Intelligent response processing method and system for measurement switch based on internet of things remote regulation and control

Abstract

The application discloses a measurement switch intelligent response processing method and system based on Internet of Things remote regulation and control, and belongs to the technical field of measurement switch control. The method forms a switch source set by uniformly compiling a measurement switch cluster and a single switch, records a trigger instruction state to generate a sample set; divides and counts abnormal trigger instructions according to the shortest time segment, and constructs a switch state matrix with a cycle period of a day; calculates the influence degree and the influence offset coefficient of the trigger instruction on the switch, and screens an abnormal association set; and constructs an abnormal trigger link and a dynamic intelligent atlas based on the association degree. The application solves the problems of disorder of traditional switch management, abnormal response lag, and difficulty in tracing, significantly improves the real-time performance, accuracy and operation and maintenance efficiency of the abnormal response of the measurement switch, and is suitable for industrial power distribution, intelligent buildings and other multi-scene measurement switch cluster regulation and control.

Description

Technical Field

[0001] This invention relates to the field of measurement switch control technology, specifically to a measurement switch intelligent response processing method and system based on remote control via the Internet of Things. Background Technology

[0002] With the deep penetration of IoT technology into industrial control, smart buildings, power distribution, and other fields, measurement switches, as core components for circuit safety monitoring and control, are facing increasingly complex application scenarios and a continuously expanding scale of clustered deployments. Traditional measurement switch control technology can no longer meet the needs of modern intelligent regulation and control, gradually exposing many key technical problems:

[0003] Traditional measurement switches are mostly independently numbered or have no unified identification, resulting in chaotic switch identities within the cluster and making it impossible to establish an anomaly correlation analysis mechanism across switches. For example, a power distribution system in an industrial plant contains multiple switch clusters, with each switch randomly numbered according to its area. When a switch triggers an anomaly due to circuit overload, it is impossible to quickly locate other potentially affected related switches within the cluster, leading to an disorderly expansion of the anomaly investigation scope and low efficiency.

[0004] Existing technologies mostly adopt batch recording modes with hourly or longer periods, without finely breaking down the monitoring period, making it difficult to capture instantaneous abnormal triggering events within a short period of time. At the same time, abnormal triggering instructions are only stored in the form of scattered logs without structured matrix mapping, which cannot intuitively present the distribution patterns of different time periods and different types of abnormalities, resulting in a lag in the judgment of abnormal trends.

[0005] Traditional technology relies on human experience to judge the impact of trigger commands on switches, which is highly subjective and prone to overlooking important abnormal commands and misjudging minor commands. For example, short-circuit trigger commands and slight voltage fluctuation trigger commands may be treated the same, which not only increases the load of ineffective responses, but may also lead to safety hazards because key abnormalities are not given priority attention.

[0006] Existing technologies lack a systematic mechanism for constructing anomaly triggering links. After an anomaly occurs, only the status of individual switches can be checked, making it impossible to trace the source and propagation path of the anomaly. For example, in a smart building, a single switch malfunction caused a chain of anomalies. Traditional technologies took 3 hours to locate the source, during which time power outages occurred in some areas. Furthermore, the lack of dynamically updated intelligent maps makes it difficult for managers to intuitively grasp the cluster's operating status, predict the trend of anomaly spread, and ensure timely response.

[0007] Traditional switches mostly operate in an independent response mode, without considering the correlation between switches within the cluster. When multiple switches trigger anomalies simultaneously, it is impossible to prioritize handling anomalies with high correlation and wide impact, resulting in resource waste and delays in responding to critical anomalies. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for intelligent response processing of measurement switches based on remote control via the Internet of Things, so as to solve the problems mentioned in the background art.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0010] The IoT-based remote control intelligent response processing system for measurement switches includes: a switch source set and trigger state construction module, a switch state matrix generation module, an abnormal association trigger instruction filtering module, and an abnormal trigger link and intelligent graph construction module.

[0011] The switch source set and trigger state construction module is used to uniformly compile the measurement switch cluster and the measurement switches within the cluster to form a switch source set, record the trigger command state of each measurement switch, and generate a trigger command state sample set.

[0012] The switch state matrix generation module is used to set the cyclic recording period and duration segment, count the abnormal trigger instruction set within the duration segment, construct the switch state matrix, and generate the measurement switch state matrix within the corresponding cyclic period.

[0013] The abnormal association trigger instruction filtering module is used to evaluate the degree of influence of the trigger instruction state on the measurement switch, calculate the influence offset coefficient, filter the abnormal association trigger instruction state and form an abnormal association set.

[0014] The abnormal triggering link and intelligent graph construction module is used to evaluate the abnormal triggering correlation between measurement switches, construct the abnormal triggering link of the switches, and generate and periodically update the intelligent graph of abnormal response of the measurement switches.

[0015] As a preferred embodiment of the present invention, the switch source set and trigger state construction module includes a switch source set compilation unit and a trigger command state sample set generation unit; the switch source set compilation unit is used to compile a unified set of each measurement switch cluster and each measurement switch within the cluster, with the measurement switch cluster as the center, to form a switch source set; the trigger command state sample set generation unit is used to record the trigger command state set of each measurement switch based on the abnormal circuit operation state, and integrate all trigger command states to form a trigger command state sample set.

[0016] As a preferred embodiment of the present invention, the switch state matrix generation module includes a cycle period and duration segment configuration unit, an abnormal trigger instruction set statistics unit, and a switch state matrix construction unit. The cycle period and duration segment configuration unit is used to set a cycle recording cycle in days and configure a uniform minimum duration scale unit within the cycle, dividing the cycle period into multiple duration segments. The abnormal trigger instruction set statistics unit is used to count the trigger instruction states generated by each measurement switch within each duration segment, and generate the abnormal trigger instruction set of the corresponding measurement switch within the duration segment. The switch state matrix construction unit is used to construct a switch state matrix model with the duration segment number as the row index and the trigger instruction state type as the column index, map the abnormal trigger instruction set into the model, and generate the measurement switch state matrix within the corresponding cycle period by setting it to 0 or 1.

[0017] As a preferred embodiment of the present invention, the abnormal association triggering command filtering module includes a triggering command state influence degree evaluation unit, an influence offset coefficient calculation unit, and an abnormal association triggering command state filtering unit. The triggering command state influence degree evaluation unit, based on the switch state matrix, counts the number of corresponding values ​​in the matrix and calculates the influence degree of each triggering command state on each measurement switch. The influence offset coefficient calculation unit, based on the influence degree of each triggering command state on the measurement switch, calculates the mean and standard deviation of the influence degree to obtain the influence offset coefficient of each triggering command state on the corresponding measurement switch. The abnormal association triggering command state filtering unit is used to compare the influence offset coefficient with a preset threshold, filter out abnormal association triggering command states that meet the threshold requirements, and record them in the abnormal association set.

[0018] As a preferred embodiment of the present invention, the abnormal triggering link and intelligent graph construction module includes a switch abnormal triggering correlation evaluation unit, an abnormal triggering link construction unit, and an intelligent graph generation and update unit. The switch abnormal triggering correlation evaluation unit, based on the abnormal correlation sets of each measurement switch within the same cluster, calculates the total number of trigger command states contained in the intersection and union of these sets, and calculates the abnormal triggering correlation between any two measurement switches. The abnormal triggering link construction unit is used to select the measurement switch with the highest abnormal triggering correlation as the relevant triggering object, construct the links of the switch abnormal triggering link, and organize all connected links to form multiple switch abnormal triggering links. The intelligent graph generation and update unit, based on the formed switch abnormal triggering links, generates an intelligent graph of the measurement switch response abnormalities of the measurement switch cluster, and updates the intelligent graph according to a preset period.

[0019] A smart response processing method for measurement switches based on remote control via the Internet of Things (IoT) includes the following steps:

[0020] Step S1: Based on the measurement switch cluster, the cluster and each measurement switch in the cluster are uniformly compiled to form a switch source set. At the same time, the trigger command status of each measurement switch based on the abnormal circuit operation state is recorded to generate a trigger command status sample set.

[0021] Step S2: Set a daily recording cycle, configure a uniform minimum duration unit within the cycle to divide the duration into segments, count the trigger command status of each measurement switch within each duration segment, generate an abnormal trigger command set, and construct a switch status matrix based on the duration segment number and trigger command status type. Map the abnormal trigger command set to the matrix to generate the corresponding switch status matrix within the cycle.

[0022] Step S3: Based on the switch state matrix, evaluate the impact of each trigger command state on each measurement switch, calculate the impact offset coefficient, and filter out abnormal associated trigger command states according to the preset impact offset coefficient threshold to form an abnormal association set;

[0023] Step S4: Based on the abnormal association set of each measurement switch, evaluate the abnormal triggering correlation between any two measurement switches in the same cluster, select the measurement switch with the highest correlation to construct the link of the switch abnormal triggering link, sort out the connected links to form the switch abnormal triggering link, generate the intelligent map of the measurement switch response abnormality of the measurement switch cluster and update it regularly.

[0024] As a preferred embodiment of the present invention, the specific implementation process of step S1 includes:

[0025] Centered on a measurement switch cluster, and uniformly compiling data for each measurement switch cluster and the IoT-controlled remote measurement switches within each cluster, a switch source set is formed, denoted as... ,in, This represents the i-th measurement switch cluster for remote control via the Internet of Things. This represents the a-th measuring switch, and A represents the total number of measuring switches;

[0026] The IoT backend records the trigger command status of each measurement switch. The trigger command status is set based on the abnormal operating state of the circuit in which the measurement switch is located. Based on the trigger command status, a trigger command status sample set is generated, denoted as... , This indicates the state of the v-th trigger command. This indicates the total number of trigger command status types.

[0027] As a preferred embodiment of the present invention, the specific implementation process of step S2 includes:

[0028] The cyclic recording period for the trigger command state of the measurement switch response is based on a day, and the shortest duration unit is uniformly configured within the cyclic period to form N duration segments within the cyclic period. Any nth duration segment is denoted as... ;

[0029] In duration Internally, the trigger command status of each measurement switch is statistically analyzed, and an abnormal trigger command set is generated. If it occurs within a time segment... Inside, the a-th measurement switch The v-th trigger command state was generated. Then the v-th trigger command state will be... Recorded to the a-th measurement switch Corresponding exception triggering instruction set middle;

[0030] Using the duration segment number as the row index and the trigger command state type number as the column index, a switch state matrix model is constructed to represent the abnormal trigger command set. Mapped to the nth row of the switch state matrix model, if an abnormal trigger instruction set is used... There is a trigger command state. Then set the nth row and vth column of the switch state matrix model to 1, if an abnormal trigger instruction set is used. There is no trigger command state in the middle. Then, set the nth row and vth column of the switch state matrix model to 0 to generate the measurement switch. The switch state matrix during the r-th cycle is denoted as... Where T is the total number of rows, i.e. the total number of duration segments contained in the cyclic recording period, and U is the total number of columns, corresponding to the total number of trigger command state types.

[0031] As a preferred embodiment of the present invention, the specific implementation process of step S3 includes:

[0032] Based on the switch state matrix, for any v-th trigger command state in the trigger command state sample set M Evaluate the status of the trigger command. For the a-th measurement switch degree of influence In the formula, F[] is a counting function used to count the number of 1s. Represents the switch state matrix The number of 1s contained in column v. Represents the switch state matrix The total number of 1s contained in each row and column of the text. This represents the current cycle recording period, where T and U are the switch state matrices, respectively. The total number of rows and columns; This indicates the trigger state of the instruction in the r-th cycle. For measuring switch The probability of phased impact, especially if the state matrix is ​​relevant. If none of the rows and columns contain the value 1, it means that the measurement switch was in the r-th cycle. If no exception is triggered, then let ;

[0033] It should be noted that, based on the time continuity and instantaneous nature of circuit abnormal triggering, the triggering of circuit abnormalities (such as short circuits and overloads) has a time distribution pattern. The triggering frequency within a short time segment (such as 10 minutes) can reflect the instantaneous impact intensity of the abnormality on the switch, avoiding the smoothing effect of long-period statistics on instantaneous abnormalities. By using the counting function F[] to count the number of 1 values ​​(abnormal triggering times) of a specific trigger instruction column (vth column) in the switch state matrix, and the ratio of the total number of 1 values ​​in the matrix (the total number of all abnormalities of the switch), that is, the proportion of a single type of abnormality in the total abnormalities, the stage impact probability is obtained.

[0034] Based on the cumulative damage effect of circuit anomalies, the impact of a single trigger command on the measurement switch is not an isolated event. Long-term repeated triggering will accelerate switch aging and reduce response sensitivity. The degree of cumulative impact can reflect the core role of the command in the switch life and operational stability, and quantify the "long-term impact weight" of the trigger command.

[0035] Quantify the state of trigger commands based on the degree of impact. For measuring switch Influence of offset coefficient In the formula, and Each trigger command status corresponds to a measurement switch. The mean and standard deviation of the degree of influence, and , ;Preset threshold for affecting offset coefficient; if it affects offset coefficient If the value is greater than or equal to the threshold affecting the offset coefficient, then the trigger command status is selected. As a measuring switch The status of the exception-related trigger command is recorded in the exception association set. In the middle, if the offset coefficient is affected If the value is less than the threshold affecting the offset coefficient, then the trigger command status will not be changed. Filtering indicates the trigger command status. For measuring switch No abnormal associations are generated, and they are not recorded in the abnormal association set. middle;

[0036] It should be noted that, with the mean α of the impact of all triggering commands as the center and the standard deviation β as the dispersion benchmark, the degree of deviation of the impact of a single type of triggering command is quantified by the ratio of the absolute deviation to the standard deviation. In the circuit system, the normal abnormal triggering of the measuring switch follows the statistical law of "most are slight and a few are serious" (approximately normal distribution). Triggering commands that deviate too much from the mean are often critical abnormalities that exceed the normal operating range, and their impact on the switch is significant.

[0037] As a preferred embodiment of the present invention, the specific implementation process of step S4 includes:

[0038] For the i-th measurement switch cluster Switch source set Except for the a-th measurement switch Any b-th measurement switch outside a≠b, based on abnormal association sets Association set with anomalies Evaluation measurement switch With measurement switch Inter-abnormal trigger correlation In the formula, Represents anomaly association set Association set with anomalies The total number of trigger command states contained in the intersection set. Represents anomaly association set Association set with anomalies The total number of trigger command states contained in the union set;

[0039] It should be noted that the abnormal trigger correlation is based on the Jaccard similarity coefficient. It quantifies the overlap of abnormal features between two switches by the ratio of the intersection (number of common trigger commands) to the union (number of all trigger commands) of the abnormal correlation sets. The value range is [0, 1], and the closer it is to 1, the stronger the correlation. Based on the abnormal propagation characteristics of the circuit system, the switches in the cluster are interconnected through the circuit topology. A critical abnormality of a switch (such as a short circuit) will be transmitted to the associated switches through the lines, resulting in a high degree of overlap in the trigger commands of the two. The correlation can reflect this physical transmission relationship.

[0040] Based on the correlation of anomaly triggers, the measurement switch with the highest correlation of anomaly triggers is selected. As a measuring switch Related triggering objects To form a link in the switch abnormal triggering chain. argmax{} is a feedback indicator function used to provide feedback on the measurement switch selected when the maximum anomaly trigger correlation is used as the correlation condition. ;

[0041] Based on switch source set By organizing the connected links, a cluster of measurement switches is formed. Several switch anomaly trigger links are used to generate a measurement switch cluster. The intelligent graph is used to detect abnormal response from the measurement switch and is updated periodically.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0043] By adopting a unified set of switch source sets and trigger state sample sets, it is easy to achieve standardized and systematic management of measurement switch clusters. It can solve the problem of broken abnormal correlation analysis caused by the lack of unified identification of traditional switches. For cross-switch collaborative response scenarios, this identification system can be directly adapted to multi-scenario cluster expansion.

[0044] By combining switch state matrix mapping, scattered abnormal data is transformed into structured data, breaking through the limitations of traditional batch recording. This not only enables fine-grained anomaly characterization but also transforms anomaly trend analysis from "lagging summary" to "real-time presentation," resulting in a significant improvement in accuracy in capturing instantaneous anomalies.

[0045] By quantifying the degree of influence and the influence offset coefficient, an objective and quantifiable abnormal correlation screening standard can be established, avoiding the subjectivity of traditional human experience judgment. At the same time, the coefficient calculation is dynamically updated based on historical data, adapting to the switching operation characteristics of different scenarios, and has strong adaptability.

[0046] Based on the correlation between switches, anomaly triggering links and dynamic intelligent graphs are constructed to visualize the anomaly propagation path, transforming anomaly tracing from "blind investigation" to "precise location". At the same time, the graph is updated regularly to support anomaly prediction, which can solve the core pain points of traditional technologies that cannot trace the source of anomalies and are difficult to predict the spread trend, thereby improving operation and maintenance efficiency. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0048] Figure 1 This is a schematic diagram of the steps of the intelligent response processing method for measurement switches based on remote control of the Internet of Things according to the present invention. Detailed Implementation

[0049] 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.

[0050] In this first embodiment: a measurement switch intelligent response processing system based on IoT remote control is provided. The system includes: a switch source set and trigger state construction module, a switch state matrix generation module, an abnormal association trigger instruction filtering module, and an abnormal trigger link and intelligent graph construction module.

[0051] The switch source set and trigger state construction module is used to uniformly compile the measurement switch cluster and the measurement switches within the cluster to form a switch source set, record the trigger command state of each measurement switch and generate a trigger command state sample set.

[0052] Specifically, the switch source set and trigger state construction module includes a switch source set compilation unit and a trigger command state sample set generation unit. The switch source set compilation unit is used to compile a unified set of each measurement switch cluster and each measurement switch within the cluster, with the measurement switch cluster as the center, to form a switch source set. The trigger command state sample set generation unit is used to record the trigger command state set of each measurement switch based on the abnormal circuit operation state, and integrate all trigger command states to form a trigger command state sample set.

[0053] The switch state matrix generation module is used to set the cyclic recording period and duration segment, count the abnormal trigger instruction set within the duration segment, construct the switch state matrix, and generate the measurement switch state matrix within the corresponding cyclic period.

[0054] Specifically, the switch state matrix generation module includes a cycle period and duration segment configuration unit, an abnormal trigger instruction set statistics unit, and a switch state matrix construction unit. The cycle period and duration segment configuration unit is used to set a cycle recording cycle in days and configure a uniform minimum duration scale unit within the cycle, dividing the cycle period into multiple duration segments. The abnormal trigger instruction set statistics unit is used to count the trigger instruction states generated by each measurement switch within each duration segment, generating the abnormal trigger instruction set of the corresponding measurement switch within that duration segment. The switch state matrix construction unit is used to construct a switch state matrix model with the duration segment number as the row index and the trigger instruction state type as the column index, mapping the abnormal trigger instruction set into the model, and generating the measurement switch state matrix within the corresponding cycle period by setting it to 0 or 1.

[0055] The abnormal association trigger command filtering module is used to evaluate the impact of trigger command status on the measurement switch, calculate the impact offset coefficient, filter abnormal association trigger command status and form an abnormal association set;

[0056] Specifically, the abnormal association trigger command filtering module includes a trigger command state influence degree evaluation unit, an influence offset coefficient calculation unit, and an abnormal association trigger command state filtering unit. The trigger command state influence degree evaluation unit calculates the influence degree of each trigger command state on each measurement switch by counting the number of corresponding values ​​in the switch state matrix. The influence offset coefficient calculation unit calculates the mean and standard deviation of the influence degree based on the influence degree of each trigger command state on the measurement switch, and obtains the influence offset coefficient of each trigger command state on the corresponding measurement switch. The abnormal association trigger command state filtering unit compares the influence offset coefficient with a preset threshold, filters out abnormal association trigger command states that meet the threshold requirements, and records them into the abnormal association set.

[0057] The abnormal triggering link and intelligent graph construction module is used to evaluate the abnormal triggering correlation between measurement switches, construct the abnormal triggering link of the switches, and generate and periodically update the intelligent graph of abnormal response of measurement switches.

[0058] Specifically, the abnormal triggering link and intelligent graph construction module includes a switch abnormal triggering correlation evaluation unit, an abnormal triggering link construction unit, and an intelligent graph generation and update unit. The switch abnormal triggering correlation evaluation unit calculates the abnormal triggering correlation between any two measurement switches based on the abnormal correlation sets of each measurement switch in the same cluster, counts the total number of trigger command states contained in the intersection and union, and calculates the abnormal triggering correlation between any two measurement switches. The abnormal triggering link construction unit selects the measurement switch with the highest abnormal triggering correlation as the relevant triggering object, constructs the link of the switch abnormal triggering link, and sorts out all connected links to form multiple switch abnormal triggering links. The intelligent graph generation and update unit generates an intelligent graph of the measurement switch response abnormalities of the measurement switch cluster based on the formed switch abnormal triggering links, and updates the intelligent graph according to a preset period.

[0059] Please see Figure 1 In this second embodiment, an intelligent response processing method for measurement switches based on remote control via the Internet of Things is provided, applicable to the first embodiment. This embodiment uses the control of measurement switch clusters in a large automotive parts manufacturing plant as an application scenario. The plant contains three measurement switch clusters (S1-S3), corresponding to the power distribution systems of the stamping workshop, assembly workshop, and warehousing area, respectively. A total of 20 measurement switches (K1-K20) are deployed. Five types of abnormal trigger commands need to be monitored: circuit overload (m1), short circuit (m2), high voltage (m3), low voltage (m4), and poor contact (m5), to ensure continuous operation of production equipment and prevent production line shutdowns due to circuit abnormalities.

[0060] The total number of measurement switches A=20, the number of clusters is 3 (S1: K1-K7, S2: K8-K14, S3: K15-K20); the number of trigger command state types U=5 (m1-m5), the trigger command state sample set M={m1, m2, m3, m4, m5}; the cycle recording period is 1 day (24 hours), the shortest duration scale unit is 10 minutes / segment, each cycle contains a total of duration segments T=24×6=144, the nth duration segment is denoted as tn (n∈[1,144]); the current cycle recording period r0=30 (historical data from the past 30 days is used for baseline calculation); the threshold for the influence offset coefficient is 0.8 (verified through historical data, this threshold can achieve an anomaly screening accuracy of 92%).

[0061] The method includes the following steps:

[0062] Step S1: Based on the measurement switch cluster, the cluster and each measurement switch in the cluster are uniformly compiled to form a switch source set. At the same time, the trigger command status of each measurement switch based on the abnormal circuit operation state is recorded to generate a trigger command status sample set.

[0063] For example, a measurement switch cluster is taken as the center, and each measurement switch cluster and each measurement switch remotely controlled by the Internet of Things within each measurement switch cluster is uniformly compiled to form a switch source set, denoted as . ,in, This represents the i-th measurement switch cluster for remote control via the Internet of Things. This represents the a-th measuring switch, and A represents the total number of measuring switches;

[0064] The IoT backend records the trigger command status of each measurement switch. The trigger command status is set based on the abnormal operating state of the circuit in which the measurement switch is located. Based on the trigger command status, a trigger command status sample set is generated, denoted as . , This indicates the state of the v-th trigger command. Indicates the total number of trigger command status types;

[0065] For example, three clusters and 20 switches are uniformly coded, with switch source sets W(S1)={K1-K7}, W(S2)={K8-K14}, and W(S3)={K15-K20}. The coding rule is "cluster number-switch sequence number" (e.g., K1=S1-01), ensuring that each switch is unique. The IoT backend records the trigger command status of each switch over 30 days, generating a sample set M={m1, m2, m3, m4, m5}.

[0066] Step S2: Set a daily recording cycle, configure a uniform minimum duration unit within the cycle to divide the duration into segments, count the trigger command status of each measurement switch within each duration segment, generate an abnormal trigger command set, and construct a switch status matrix based on the duration segment number and trigger command status type. Map the abnormal trigger command set to the matrix to generate the corresponding switch status matrix within the cycle.

[0067] For example, the cyclic recording period for the trigger command state of the measurement switch response is taken as a day, and the shortest duration unit is uniformly configured within the cyclic period to form N duration segments within the cyclic period, and any nth duration segment is denoted as ;

[0068] In duration Internally, the trigger command status of each measurement switch is statistically analyzed, and an abnormal trigger command set is generated. If it occurs within a time segment... Inside, the a-th measurement switch The v-th trigger command state was generated. Then the v-th trigger command state will be... Recorded to the a-th measurement switch Corresponding exception triggering instruction set middle;

[0069] Using the duration segment number as the row index and the trigger command state type number as the column index, a switch state matrix model is constructed to represent the abnormal trigger command set. Mapped to the nth row of the switch state matrix model, if an abnormal trigger instruction set is used... There is a trigger command state. Then set the nth row and vth column of the switch state matrix model to 1, if an abnormal trigger instruction set is used. There is no trigger command state in the middle. Then, set the nth row and vth column of the switch state matrix model to 0 to generate the measurement switch. The switch state matrix during the r-th cycle is denoted as... Where T is the total number of rows, i.e. the total number of duration segments contained in the cyclic recording period, and U is the total number of columns, corresponding to the total number of trigger command state types;

[0070] For example, by statistically analyzing the trigger commands of each switch in 10-minute segments, an abnormal trigger command set is generated. K3 (S1-03, the main circuit switch of the stamping workshop) generates a short-circuit trigger command m2 at t23 (3 hours and 50 minutes, i.e., n=23), which is recorded in K3(t23)={m2}. If there is no abnormality in a certain segment, the command set is empty. Using the duration segment number as the row index and the trigger command type as the column index, a 144×5 state matrix R144×5(Ka, r) ​​is constructed. If a trigger command exists, it is set to 1; otherwise, it is set to 0. The matrix clearly shows the abnormal distribution of K3 over 30 days, where m2 is triggered 12 times, concentrated in the daily peak production period (t48-t72, i.e., 8:00-12:00).

[0071] Step S3: Based on the switch state matrix, evaluate the impact of each trigger command state on each measurement switch, calculate the impact offset coefficient, and filter out abnormal associated trigger command states according to the preset impact offset coefficient threshold to form an abnormal association set;

[0072] For example, based on the switch state matrix, for any v-th trigger command state in the trigger command state sample set M Evaluate the status of the trigger command. For the a-th measurement switch degree of influence In the formula, F[] is a counting function used to count the number of 1s. Represents the switch state matrix The number of 1s contained in column v. Represents the switch state matrix The total number of 1s contained in each row and column of the text. This represents the current cycle recording period, where T and U are the switch state matrices, respectively. The total number of rows and columns; This indicates the trigger state of the instruction in the r-th cycle. For measuring switch The probability of phased impact, especially if the state matrix is ​​relevant. If none of the rows and columns contain the value 1, it means that the measurement switch was in the r-th cycle. If no exception is triggered, then let ;

[0073] Quantify the state of trigger commands based on the degree of impact. For measuring switch Influence of offset coefficient In the formula, and Each trigger command status corresponds to a measurement switch. The mean and standard deviation of the degree of influence, and , ;Preset threshold for affecting offset coefficient; if it affects offset coefficient If the value is greater than or equal to the threshold affecting the offset coefficient, then the trigger command status is selected. As a measuring switch The status of the exception-related trigger command is recorded in the exception association set. In the middle, if the offset coefficient is affected If the value is less than the threshold affecting the offset coefficient, then the trigger command status will not be changed. Filtering indicates the trigger command status. For measuring switch No abnormal associations are generated, and they are not recorded in the abnormal association set. middle;

[0074] For example, to calculate the influence of trigger commands on the switch, P(mv, Ka), taking the influence of m2 on K3 as an example: statistically analyze the state matrix of each cycle within 30 days, the average probability of stage influence is P(m2, K3) = (12+10+11+...+13) / 30≈0.42, calculate the average influence of all trigger commands on K3, α=0.3, standard deviation β=0.15, influence offset coefficient η=|0.42-0.3| / 0.15=0.8, reaching the threshold of 0.8, and include m2 in the abnormal association set of K3 G(K3)={m2, m5} (η=0.83 for m5).

[0075] Step S4: Based on the abnormal association set of each measurement switch, evaluate the abnormal triggering correlation between any two measurement switches in the same cluster, select the measurement switch with the highest correlation to construct the link of the switch abnormal triggering link, sort out the connected links to form the switch abnormal triggering link, generate the intelligent map of the measurement switch response abnormality of the measurement switch cluster and update it regularly.

[0076] For example, for the i-th measurement switch cluster Switch source set Except for the a-th measurement switch Any b-th measurement switch outside a≠b, based on abnormal association sets Association set with anomalies Evaluation measurement switch With measurement switch Inter-abnormal trigger correlation In the formula, Represents anomaly association set Association set with anomalies The total number of trigger command states contained in the intersection set. Represents anomaly association set Association set with anomalies The total number of trigger command states contained in the union set;

[0077] Based on the correlation of anomaly triggers, the measurement switch with the highest correlation of anomaly triggers is selected. As a measuring switch Related triggering objects To form a link in the switch abnormal triggering chain. argmax{} is a feedback indicator function used to provide feedback on the measurement switch selected when the maximum anomaly trigger correlation is used as the correlation condition. ;

[0078] Based on switch source set By organizing the connected links, a cluster of measurement switches is formed. Several switch anomaly trigger links are used to generate a measurement switch cluster. The intelligent graph of abnormal measurement switch response is updated regularly;

[0079] For example, calculate the correlation degree between switches within the same cluster: L(Ka, Kb) = NUM(G(Ka)∩G(Kb)) / NUM(G(Ka)∪G(Kb)). For example, G(K3) = {m2, m5}, G(K7) = {m2, m3}, intersection count = 1, union count = 3, L(K3, K7) = 1 / 3 ≈ 0.33; G(K3) = {m2, m5}, G(K5) = {m2, m5}, intersection count = 2, union count = 2, L(K3, K5) = 1, select K5 as the relevant triggering object of K3, and construct the chain K3→K5. All connected links are identified, forming three abnormal triggering links (S1 cluster: K3→K5→K7; S2 cluster: K10→K12; S3 cluster: K18→K20), generating an intelligent graph; after K3 triggers an m2 abnormality on a certain workday, the graph quickly locates the source as a poor contact (m5) in K5.

[0080] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0081] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent response processing of measurement switches based on remote control via the Internet of Things, characterized in that, The method includes the following steps: Step S1: Based on the measurement switch cluster, the cluster and each measurement switch in the cluster are uniformly compiled to form a switch source set. At the same time, the trigger command status of each measurement switch based on the abnormal circuit operation state is recorded to generate a trigger command status sample set. Step S2: Set a daily recording cycle, configure a uniform minimum duration unit within the cycle to divide the duration into segments, count the trigger command status of each measurement switch within each duration segment, generate an abnormal trigger command set, and construct a switch status matrix based on the duration segment number and trigger command status type. Map the abnormal trigger command set to the matrix to generate the corresponding switch status matrix within the cycle. Step S3: Based on the switch state matrix, evaluate the impact of each trigger command state on each measurement switch, calculate the impact offset coefficient, and filter out abnormal associated trigger command states according to the preset impact offset coefficient threshold to form an abnormal association set; Step S4: Based on the abnormal association set of each measurement switch, evaluate the abnormal triggering correlation between any two measurement switches in the same cluster, select the measurement switch with the highest correlation to construct the link of the switch abnormal triggering link, sort out the connected links to form the switch abnormal triggering link, generate the intelligent map of the measurement switch response abnormality of the measurement switch cluster and update it regularly. The specific implementation process of step S3 includes: Based on the switch state matrix, for any v-th trigger command state in the trigger command state sample set M Evaluate the status of the trigger command. For the a-th measurement switch degree of influence In the formula, F[] is a counting function used to count the number of 1s. Represents the switch state matrix The number of 1s contained in column v. Represents the switch state matrix The total number of 1s contained in each row and column of the text. This represents the current cycle recording period, where T and U are the switch state matrices, respectively. The total number of rows and columns; This indicates the trigger state of the instruction in the r-th cycle. For measuring switch The probability of the stage-specific impact, if the state matrix If none of the rows and columns contain the value 1, it means that the measurement switch was in the r-th cycle. If no exception is triggered, then let ; Based on the degree of impact, quantify the state of the trigger command. For measuring switch Influence of offset coefficient In the formula, and Each trigger command status corresponds to a measurement switch. The mean and standard deviation of the degree of influence, and ;Preset threshold for affecting offset coefficient; if it affects offset coefficient If the value is greater than or equal to the threshold affecting the offset coefficient, then the trigger command status is selected. As a measuring switch The status of the exception-related trigger command is recorded in the exception association set. In the middle, if the offset coefficient is affected If the value is less than the threshold affecting the offset coefficient, then the trigger command status will not be changed. Filtering indicates the trigger command status. For measuring switch No abnormal associations are generated, and they are not recorded in the abnormal association set. middle.

2. The intelligent response processing method for measurement switches based on remote control via the Internet of Things as described in claim 1, characterized in that, The specific implementation process of step S1 includes: Centered on a measurement switch cluster, and uniformly compiling data for each measurement switch cluster and the IoT-controlled remote measurement switches within each cluster, a switch source set is formed, denoted as […]. ,in, This represents the i-th measurement switch cluster for remote control via the Internet of Things. This represents the a-th measuring switch, and A represents the total number of measuring switches; The IoT backend records the trigger command status of each measurement switch. The trigger command status is set based on the abnormal operating state of the circuit in which the measurement switch is located. Based on the trigger command status, a trigger command status sample set is generated, denoted as... This indicates the state of the v-th trigger command. This indicates the total number of trigger command status types.

3. The intelligent response processing method for measurement switches based on remote control via the Internet of Things as described in claim 1, characterized in that, The specific implementation process of step S2 includes: The cyclic recording period for the trigger command state of the measurement switch response is based on a day, and the shortest duration unit is uniformly configured within the cyclic period to form N duration segments within the cyclic period. Any nth duration segment is denoted as... ; In duration Internally, the trigger command status of each measurement switch is statistically analyzed, and an abnormal trigger command set is generated. If it occurs within a time segment... Inside, the a-th measurement switch The v-th trigger command state was generated. Then the v-th trigger command state will be... Recorded to the a-th measurement switch Corresponding generated exception trigger instruction set middle; Using the duration segment number as the row index and the trigger command state type number as the column index, a switch state matrix model is constructed to represent the abnormal trigger command set. Mapped to the nth row of the switch state matrix model, if an abnormal trigger instruction set is used... There is a trigger command state. Then set the nth row and vth column of the switch state matrix model to 1, if an abnormal trigger instruction set is used. There is no trigger command state in the middle. Then, set the nth row and vth column of the switch state matrix model to 0 to generate the measurement switch. The switch state matrix during the r-th cycle is denoted as... Where T is the total number of rows, i.e. the total number of duration segments contained in the cyclic recording period, and U is the total number of columns, corresponding to the total number of trigger command state types.

4. The intelligent response processing method for measurement switches based on remote control via the Internet of Things as described in claim 1, characterized in that, The specific implementation process of step S4 includes: For the i-th measurement switch cluster Switch source set Except for the a-th measurement switch Any b-th measurement switch outside a≠b, based on abnormal association sets Association set with anomalies Evaluation measurement switch With measurement switch Inter-abnormal trigger correlation In the formula, Represents anomaly association set Association set with anomalies The total number of trigger command states contained in the intersection set. Represents anomaly association set Association set with anomalies The total number of trigger command states contained in the union set; Based on the correlation of anomaly triggers, the measurement switch with the highest correlation of anomaly triggers is selected. As a measuring switch Related triggering objects To form a link in the switch abnormal triggering chain. argmax{} is a feedback indicator function used to provide feedback on the measurement switch selected when the maximum anomaly trigger correlation is used as the correlation condition. ; Based on switch source set By organizing the connected links, a cluster of measurement switches is formed. Several switch anomaly trigger links are used to generate a measurement switch cluster. The intelligent graph is used to detect abnormal response from the measurement switch and is updated periodically.

5. A measurement switch intelligent response processing system based on IoT remote control, executing the measurement switch intelligent response processing method based on IoT remote control as described in claim 1, characterized in that, The system includes: a switch source set and trigger state construction module, a switch state matrix generation module, an anomaly association trigger instruction filtering module, and an anomaly trigger link and intelligent graph construction module; The switch source set and trigger state construction module is used to uniformly compile the measurement switch cluster and the measurement switches within the cluster to form a switch source set, record the trigger command state of each measurement switch, and generate a trigger command state sample set. The switch state matrix generation module is used to set the cyclic recording period and duration segment, count the abnormal trigger instruction set within the duration segment, construct the switch state matrix, and generate the measurement switch state matrix within the corresponding cyclic period. The abnormal association trigger instruction filtering module is used to evaluate the degree of influence of the trigger instruction state on the measurement switch, calculate the influence offset coefficient, filter the abnormal association trigger instruction state and form an abnormal association set. The abnormal triggering link and intelligent graph construction module is used to evaluate the abnormal triggering correlation between measurement switches, construct the abnormal triggering link of the switches, and generate and periodically update the intelligent graph of abnormal response of the measurement switches.

6. The intelligent response processing system for measurement switches based on remote control via the Internet of Things as described in claim 5, characterized in that, The switch source set and trigger state construction module includes a switch source set compilation unit and a trigger command state sample set generation unit. The switch source set compilation unit is used to compile a unified set of each measurement switch cluster and each measurement switch within the cluster, with the measurement switch cluster as the center, to form a switch source set. The trigger command state sample set generation unit is used to record the trigger command state set of each measurement switch based on the abnormal circuit operation state, and integrate all trigger command states to form a trigger command state sample set.

7. The intelligent response processing system for measurement switches based on remote control via the Internet of Things as described in claim 5, characterized in that, The switch state matrix generation module includes a cycle period and duration segment configuration unit, an abnormal trigger instruction set statistics unit, and a switch state matrix construction unit. The cycle period and duration segment configuration unit is used to set a cycle recording cycle in days and configure a uniform minimum duration scale unit within the cycle, dividing the cycle period into multiple duration segments. The abnormal trigger instruction set statistics unit is used to count the trigger instruction states generated by each measurement switch within each duration segment, generating the abnormal trigger instruction set for the corresponding measurement switch within that duration segment. The switch state matrix construction unit is used to construct a switch state matrix model with the duration segment number as the row index and the trigger instruction state type as the column index, mapping the abnormal trigger instruction set into the model, and generating the measurement switch state matrix within the corresponding cycle period by setting it to 0 or 1.

8. The intelligent response processing system for measurement switches based on remote control via the Internet of Things as described in claim 5, characterized in that, The abnormal association trigger command filtering module includes a trigger command state influence degree evaluation unit, an influence offset coefficient calculation unit, and an abnormal association trigger command state filtering unit. The trigger command state influence degree evaluation unit, based on the switch state matrix, counts the number of corresponding values ​​in the matrix and calculates the influence degree of each trigger command state on each measurement switch. The influence offset coefficient calculation unit, based on the influence degree of each trigger command state on the measurement switch, calculates the mean and standard deviation of the influence degree to obtain the influence offset coefficient of each trigger command state on the corresponding measurement switch. The abnormal association trigger command state filtering unit compares the influence offset coefficient with a preset threshold, filters out abnormal association trigger command states that meet the threshold requirements, and records them in the abnormal association set.

9. The intelligent response processing system for measurement switches based on remote control via the Internet of Things as described in claim 5, characterized in that, The abnormal triggering link and intelligent graph construction module includes a switch abnormal triggering correlation evaluation unit, an abnormal triggering link construction unit, and an intelligent graph generation and update unit. The switch abnormal triggering correlation evaluation unit, based on the abnormal correlation sets of each measurement switch within the same cluster, calculates the total number of trigger command states contained in the intersection and union of these sets, and calculates the abnormal triggering correlation between any two measurement switches. The abnormal triggering link construction unit selects the measurement switch with the highest abnormal triggering correlation as the relevant triggering object, constructs the links of the switch abnormal triggering link, and organizes all connected links to form multiple switch abnormal triggering links. The intelligent graph generation and update unit, based on the formed switch abnormal triggering links, generates an intelligent graph of the measurement switch response abnormalities in the measurement switch cluster and updates the intelligent graph according to a preset period.

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