A power cable grounding resistance control system and method based on the Internet of Things

Abstract

This invention discloses a power cable grounding resistance control system and method based on the Internet of Things (IoT), relating to the field of IoT technology. The system acquires false alarm records of power cable grounding resistance, collects environmental characteristics within a target time period, classifies the false alarm records according to the environmental characteristics, assigning each category as a false alarm mode, collects the change characteristics of environmental parameters and grounding resistance values ​​for a specific false alarm mode, obtains the grounding resistance change characteristics corresponding to the change characteristics of a specific environmental parameter, establishes a relationship model between the change in environmental parameters and the change in grounding resistance through a feature matrix, calculates the dynamic relationship coefficient between environmental parameters and grounding resistance, collects environmental characteristics within the current time period, matches false alarm modes, adjusts the dynamic threshold of grounding resistance according to the dynamic relationship coefficient, and filters alarm information when the sensor return value of the grounding resistance value does not exceed the dynamic threshold.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, specifically to an IoT-based power cable grounding resistance control system and method. Background Technology

[0002] In IoT-based remote monitoring systems for power cable grounding resistance, false alarms have become a key technical bottleneck restricting the practical application of these systems. Existing monitoring systems generally employ a "sensor array + wireless transmission" architecture, using non-contact current-sensing clamps or high-precision grounding resistance testers for data acquisition. However, this approach suffers from an extremely high false alarm rate in practical applications. For example, changes in the natural environment, fluctuations in temperature and humidity, and electromagnetic interference can all cause false alarms regarding grounding resistance values. Furthermore, many systems still rely on static threshold judgments and lack dynamic models, leading to an even higher probability of false alarms when external environmental changes cause sudden changes in soil conductivity. Summary of the Invention

[0003] The purpose of this invention is to provide a power cable grounding resistance management system and method based on the Internet of Things to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for controlling the grounding resistance of power cables based on the Internet of Things, the method comprising:

[0005] Step S100: Obtain false alarm records of power cable grounding resistance, take the time period including the false alarm records as a target time period, collect environmental characteristics in the target time period, classify the false alarm records according to the environmental characteristics, and take each category as a false alarm mode.

[0006] Step S200: Collect the variation characteristics of environmental parameters and grounding resistance values ​​for a certain false alarm mode;

[0007] Step S300: Obtain the grounding resistance change characteristics corresponding to the change characteristics of a certain environmental parameter, establish a relationship model between the change of environmental parameter and the change of grounding resistance through the feature matrix, and calculate the dynamic relationship coefficient between environmental parameter and grounding resistance.

[0008] Step S400: Collect environmental characteristics in the current time period, match false alarm patterns, adjust the dynamic threshold of grounding resistance according to the dynamic relationship coefficient, and filter alarm information when the sensor return value of grounding resistance does not exceed the dynamic threshold.

[0009] Furthermore, step S100 includes:

[0010] Step S101: Real-time acquisition of grounding resistance values ​​and environmental data of related power equipment of the power cable through a sensor array deployed on the grounding resistance monitoring node of the power cable;

[0011] Step S102: Compare the grounding resistance value with the static threshold of the resistance value. When the grounding resistance value triggers an alarm but is manually verified as a false alarm, a false alarm record is generated. The alarm strategy triggered when the false alarm is generated is obtained from the false alarm record.

[0012] Step S103: Set the time period to Tc, and define a time period range that includes time false alarm records as the target time period;

[0013] Step S104: Collect environmental characteristics within the target time period, including temperature and humidity, electromagnetic interference intensity, and soil conductivity;

[0014] Step S105: Collect the feature values ​​of environmental data and the alarm strategies in the false alarm records as input parameters, perform cluster analysis on the input parameters, and divide the environmental features and corresponding alarm strategies into several categories, with each category corresponding to a false alarm mode.

[0015] Furthermore, step S200 includes:

[0016] Step S201: Divide the target time period into n unit intervals, and obtain the change in each environmental parameter and the change in grounding resistance value in each unit interval;

[0017] Step S202: Collect the changes in environmental parameters and grounding resistance values ​​in each unit interval to obtain the dynamic change vector corresponding to each unit interval;

[0018] Step S203: Collect the dynamic change vectors of n unit intervals to obtain the dynamic change matrix D, D=[d1, d2, d3, ..., d n ] T d1, d2, d3, ... and d n These represent the dynamic change vectors of the 1st, 2nd, 3rd, ..., and nth unit intervals, respectively;

[0019] Step S204: Obtain the change in grounding resistance value for each unit interval, and obtain the resistance value change matrix R, R = [r1, r2, r3, ..., r n ] T Where r1, r2, r3, ... and r n These represent the changes in grounding resistance values ​​for the 1st, 2nd, 3rd, ..., and nth unit intervals, respectively.

[0020] By dividing the target time period into multiple unit intervals and collecting the changes in environmental parameters and grounding resistance values ​​within each interval, it is possible to deeply explore the changing characteristics of false alarm patterns at different time stages, which helps to more comprehensively grasp the dynamic characteristics of false alarms.

[0021] Furthermore, step S300 includes:

[0022] Step S301: Set the dynamic relationship coefficient matrix B, B=[β0, β1, β2, β3, ..., β m ] T , where β1, β2, β3,…and β m β0 represents the dynamic relationship coefficients corresponding to the first, second, third, ... and mth environmental features, respectively, and β0 represents the bias coefficient.

[0023] Step S302: Obtain the augmented matrix X of the dynamically changing matrix D, and establish the matrix equation:

[0024] B = (X) T X) -1 X T R is used to solve for the dynamic relationship coefficients.

[0025] By dividing the target time period into multiple unit intervals and collecting the changes in environmental parameters and grounding resistance values ​​within each interval, it is possible to deeply explore the changing characteristics of false alarm patterns at different time stages, which helps to more comprehensively grasp the dynamic characteristics of false alarms.

[0026] By collecting the dynamic change vectors to obtain the dynamic change matrix D, and obtaining the resistance value change matrix R, key data support is provided for the subsequent establishment of a dynamic relationship model between environmental parameters and grounding resistance, enabling the model to more accurately reflect the actual changes.

[0027] Furthermore, step S400 includes:

[0028] Step S401: Record the current time period as the time period with a current time length of Tc, collect the environmental data of the relevant power equipment of the power cable in the current time period, collect the feature value of the environmental data, match the false alarm pattern corresponding to the feature value and record it as the target false alarm pattern, and record the alarm strategy included in the target false alarm pattern as the target alarm strategy.

[0029] Step S402: Calculate the fluctuation threshold R of the grounding resistance value. th , , where β i Ui represents the dynamic relationship coefficient corresponding to the i-th environmental feature, and Ui represents the change in the value of the i-th environmental feature within a unit interval.

[0030] Step S403: Obtain the static threshold Rs of the resistance value, and calculate the dynamic boundary, where the dynamic boundary includes the upper boundary Gup and the lower boundary Gdown, Gup=max(Rs, Rs+R th ), Gdown=min(Rs, Rs+Rth (max) represents the function for finding the maximum value, and min represents the function for finding the minimum value;

[0031] Step S404: Obtain the sensor return value R0 of the power cable grounding resistance value. When R0∈(Gdown,Gup), obtain the alarm information of the current power cable resistance value being abnormal. When the alarm information is the alarm information generated by the target alarm strategy, prevent the sending of the alarm information.

[0032] Adjusting the dynamic threshold of grounding resistance based on dynamic relationship coefficients and filtering alarm information can effectively avoid false alarms caused by changes in environmental factors, improve the accuracy of alarm information, and reduce the unnecessary workload of maintenance personnel.

[0033] To better implement the above methods, an Internet of Things-based power cable grounding resistance management system is also proposed.

[0034] The system includes: a historical data management module, a feature management module, a relational model management module, and an alarm filtering module.

[0035] Furthermore, the historical data management module includes: a sensor management unit, an alarm record management unit, and a cluster analysis unit. The sensor management unit is used to manage the network of sensors that collect environmental parameters and grounding resistance values. The alarm record management unit is used to obtain the alarm strategy triggered when a false alarm occurs. The cluster analysis unit is used to perform cluster analysis on the feature values ​​of environmental data and the alarm strategies in the false alarm records.

[0036] Furthermore, the feature management module includes: an interval management unit, an environmental parameter management unit, a resistance value management unit, and a matrix management unit. The interval management unit is used to manage the unit interval within the target time period, the environmental parameter management unit is used to manage the changes in environmental parameters within the unit interval, the resistance value management unit is used to manage the changes in grounding resistance values ​​within the unit interval, and the matrix management unit is used to manage the dynamic change matrix and the resistance value change matrix.

[0037] Furthermore, the relational model management module includes a coefficient management unit and a coefficient calculation unit. The coefficient management unit is used to manage the dynamic relational coefficient matrix, and the coefficient calculation unit is used to solve for the dynamic relational coefficients.

[0038] Furthermore, the alarm filtering module includes: an alarm strategy matching unit, a fluctuation threshold management unit, a dynamic boundary management unit, and an alarm judgment unit. The alarm strategy matching unit is used to collect environmental data of relevant power equipment of the current power cable and match the alarm strategy for the current time period. The fluctuation threshold management unit is used to calculate the fluctuation threshold of the grounding resistance value. The dynamic boundary management unit is used to manage the dynamic boundary. The alarm judgment unit is used to filter alarm information that meets the filtering conditions.

[0039] Compared with existing technologies, the beneficial effects of this invention are as follows: By collecting, classifying, and analyzing false alarm records, this invention accurately identifies false alarm patterns caused by different environmental factors, establishes a dynamic relationship model based on the changing characteristics of environmental parameters, calculates dynamic relationship coefficients, and then adjusts the dynamic threshold of grounding resistance. This dynamic adjustment mechanism enables the system to better adapt to environmental changes, such as fluctuations in temperature and humidity, changes in electromagnetic interference, and changes in soil conductivity, avoiding false alarms or missed alarms caused by fixed thresholds. Real-time collection and matching of environmental characteristics in the current time period quickly identifies the false alarm patterns corresponding to the current environmental characteristics and adjusts the alarm strategy accordingly. This allows the system to flexibly cope with various complex operating environments, enhancing the system's adaptability and environmental awareness. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the structure of a power cable grounding resistance control system based on the Internet of Things according to the present invention;

[0041] Figure 2 This is a flowchart illustrating a method for controlling the grounding resistance of power cables based on the Internet of Things according to the present invention. Detailed Implementation

[0042] 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.

[0043] Example: Figures 1-2 As shown, the present invention provides a technical solution: a method for controlling the grounding resistance of power cables based on the Internet of Things.

[0044] Step S100: Obtain false alarm records of power cable grounding resistance, take the time period including the false alarm records as a target time period, collect environmental characteristics in the target time period, classify the false alarm records according to the environmental characteristics, and take each category as a false alarm mode.

[0045] Step S100 includes:

[0046] Step S101: Collect grounding resistance values ​​and environmental data of relevant power equipment of the power cable in real time through a sensor array deployed on the power cable grounding resistance monitoring node;

[0047] Step S102: Compare the grounding resistance value with the static threshold of the resistance value. When the grounding resistance value triggers an alarm but is manually verified as a false alarm, a false alarm record is generated. The alarm strategy triggered when the false alarm is generated is obtained from the false alarm record.

[0048] Step S103: Set the time period to Tc, and define a time period range that includes time false alarm records as the target time period;

[0049] Step S104: Collect environmental characteristics within the target time period, including temperature and humidity, electromagnetic interference intensity, and soil conductivity;

[0050] Step S105: Collect the feature values ​​of environmental data and the alarm strategies in the false alarm records as input parameters, perform cluster analysis on the input parameters, and divide the environmental features and corresponding alarm strategies into several categories, with each category corresponding to a false alarm mode.

[0051] In this embodiment, environmental parameters are collected when a false alarm of the grounding cable resistance value occurs. These environmental parameters include temperature (T / ℃), humidity (H / %RH), electromagnetic interference (EMI / V / m), and soil conductivity (C / mS / m). Alarm tags are set according to the alarm conditions that trigger the alarm.

[0052] When a false alarm occurs, environmental data of the relevant power equipment is sampled, and 10 data points are collected and represented by a1~a10 respectively.

[0053] a1: T1=28, H1=85, EMI1=12, C1=15, alarm tag 1;

[0054] a2: T2=28, H2=86, EMI2=11, C2=14, alarm tag 1;

[0055] a3: T3=32, H3=60, EMI3=25, C3=20, alarm tag 2;

[0056] a4: T4=32, H4=58, EMI4=24, C4=19, alarm tag 2;

[0057] a5: T5=25, H5=90, EMI5=8, C5=12, alarm tag 1;

[0058] a6: T6=25, H6=92, EMI6=7, C6=11, alarm tag 1;

[0059] a7: T7=30, H7=70, EMI7=30, C7=22, alarm tag 2;

[0060] a8: T8=30, H8=72, EMI8=29, C8=23, alarm tag 2;

[0061] a9: T9=27, H9=88, EMI9=9, C9=13, alarm tag 1;

[0062] a10: T10=27, H10=89, EMI10=10, C10=12, alarm tag 1;

[0063] The K-means algorithm was used to classify the 10 data points, with a1, a5, and a9 belonging to the first class and a3 and a7 belonging to the second class.

[0064] In the first category, for alarm label 1, the relative humidity is greater than or equal to 85%RH, the electromagnetic interference is less than 10 V / m, and the soil conductivity is less than 15mS / m.

[0065] In the second category, for alarm tag 2, the temperature is greater than or equal to 30℃, the electromagnetic interference is greater than 20 V / m, and the soil conductivity is greater than 20mS / m.

[0066] Step S200: Collect the variation characteristics of environmental parameters and grounding resistance values ​​for a certain false alarm mode;

[0067] Step S200 includes:

[0068] Step S201: Divide the target time period into n unit intervals, and obtain the change in each environmental parameter and the change in grounding resistance value in each unit interval;

[0069] Step S202: Collect the changes in environmental parameters and grounding resistance values ​​in each unit interval to obtain the dynamic change vector corresponding to each unit interval;

[0070] Step S203: Collect the dynamic change vectors of n unit intervals to obtain the dynamic change matrix D, D=[d1, d2, d3, ..., d n ] T d1, d2, d3, ... and d n These represent the dynamic change vectors of the 1st, 2nd, 3rd, ..., and nth unit intervals, respectively;

[0071] Step S204: Obtain the change in grounding resistance value for each unit interval, and obtain the resistance value change matrix R, R = [r1, r2, r3, ..., r n ] T Where r1, r2, r3, ... and r n These represent the changes in grounding resistance values ​​for the 1st, 2nd, 3rd, ..., and nth unit intervals, respectively.

[0072] In this embodiment, the target time period is divided into 5 unit intervals, denoted as t1, t2, t3, t4 and t5 respectively. The changes in environmental parameters in each unit interval are collected, including temperature change ΔT (°C), humidity change ΔH (%RH), soil conductivity change ΔC (mS / m), and electromagnetic interference change ΔEMI (V / m).

[0073] The dynamic change vector d1 corresponding to the unit interval t1 is d1 = [2, 5, 1, 1].

[0074] The dynamic change vector d2 corresponding to the unit interval t2 is d2=[3,-2,1,-2];

[0075] The dynamic change vector d3 corresponding to the unit interval t3 is given by d3 = [-7, -8, -3, +3].

[0076] The dynamic change vector d4 corresponding to the unit interval t4 is d4 = [+12, +15, +4, -2];

[0077] The dynamic change vector d5 corresponding to the unit interval t5 is given by d5 = [-2, -3, -1, 1].

[0078] Establish a dynamic change matrix D, ;

[0079] Obtain the change in grounding resistance value in each unit interval: r1=-1, r2=-1, r3=4, r4=-5, r5=1;

[0080] Establish the resistance value change matrix R, .

[0081] Step S300: Obtain the grounding resistance change characteristics corresponding to the change characteristics of a certain environmental parameter, establish a relationship model between the change of environmental parameter and the change of grounding resistance through the feature matrix, and calculate the dynamic relationship coefficient between environmental parameter and grounding resistance.

[0082] Step S300 includes:

[0083] Step S301: Set the dynamic relationship coefficient matrix B, B=[β0, β1, β2, β3, ..., β m ] T, where β1, β2, β3,…and β m β0 represents the dynamic relationship coefficients corresponding to the first, second, third, ... and mth environmental features, respectively, and β0 represents the bias coefficient.

[0084] Step S302: Obtain the augmented matrix X of the dynamically changing matrix D, and establish the matrix equation:

[0085] B = (X) T X) -1 X T R is used to solve for the dynamic relationship coefficients.

[0086] Construct an augmented matrix X, X=[1,D], and substitute it into the dynamic transformation matrix to obtain: According to the matrix equation B=(X T X) -1 X T R, where ;

[0087] Calculations yielded the following values: β0 = 0.82, β1 = -0.45, β2 = -0.21, β3 = -1.33, and β4 = 0.28. The units for β0 are Ω, β1 is Ω / ℃, β2 is Ω / %RH, β3 is Ω / mS / m, and β4 is Ω / V / m.

[0088] Step S400: Collect environmental characteristics in the current time period, match false alarm patterns, adjust the dynamic threshold of grounding resistance according to the dynamic relationship coefficient, and filter alarm information when the sensor return value of grounding resistance does not exceed the dynamic threshold.

[0089] Step S400 includes:

[0090] Step S401: Record the current time period as the time period with a current time length of Tc, collect the environmental data of the relevant power equipment of the power cable in the current time period, collect the feature value of the environmental data, match the false alarm pattern corresponding to the feature value and record it as the target false alarm pattern, and record the alarm strategy included in the target false alarm pattern as the target alarm strategy.

[0091] Step S402: Calculate the fluctuation threshold R of the grounding resistance value. th , , where β i Ui represents the dynamic relationship coefficient corresponding to the i-th environmental feature, and Ui represents the change in the value of the i-th environmental feature within a unit interval.

[0092] Step S403: Obtain the static threshold Rs of the resistance value, and calculate the dynamic boundary, where the dynamic boundary includes the upper boundary Gup and the lower boundary Gdown, Gup=max(Rs, Rs+Rth ), Gdown=min(Rs, Rs+R th (max) represents the function for finding the maximum value, and min represents the function for finding the minimum value;

[0093] Step S404: Obtain the sensor return value R0 of the power cable grounding resistance value. When R0∈(Gdown,Gup), obtain the alarm information of the current power cable resistance value being abnormal. When the alarm information is the alarm information generated by the target alarm strategy, prevent the alarm information from being sent.

[0094] In this embodiment, environmental features in the current time period are collected, false alarm patterns are matched, and alarm conditions corresponding to the false alarm patterns are obtained.

[0095] The temperature change ΔT, humidity change ΔH, soil conductivity change ΔC, and electromagnetic interference change ΔEMI are collected within a unit interval. The fluctuation threshold of the grounding resistance value is calculated through the dynamic relationship coefficients corresponding to the environmental characteristics.

[0096] The system acquires dynamic boundaries. When the sensor returns a resistance value within the dynamic boundary range, if an alarm message indicating an abnormal grounding resistance value is detected, the system acquires the triggering alarm conditions. If the alarm conditions are the same as those corresponding to the false alarm mode, the alarm message is marked as a suspected false alarm and the alarm is not executed temporarily. If the alarm conditions are different from those corresponding to the false alarm mode, the alarm is executed.

[0097] When the sensor returns a resistance value outside the dynamic boundary range, and an alarm is triggered when an abnormal grounding resistance value is detected, an alarm is triggered.

[0098] The system includes: a historical data management module, a feature management module, a relational model management module, and an alarm filtering module;

[0099] The historical data management module is used to manage the historical records of false alarms in the grounding resistance of power cables. The historical data management module includes a sensor management unit, an alarm record management unit, and a cluster analysis unit. The sensor management unit is used to manage the network of sensors that collect environmental parameters and grounding resistance values. The alarm record management unit is used to obtain the alarm strategies triggered when false alarms occur. The cluster analysis unit is used to perform cluster analysis on the feature values ​​of environmental data and the alarm strategies in the false alarm records.

[0100] The feature management module is used to manage the changing characteristics of environmental parameters and the changing characteristics of grounding resistance values. The feature management module includes: an interval management unit, an environmental parameter management unit, a resistance value management unit, and a matrix management unit. The interval management unit is used to manage unit intervals within a target time period, the environmental parameter management unit is used to manage the changes in environmental parameters within a unit interval, the resistance value management unit is used to manage the changes in grounding resistance values ​​within a unit interval, and the matrix management unit is used to manage the dynamic change matrix and the resistance value change matrix.

[0101] The relational model management module is used to manage the relational model between changes in environmental parameters and changes in grounding resistance. The relational model management module includes a coefficient management unit and a coefficient calculation unit. The coefficient management unit is used to manage the dynamic relational coefficient matrix, and the coefficient calculation unit is used to solve for the dynamic relational coefficients.

[0102] The alarm filtering module is used to filter alarm information when the sensor return value of the grounding resistance value does not exceed the dynamic threshold. The alarm filtering module includes an alarm strategy matching unit, a fluctuation threshold management unit, a dynamic boundary management unit, and an alarm judgment unit. The alarm strategy matching unit is used to collect environmental data of relevant power equipment of the current power cable and match the alarm strategy for the current time period. The fluctuation threshold management unit is used to calculate the fluctuation threshold of the grounding resistance value. The dynamic boundary management unit is used to manage the dynamic boundary. The alarm judgment unit is used to filter alarm information that meets the filtering conditions.

[0103] 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. A method for controlling the grounding resistance of power cables based on the Internet of Things, characterized in that: The methods include: Step S100: Obtain false alarm records of power cable grounding resistance, take the time period including the false alarm records as a target time period, collect environmental characteristics in the target time period, classify the false alarm records according to the environmental characteristics, and take each category as a false alarm mode. Step S200: Collect the variation characteristics of environmental parameters and grounding resistance values ​​for a certain false alarm mode; Step S300: Obtain the grounding resistance change characteristics corresponding to the change characteristics of a certain environmental parameter, establish a relationship model between the change of environmental parameter and the change of grounding resistance through the feature matrix, and calculate the dynamic relationship coefficient between environmental parameter and grounding resistance. Step S400: Collect environmental characteristics in the current time period, match false alarm patterns, adjust the dynamic threshold of grounding resistance according to the dynamic relationship coefficient, and filter alarm information when the sensor return value of grounding resistance does not exceed the dynamic threshold. Step S100 includes: Step S101: Real-time acquisition of grounding resistance values ​​and environmental data of related power equipment of the power cable through a sensor array deployed on the power cable grounding resistance monitoring node; Step S102: Compare the grounding resistance value with the static threshold of the resistance value. When the grounding resistance value triggers an alarm but is manually verified as a false alarm, a false alarm record is generated. The alarm strategy triggered when the false alarm is generated is obtained from the false alarm record. Step S103: Set the time period to Tc, and define a time period range including time false alarm records as the target time period; Step S104: Collect environmental characteristics within the target time period, including temperature and humidity, electromagnetic interference intensity, and soil conductivity; Step S105: Collect the feature values ​​of environmental data and the alarm strategies in the false alarm records as input parameters, perform cluster analysis on the input parameters, and divide the environmental features and corresponding alarm strategies into several categories, with each category corresponding to a false alarm mode.

2. The method for controlling the grounding resistance of power cables based on the Internet of Things according to claim 1, characterized in that: Step S200 includes: Step S201: Divide the target time period into n unit intervals, and obtain the change in each environmental parameter and the change in grounding resistance value in each unit interval; Step S202: Collect the changes in environmental parameters and grounding resistance values ​​in each unit interval to obtain the dynamic change vector corresponding to each unit interval; Step S203: Collect the dynamic change vectors of n unit intervals to obtain the dynamic change matrix D, D=[d1, d2, d3, ..., d n ] T d1, d2, d3, ... and d n These represent the dynamic change vectors of the 1st, 2nd, 3rd, ..., and nth unit intervals, respectively; Step S204: Obtain the change in grounding resistance value for each unit interval, and obtain the resistance value change matrix R, R = [r1, r2, r3, ..., r n ] T Where r1, r2, r3, ... and r n These represent the changes in grounding resistance values ​​for the 1st, 2nd, 3rd, ..., and nth unit intervals, respectively.

3. The method for controlling the grounding resistance of power cables based on the Internet of Things according to claim 2, characterized in that: Step S300 includes: Step S301: Set the dynamic relationship coefficient matrix B, B=[β0, β1, β2, β3, ..., β m ] T , where β1, β2, β3,…and β m β0 represents the dynamic relationship coefficients corresponding to the first, second, third, ... and mth environmental features, respectively, and β0 represents the bias coefficient. Step S302: Obtain the augmented matrix X of the dynamically changing matrix D, and establish the matrix equation: B = (X) T X) -1 X T R is used to solve for the dynamic relationship coefficients.

4. The method for controlling the grounding resistance of power cables based on the Internet of Things according to claim 3, characterized in that: Step S400 includes: Step S401: Record the current time period as the time period with a current time length of Tc, collect the environmental data of the relevant power equipment of the power cable in the current time period, collect the feature value of the environmental data, match the false alarm pattern corresponding to the feature value and record it as the target false alarm pattern, and record the alarm strategy included in the target false alarm pattern as the target alarm strategy. Step S402: Calculate the fluctuation threshold R of the grounding resistance value. th , , where β i Ui represents the dynamic relationship coefficient corresponding to the i-th environmental feature, and Ui represents the change in the value of the i-th environmental feature within a unit interval. Step S403: Obtain the static threshold Rs of the resistance value, and calculate the dynamic boundary, where the dynamic boundary includes the upper boundary Gup and the lower boundary Gdown, Gup=max(Rs, Rs+R th ), Gdown=min(Rs, Rs+R th (max) represents the function for finding the maximum value, and min represents the function for finding the minimum value; Step S404: Obtain the sensor return value R0 of the power cable grounding resistance value. When R0∈(Gdown,Gup), obtain the alarm information of the current power cable resistance value being abnormal. When the alarm information is the alarm information generated by the target alarm strategy, prevent the sending of the alarm information.

5. A power cable grounding resistance control system based on the Internet of Things (IoT), used to execute the power cable grounding resistance control method based on the IoT as described in any one of claims 1-4, characterized in that: The system includes: Historical data management module, feature management module, relational model management module, and alarm filtering module; The historical data management module manages the historical records of false alarms in the grounding resistance of power cables; the feature management module manages the change characteristics of environmental parameters and the change characteristics of grounding resistance values; the relationship model management module manages the relationship model between the change in environmental parameters and the change in grounding resistance; and the alarm filtering module filters alarm information when the sensor return value of the grounding resistance value does not exceed the dynamic threshold.

6. The power cable grounding resistance control system based on the Internet of Things according to claim 5, characterized in that: The historical data management module includes: a sensor management unit, an alarm record management unit, and a cluster analysis unit; The sensor management unit is used to manage the network of sensors that collect environmental parameters and grounding resistance values; The alarm log management unit is used to obtain the alarm policies triggered when false alarms occur; The clustering analysis unit is used to perform clustering analysis on the feature values ​​of environmental data and the alarm strategies in false alarm records.

7. The power cable grounding resistance control system based on the Internet of Things according to claim 5, characterized in that: The feature management module includes: an interval management unit, an environmental parameter management unit, a resistance value management unit, and a matrix management unit; Interval management units are used to manage unit intervals within a target time period; The environmental parameter management unit is used to manage the changes in environmental parameters within a unit interval; The resistance value management unit is used to manage the changes in grounding resistance values ​​within a unit interval; The matrix management unit is used to manage dynamically changing matrices and resistance value change matrices.

8. A power cable grounding resistance control system based on the Internet of Things according to claim 5, characterized in that: The relational model management module includes: a coefficient management unit and a coefficient calculation unit; The coefficient management unit is used to manage the dynamic relationship coefficient matrix; The coefficient calculation unit is used to solve for dynamic relationship coefficients.

9. A power cable grounding resistance control system based on the Internet of Things according to claim 5, characterized in that: The alarm filtering module includes: an alarm policy matching unit, a fluctuation threshold management unit, a dynamic boundary management unit, and an alarm judgment unit; The alarm strategy matching unit is used to collect environmental data of relevant power equipment of the current power cable and match alarm strategies for the current time period. The fluctuation threshold management unit is used to calculate the fluctuation threshold of the grounding resistance value; The dynamic boundary management unit is used to manage dynamic boundaries; the alarm judgment unit is used to filter alarm information that meets the filtering conditions.

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