CN110262313B - Electric power material key point remote monitoring system based on internet of things technology - Google Patents
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Abstract
The invention provides an electric power material key point remote monitoring system based on the technology of the Internet of things, which comprises a data acquisition module, a data analysis module, a data intelligent alarm module and an intelligent terminal module; the data acquisition module converts the acquired monitoring data of different data types into a uniform data format, encrypts the monitoring data and sends the encrypted monitoring data to the cloud. The different data types of monitoring data include real-time monitoring data, conductor resistance data, and insulation run length data. The data analysis module compares the data acquired by the data acquisition module with a nominal value to acquire a deviation degree; and when the deviation degree data is greater than the threshold value, alarming. The invention forms a new material quality management mode of advanced management and control, real-time monitoring, intelligent analysis and process management and inspection, realizes the on-site evidence-based remote real-time automatic monitoring of electric materials from multi-contract time, and the passive lagging supervision to the active supervision, reduces the labor intensity and labor cost of personnel, and improves the efficiency of material quality management.
Description
Technical Field
The invention belongs to the technical field of power material quality monitoring, and particularly relates to a power material key point remote monitoring system based on the technology of the Internet of things.
Background
The material management work is an important support for power grid construction and production, and the intensive material management is the overall planning and intensive management from the strategic height of the sustainable development of the national grid company, so that the maximum benefit is obtained at the minimum cost. The safe and stable operation of the power grid depends on the electric power materials with excellent quality, the quality control work of the materials used by the whole power grid is enhanced, and the method is a basis and guarantee for making good the material management and improving the safe and stable operation of the power grid and is very important. The guarantee of the quality of the supplies undoubtedly influences the safety and the continuous stability of the whole power grid.
The key point of the electric power materials refers to the key point of delivery inspection of the quality of the electric power materials, at the present stage, the quality management form of the materials is severe, the quality control of the materials at the production stage of an important material supplier of 220kV or below is only limited to the witness work of the key point, and the quality control of the materials at the production stage of 10KV or below distribution network type materials with large purchase quantity and important items is still blank.
The material quality management mode is lagged, the current material quality management mode is the traditional spot check mode, and material quality management personnel sample on site, so that the defects of scattered places, low sampling efficiency, small sample coverage and the like exist. Under the sampling inspection mode, the risk that unqualified materials flow into the power grid is high, and hidden dangers are brought to safe and reliable operation of the power grid. The labor intensity of quality management personnel is high, at present, key equipment in electric power materials needs the materials management personnel to carry out witness work of key procedures to the site, the consumed manpower, material resources and financial resources are more, the places are scattered, particularly, the projects of power grid infrastructure, technical improvement, overhaul and the like in the south of China are increased year by year, the purchase amount of the materials is increased year by year, the witness work amount is increased sharply, at least 2 persons are needed for witness work at each time, 3 days are consumed, and on-site account witness, form recording, table compilation, data arrangement and filing and the like all need offline manual operation; if multiple suppliers need the key process for witness work at the same time, the working intensity of personnel is high, the working time is short, the business requirements are difficult to meet, and the delivery progress is influenced.
Disclosure of Invention
The invention provides a remote monitoring system for key points of electric power materials based on the technology of the Internet of things. The method realizes the on-site evidence of the electric power materials from multi-contract time to remote real-time automatic monitoring, and realizes the passive hysteresis supervision to the active supervision.
In order to achieve the purpose, the remote monitoring system for the key points of the power materials based on the technology of the internet of things comprises a data acquisition module and a data analysis module;
the data acquisition module is communicated with monitoring equipment through an RS485 communication interface, acquired monitoring data of different data types are respectively stored in different memories or databases, then the monitoring data of different data types are all converted into a uniform data format, and the data of the uniform data format is encrypted by DES and then sent to the cloud; the monitoring data of different data types comprises real-time monitoring data, conductor resistance data and insulation extension length data;
the data analysis module compares the monitoring real-time data with the nominal value of the detection real-time data to obtain the deviation degree of the monitoring real-time data; optimizing the nominal value of the conductor resistance to obtain a second nominal value, and comparing the data of the conductor resistance with the second nominal value to obtain the deviation degree of the data of the conductor resistance; and comparing the data of the insulation extension length with the nominal value of the insulation extension length to obtain the deviation degree of the insulation extension length.
Furthermore, the remote monitoring system also comprises a data intelligent alarm module and an intelligent terminal module;
the data intelligent alarm module is used for acquiring the deviation degree of monitoring real-time data, the deviation degree of data of conductor resistance and the deviation degree of insulation extension length which are analyzed by the data analysis module; when any data of the deviation degree of the monitored real-time data, the deviation degree of the data of the conductor resistance and the deviation degree of the insulation extension length is larger than a threshold value, alarming;
the intelligent terminal module is used for setting different login authorities, is in real-time butt joint with monitoring data of the electric power materials, checks the change trend of the monitoring data, and pushes information to related personnel after the data intelligent alarm module gives an alarm.
Further, the processing method for monitoring the real-time data comprises the following steps: storing the monitored real-time data into a PLC memory, and configuring an APU2004 module at a PLC output interface; the APU2004 module stores the monitoring real-time data output by the PLC to the local of the APU2004 in batches, and each batch generates a first number which is also stored with the monitoring real-time data at the same time.
Further, the method for processing the data of the conductor resistance comprises the following steps: the DC3000 system stores the data of the conductor resistance in a relational database, reads a plurality of tables of the relational database, forms a uniform data format and generates a second serial number for the data;
the DC3000 system is a parameter setting and calculating system of a cable insulation sheath thermal extension tester.
Further, the processing method of the data of the insulation extension length comprises the following steps: the QJ36B-2 system stores the data of the insulation run length to a file-type database; copying the data of the conductor resistance and the insulation extension length to form a uniform data format, generating a third serial number for the data of the insulation extension length, and storing the third serial number on the hard disk;
the QJ36B-2 system is a resistance testing instrument for cables and wires.
Further, the data in the unified data format is encrypted by DES and then sent to the cloud end in such a way that the data acquisition module sends the data to the cloud end one by one according to the sequence of the first number, the second number or the third number; and the cloud end immediately sends N confirmation serial numbers to the data acquisition module after receiving N records in unit time.
Further, the method for optimizing the nominal value of the conductor resistance to obtain the second nominal value comprises the following steps: making an original nominal value into a training set according to a detection record, and carrying out self-learning by using TensorFlow; then, acquiring deviation by adopting a quadratic cost function Square, and adjusting optimization parameters by combining a gradient descent optimizer;
the formula of self-learning is nominal value R-w 1 c + w2 d + b, wherein w1 and w2 are both weights, c is temperature, d is humidity, and b is offset.
Further, the calculation method for the deviation degree of the monitoring real-time data comprises the following steps:
the degree of deviation of the data of the conductor resistance is as follows:
the deviation degree of the insulation extension length is as follows:
furthermore, the intelligent terminal module is used for setting different login authorities, including that a supplier checks monitoring data of products provided by the supplier, a material manager checks monitoring data of all products, and the manager manages a remote monitoring system except all detection data.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
the embodiment of the invention provides an electric power material key point remote monitoring system based on the technology of the Internet of things, which comprises a data acquisition module, a data analysis module, a data intelligent alarm module and an intelligent terminal module; the data acquisition module is communicated with the monitoring equipment through an RS485 communication interface. The collected monitoring data of different data types are respectively stored in different internal memories or databases, then the monitoring data of different data types are all converted into a uniform data format, and the data of the uniform data format is encrypted by DES and then sent to the cloud. The different data types of monitoring data include real-time monitoring data, conductor resistance data, and insulation run length data. The data analysis module compares the monitoring real-time data with the nominal value of the detection real-time data to obtain the deviation degree of the monitoring real-time data; optimizing the nominal value of the conductor resistance to obtain a second nominal value, and comparing the data of the conductor resistance with the second nominal value to obtain the deviation degree of the data of the conductor resistance; and comparing the data of the insulation extension length with the nominal value of the insulation extension length to obtain the deviation degree of the insulation extension length. The intelligent alarm module is used for acquiring the deviation degree of monitoring real-time data, the deviation degree of conductor resistance data and the deviation degree of insulation extension length which are analyzed by the data analysis module; and when any deviation degree data is larger than a threshold value, alarming. The invention provides an electric power material key point remote monitoring system based on the technology of the Internet of things. By developing the remote monitoring system for the key points of the electric power materials based on the internet of things technology, a new material quality management mode of advanced management and control, real-time monitoring, intelligent analysis and process management and inspection is formed, the electric power materials are monitored remotely and automatically in real time from on-site evidence meeting in many appointment, and are supervised passively and lagged to active supervision, the labor intensity and labor cost of personnel are reduced, the efficiency, efficiency and lean level of material quality management are improved, and good benefits are generated. When the direct benefit is generated, the cost of human resources is saved. The indirect benefit is that the former mode of manual sampling and sampling from the materials to the goods is changed, the original sampling and sampling of the materials is changed into full detection, the quality supervision of the materials is moved to the production stage from the goods sampling and sampling, and the outflow of unqualified materials is thoroughly stopped. The qualification rate of the material spot check in the prior art is 93.36%, and the quality qualification rate of the material can be at least improved to 99% by advancing quality supervision and enlarging the quality detection coverage. In addition, the material guarantee capability of safe operation of the power grid is improved, the brand image of national grid companies is improved, and indirect economic benefits brought by reduction of unqualified material replacement and delay of project completion cycle are more considerable.
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Fig. 1 is a schematic structural diagram of an electric power material key point remote monitoring system based on the internet of things technology according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
Example 1
The embodiment 1 of the invention provides an electric power material key point remote monitoring system based on the technology of the Internet of things, which comprises a data acquisition module, a data analysis module, an intelligent alarm module and an intelligent terminal module; fig. 1 is a schematic structural diagram of an electric power material key point remote monitoring system based on the internet of things technology according to embodiment 1 of the present invention.
The data acquisition module is communicated with monitoring equipment through an RS485 communication interface, wherein the monitoring equipment comprises cable monitoring equipment and transformer monitoring equipment. Respectively storing the collected monitoring data of different data types into different internal memories or databases, then converting the monitoring data of different data types into a uniform data format, encrypting the data of the uniform data format by using a Data Encryption Standard (DES), and then sending the data to a cloud end; wherein the different data types of monitoring data include real-time monitoring data, conductor resistance data, and insulation run length data. The monitoring equipment mainly comprises cable detection equipment and transformer monitoring equipment.
The processing method for monitoring the real-time data comprises the following steps: storing cable monitoring real-time data and transformer monitoring real-time data into a PLC memory, and configuring an APU2004 module at a PLC output interface; the APU2004 module stores the monitoring real-time data output by the PLC to the APU2004 locally in batches, and each batch generates a first number which is also stored with the monitoring real-time data at the same time. The APU2004 module is provided with an LTE (4G) wireless communication function and can also provide functions of data storage and forwarding.
The data processing method for the conductor resistance comprises the following steps: the DC3000 system stores the data of the conductor resistance in a relational database, reads a plurality of tables of the relational database, forms a uniform data format and generates a second serial number for the data;
the DC3000 system is a parameter setting and calculating system of the cable insulation sheath thermal extension tester.
The processing method of the data of the insulation extension length comprises the following steps: the QJ36B-2 system stores the data of the insulation run length to a file-type database; copying the data of the conductor resistance and the insulation extension length to form a uniform data format, generating a third standard number for the data of the insulation extension length, and storing the third standard number on the hard disk; the value of the third number of (a) may be set to:
the QJ36B-2 system is a resistance testing instrument for cables and wires.
After the data in the unified data format is encrypted by the DES, the data is sent to the cloud end in a way that the data acquisition module sends the data to the cloud end one by one according to the sequence of the first number, the second number or the third number; and the cloud end immediately sends N confirmation serial numbers to the data acquisition module after receiving the N records in unit time. The data acquisition module sends the data to the cloud end one by one according to the sequence of the serial numbers, and data needs to be encrypted by a DES before being sent, so that the data is prevented from being tampered or forged. Under the condition of ensuring efficiency and timeliness, the cloud platform does not need single confirmation on data, 10 confirmation serial numbers are sent to the data acquisition module immediately after 10 data are received within 1 minute, if the number is less than 10, the received confirmation serial numbers are sent to the data acquisition module within 1 minute, and if the number is not received within 1 minute and 30 seconds, the cloud platform automatically sends the repeated serial numbers, so that the integrity of the data is ensured.
And after the monitoring starts, transmitting the video data stream of the monitoring site to a cloud background for storage, wherein the cloud background stores the whole monitored video data stream to a hard disk, and the file name is equipment ID + detection time and is used as the monitoring witness data.
The data analysis module compares the monitoring real-time data with the nominal value of the detection real-time data to obtain the deviation degree of the monitoring real-time data; the monitoring real-time data comprises cable monitoring real-time data and transformer monitoring real-time data.
The data analysis module receives cable monitoring real-time data, wherein the data analysis module comprises outer sheath thickness (mm), armor thickness (mm), copper shielding lap joint rate (%), conductor shielding thickness (mm), insulating layer thickness (mm), insulating shielding thickness (mm) and eccentricity (%), and also collects temperature and humidity data of the environment.
Table 1 below gives a table of real-time data, nominal values and allowed minimum values for cable monitoring.
The data analysis module receives real-time transformer monitoring data, wherein the real-time transformer monitoring data comprises voltage ratio measurement and connection label verification, winding resistance measurement, direct current insulation resistance measurement of the winding to the ground and between windings, no-load loss and no-load current measurement, short-circuit impedance and load loss measurement, external application voltage withstand test, induction voltage withstand test, temperature rise test, partial discharge measurement test and lightning stroke impact test.
Table 2 below gives the voltage ratio measurements and bond designation verification in real time data for transformer monitoring with winding temperature of 16 ℃; the ambient temperature is 17 ℃; relative humidity 46%; atmospheric pressure 102.7 kpa.
The winding resistance measurements in real-time data for transformer monitoring are given in table 3 below, where the winding temperature is 16 ℃; the ambient temperature is 17 ℃; relative humidity 46%; atmospheric pressure 102.7 kpa.
Table 4 below gives the winding-to-ground and winding-to-winding dc insulation resistance measurements in real-time data for transformer monitoring, where the winding temperature is 16 ℃; the ambient temperature is 17 ℃; relative humidity 46%; atmospheric pressure 102.7 kpa.
Short circuit impedance and load loss measurements and short circuit impedance and load loss measurements in real time data for transformer monitoring are given in table 5 below, where the winding temperature is 16 ℃; the ambient temperature is 17 ℃; relative humidity 46%; atmospheric pressure 102.7 kpa.
Table 6 below gives the applied voltage withstand test in monitoring real time data for a transformer with a winding temperature of 16 ℃; the ambient temperature is 17 ℃; relative humidity 46%; atmospheric pressure 102.7 kpa.
Table 7 below gives the induced voltage withstand test in monitoring real time data for a transformer, where the winding temperature is 16 ℃; the ambient temperature is 17 ℃; relative humidity 46%; atmospheric pressure 102.7 kpa.
Table 8 below gives the temperature rise test in real time data for transformer monitoring, where the ambient temperature is 14.5 ℃; relative humidity 42%; atmospheric pressure 102.5 kpa.
The method also comprises a partial discharge measurement test, a lightning stroke impact test and other tests. Comparing the real-time monitoring data of the transformer with the nominal value of the real-time monitoring data to obtain the deviation degree of the real-time monitoring data;
optimizing the nominal value of the conductor resistance to obtain a second nominal value, and comparing the data of the conductor resistance with the second nominal value to obtain the deviation degree of the data of the conductor resistance; and comparing the data of the insulation extension length with the nominal value of the insulation extension length to obtain the deviation degree of the insulation extension length.
The data analysis module receives conductor inspection, temperature and humidity data, the resistance measured under the length of the resistance value of 1 meter is R1 omega/m, and R1 omega/km is compared with a nominal value. The nominal value is influenced by temperature and humidity, the influence of the temperature is only considered in the prior method, coefficients of a plurality of gears are given, the error judgment of 1% is carried out on the cable insulation resistance multiplied by the temperature conversion coefficient when the cable insulation resistance is t ℃ and the nominal value is 0.0754 omega/km, and the coefficient is 0.48 when the temperature is 0 ℃; the coefficient is 0.57 at 5 ℃; the coefficient is 0.70 at 10 ℃; the coefficient is 0.85 at 15 ℃; the coefficient is 1.0 at 20 ℃; the coefficient at 25 ℃ is 1.13; the coefficient at 30 ℃ is 1.41; the coefficient at 35 ℃ is 1.66; the coefficient at 40 ℃ was 1.92. The method for optimizing the nominal value of the conductor resistance to obtain the second nominal value comprises the following steps: making an original nominal value into a training set according to a detection record, and carrying out self-learning by using TensorFlow; then, acquiring deviation by adopting a quadratic cost function Square, and adjusting optimization parameters by combining a gradient descent optimizer GradientDescementOptimizer; and optimizing by using the training set, acquiring a second nominal value, comparing the second nominal value with the input nominal value by 1% of error, recording a judgment result and informing an alarm module.
The formula for self-learning is nominal value R w1 c w2 d + b, where w1 and w2 are both weights, c is temperature, d is humidity, and b is offset.
The calculation method for monitoring the deviation degree of the real-time data comprises the following steps:
the degree of deviation of the data of the conductor resistance is:
the degree of deviation of the insulation extension length is:
and the deviation degree data is stored and is informed to the alarm module. The deviation degree data is regarded as evaluation data of a supplier, deviation data is generated in each detection, the supplier is evaluated every year, the average deviation degree is the sum of deviation in each detection/detection times, and the supplier is regarded as unqualified supplier with the value less than 98%.
The insulation elongation measurements include pre-test length (mm), post-hot elongation (mm), post-repair length (mm), maximum elongation under load (mm), maximum permanent elongation after cooling (mm), and maximum nominal elongation under load (mm). And comparing the measured data with a standard value to obtain the deviation rate of the insulation extension length.
The data intelligent alarm module is used for acquiring the deviation degree of monitoring real-time data, the deviation degree of data of conductor resistance and the deviation degree of insulation extension length which are analyzed by the data analysis module; and when any data of the deviation degree of the monitored real-time data, the deviation degree of the data of the conductor resistance and the deviation degree of the insulation extension length is larger than a threshold value, alarming.
The intelligent terminal module is used for setting different login authorities, is in real-time butt joint with monitoring data of the electric power materials, checks the change trend of the monitoring data, and pushes information to related personnel after the data intelligent alarm module gives an alarm. The intelligent terminal needs user identity authentication, different users are assigned with different roles, the roles are important means for controlling display contents, and a system has a supplier, a material manager and a system manager. The supplier checks the monitoring data of the products provided by the supplier, and the material manager checks the detection data of all the products. Besides all monitoring data are checked, the administrator can also manage the remote monitoring system, such as adding personnel.
The intelligent terminal is in butt joint with the field quality control data, and the variation trend of each parameter is checked in real time; and the system automatically pushes the alarm information to related personnel after the alarm.
The intelligent power material quality monitoring and management system is divided into a three-layer structure, a comprehensive sensing layer, a ubiquitous network layer and an intelligent application layer according to a ubiquitous Internet of things architecture.
The comprehensive sensing layer comprises an intelligent monitoring device or system and a field camera.
The ubiquitous network layer comprises the steps that data and videos are sent to a cloud database server through a 4G network or a 5G network;
the intelligent application layer comprises the following components: the cloud database server realizes big data storage; mobile application: and the intelligent terminal logs in the cloud database server through the 4G network. Intelligent application: and the monitoring center computer logs in the cloud WEB server through the Ethernet.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the present invention as defined in the accompanying claims.
Claims (8)
1. The remote monitoring system for the key points of the electric power materials based on the technology of the Internet of things is characterized by comprising a data acquisition module and a data analysis module;
the data acquisition module is communicated with monitoring equipment through an RS485 communication interface, acquired monitoring data of different data types are respectively stored in different memories or databases, then the monitoring data of different data types are all converted into a uniform data format, and the data of the uniform data format is encrypted by DES and then sent to the cloud; the monitoring data of different data types comprises real-time monitoring data, conductor resistance data and insulation extension length data;
the data analysis module compares the monitoring real-time data with the nominal value of the detection real-time data to obtain the deviation degree of the monitoring real-time data; optimizing the nominal value of the conductor resistance to obtain a second nominal value, and comparing the data of the conductor resistance with the second nominal value to obtain the deviation degree of the data of the conductor resistance; comparing the data of the insulation extension length with the nominal value of the insulation extension length to obtain the deviation degree of the insulation extension length; the method for optimizing the nominal value of the conductor resistance to obtain the second nominal value comprises the following steps: making an original nominal value into a training set according to a detection record, and carrying out self-learning by using TensorFlow; then, acquiring deviation by adopting a quadratic cost function Square, and adjusting optimization parameters by combining a gradient descent optimizer;
the formula of self-learning is nominal value R-w 1 c + w2 d + b, wherein w1 and w2 are both weights, c is temperature, d is humidity, and b is offset.
2. The electric power material key point remote monitoring system based on the technology of the internet of things according to claim 1, characterized in that the remote monitoring system further comprises a data intelligent alarm module and an intelligent terminal module;
the intelligent data alarm module is used for acquiring the deviation degree of monitoring real-time data, the deviation degree of data of conductor resistance and the deviation degree of insulation extension length which are analyzed by the data analysis module; when any data of the deviation degree of the monitored real-time data, the deviation degree of the data of the conductor resistance and the deviation degree of the insulation extension length is larger than a threshold value, alarming;
the intelligent terminal module is used for setting different login authorities, is in real-time butt joint with monitoring data of the electric power materials, checks the change trend of the monitoring data, and pushes information to related personnel after the data intelligent alarm module gives an alarm.
3. The electric power material key point remote monitoring system based on the technology of the internet of things according to claim 1, wherein a processing method for monitoring real-time data is as follows: storing the monitored real-time data into a PLC memory, and configuring an APU2004 module at a PLC output interface; the APU2004 module stores the monitoring real-time data output by the PLC to the local of the APU2004 in batches, and each batch generates a first number which is also stored with the monitoring real-time data at the same time.
4. The electric power material key point remote monitoring system based on the technology of the internet of things according to claim 1, wherein the data processing method of the conductor resistance comprises the following steps: the DC3000 system stores the data of the conductor resistance in a relational database, reads a plurality of tables of the relational database, forms a uniform data format and generates a second serial number for the data;
the DC3000 system is a parameter setting and calculating system of a cable insulation sheath thermal extension tester.
5. The electric power material key point remote monitoring system based on the technology of the internet of things according to claim 1, wherein the processing method of the data of the insulation extension length comprises the following steps: the QJ36B-2 system stores the data of the insulation run length to a file-type database; copying the data of the conductor resistance and the insulation extension length to form a uniform data format, generating a third serial number for the data of the insulation extension length, and storing the third serial number on the hard disk;
the QJ36B-2 system is a resistance testing instrument for cables and wires.
6. The electric power material key point remote monitoring system based on the internet of things technology according to any one of claims 3 to 5, wherein the data in the unified data format is encrypted by DES and then sent to the cloud end by the method that the data acquisition module sends the data to the cloud end one by one according to the sequence of the first number, the second number or the third number; and the cloud end immediately sends N confirmation serial numbers to the data acquisition module after receiving N records in unit time.
7. The remote monitoring system for key points of electric power materials based on the technology of the internet of things as claimed in claim 1, wherein the calculation method for the deviation degree of the monitored real-time data is
The degree of deviation of the data of the conductor resistance is
The degree of deviation of the insulation extension length is
8. The electric power material key point remote monitoring system based on the internet of things technology as claimed in claim 2, wherein the intelligent terminal module is used for setting different login permissions including that a supplier checks monitoring data of products provided by the supplier, a material manager checks monitoring data of all products, and a manager manages the remote monitoring system except all the detection data.
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| US8965698B2 (en) * | 2010-12-29 | 2015-02-24 | Nexans | Real time thermal line ratings for overhead transmission line |
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| US8965698B2 (en) * | 2010-12-29 | 2015-02-24 | Nexans | Real time thermal line ratings for overhead transmission line |
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