of openings in the total number and the proportion of the number of closings in the total number, the total number being the sum of the number of openings and the number of closings; Step S3-2, sequentially calculating all switches using the above method to generate a switch state judgment standard library including switch unique identifier and update times.

5. The intelligent switch cabinet integrated monitoring method according to claim 4, characterized in that: The specific steps of step S4 are as follows: Step S4-1, after detecting the change of the switch state, collecting the unique identifier of the state transition switch, the current state data and the timestamp of this state update, arranging these information according to the unified data format set in step S1, matching the corresponding switch state sequence in the reference state library, and extracting the state probability of all other switches corresponding to the opening state and the closing state under the update number of the switch from the switch state judgment standard library; Step S4-2, collecting the actual state data of all other switches associated with the state transition switch, counting the actual opening times and the actual closing times of each other switch, calculating the proportion of the actual opening times in the actual total times and the proportion of the actual closing times in the actual total times of each other switch, the actual total times being the sum of the actual opening times and the actual closing times, and calculating the matching degree of the current state and the standard state by formula, the calculation formula being: ； wherein represents the matching degree, represents a summation operation on all associated other switches, represents the actual state proportion of the i-th other switch in the current state of the state transformation switch, represents the state probability of the i-th other switch in the switch state judgment standard library, k represents the total number of other switches associated with the state transformation switch, and if the matching degree is lower than a preset threshold, a warning is issued and manual verification is performed.

6. An intelligent switch cabinet integrated monitoring system applied to the intelligent switch cabinet integrated monitoring method of any one of claims 1-5, characterized in that: The integrated monitoring system comprises a data acquisition module, a state sequence module, a probability standard module and a matching and early warning module; The data acquisition module is used for collecting key data of each switch in the normal operation process and forming historical normal data records after arranging the key data; the state sequence module is used for counting the update number of the switch from the historical normal data records and generating a state sequence, and integrating data to build a reference state library; The probability standard module is used for extracting data from the reference state library to calculate the switch state probability, and repeatedly operating all switches to build a switch state judgment standard library; the matching and early warning module is used for collecting relevant data after the switch state changes and calculating the matching degree, and deciding whether to issue a warning and trigger manual verification according to the matching degree result; The output end of the data acquisition module is electrically connected to the input end of the state sequence module; the output end of the state sequence module is electrically connected to the input end of the probability standard module; and the output end of the probability standard module is electrically connected to the input end of the matching and early warning module.

7. The integrated monitoring system of the intelligent switch cabinet according to claim 1, characterized in that: The data acquisition module comprises a state data acquisition unit and a historical record generation unit; The state data acquisition unit is used for collecting state data of each switch in the normal operation process, daily refreshed update numbers and timestamps when each state update occurs; and the historical record generation unit is used for arranging the collected state data, update numbers and update timestamps of each switch in a unified data format, and forming historical normal data records by adding a unique identifier randomly generated by the system.

8. The integrated monitoring system of the intelligent switch cabinet according to claim 1, characterized in that: The state sequence module comprises an update number counting unit and a reference library building unit; The update number counting unit is used for counting the update number corresponding to each switch from the historical normal data records, and building a switch state sequence and recording all formed state sets according to the switch state as a standard; The reference library construction unit is configured to integrate the state sets of all switches, classify the switches according to switch identifiers and state data, and construct a reference state library with the switch unique identifiers as the first index and the update times as the second index. 9.The intelligent switch cabinet integrated monitoring system according to claim 1, characterized in that: The probability standard module comprises a state probability calculation unit and a standard library construction unit. The state probability calculation unit is configured to extract all switch state sequences corresponding to a certain switch and a certain update time from the reference state library, count the opening times and closing times of other switches based on the switch state, and calculate the state probabilities of other switches in different states of the switch; and the standard library construction unit is configured to sequentially perform state probability calculation operations on all switches to generate a switch state judgment standard library comprising switch unique identifiers and update times. 10.The intelligent switch cabinet integrated monitoring system according to claim 1, characterized in that: The matching early warning module comprises an actual data acquisition unit and a matching early warning triggering unit. The actual data acquisition unit is configured to acquire the unique identifier, current state data, this update timestamp of the switch, and the actual state data of associated other switches after detecting a change in the switch state. The matching early warning triggering unit is configured to count the actual opening times and actual closing times of associated other switches, calculate the actual proportion, calculate the matching degree between the current state and the standard state by a formula, and issue an early warning and perform manual verification if the matching degree is lower than a preset threshold.

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