Cable monitoring system and monitoring method

Abstract

The invention relates to the technical field of cable temperature measurement, and discloses a cable monitoring system and method, and the system comprises a data collection module, a read-write processing module, and a monitoring management module. According to the cable monitoring system and monitoring method, a fastening nut of a cable joint is used as a carrier, RFID electronic tags are fixedly installed in switch cabinets, each RFID electronic tag is provided with a temperature sensor used for collecting sensing temperature of a monitoring node in real time, and each switch cabinet is provided with a monitoring device used for collecting an environment data set in real time. The read-write processing module analyzes the change trend of the sensing temperature of each monitoring node and the influence degree of environmental factors on different types of RFID electronic tags, and generates a corresponding fluctuation data set, a first influence coefficient, a second influence coefficient, a third influence coefficient and a fourth influence coefficient, so that the comprehensive monitoring precision is high; the monitoring management module judges whether error correction needs to be carried out on the fluctuation data set, and the error correction capability is high.

Description

Technical Field

[0001] The present invention relates to the technical field of cable temperature measurement, and in particular to a cable monitoring system and a monitoring method. Background Art

[0002] Switch cabinet and cable temperature measurement is a routine item in substation operation and maintenance. Power equipment temperature measurement mainly uses infrared temperature measurement, optical fiber temperature measurement and other technologies to detect the temperature rise status of equipment. Infrared temperature measurement technology has great limitations due to the weak penetration of infrared rays and the influence of different materials, temperature and other environmental factors on the emissivity. It cannot accurately display the temperature of the closed environment inside high-voltage electrical equipment in the form of thermal images. The optical fiber temperature measurement system has a complex structure, high technical requirements, and many equipment connection lines. It is difficult and costly to wire in the switch cabinet. At the same time, the optical fiber is easy to bend, easy to break, not resistant to high temperature, and easy to reduce insulation after dust accumulation. It is not easy to be widely promoted and used in voltage levels of 110kV and below. Therefore, in order to meet the temperature monitoring needs inside the equipment, two wireless temperature measurement technologies are mainly studied: active wireless temperature measurement and passive wireless temperature measurement. The active wireless temperature measurement method mainly refers to the combination of temperature sensors and wireless communication technology. The temperature sensor measures the heating element and transmits the measured temperature signal to the terminal monitoring device through a wireless chip. The terminal monitoring device receives the temperature signal and converts it into temperature information for display. Active wireless temperature measurement can directly monitor the temperature change of cable joints. It has the advantages of low cost, no wiring required, and good stability. However, the temperature sensor needs a battery or a small CT to be powered to work. Passive wireless temperature measurement technology uses surface acoustic wave sensing technology. When the temperature of the object being measured changes, the resonant frequency of the component changes accordingly. The temperature of the object being measured is obtained by measuring the resonant frequency. The core component of the surface acoustic wave temperature sensor is the surface acoustic wave resonator.

[0003] At present, during the installation of cable terminal joints, accidents frequently occur due to loose bolts or poor contact at the terminal blocks. The most obvious manifestation is the abnormal increase in temperature at the connection point. Traditional cable joint temperature monitoring methods can only measure the temperature of the outer surface of the cable joint, while the actual connection point temperature is located inside the joint and cannot be measured directly. The error differences between different types of power tags and the power equipment being measured are often ignored, resulting in low accuracy in temperature measurement and poor error correction capabilities. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a cable monitoring system and a monitoring method, which have the advantages of high comprehensive monitoring accuracy and strong error correction capability, and solve the problems of low accuracy and poor error correction capability of traditional cable joint temperature monitoring methods.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solutions: a cable monitoring system, including a data acquisition module, a read-write processing module and a monitoring management module;

[0008] The data acquisition module is composed of a sensor data unit and an environment data unit. The sensor data unit collects a temperature data set through a network connection to an RFID temperature sensor. The temperature data set includes the sensor temperatures of all monitoring nodes. The environment data unit collects an environment data set through a network connection to a monitoring device. The environment data set includes the environment monitoring data of all switch cabinets.

[0009] The read-write processing module is composed of a primary processing unit and an environmental assessment unit. The primary processing unit is provided with a fixed-duration monitoring cycle Q, and then combined with the temperature data set, analyzes the change trend of the sensor temperature of each monitoring node, and generates a corresponding fluctuation data set Bdsj. The environmental assessment unit analyzes the influence of environmental factors on different types of RFID electronic tags according to the environmental data set, and generates the corresponding first influence coefficient OYX, second influence coefficient TYX, third influence coefficient HYX and fourth influence coefficient FYX;

[0010] The monitoring management module is provided with a fixed range of the first impact threshold OYU, the second impact threshold TYU, the third impact threshold HYU and the fourth impact threshold FYU, and then combined with the first impact coefficient OYX, the second impact coefficient TYX, the third impact coefficient HYX and the fourth impact coefficient FYX, to determine whether error correction is required for the fluctuation data group Bdsj, and output the corresponding judgment result.

[0011] Preferably, the expression of the temperature data set is {J1 g 、J2 g 、J3 g 、...、JN g}, J1 g To JN g They are the sensing temperatures from the first detection node to the Nth monitoring node respectively. The sensing temperature is the internal temperature of the cable joint. g represents the switch cabinet number where a single monitoring node is located. The types of RFID electronic tags for each monitoring node are different, including tuning fork tags, clip tags, plum blossom contact tags and plug tags.

[0012] Preferably, the expression of the environmental data set is {G1 s , G2 s 、G3 s ,...,GN s}, G1 s To GN sThey are the environmental monitoring data of the first to the Nth switch cabinets, respectively. The environmental monitoring data include vibration frequency, suspended dust concentration, magnetic field strength, vibration amplitude, corrosive gas concentration and humidity. s represents the specific time for obtaining the environmental monitoring data of a single switch cabinet. Each switch cabinet corresponds to a monitoring node.

[0013] Preferably, the calculation process of the fluctuation data set Bdsj is as follows:

[0014] According to the temperature data set, the sensor temperature of the i-th monitoring node in the monitoring period Q is extracted and marked as {Wi1, Wi2, Wi3, ..., Wi e}, Wi1 to Wi e are the sensing temperatures of the i-th monitoring node from the first time point to the e-th time point;

[0015]

[0016] In the formula, maxWi represents the highest sensor temperature of the ith monitoring node within the monitoring period Q, minWi represents the lowest sensor temperature of the ith monitoring node within the monitoring period Q, and maxWi-minWi represents the temperature extreme difference of the ith monitoring node. It represents the average sensor temperature of the i-th monitoring node in the monitoring period Q, which is recorded as Wi f represents the sensor temperature of the i-th monitoring node at the f-th time point, f∈e, It means that according to the standard deviation formula, the fluctuation rate of the sensor temperature of the i-th monitoring node within the monitoring period Q is obtained.

[0017] Preferably, the calculation process of the first influence coefficient OYX is as follows:

[0018] Extract the environmental monitoring data of the i-th switch cabinet from the environmental data set. The type of RFID electronic tag set in the i-th switch cabinet is a tuning fork tag, and the vibration frequency of the i-th switch cabinet is marked as ZP i , the suspended dust concentration of the i-th switch cabinet is marked as XH i , the magnetic field strength of the i-th switch cabinet is marked as CQ i ;

[0019] OYX=α1×ZP i +α2×XH i +α3×CQ i +α4×(BJ-JD i )

[0020] In the formula, α1 represents the evaluation weight for vibration frequency, α2 represents the evaluation weight for suspended dust concentration, α3 represents the evaluation weight for magnetic field strength, BJ represents the standard angle between the tuning fork label and the busbar connection point, JD represents the standard angle between the tuning fork label and the busbar connection point, i Indicates the tilt angle between the tuning fork label and the busbar connection point, BJ-JD i represents the difference between the tilt angle and the standard angle, α4 represents the evaluation weight for the angle difference, α1+α2+α3+α4=1, α1×ZP i +α2×XH i +α3×CQ i +α4×(BJ-JD i ) represents the first influence coefficient for evaluating the tuning fork tag data error obtained by combining the vibration frequency, suspended dust concentration, magnetic field strength and angle difference according to α1, α2, α3 and α4.

[0021] Preferably, the calculation process of the second influence coefficient TYX is as follows:

[0022] Extract the environmental monitoring data of the kth switch cabinet from the environmental data set. The type of RFID electronic tag set in the kth switch cabinet is a clip tag, and the vibration amplitude of the kth switch cabinet is marked as ZF k , the corrosive gas concentration of the kth switch cabinet is marked as FQ k ;

[0023] TYX=β1×ZF k +β2×FQ k +β3×(BL-JL k )

[0024] In the formula, β1 represents the evaluation weight for vibration amplitude, β2 represents the evaluation weight for corrosive gas concentration, BL represents the standard clamping force between the clip label and the busbar, and JL represents the standard clamping force between the clip label and the busbar. k Indicates the actual clamping force between the clip label and the busbar, BL-JL k represents the difference between the actual clamping force and the standard clamping force, β3 represents the evaluation weight for the clamping force difference, β1+β2+β3=1, β1×ZF k +β2×FQ k +β3×(BL-JL k ) represents the second influence coefficient for evaluating the clip tag data error obtained by combining the vibration amplitude, corrosive gas concentration and clamping force difference according to β1, β2 and β3.

[0025] Preferably, the calculation process of the third influence coefficient HYX is as follows:

[0026] Extract the environmental monitoring data of the jth switch cabinet from the environmental data set. The type of RFID electronic tag set in the jth switch cabinet is a plum blossom contact tag, and the vibration frequency of the jth switch cabinet is marked as ZP j , the vibration amplitude of the jth switch cabinet is marked as ZF j ;

[0027]

[0028] In the formula, represents the evaluation weight for the vibration frequency, Denotes the evaluation weight for vibration amplitude, ΔJM j Indicates the change in contact area between the plum blossom contact label and the contact of the power equipment. represents the evaluation weight for the contact area change, Indicates according to and The third influence coefficient for evaluating the data error of the plum blossom contact label is obtained by combining the vibration frequency, vibration amplitude and contact area change.

[0029] Preferably, the calculation process of the fourth influence coefficient FYX is as follows:

[0030] Extract the environmental monitoring data of the u-th switch cabinet from the environmental data set. The type of RFID electronic tag set in the u-th switch cabinet is a plug tag, and the vibration frequency of the u-th switch cabinet is marked as ZP u , mark the humidity of the u-th switch cabinet as SD u ;

[0031] FYX=ω1×ZP u +ω2×SD u +ω3×ΔDT u

[0032] In the formula, ω1 represents the evaluation weight for vibration frequency, ω2 represents the evaluation weight for humidity, and ΔDT u represents the change in the volume of the plug label, ω3 represents the evaluation weight for the volume change, ω1+ω2+ω3=1, ω1×ZP u +ω2×SD u +ω3×ΔDT u It means that according to the weights of ω1, ω2 and ω3, the vibration frequency, humidity and volume change are combined to obtain the fourth influence coefficient for evaluating the plug label data error.

[0033] Preferably, when any value of the first influence coefficient OYX, the second influence coefficient TYX, the third influence coefficient HYX and the fourth influence coefficient FYX exceeds the first influence threshold OYU, the second influence threshold TYU, the third influence threshold HYU or the fourth influence threshold FYU, it indicates that the influence coefficient has exceeded the standard, and the data error of the type of RFID electronic tag corresponding to the influence coefficient has exceeded the standard, and the fluctuation data group Bdsj needs to be corrected for the error. The error correction process of the fluctuation data group Bdsj is as follows:

[0034] S11, extracting the sensor temperatures of all monitoring nodes of the same type in the temperature data set according to the type of RFID electronic tag corresponding to the influence coefficient;

[0035] S12, setting a conversion coefficient θ, calculating the product of the sensor temperature of all monitoring nodes of the same type and the conversion coefficient θ, which is the sensor temperature after error correction;

[0036] S13. Recalculate the fluctuation data group Bdsj according to the error-corrected sensor temperature to complete the error correction process of the fluctuation data group Bdsj.

[0037] A cable monitoring method comprises the following steps:

[0038] Step 1: Using the fastening nut of the cable connector as a carrier, N RFID electronic tags are fixedly installed in N switch cabinets. Each RFID electronic tag is provided with a temperature sensor for real-time collection of the sensing temperature of the monitoring node and forming a temperature data set;

[0039] Step 2: Each switch cabinet is equipped with a monitoring device to collect vibration frequency, suspended dust concentration, magnetic field strength, vibration amplitude, corrosive gas concentration and humidity in real time and form an environmental data set;

[0040] Step 3: Use RFID radio frequency identification wireless communication technology to send the temperature data set and environmental data set to the reader / writer. The reader / writer analyzes the change trend of the sensor temperature of each monitoring node and the influence of environmental factors on different types of RFID electronic tags, generates the corresponding fluctuation data group Bdsj, the first influence coefficient OYX, the second influence coefficient TYX, the third influence coefficient HYX and the fourth influence coefficient FYX, and forwards them to the monitoring background;

[0041] Step 4: The monitoring background is set with a fixed range of the first impact threshold OYU, the second impact threshold TYU, the third impact threshold HYU and the fourth impact threshold FYU, and then combined with the first impact coefficient OYX, the second impact coefficient TYX, the third impact coefficient HYX and the fourth impact coefficient FYX, to determine whether error correction is needed for the fluctuation data group Bdsj, and output the corresponding judgment result.

[0042] Compared with the prior art, the present invention provides a cable monitoring system and a monitoring method, which have the following beneficial effects:

[0043] 1. The present invention uses the fastening nut of the cable connector as a carrier to fix N RFID electronic tags in N switch cabinets. Each RFID electronic tag is provided with a temperature sensor for real-time acquisition of the sensing temperature of the monitoring node and forms a temperature data set. Different types of RFID electronic tags are flexibly applicable to switch cabinets in different environments. Each switch cabinet is provided with a monitoring device for real-time acquisition of vibration frequency, suspended dust concentration, magnetic field strength, vibration amplitude, corrosive gas concentration and humidity and forms an environmental data set. The read-write processing module analyzes the change trend of the sensing temperature of each monitoring node and the influence of environmental factors on different types of RFID electronic tags, and generates corresponding fluctuation data groups Bdsj, the first influence coefficient OYX, the second influence coefficient TYX, the third influence coefficient HYX and the fourth influence coefficient FYX, so as to achieve high comprehensive monitoring accuracy.

[0044] 2. The present invention sets a fixed range of the first impact threshold OYU, the second impact threshold TYU, the third impact threshold HYU and the fourth impact threshold FYU through the monitoring management module, and then combines the first impact coefficient OYX, the second impact coefficient TYX, the third impact coefficient HYX and the fourth impact coefficient FYX to determine whether it is necessary to perform error correction on the fluctuation data group Bdsj. When any value exceeds the first impact threshold OYU, the second impact threshold TYU, the third impact threshold HYU or the fourth impact threshold FYU, it indicates that the impact coefficient has exceeded the standard, and the data error of the RFID electronic tag corresponding to the type of the impact coefficient has exceeded the standard, and it is necessary to perform error correction on the fluctuation data group Bdsj. The corrected fluctuation data group Bdsj can be used by management personnel to accurately judge the operating status of the switch cabinet, and the error correction capability is strong. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0046] Figure 2 It is a schematic diagram of the system flow of the present invention;

[0047] Figure 3 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] During the installation of the cable terminal joint, accidents often occur due to loose bolts or poor contact at the terminal. The most obvious manifestation is the abnormal increase in the temperature of the connection point. The traditional cable joint temperature monitoring method can only measure the temperature of the outer surface of the cable joint, while the actual connection point temperature is located inside the joint and cannot be directly measured. The error difference between different types of power tags and the measured power equipment is often ignored, resulting in low temperature measurement accuracy and poor error correction ability. Therefore, a cable monitoring system and monitoring method are provided. Please refer to Figure 1-Figure 2 , a cable monitoring system, including a data acquisition module, a read-write processing module and a monitoring management module;

[0050] The data acquisition module consists of a sensor data unit and an environmental data unit. The sensor data unit collects the temperature data set through a network connection to the RFID temperature sensor. The temperature data set includes the sensor temperatures of all monitoring nodes. The expression of the temperature data set is {J1 g 、J2 g 、J3 g 、...、JN g}, J1 g To JN g They are the sensing temperatures of the first detection node to the Nth monitoring node, respectively. The sensing temperature is the internal temperature of the cable joint. g represents the switch cabinet number where a single monitoring node is located. The types of RFID electronic tags of each monitoring node are different, including tuning fork tags, clip tags, plum blossom contact tags and plug tags. Each monitoring node sends the collected monitoring data to the reader through RFID. The reader communicates with the cloud server using the Modbus485 communication protocol to achieve real-time uploading of monitoring data for the power supply network operation and maintenance personnel to check. For switch cabinets far away from the city center and relatively isolated substations, DTU wireless transmission to the cloud can be used;

[0051] The environmental data unit collects environmental data sets through network connection monitoring devices. The environmental data sets include environmental monitoring data of all switch cabinets. The expression of environmental data sets is {G1 s , G2 s 、G3 s ,...,GN s}, G1 s To GN sThese are the environmental monitoring data of the first to Nth switch cabinets, including vibration frequency, suspended dust concentration, magnetic field strength, vibration amplitude, corrosive gas concentration and humidity. s represents the specific time for obtaining the environmental monitoring data of a single switch cabinet. Each switch cabinet corresponds to a monitoring node, which comprehensively monitors the internal environmental data of the switch cabinet, which is conducive to the subsequent rapid evaluation of the accuracy of the sensor temperature.

[0052] The read-write processing module consists of a primary processing unit and an environmental assessment unit. The primary processing unit is set with a fixed-duration monitoring cycle Q. Combined with the temperature data set, it analyzes the change trend of the sensor temperature of each monitoring node and generates the corresponding fluctuation data group Bdsj. The calculation process is as follows:

[0053] According to the temperature data set, the sensor temperature of the i-th monitoring node in the monitoring period Q is extracted and marked as {Wi1, Wi2, Wi3, ..., Wi e}, Wi1 to Wi e are the sensing temperatures of the i-th monitoring node from the first time point to the e-th time point;

[0054]

[0055] In the formula, maxWi represents the highest sensor temperature of the ith monitoring node within the monitoring period Q, minWi represents the lowest sensor temperature of the ith monitoring node within the monitoring period Q, and maxWi-minWi represents the temperature extreme difference of the ith monitoring node. It represents the average sensor temperature of the i-th monitoring node in the monitoring period Q, which is recorded as Wix represents the sensor temperature of the i-th monitoring node at the f-th time point, f∈e, It means that according to the standard deviation formula, the fluctuation rate of the sensor temperature of the i-th monitoring node within the monitoring period Q is obtained, and the change trend of the sensor temperature is analyzed in multiple dimensions, realizing the long-term autonomous monitoring function of the system;

[0056] The environmental assessment unit analyzes the influence of environmental factors on different types of RFID electronic tags according to the environmental data set, and generates corresponding first influence coefficient OYX, second influence coefficient TYX, third influence coefficient HYX and fourth influence coefficient FYX;

[0057] The calculation process of the first influence coefficient OYX is as follows:

[0058] Extract the environmental monitoring data of the i-th switch cabinet from the environmental data set. The type of RFID electronic tag set in the i-th switch cabinet is a tuning fork tag, and the vibration frequency of the i-th switch cabinet is marked as ZP i , the suspended dust concentration of the i-th switch cabinet is marked as XH i, the magnetic field strength of the i-th switch cabinet is marked as CQ i ;

[0059] OYX=α1×ZP i +α2×XH i +α3×CQ i +α4×(BJ-JD i )

[0060] In the formula, α1 represents the evaluation weight for vibration frequency, α2 represents the evaluation weight for suspended dust concentration, α3 represents the evaluation weight for magnetic field strength, BJ represents the standard angle between the tuning fork label and the busbar connection point, JD represents the standard angle between the tuning fork label and the busbar connection point, i Indicates the tilt angle between the tuning fork label and the busbar connection point, BJ-JD i represents the difference between the tilt angle and the standard angle, α4 represents the evaluation weight for the angle difference, α1+α2+α3+α4=1, α1×ZP i +α2×XH i +α3×CQ i +α4×(BJ-JD i ) indicates that according to α1, α2, α3 and α4, the first influence coefficient for evaluating the tuning fork tag data error is obtained by combining the vibration frequency, suspended dust concentration, magnetic field strength and angle difference. The tuning fork tag is suitable for the busbar connection point. Based on the principle of mechanical resonance, if the vibration frequency of the power equipment exceeds the normal range, the tuning fork tag will be poorly fitted and the sensor data will have obvious errors;

[0061] The calculation process of the second influence coefficient TYX is as follows:

[0062] Extract the environmental monitoring data of the kth switch cabinet from the environmental data set. The type of RFID electronic tag set in the kth switch cabinet is a clip tag, and the vibration amplitude of the kth switch cabinet is marked as ZF k , the corrosive gas concentration of the kth switch cabinet is marked as FQ k ;

[0063] TYX=β1×ZF k +β2×FQ k +β3×(BL-JL k )

[0064] In the formula, β1 represents the evaluation weight for vibration amplitude, β2 represents the evaluation weight for corrosive gas concentration, BL represents the standard clamping force between the clip label and the busbar, and JL represents the standard clamping force between the clip label and the busbar. k Indicates the actual clamping force between the clip label and the busbar, BL-JL krepresents the difference between the actual clamping force and the standard clamping force, β3 represents the evaluation weight for the clamping force difference, β1+β2+β3=1, β1×ZF k +β2×FQ k +β3×(BL-JL k ) indicates that according to β1, β2 and β3, the vibration amplitude, corrosive gas concentration and clamping force difference are combined to obtain the second influence coefficient for evaluating the clip tag data error. The clip tag is directly clipped on the busbar. Severe vibration may cause the clip to loosen, resulting in poor contact or signal loss. Excessive clamping force may also cause the busbar to deform, affecting the electrical contact performance.

[0065] The calculation process of the third influence coefficient HYX is as follows:

[0066] Extract the environmental monitoring data of the jth switch cabinet from the environmental data set. The type of RFID electronic tag set in the jth switch cabinet is a plum blossom contact tag, and the vibration frequency of the jth switch cabinet is marked as ZP j , the vibration amplitude of the jth switch cabinet is marked as ZF j ;

[0067]

[0068] In the formula, represents the evaluation weight for the vibration frequency, Denotes the evaluation weight for vibration amplitude, ΔJM j Indicates the change in contact area between the plum blossom contact label and the contact of the power equipment. represents the evaluation weight for the contact area change, Indicates according to and Weight, combining vibration frequency, vibration amplitude and contact area change, to obtain the third influence coefficient used to evaluate the data error of plum blossom contact label. The plum blossom contact label is designed with multiple points of contact. High-frequency vibration may cause the contact to loosen and increase the contact resistance, thus affecting the temperature measurement.

[0069] The calculation process of the fourth influence coefficient FYX is as follows:

[0070] Extract the environmental monitoring data of the u-th switch cabinet from the environmental data set. The type of RFID electronic tag set in the u-th switch cabinet is a plug tag, and the vibration frequency of the u-th switch cabinet is marked as ZP u , mark the humidity of the u-th switch cabinet as SD u ;

[0071] FYX=ω1×ZP u +ω2×SD u +ω3×ΔDTu

[0072] In the formula, ω1 represents the evaluation weight for vibration frequency, ω2 represents the evaluation weight for humidity, and ΔDT u represents the change in the volume of the plug label, ω3 represents the evaluation weight for the volume change, ω1+ω2+ω3=1, ω1×ZP u +ω2×SD u +ω3×ΔDT u It means that according to the weights of ω1, ω2 and ω3, the fourth influence coefficient used to evaluate the data error of the plug tag is obtained by combining the vibration frequency, humidity and volume change. The plug tag is usually installed in the closed holes of equipment such as switch cabinets to monitor the internal temperature or environment. Not only will the material of the tag itself change in volume due to temperature, but vibration will also cause the plug to loosen or fall off, affecting the stability of the sensor;

[0073] The monitoring management module is provided with a fixed range of the first impact threshold OYU, the second impact threshold TYU, the third impact threshold HYU and the fourth impact threshold FYU, and then combined with the first impact coefficient OYX, the second impact coefficient TYX, the third impact coefficient HYX and the fourth impact coefficient FYX, to determine whether it is necessary to perform error correction on the fluctuation data group Bdsj. When any value of the first impact coefficient OYX, the second impact coefficient TYX, the third impact coefficient HYX and the fourth impact coefficient FYX exceeds the first impact threshold OYU, the second impact threshold TYU, the third impact threshold HYU or the fourth impact threshold FYU, it indicates that the impact coefficient has exceeded the standard, and the data error of the type of RFID electronic tag corresponding to the impact coefficient has exceeded the standard, and the fluctuation data group Bdsj needs to be corrected for error. The error correction process of the fluctuation data group Bdsj is as follows:

[0074] S11, extracting the sensor temperatures of all monitoring nodes of the same type in the temperature data set according to the type of RFID electronic tag corresponding to the influence coefficient;

[0075] S12, setting a conversion coefficient θ, calculating the product of the sensor temperature of all monitoring nodes of the same type and the conversion coefficient θ, which is the sensor temperature after error correction;

[0076] S13. Recalculate the fluctuation data group Bdsj according to the error-corrected sensor temperature to complete the error correction process of the fluctuation data group Bdsj. The error-corrected fluctuation data group Bdsj can be used by the management personnel to accurately judge the operating status of the switch cabinet.

[0077] See also Figure 3 , a cable monitoring method, comprising the following steps:

[0078] Step 1: Using the fastening nut of the cable joint as a carrier, N RFID electronic tags are fixedly installed in N switch cabinets. Each RFID electronic tag is provided with a temperature sensor for real-time collection of the sensing temperature of the monitoring node and forming a temperature data set. Different types of RFID electronic tags are flexibly applicable to switch cabinets in different environments. RFID electronic tags include but are not limited to nut-type tags that are surface-attached or embedded in the conductor joint.

[0079] Step 2: Each switch cabinet is equipped with a monitoring device to collect vibration frequency, suspended dust concentration, magnetic field strength, vibration amplitude, corrosive gas concentration and humidity in real time and form an environmental data set;

[0080] Step 3: Use RFID radio frequency identification wireless communication technology to send the temperature data set and environmental data set to the reader / writer, realize the conductor contact between the wireless passive RFID sensor and the connection point, so as to ensure the accuracy of the measurement, and effectively solve the problem that the sensor affects the electrical performance of the cable joint inside the cable joint, and reduce the actual loss caused by loose bolts or poor contact of the cable joint. The reader / writer analyzes the change trend of the sensor temperature of each monitoring node and the influence of environmental factors on different types of RFID electronic tags, generates the corresponding fluctuation data group Bdsj, the first influence coefficient OYX, the second influence coefficient TYX, the third influence coefficient HYX and the fourth influence coefficient FYX, and forwards them to the monitoring background, with high comprehensive monitoring accuracy;

[0081] Step 4: The monitoring background is set with a fixed range of the first impact threshold OYU, the second impact threshold TYU, the third impact threshold HYU and the fourth impact threshold FYU, and then combined with the first impact coefficient OYX, the second impact coefficient TYX, the third impact coefficient HYX and the fourth impact coefficient FYX, to determine whether error correction is needed for the fluctuation data group Bdsj, and output the corresponding judgment result, with strong error correction capability.

[0082] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cable monitoring system, characterized in that: It includes data acquisition module, reading and writing processing module and monitoring management module; The data acquisition module is composed of a sensor data unit and an environment data unit. The sensor data unit collects a temperature data set through a network connection to an RFID temperature sensor. The temperature data set includes the sensor temperatures of all monitoring nodes. The environment data unit collects an environment data set through a network connection to a monitoring device. The environment data set includes the environment monitoring data of all switch cabinets. The read-write processing module is composed of a primary processing unit and an environmental assessment unit. The primary processing unit is provided with a fixed-duration monitoring cycle Q, and then combined with the temperature data set, analyzes the change trend of the sensor temperature of each monitoring node, and generates a corresponding fluctuation data set Bdsj. The environmental assessment unit analyzes the influence of environmental factors on different types of RFID electronic tags according to the environmental data set, and generates the corresponding first influence coefficient OYX, second influence coefficient TYX, third influence coefficient HYX and fourth influence coefficient FYX; The monitoring management module is provided with a fixed range of the first impact threshold OYU, the second impact threshold TYU, the third impact threshold HYU and the fourth impact threshold FYU, and then combined with the first impact coefficient OYX, the second impact coefficient TYX, the third impact coefficient HYX and the fourth impact coefficient FYX, to determine whether error correction is required for the fluctuation data group Bdsj, and output the corresponding judgment result.

2. A cable monitoring system according to claim 1, characterized in that: The expression of the temperature data set is {J1 g 、J2 g 、J3 g 、...、JN g }, J1 g To JN g They are the sensing temperatures from the first detection node to the Nth monitoring node respectively. The sensing temperature is the internal temperature of the cable joint. g represents the switch cabinet number where a single monitoring node is located. The types of RFID electronic tags for each monitoring node are different, including tuning fork tags, clip tags, plum blossom contact tags and plug tags.

3. A cable monitoring system according to claim 2, characterized in that: The expression of the environmental data set is {G1 s , G2 s , G3 s ,...,GN s }, G1 s To GN s They are the environmental monitoring data of the first to the Nth switch cabinets, respectively. The environmental monitoring data include vibration frequency, suspended dust concentration, magnetic field strength, vibration amplitude, corrosive gas concentration and humidity. s represents the specific time for obtaining the environmental monitoring data of a single switch cabinet. Each switch cabinet corresponds to a monitoring node.

4. A cable monitoring system according to claim 3, characterized in that: The calculation process of the fluctuation data group Bdsj is as follows: According to the temperature data set, the sensor temperature of the i-th monitoring node in the monitoring period Q is extracted and marked as {Wi1, Wi2, Wi3, ..., Wi e }, Wi1 to Wi e are the sensing temperatures of the i-th monitoring node from the first time point to the e-th time point; In the formula, maxWi represents the highest sensor temperature of the ith monitoring node within the monitoring period Q, minWi represents the lowest sensor temperature of the ith monitoring node within the monitoring period Q, and maxWi-minWi represents the temperature extreme difference of the ith monitoring node. It represents the average sensor temperature of the i-th monitoring node in the monitoring period Q, which is recorded as Wi f represents the sensor temperature of the i-th monitoring node at the f-th time point, It means that according to the standard deviation formula, the fluctuation rate of the sensor temperature of the i-th monitoring node within the monitoring period Q is obtained.

5. A cable monitoring system according to claim 4, characterized in that: The calculation process of the first influence coefficient OYX is as follows: Extract the environmental monitoring data of the i-th switch cabinet from the environmental data set. The type of RFID electronic tag set in the i-th switch cabinet is a tuning fork tag, and the vibration frequency of the i-th switch cabinet is marked as ZP i , the suspended dust concentration of the i-th switch cabinet is marked as XH i , the magnetic field strength of the i-th switch cabinet is marked as CQ i ; OYX=α1×ZP i +α2×XH i +α3×CQ i +α4×(BJ-JD i ) In the formula, α1 represents the evaluation weight for vibration frequency, α2 represents the evaluation weight for suspended dust concentration, α3 represents the evaluation weight for magnetic field strength, BJ represents the standard angle between the tuning fork label and the busbar connection point, JD represents the standard angle between the tuning fork label and the busbar connection point, i Indicates the tilt angle between the tuning fork label and the busbar connection point, BJ-JD i represents the difference between the tilt angle and the standard angle, α4 represents the evaluation weight for the angle difference, α1+α2+α3+α4=1, α1×ZP i +α2×XH i +α3×CQ i +α4×(BJ-JD i ) represents the first influence coefficient for evaluating the tuning fork tag data error obtained by combining the vibration frequency, suspended dust concentration, magnetic field strength and angle difference according to α1, α2, α3 and α4.

6. A cable monitoring system according to claim 5, characterized in that: The calculation process of the second influence coefficient TYX is as follows: Extract the environmental monitoring data of the kth switch cabinet from the environmental data set. The type of RFID electronic tag set in the kth switch cabinet is a clip tag, and the vibration amplitude of the kth switch cabinet is marked as ZF k , the corrosive gas concentration of the kth switch cabinet is marked as FQ k ; TYX=β1×ZF k +β2×FQ k +β3×(BL-JL k ) In the formula, β1 represents the evaluation weight for vibration amplitude, β2 represents the evaluation weight for corrosive gas concentration, BL represents the standard clamping force between the clip label and the busbar, and JL represents the standard clamping force between the clip label and the busbar. k Indicates the actual clamping force between the clip label and the busbar, BL-JL k represents the difference between the actual clamping force and the standard clamping force, β3 represents the evaluation weight for the clamping force difference, β1+β2+β3=1, β1×ZF k +β2×FQ k +β3×(BL-JL k ) represents the second influence coefficient for evaluating the clip tag data error obtained by combining the vibration amplitude, corrosive gas concentration and clamping force difference according to β1, β2 and β3.

7. A cable monitoring system according to claim 6, characterized in that: The calculation process of the third influence coefficient HYX is as follows: Extract the environmental monitoring data of the jth switch cabinet from the environmental data set. The type of RFID electronic tag set in the jth switch cabinet is a plum blossom contact tag, and the vibration frequency of the jth switch cabinet is marked as ZP j , the vibration amplitude of the jth switch cabinet is marked as ZF j ; In the formula, represents the evaluation weight for the vibration frequency, Denotes the evaluation weight for vibration amplitude, ΔJM j Indicates the change in contact area between the plum blossom contact label and the contact of the power equipment. represents the evaluation weight for the contact area change, Indicates according to and The third influence coefficient for evaluating the data error of the plum blossom contact label is obtained by combining the vibration frequency, vibration amplitude and contact area change.

8. A cable monitoring system according to claim 7, characterized in that: The calculation process of the fourth influence coefficient FYX is as follows: Extract the environmental monitoring data of the u-th switch cabinet from the environmental data set. The type of RFID electronic tag set in the u-th switch cabinet is a plug tag, and the vibration frequency of the u-th switch cabinet is marked as ZP u , mark the humidity of the u-th switch cabinet as SD u ; FYX=ω1×ZP u +ω2×SD u +ω3×ΔDT u In the formula, ω1 represents the evaluation weight for vibration frequency, ω2 represents the evaluation weight for humidity, and ΔDT u represents the change in the volume of the plug label, ω3 represents the evaluation weight for the volume change, ω1+ω2+ω3=1, ω1×ZP u +ω2×SD u +ω3×ΔDT u It means that according to the weights of ω1, ω2 and ω3, the vibration frequency, humidity and volume change are combined to obtain the fourth influence coefficient for evaluating the plug label data error.

9. A cable monitoring system according to claim 8, characterized in that: When any value of the first influence coefficient OYX, the second influence coefficient TYX, the third influence coefficient HYX and the fourth influence coefficient FYX exceeds the first influence threshold OYU, the second influence threshold TYU, the third influence threshold HYU or the fourth influence threshold FYU, it indicates that the influence coefficient has exceeded the standard, and the data error of the type of RFID electronic tag corresponding to the influence coefficient has exceeded the standard, and the fluctuation data group Bdsj needs to be corrected for the error. The error correction process of the fluctuation data group Bdsj is as follows: S11, extracting the sensor temperatures of all monitoring nodes of the same type in the temperature data set according to the type of RFID electronic tag corresponding to the influence coefficient; S12, setting a conversion coefficient θ, calculating the product of the sensor temperature of all monitoring nodes of the same type and the conversion coefficient θ, which is the sensor temperature after error correction; S13. Recalculate the fluctuation data group Bdsj according to the error-corrected sensor temperature to complete the error correction process of the fluctuation data group Bdsj.

10. A cable monitoring method, applied to a cable monitoring system as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Using the fastening nut of the cable connector as a carrier, N RFID electronic tags are fixedly installed in N switch cabinets. Each RFID electronic tag is provided with a temperature sensor for real-time collection of the sensing temperature of the monitoring node and forming a temperature data set; Step 2: Each switch cabinet is equipped with a monitoring device to collect vibration frequency, suspended dust concentration, magnetic field strength, vibration amplitude, corrosive gas concentration and humidity in real time and form an environmental data set; Step 3: Use RFID radio frequency identification wireless communication technology to send the temperature data set and environmental data set to the reader / writer. The reader / writer analyzes the change trend of the sensor temperature of each monitoring node and the influence of environmental factors on different types of RFID electronic tags, generates the corresponding fluctuation data group Bdsj, the first influence coefficient OYX, the second influence coefficient TYX, the third influence coefficient HYX and the fourth influence coefficient FYX, and forwards them to the monitoring background; Step 4: The monitoring background is set with a fixed range of the first impact threshold OYU, the second impact threshold TYU, the third impact threshold HYU and the fourth impact threshold FYU, and then combined with the first impact coefficient OYX, the second impact coefficient TYX, the third impact coefficient HYX and the fourth impact coefficient FYX, to determine whether error correction is needed for the fluctuation data group Bdsj, and output the corresponding judgment result.

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