CN117932275A - Artificial intelligence-based fireproof and explosion-proof blanket monitoring method and system for cable connector - Google Patents
Artificial intelligence-based fireproof and explosion-proof blanket monitoring method and system for cable connector Download PDFInfo
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Abstract
The invention relates to the technical field of electric power and electric protection, in particular to a fire-proof and explosion-proof blanket monitoring method and a fire-proof and explosion-proof blanket monitoring system for a cable joint based on artificial intelligence, which comprise the steps of acquiring data information of the fire-proof and explosion-proof blanket and the cable joint, performing data preprocessing according to the acquired data information, and establishing a comprehensive fire-proof and explosion-proof degree model of the fire-proof and explosion-proof blanket to obtain fire-proof and explosion-proof performance of the fire-proof and explosion-proof blanket; constructing a cable joint abnormality recognition neural network model through the preprocessed data information, judging according to a recognition calculation result, and providing data support for a follow-up monitoring process; and comprehensively judging according to the analysis of the fire-resistant explosion degree of the fire-resistant explosion blanket and the output result of the abnormal recognition model of the cable joint, and then early warning and real-time monitoring. According to the invention, through monitoring of the fireproof and explosion-proof blanket, the abnormal condition of the cable joint is early warned, measures are responded in time, the accuracy and the efficiency of monitoring are improved, and the safety and the reliability of the cable joint are ensured.
Description
Technical Field
The invention relates to the field of electric power and electric protection, in particular to a fire-proof and explosion-proof blanket monitoring method and system for a cable joint based on artificial intelligence.
Background
The cable joint is an important component for connecting cables, is used in power systems, communication networks and industrial equipment, often works in high-pressure environments, is monitored by manual inspection or simple equipment due to the fact that the cable joint is in a high-load working state for a long time, but overload, short circuit or other faults can occur in the process, and dangerous conditions such as over-temperature, fire or explosion of the joint are caused. At present, a fireproof and explosion-proof cover plate is used for coating the high-voltage cable joint, and various cable joint protection blankets are used for avoiding the large-range fire explosion caused by the explosion-proof high-voltage cable joint.
However, the above technical means have at least the following problems: the traditional cable joint monitoring method has low working efficiency and excessive cost of manpower and material resources, and the condition of missing error detection and detection still exists, so that the waste and excessive consumption of fireproof and explosion-proof articles are caused; when the fault problem of the cable joint is found, the response cannot be timely made, and further protection cannot be effectively carried out; the monitoring process and the method are not adjusted in time according to the related data information, so that the early warning result is low in accuracy, the monitoring process loses practical significance, and the risks of fire and explosion accidents can be possibly caused; reducing the reliability and stability of the power system.
Therefore, there is a need to design a fire-proof and explosion-proof blanket monitoring method and system for cable joints based on artificial intelligence, which ensures the safety and reliability of the cable joints.
Disclosure of Invention
The invention provides a fire-proof and explosion-proof blanket monitoring method and a fire-proof and explosion-proof blanket monitoring system for a cable joint based on artificial intelligence, which aim to early warn of abnormal conditions of the cable joint through monitoring of the fire-proof and explosion-proof blanket, respond to measures in time, improve the accuracy and efficiency of monitoring and provide a more comprehensive solution for preventing fire and explosion; reduces potential losses and improves reliability and stability of the system. In addition, the fireproof and explosion-proof blanket is used as protective equipment, can effectively isolate a fire source and has the characteristics of high temperature resistance and corrosion resistance.
The technical scheme of the invention is as follows:
the artificial intelligence-based fireproof and explosion-proof blanket monitoring method for the cable joint comprises the following steps of:
S1, acquiring data information of a fireproof and explosion-proof blanket and a cable joint, preprocessing data according to the acquired data information, and establishing a comprehensive fireproof explosion degree model of the fireproof and explosion-proof blanket to obtain fireproof explosion performance of the fireproof and explosion-proof blanket;
S2, constructing a cable joint abnormality recognition neural network model through the preprocessed data information, judging according to a recognition calculation result, and providing data support for a follow-up monitoring process;
And S3, comprehensively judging and then early warning and monitoring in real time according to the analysis of the fire-resistant explosion-proof blanket fire-resistant explosion degree and the output result of the cable joint abnormality identification model.
Further, the step S1 specifically includes:
the comprehensive fire-resistant explosion-proof blanket model comprises the following steps:
;
wherein, Representing the pretreated cable interface and the total data set of the fireproof and explosion-proof blanket; /(I)Representing the status parameters of a fire and explosion blanket,/>，/>Representing the number of the fireproof and explosion-proof state parameter sets; /(I)Representing feedback response parameters,/>，/>Representing the number of feedback response parameter sets; /(I)Representing daily dimension data,/>，/>Representing daily operation and maintenance data set/>Is the number of (3); /(I)The fire-proof performance of the fire-proof and explosion-proof blanket is shown.
Further, the step S2 specifically includes:
the cable joint abnormality recognition neural network model comprises an input layer, a recognition layer, an optimization enhancement layer and an output layer, and the abnormal recognition result of the cable joint is obtained through model training and output.
Further, the output in the identification layer is，
;
Wherein,Representing intermediate results; /(I)An input representing an identification layer; ; /(I)Representing the bias of the recognition layer; /(I)Representing the adjustment parameters; /(I)Representing a learning factor; /(I)Representing the output result of the identification layer; /(I)Representing a threshold value, and presetting according to a large number of experiments and combining expert advice; /(I)Representing an activation function.
Further, the step S3 specifically includes:
Setting a protection range of the fireproof and explosion-proof blanket according to the overall state condition of the cable joint; acquiring vibration frequency in fireproof and explosion-proof blanket cladding space through sensor And according to the vibration frequency, obtaining the adjusting parameter/>, of the protection range of the fireproof and explosion-proof blanket。
Further, according to the adjusting parameters of the protection range of the fireproof and explosion-proof blanketComparing the comparison quantity with a preset protection range of the fireproof and anti-explosion blanket, and adjusting the protection range of the fireproof and anti-explosion blanket according to a comparison result; and compensating the abnormal condition of the cable joint by utilizing the protection range of the adjusted fireproof and explosion-proof blanket and the fireproof explosion-proof degree of the fireproof and explosion-proof blanket.
Further, judging through the abnormal condition of the compensated cable joint, if the condition of the cable joint is good, the operation can be continued safely; if the cable joint has abnormal conditions, an alarm is sent out timely to inform workers, and meanwhile, the fireproof and explosion-proof blanket can be triggered automatically and work is started.
An artificial intelligence-based fire-proof and explosion-proof blanket monitoring system for a cable joint comprises the following parts:
The system comprises a data acquisition module, a data preprocessing module, a comprehensive analysis module, an abnormality identification module, an early warning monitoring module and a user interface module;
The data acquisition module is used for acquiring the data information of the whole fireproof and explosion-proof blanket coated cable joint through the sensor equipment, converting the analog signal into a digital signal and transmitting the digital signal to the data preprocessing module for subsequent processing and analysis;
The data preprocessing module is used for preprocessing the acquired data to obtain processed data, and sending the processed data to the comprehensive analysis module and the abnormality identification module;
The comprehensive analysis module is used for comprehensively analyzing the preprocessed data information to obtain the influence condition of different parameters on the fire-resistant explosion degree of the fireproof and explosion-proof blanket, and sending an analysis result to the early warning and monitoring module;
the abnormal recognition module is used for recognizing and analyzing abnormal conditions at the cable joint and transmitting recognition results to the early warning and monitoring module;
the early warning monitoring module is used for carrying out comprehensive early warning judgment and real-time monitoring according to the obtained comprehensive fire-explosion-proof degree of the fireproof and explosion-proof blanket and the abnormal recognition condition of the cable connector, and transmitting early warning monitoring data to the user interface module;
and the user interface module is used for enabling a user to interact with the system, displaying real-time data, alarm information and history records, and providing user configuration.
The beneficial effects are that:
1. The comprehensive fire-explosion-proof degree model of the fire-explosion-proof blanket is used for measuring the fire resistance and explosion-proof capacity of the fire-explosion-proof blanket, and better evaluates and manages the performance of the fire-explosion-proof blanket so as to reduce the safety risk, provide data support for the subsequent monitoring process, effectively reduce the damage of fire and explosion accidents caused by cable joints and reduce the potential personal injury and property loss.
2. According to the invention, a large amount of data can be learned and analyzed by constructing the cable joint abnormality recognition neural network model, so that the abnormal condition of the cable joint can be accurately recognized, and a tiny abnormal mode can be found compared with the neural network model in the traditional method; the model can monitor the state of the cable joint in real time, does not need manual intervention, improves the production efficiency, reduces the labor cost, and can continuously monitor a large number of cable joint working conditions; when abnormal problems occur, an alarm can be sent out timely, the problems can be quickly responded and solved through the fireproof and explosion-proof blanket, and potential risks and losses are reduced. The abnormal state of the cable joint can cause instability and potential safety risk of the power system, and the state of the cable joint can be better monitored and controlled by establishing a cable joint abnormal recognition neural network model to perform abnormal recognition, so that the safety and stability of the power system are improved.
3. By monitoring the fireproof and explosion-proof blanket, the invention can early warn the abnormal condition of the cable joint in advance, respond to measures in time, improve the accuracy and efficiency of monitoring, and provide a more comprehensive solution for preventing fire and explosion. In addition, the fireproof and explosion-proof blanket is used as protective equipment, can effectively isolate a fire source and has the characteristics of high temperature resistance and corrosion resistance.
4. According to the invention, the protection range of the fireproof and explosion-proof blanket is set according to the overall state of the cable joint, so that the cable joint can be ensured to be completely covered, dangerous substances such as sparks, flames or explosions are prevented from escaping, and meanwhile, the optimal protection effect is provided by adjusting the protection range, and the risks of fire and explosion accidents are reduced; according to the actual vibration frequency, the adjusting parameters of the protection range of the fireproof and explosion-proof blanket are obtained, unnecessary waste is avoided, resources are utilized more efficiently, the accurate coverage range of the fireproof and explosion-proof blanket can ensure that all key parts are effectively protected, and the reliability and stability of the system are improved. The abnormal condition of the cable joint is compensated by utilizing the adjusted protection range and the fire-resistant explosion-proof blanket, so that the state of the cable joint can be accurately judged, more accurate parameters are obtained, and the resource utilization efficiency is improved; meanwhile, measures can be taken in advance, and the maintenance cost and the power failure time caused by potential faults and accidents are avoided.
5. In the invention, once the abnormal condition at the cable joint is detected, the system automatically gives an alarm to inform staff, so that quick response and treatment are realized to prevent further deterioration of the situation; the time window for accident occurrence is reduced, the loss caused by fire and explosion is reduced to the greatest extent, and the safety of equipment and personnel is protected.
Drawings
FIG. 1 is a flow chart of an artificial intelligence based fire and explosion blanket monitoring method for a cable joint of the present invention;
fig. 2 is a block diagram of an artificial intelligence based fire and explosion blanket monitoring system for a cable joint of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments. It should also be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention.
In the embodiment, the fireproof and explosion-proof blanket comprises a fireproof and explosion-proof inner layer and a plain weave surface layer coated on the fireproof and explosion-proof inner layer, wherein the plain weave surface layer is formed by hot pressing a plurality of layers of ultra-high molecular weight polyethylene fiber base cloth which is soaked with vinyl ester resin, the vinyl ester resin particles are discretely arranged on the ultra-high molecular weight polyethylene fiber base cloth in a mode of 12mm spacing, and the flexible fireproof and explosion-proof inner layer is a honeycomb sandwich structure formed by orderly arranging composite honeycomb cells; when the surface fabric is impacted, the surface fabric is broken by shearing and absorbs a part of energy, the gaps among the resin particles enable the surface material of the fabric to have good flexibility and certain toughness, and the inner layer further improves the impact resistance and the energy absorption performance by combining special materials and special honeycomb structures, so that the purposes of fire prevention and explosion prevention are achieved; the monitoring system divides the cable joints into grids, and the protection range can be intelligently regulated and controlled when the cable joints fail. The fire-proof and explosion-proof blanket is arranged on the cable joint, so that the damage caused by spontaneous combustion and explosion of the cable fault can be reduced.
Referring to fig. 1, the embodiment provides an artificial intelligence-based fire and explosion blanket monitoring method for a cable joint, which comprises the following steps:
s1, acquiring data information of a fireproof and explosion-proof blanket and a cable joint, preprocessing data according to the acquired data information, and establishing a comprehensive fireproof explosion degree model of the fireproof and explosion-proof blanket to obtain fireproof explosion performance of the fireproof and explosion-proof blanket.
A fireproof and explosion-proof blanket is arranged on a cable joint, a plurality of sensors, a data interface and the like are arranged on a blanket body, related data information is collected and recorded, and related data information when the cable interface works is collected; and performing data preliminary processing, and performing data analysis based on the result of the data preliminary processing.
In the embodiment, a smoke sensor is arranged on the surface and inside the fireproof and explosion-proof blanket and is used for monitoring whether smoke is generated near the joint; the temperature sensor is arranged in the fireproof and explosion-proof blanket, so that the temperature around the joint can be monitored in real time; the pressure sensor is arranged on the fireproof and explosion-proof blanket, so that the pressure change near the joint can be monitored, and the pressure sensor is used for detecting potential leakage or pressure abnormality; installing a gas sensor to monitor the concentration of gas near the joint; the vibration sensor is used for measuring the vibration intensity and frequency of the joint; the inclination sensor is used for measuring the inclination angle of the joint; the water immersion sensor can detect the water level or water immersion condition of the cable interface; the sensor on the fireproof and explosion-proof blanket can be connected with the monitoring system and the alarm device through the data interface.
The data are acquired and stored according to the determined data acquisition frequency through the installed sensor, so that the real-time effectiveness of the data is ensured; the fireproof and explosion-proof blanket body can be attached to and coated with the cable connector to monitor the fireproof and explosion-proof blanket body, and different sensors are arranged to collect the fireproof and explosion-proof blanket body to obtain a data set; Acquiring parameter data of the cable and the interface thereof to obtain a data set/>；
Finally obtaining the total data set of the cable interface and the fireproof and explosion-proof blanket,Which is provided with，/>Representing a smoke sensor,/>Representing a temperature sensor,/>Representing a water immersion sensor,/>Representing a gas sensor,/>Representing vibration sensor,/>Representing a pressure sensor, for example: the data set acquired by the smoke sensor is/>，/>，/>Any of the elements may be composed ofRepresentation of/>Indicating the/>, in the temperature sensorData; /(I)，/>Representing the number of parameters of the cable and its interface, i.e./>Any of the elements may be represented by/>Representation of/>Representing the/>, of the acquired parameters of the cable and its interface itselfAnd each.
Data preprocessing is then performed to identify and process missing values, outliers and duplicate values, and the original data stored in the database may be queried for population or missing items may be deleted from the data table, for example: the smoke concentration at the cable joint obtained through statistics is obviously smaller than the average value, and in order to ensure the accuracy of subsequent data analysis, the duplicate removal operation is carried out on the data with the excessively small value; and then, the obtained data are applied to a unified format, standardized and the consistency of the data is ensured. Obtaining a cable interface after data preprocessing and a total data set of fireproof and explosion-proof blankets，/>。
Constructing a comprehensive fire-explosion-proof degree model of the fire-explosion-proof blanket, comprehensively considering the influence of various parameters on the fire-explosion-proof blanket, and specifically, the method comprises the following steps of:
；
wherein, Representing the pretreated cable interface and the total data set of the fireproof and explosion-proof blanket; /(I)Representing the status parameters of a fire and explosion blanket,/>，/>The number of the fireproof and explosion-proof state parameter sets is represented, and the state parameters of the fireproof and explosion-proof blanket comprise materials, heat radiation quantity and the like; /(I)Representing feedback response parameters,/>，/>Representing the number of feedback response parameter sets, the feedback response parameters including response position, response time, etc.; /(I)Representing daily dimension data,/>，/>Representing daily operation and maintenance data set/>The daily maintenance and operation data comprise the number of times of occurrence of accidents, the duration of solving the accidents and the like; /(I)The fire-proof performance of the fire-proof and explosion-proof blanket is shown.，/>The number of elements in each subset is/>, respectively、/>、、/>、/>、/>；/>；
The calculation process is as follows:
；
wherein, Representing a specific influence value of the pretreated cable interface and the total data set of the fireproof and explosion-proof blanket on the comprehensive fireproof explosion degree of the model; /(I)、/>、/>Respectively representing corresponding adjustment coefficients in the preprocessed total data set; /(I)Represents the/>The influence of the individual pressure value test parameters on the installed sensor; /(I)、/>、/>、/>、/>Respectively representing corresponding weight parameters, and representing the influence of different data sets on the comprehensive fire-explosion degree of the fire-explosion blanket; /(I)Representing the average value of the response parameters to feedback.
The comprehensive fire-explosion-proof degree model of the fire-explosion-proof blanket is used for measuring the fire resistance and explosion-proof capacity of the fire-explosion-proof blanket, and better evaluates and manages the performance of the fire-explosion-proof blanket so as to reduce the safety risk, provide data support for the subsequent monitoring process, effectively reduce the damage of fire and explosion accidents caused by cable joints and reduce the potential personal injury and property loss.
S2, constructing a cable joint abnormality identification neural network model according to the data information of the fireproof and explosion-proof blanket and the cable joint obtained after data preprocessing, judging according to an identification calculation result, providing data support for a subsequent monitoring process, and helping a monitoring system to respond.
According to the obtained data, the cable interface after pretreatment and the total data set of the fireproof and explosion-proof blanketEstablishing a cable joint abnormality recognition neural network model by deep learning, and performing/>Input data set organized into cable joint anomaly identification modelWherein/>Representing the/>, in the input datasetFirst/>, of the input dataAttribute value/>Representing the number of input data,/>Representing the number of input data attributes. The cable joint abnormality recognition neural network model comprises an input layer, a recognition layer, an optimization enhancement layer and an output layer, and the abnormal recognition result of the cable joint is obtained through model training and output. The specific process is as follows:
the input layer of the cable joint abnormality recognition neural network model is provided with Neurons, input arbitrary data/>The attribute value is input into the input layer as/>And/>. Wherein the input layer is fully connected with the identification layer, and the input of the identification layer is/>，/>Wherein/>Representing a connection weight value between the input layer and the recognition layer,/>Representing the bias of the recognition layer; the specific calculation process in the identification layer is as follows:
；
wherein, Representing intermediate results; /(I)Representing the adjustment parameters; /(I)Represents the/>The number of cable joint related data corresponding to each attribute value,/>；/>Representing a learning factor; /(I)Representing the output result of the identification layer; /(I)Representing a threshold value, and presetting according to a large number of experiments and combining expert advice; /(I)Representing an activation function.
Judging in the identification layer if the output result isTransmitting the calculation result to an optimized enhancement layer for further processing; if the output result in the identification layer is-1, the result is directly transmitted to the output layer, meanwhile, the red primary early warning is started, and the fire-proof and explosion-proof blanket is applied to carry out emergency treatment on the cable joint.
Processing the output result in the identification layer in the optimized enhancement layer to ensure the accuracy of the data, wherein the specific process is as follows:
；
wherein, Representing an input optimizing the enhancement layer; /(I)Representing a connection weight value between the identification layer and the optimized enhancement layer; representing a bias to optimize the enhancement layer; /(I) Representing an output of the optimized enhancement layer; /(I)Representing the enhancement factor; /(I)Representing the weight parameters.
The output layer outputs the final result，/>Wherein/>Representing the output result of the output layer,/>Representing the connection weight value between the optimized enhancement layer and the output layer,/>Representing the bias of the output layer.
According to the invention, a large amount of data can be learned and analyzed by constructing the cable joint abnormality recognition neural network model, so that the abnormal condition of the cable joint can be accurately recognized, and a tiny abnormal mode can be found compared with the neural network model in the traditional method; the model can monitor the state of the cable joint in real time, does not need manual intervention, improves the production efficiency, reduces the labor cost, and can continuously monitor a large number of cable joint working conditions; when abnormal problems occur, an alarm can be sent out timely, the problems can be quickly responded and solved through the fireproof and explosion-proof blanket, and potential risks and losses are reduced. The abnormal state of the cable joint can cause instability and potential safety risk of the power system, and the state of the cable joint can be better monitored and controlled by establishing a cable joint abnormal recognition neural network model to perform abnormal recognition, so that the safety and stability of the power system are improved.
S3, comprehensively early warning and monitoring according to analysis of fire and explosion resistance degree of the fireproof and explosion-proof blanket and output results of the abnormal recognition model of the cable connector.
The protection range of the fireproof and explosion-proof blanket is set according to the overall state condition of the cable joint, and is regulated and controlled by the intelligent monitoring system, so that the resource waste can be reduced, and certain cost can be saved; the overall state of the cable joint comprises a sound level, a general level and a dangerous level; the protection range of the preset fireproof and explosion-proof blanket is as follows; Setting the protection range of the fireproof and explosion-proof blanket to be/>, if the overall state of the cable joint is at a perfect level，/>; If the overall state of the cable joint is of a general level, setting the protection range of the fireproof and explosion-proof blanket to be/>，/>; If the overall state of the cable joint is dangerous, setting the protection range of the fireproof and explosion-proof blanket to be/>，/>; Wherein/>The parameters are the adjusting parameters of the protection range of the corresponding fireproof and explosion-proof blanket in the sound level; /(I)The parameters are the adjusting parameters of the protection range of the corresponding fireproof and explosion-proof blanket in the general level; /(I)Is the adjusting parameter of the protection range of the corresponding fireproof and explosion-proof blanket at the dangerous level, and/>。
Acquiring vibration frequency in fireproof and explosion-proof blanket cladding space through sensorNamely vibration frequency at the cable joint, and obtaining the adjusting parameter/>, of the protection range of the fireproof and explosion-proof blanket according to the vibration frequencyThe specific calculation process is as follows:
；
wherein, Representing an adjustment amplitude coefficient; /(I)Representing the mean; /(I)Representing standard deviation.
Then according to the adjusting parameters of the protection range of the fireproof and explosion-proof blanketComparing the comparison amount of the protection range of the fireproof and anti-explosion blanket with the comparison amount of the protection range of the preset fireproof and anti-explosion blanket, and adjusting the protection range of the fireproof and anti-explosion blanket according to the comparison result, wherein the comparison amount of the protection range of the fireproof and anti-explosion blanket is defined as/>，/>Is an integer greater than;
If it is The protection range of the fireproof and explosion-proof blanket is adjusted to be/>， />；
If it isThe protection range of the fireproof and explosion-proof blanket is adjusted to be/>，/>；
If 0 isThe protection range of the fireproof and explosion-proof blanket is adjusted to be/>，/>。
Meanwhile, according to the protection range of the adjusted fireproof and explosion-proof blanketFire-resistant explosion-proof blanketCompensating the abnormal condition of the cable joint, and defining the preset vibration frequency as/>The specific process is as follows:
；
；
wherein, Representing the abnormal condition of the cable joint after compensation; /(I)Indicating a desired cable joint condition; Indicating in monitoring process/> The cable joint condition at the moment; /(I)Representing the adaptation factor; /(I)Compensation coefficients representing a protection range; /(I)A compensation coefficient indicating the degree of fire resistance, and/>、/>Are all greater than 0.
Judging according to the abnormal condition of the compensated cable joint, ifThe condition of the cable joint is good, and the operation can be continued safely; if/>And judging that the abnormal condition exists at the cable joint, and sending an alarm to inform related personnel in time. Specifically, when the monitoring system detects that the joint is abnormal, the application of the fireproof and explosion-proof blanket is automatically triggered. The fireproof and explosion-proof blanket is composed of fireproof materials and explosion-proof materials, and can effectively inhibit fire and explosion and protect safety of surrounding equipment and personnel.
According to the invention, the protection range of the fireproof and explosion-proof blanket is set according to the overall state of the cable joint, so that the cable joint can be ensured to be completely covered, dangerous substances such as sparks, flames or explosions are prevented from escaping, and meanwhile, the optimal protection effect is provided by adjusting the protection range, and the risks of fire and explosion accidents are reduced; according to the actual vibration frequency, the adjusting parameters of the protection range of the fireproof and explosion-proof blanket are obtained, unnecessary waste is avoided, resources are utilized more efficiently, the accurate coverage range of the fireproof and explosion-proof blanket can ensure that all key parts are effectively protected, and the reliability and stability of the system are improved. The abnormal condition of the cable joint is compensated by utilizing the adjusted protection range and the fire-resistant explosion-proof blanket, so that the state of the cable joint can be accurately judged, more accurate parameters are obtained, and the resource utilization efficiency is improved; meanwhile, measures can be taken in advance, and the maintenance cost and the power failure time caused by potential faults and accidents are avoided.
Once an abnormal condition exists at the cable joint, the system automatically gives an alarm to inform staff, so that quick response and treatment are realized to prevent further deterioration of the situation; the time window for accident occurrence is reduced, the loss caused by fire and explosion is reduced to the greatest extent, and the safety of equipment and personnel is protected.
In this embodiment, the user interface may monitor the data in real time, display key parameters and status information of the cable connector, including real-time readings of data such as temperature, humidity, current, etc., and graphs, curves or indicators to display the trend of the data; when the cable joint is abnormal, the system triggers an alarm, the user interface displays corresponding alarm information, workers can be reminded by means of sound, popup window, notification and the like, meanwhile, a fireproof and explosion-proof blanket is started, and the working condition of the fireproof and explosion-proof blanket is fed back to the user interface; the system will also record historical data of the cable joint and provide the user with query and analysis functions, and the user can view the data records over a specific period of time.
Referring to fig. 2, the present embodiment provides an artificial intelligence based fire and explosion blanket monitoring system for a cable joint, including the following:
The system comprises a data acquisition module, a data preprocessing module, a comprehensive analysis module, an abnormality identification module, an early warning monitoring module and a user interface module;
The data acquisition module is used for acquiring the data information of the whole fireproof and explosion-proof blanket coated cable joint through the sensor equipment, converting the analog signal into a digital signal and transmitting the digital signal to the data preprocessing module for subsequent processing and analysis;
The data preprocessing module is used for preprocessing the acquired data to obtain processed data, and sending the processed data to the comprehensive analysis module and the abnormality identification module;
The comprehensive analysis module is used for comprehensively analyzing the preprocessed data information to obtain the influence condition of different parameters on the fire-resistant explosion degree of the fireproof and explosion-proof blanket, and sending an analysis result to the early warning and monitoring module;
the abnormal recognition module is used for recognizing and analyzing abnormal conditions at the cable joint and transmitting recognition results to the early warning and monitoring module;
the early warning monitoring module is used for carrying out comprehensive early warning judgment and real-time monitoring according to the obtained comprehensive fire-explosion-proof degree of the fireproof and explosion-proof blanket and the abnormal recognition condition of the cable connector, and transmitting early warning monitoring data to the user interface module;
the user interface module is used for enabling a user to interact with the system, displaying real-time data, alarm information, history records and the like, and providing user configuration; typically a Graphical User Interface (GUI) or Web-based interface.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (8)
1. The artificial intelligence-based fireproof and explosion-proof blanket monitoring method for the cable connector is characterized by comprising the following steps of:
S1, acquiring data information of a fireproof and explosion-proof blanket and a cable joint, preprocessing data according to the acquired data information, and establishing a comprehensive fireproof explosion degree model of the fireproof and explosion-proof blanket to obtain fireproof explosion performance of the fireproof and explosion-proof blanket;
S2, constructing a cable joint abnormality recognition neural network model through the preprocessed data information, judging according to a recognition calculation result, and providing data support for a follow-up monitoring process;
And S3, comprehensively judging and then early warning and monitoring in real time according to the analysis of the fire-resistant explosion-proof blanket fire-resistant explosion degree and the output result of the cable joint abnormality identification model.
2. The method for monitoring fire and explosion protection blanket for cable joint according to claim 1, wherein said step S1 specifically comprises:
the comprehensive fire-resistant explosion-proof blanket model comprises the following steps:
;
wherein, Representing the pretreated cable interface and the total data set of the fireproof and explosion-proof blanket; /(I)Representing the status parameters of a fire and explosion blanket,/>，/>Representing the number of the fireproof and explosion-proof state parameter sets; /(I)Representing feedback response parameters,/>，/>Representing the number of feedback response parameter sets; /(I)Representing the daily dimensional operation and maintenance data,，/>Representing daily operation and maintenance data set/>Is the number of (3); /(I)The fire-proof performance of the fire-proof and explosion-proof blanket is shown.
3. The method for monitoring fire and explosion protection blanket for cable joint according to claim 1, wherein said step S2 specifically comprises:
the cable joint abnormality recognition neural network model comprises an input layer, a recognition layer, an optimization enhancement layer and an output layer, and the abnormal recognition result of the cable joint is obtained through model training and output.
4. The artificial intelligence based fire and explosion blanket monitoring method for cable joints according to claim 3, wherein the output in the identification layer is，
;
Wherein,Representing intermediate results; /(I)An input representing an identification layer; /(I)Representing the bias of the recognition layer; /(I)Representing the adjustment parameters; representing a learning factor; /(I) Representing the output result of the identification layer; /(I)Representing a threshold value, and presetting according to a large number of experiments and combining expert advice; /(I)Representing an activation function.
5. The method for monitoring fire and explosion protection blanket for cable joint according to claim 1, wherein said step S3 specifically comprises:
Setting a protection range of the fireproof and explosion-proof blanket according to the overall state condition of the cable joint; acquiring vibration frequency in fireproof and explosion-proof blanket cladding space through sensor And according to the vibration frequency, obtaining the adjusting parameter/>, of the protection range of the fireproof and explosion-proof blanket。
6. The artificial intelligence based fire and explosion blanket monitoring method for cable joints according to claim 5, wherein the parameters of the fire and explosion blanket are adjusted according to the protection range of the fire and explosion blanketComparing the comparison quantity with a preset protection range of the fireproof and anti-explosion blanket, and adjusting the protection range of the fireproof and anti-explosion blanket according to a comparison result; and compensating the abnormal condition of the cable joint by utilizing the protection range of the adjusted fireproof and explosion-proof blanket and the fireproof explosion-proof degree of the fireproof and explosion-proof blanket.
7. The artificial intelligence based fire and explosion blanket monitoring method for cable joints according to any one of claims 1 to 6, wherein the abnormal condition of the cable joints after compensation is used for judging, and if the condition of the cable joints is good, the safe operation can be continued; if the cable joint has abnormal conditions, an alarm is sent out timely to inform workers, and meanwhile, the fireproof and explosion-proof blanket can be triggered automatically and work is started.
8. The artificial intelligence-based fireproof and anti-explosion blanket monitoring system for the cable joint is applied to the artificial intelligence-based fireproof and anti-explosion blanket monitoring method for the cable joint, and is characterized by comprising the following parts:
The system comprises a data acquisition module, a data preprocessing module, a comprehensive analysis module, an abnormality identification module, an early warning monitoring module and a user interface module;
The data acquisition module is used for acquiring the data information of the whole fireproof and explosion-proof blanket coated cable joint through the sensor equipment, converting analog signals into digital signals and transmitting the digital signals to the data preprocessing module for subsequent processing and analysis;
the data preprocessing module is used for preprocessing the acquired data to obtain processed data, and sending the processed data to the comprehensive analysis module and the abnormality identification module;
The comprehensive analysis module is used for comprehensively analyzing the preprocessed data information to obtain the influence condition of different parameters on the fire-resistant explosion degree of the fireproof and explosion-proof blanket, and sending an analysis result to the early warning and monitoring module;
the abnormality identification module is used for identifying and analyzing abnormal conditions at the cable joint and transmitting the identification result to the early warning and monitoring module;
The early warning and monitoring module is used for carrying out comprehensive early warning judgment and real-time monitoring according to the comprehensive fire-explosion-proof degree of the obtained fire-proof and explosion-proof blanket and the abnormal recognition condition of the cable connector, and transmitting early warning and monitoring data to the user interface module;
The user interface module enables a user to interact with the system, is used for displaying real-time data, alarm information and history records, and provides user configuration.
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