CN117875901A - Intelligent command management system and method based on gas emergency repair - Google Patents

Abstract

The invention relates to the field of gas emergency repair command management, in particular to an intelligent command management system and method based on gas emergency repair, comprising a data acquisition module, an accident prediction module, a scheduling distribution module, a real-time monitoring module and an early warning management module; the gas pipeline equipment is monitored in real time by using a sensor and monitoring equipment, training data are extracted from the acquired historical monitoring data and combined with the gas accident recording condition, and a gas accident prediction neural network model is established; according to the accident type, the geographical position and the availability of the rush-repair resources, the Monte Carlo tree algorithm is utilized to carry out intelligent scheduling and reasonable distribution on the rush-repair personnel and equipment, and a command decision plan is obtained; in the process of emergency repair, emergency repair personnel and emergency resources are monitored in real time, the risk level of the gas accident is preset, the gas emergency repair budget is set according to the risk level, the safety prevention and control range index of the gas accident is obtained through calculation, and the gas emergency repair effect is judged.

Description

Intelligent command management system and method based on gas emergency repair

Technical Field

The invention relates to the field of gas emergency repair command management, in particular to an intelligent command management system and method based on gas emergency repair.

Background

Gas is one of important energy sources in our life, and gas facilities include gas pipelines, valves, pressure regulators and the like, but a series of faults and other emergency situations may occur in a gas system, and quick response and rush repair are required to ensure personnel safety and normal operation of equipment.

In traditional gas emergency repair management, the method often depends on manual inspection and repair, and has the problems of long response time, human negligence, untimely manual dispatching and commanding, unsmooth information transmission, misjudgment and the like, and the problems of low emergency repair efficiency, increased safety risk and easy accident occurrence and loss aggravation are caused. The traditional management mode is usually passive, corresponding rush repair measures are adopted only after faults occur, the possibility of equipment faults cannot be predicted in advance, preventive maintenance cannot be carried out, unexpected faults can occur to the equipment, and the downtime and the maintenance cost are increased. When a gas accident happens, the accident place cannot be found in time, and effective urgent measures cannot be made. The scheduling of personnel and supplies may lack efficient coordination and optimization. Failure in accurately assessing the emergency degree of the fault and the availability of the resource can lead to waste or deficiency of the resource, and the rush repair efficiency and the cost management are affected.

Disclosure of Invention

The invention provides an intelligent command management system and method based on gas emergency repair, which aims to improve the safety, the emergency repair efficiency and the management level of gas facilities, reduce the loss caused by faults, ensure the safety of public lives and properties and improve the management level of the command system.

The technical scheme of the invention is as follows:

intelligent command management system and method based on gas emergency repair, comprising the following parts:

the system comprises a data acquisition module, an accident prediction module, a scheduling distribution module, a real-time monitoring module and an early warning management module;

the data acquisition module acquires real-time monitoring data information of the gas pipeline and gas accident recording condition information through a sensor, monitoring equipment and the like, and transmits the acquired data to the accident prediction module after integration;

the accident prediction module is used for carrying out data analysis according to the acquired data information, analyzing the running state of the gas system, predicting potential accident risks, positioning accident positions when the gas is in an accident, and sending analysis results to the dispatching distribution module, the real-time monitoring module and the early warning management module;

the dispatching distribution module is used for optimally dispatching and distributing the gas emergency repair tasks according to the result of the accident prediction module and the real-time monitoring data;

the real-time monitoring module obtains an index of a prevention and control range according to an accident prediction result of the gas system and by combining the running state and the equipment performance of the gas pipeline, so that problems can be found conveniently and timely, and corresponding measures can be taken;

the early warning management module monitors emergency in the gas system through data interaction with the real-time monitoring module and the accident prediction module and timely sends out early warning notification.

Intelligent command management system and method based on gas emergency repair, comprising the following steps:

s1, monitoring gas pipeline equipment in real time by using a sensor and monitoring equipment, extracting training data from acquired historical monitoring data and combining with gas accident recording conditions, and establishing a gas accident prediction neural network model;

s2, intelligent scheduling and reasonable distribution are carried out on emergency repair personnel and equipment by utilizing a Monte Carlo tree algorithm according to the accident type, the geographical position and the availability of emergency repair resources, so as to obtain a command decision plan;

s3, monitoring emergency repair personnel and emergency resources in real time in the emergency repair process, presetting the risk level of the gas accident according to the prediction of the gas accident and the positioning judgment of the accident, setting the gas emergency repair budget according to the risk level, and judging the gas emergency repair effect by obtaining the safety prevention and control range index of the gas accident through calculation and analysis.

Further, the step S1 specifically includes:

the gas accident prediction neural network model is characterized in that training data is firstly divided into a training set and a testing set, the training set is the acquired historical data of a gas pipeline and accident label information, the gas accident prediction neural network model is fitted through the training set, for each training sample data, a predicted value of the model is calculated through forward propagation, loss between the predicted value and a real label is calculated, then the Adam algorithm is used for calculating the current gradient, and weight parameters of the model are updated according to the gradient.

Furthermore, a gating function is introduced into the gas accident prediction neural network model, and the specific process is as follows:

will beInput into a gas accident prediction neural network model, and represent the initial state thereof as +.>，

；

Wherein,representing acquired gas pipeline related data information, < >>Representing the number of elements in the gas pipeline related data information; />A weight value representing an initial state; />Representing the bias at the initial state;

the updating door determines the degree to which the gas state information at the previous moment is brought into the current state to judge whether a potential accident occurs or not; the forgetting door help model ignores information irrelevant or unimportant to the current state so as to reduce the influence of interference and noise on accident prediction; the reset gate determines the information fusion degree of the input at the current moment and the hidden state at the previous moment.

Further, the step S2 specifically includes:

the search process of the Monte Carlo tree comprises the following steps: starting from the root node, evaluating the value of each child node according to UCB strategy, and selecting the child node with the largest UCB value for expansion; when selecting the child nodes, calculating an upper bound confidence interval of each node, and then selecting the node with the highest upper bound; expanding the selected child node and adding the child node into a tree; simulation is carried out on the newly expanded nodes, and random selection is carried out on possible actions generated by the nodes by adopting a random strategy or a heuristic strategy; and updating node information on a path from the extension node to the root node according to the simulation result.

Further, the step S3 specifically includes:

presetting the risk level of the gas accident according to the prediction of the gas accident and the positioning judgment of the accident, and setting the gas emergency repair budget according to the risk level; and acquiring the gas flow of the gas accident site through a sensor, and then calculating the budget correction parameter of the gas emergency repair.

Further, presetting a gas emergency repair budget correction parameter, comparing the gas emergency repair budget correction parameter with the preset gas emergency repair budget correction parameter, and then adjusting the gas emergency repair budget according to a comparison result; and judging whether to optimally support the regulated gas emergency repair budget according to a comparison result of the gas flow and the expected gas flow.

Further, the safety prevention and control range of the gas accident is determined according to the gas emergency repair budget obtained after the optimization support, and the gas emergency repair effect is judged through the safety prevention and control range index.

The beneficial effects are that: 1. according to the invention, through the gas accident prediction neural network model, potential accident modes and abnormal behaviors can be identified when the gas equipment works normally, problems are found in advance before the actual occurrence of the accident, and an early warning notification is sent out, so that preventive measures are taken in time, and the occurrence of the accident is avoided or the influence of the accident is reduced; once the accident problem occurs to the gas, the gas accident prediction neural network model can rapidly position the accident position, reduce the time and cost of accident investigation and avoid unnecessary loss and delay. The system and the method have the advantages that the emergency repair resources are accurately scheduled to be used as a bedding, the resource waste and unnecessary manpower and material resource cost are avoided, the emergency repair efficiency can be improved through reasonable resource utilization, the maintenance cost is reduced, the downtime of a gas system is reduced, and the production efficiency and the user satisfaction are improved.

2. According to the emergency repair method, the emergency repair personnel and equipment resources are reasonably allocated according to the severity and the emergency degree of the accident, so that enough resources are input for emergency repair under the conditions of high risk and emergency, and the accident treatment efficiency is improved; by presetting the gas emergency repair budget, resource allocation and repair work can be performed within the budget range, so that the cost is effectively controlled, and the waste of resources and unnecessary expense are avoided. Meanwhile, budget correction parameters are calculated according to gas flow data of a gas accident site, and cost is controlled more accurately. By judging whether to optimally support the regulated gas emergency repair budget, reasonable utilization of the emergency repair resources can be ensured, the working efficiency is improved, the overall emergency repair cost is reduced, the accident handling time is shortened, the loss is reduced, and the coping capacity for gas accidents is enhanced.

3. According to the invention, through judging the gas emergency repair effect, real-time monitoring and improvement can be performed, if the defect or the unsatisfactory emergency repair effect is found, measures can be taken in time to adjust and improve, the emergency repair capability of gas accidents is improved, the emergency repair budget and the prevention and control strategy are continuously optimized and perfected, and the overall safety and efficiency are improved; and the rush repair budget and prevention and control measures are improved and optimized in time, so that the capability of preventing and coping with gas accidents is improved.

4. According to the invention, factors such as accident types, geographic positions, availability of rush-repair resources and the like can be comprehensively considered through a Monte Carlo tree algorithm, and an optimal resource allocation scheme is found through simulating and evaluating different decision paths, so that the existing gas rush-repair personnel and equipment resources are utilized to the greatest extent; through an intelligent decision plan, emergency repair personnel and equipment can be effectively and optimally distributed to the accident scene, the emergency repair time and response delay are reduced, and the emergency repair efficiency is improved; the decision basis based on the gas data is provided, subjectivity and randomness of the decision are reduced, and scientificity and accuracy of the decision are enhanced. By intelligent scheduling and reasonable distribution, human misjudgment and decision errors are reduced, and the quality of command decisions is improved.

5. According to the invention, the risk of the gas pipeline can be estimated and predicted through a data processing and analyzing technology, potential problems and hidden dangers are found, and early warning signals are timely sent out to remind related personnel to take necessary measures, so that disaster accidents are effectively prevented; if the gas accident happens, the management system can quickly identify the problem, and quick response and high-efficiency treatment are realized through intelligent decision and resource optimization, so that the expansion and damage of the accident are reduced to the greatest extent. In addition, the management system can realize quick connection and coordination between the emergency repair site and the command center through a remote operation and control technology, so that complicated communication and information transmission processes are avoided, the emergency repair response time is shortened, and the gas supply is quickly restored; the on-site personnel and the command center realize real-time information sharing and transmission, so that the rush repair team can rapidly acquire the required instructions and data, and the communication efficiency and the collaborative operation capability are improved.

Drawings

FIG. 1 is a flow chart of an intelligent command management method based on gas emergency repair of the invention;

fig. 2 is a block diagram of the intelligent command management system based on gas emergency repair.

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.

Referring to fig. 1, the embodiment provides an intelligent command management method based on gas emergency repair, which comprises the following steps:

s1, monitoring gas pipeline equipment in real time by using a sensor and monitoring equipment, extracting training data from acquired historical monitoring data and combining with gas accident recording conditions, and establishing a gas accident prediction neural network model. Detecting abnormal conditions and sending out early warning signals, and timely responding and processing problems.

And (3) timely positioning according to the obtained predicted accident situation, and reducing positioning time so as to timely respond and process the problems.

The method comprises the steps of building a gas accident prediction neural network model based on a cyclic neural network, firstly dividing training data into a training set and a testing set, wherein the training set is acquired historical data of a gas pipeline and accident label information, fitting the gas accident prediction neural network model through the training set, calculating a predicted value of the model through forward propagation for each training sample data, and calculating loss between the predicted value (namely output estimation of the model on input data) and a real label. The current gradient is then calculated using Adam's algorithm and the weight parameters of the model are updated according to the gradient.

For any one training sample，/>，/>Representing acquired gas pipeline related data information, < >>Representing the number of elements in the gas pipeline related data information.

By introducing a gating function, a specific training process in the gas accident prediction neural network model is as follows:

will beInput into a gas accident prediction neural network model, and represent the initial state thereof as +.>，

；

Wherein,a weight value representing an initial state; />Representing the bias at the initial state.

The updating door determines the degree to which the gas state information at the previous moment is brought into the current state to judge whether a potential accident happens or not, and the specific process is as follows:

；

wherein,is indicated at->Updating the output of the gate; />A weight value representing an update gate; />Adjusting a constant representation;representing an activation function; />A bias vector representing an update gate; />Is indicated at->State at that time.

The forgetting door can help the model to ignore information irrelevant or unimportant to the current state so as to reduce the influence of interference and noise on accident prediction, and the concrete process is as follows:

；

wherein,an output representing a forget gate; />A weight value representing a forget gate; />A bias vector representing a forget gate; />The weight value indicating the last time.

The reset gate determines the information fusion degree of the input at the current moment and the hidden state at the previous moment, and the specific process is as follows:

；

wherein,an output representing a reset gate; />A weight value representing a reset gate; />Representing the time difference; />Representing the offset vector of the reset gate.

And then output the result，/>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Weight value representing output result, +.>Indicating the bias of the output result.

Comparing the output result with the safety standard threshold range of the fuel gas (the standard threshold range is set by classification according to different safety standards of different types of the fuel gas, the fuel gas comprises natural gas, liquefied petroleum gas, artificial fuel gas and the like), and defining the safety standard threshold range of the fuel gas as. In normal operation, if->Not belong to->An early warning is sent out, so that potential safety hazards are prevented from being further amplified, and adverse consequences are caused; when the command management system receives the accident repair information, the gas accidentThe predictive neural network model can quickly locate the location of the incident based on information and guidance provided by the model.

According to the invention, through the gas accident prediction neural network model, potential accident modes and abnormal behaviors can be identified when the gas equipment works normally, problems are found in advance before the actual occurrence of the accident, and an early warning notification is sent out, so that preventive measures are taken in time, and the occurrence of the accident is avoided or the influence of the accident is reduced; once the accident problem occurs to the gas, the gas accident prediction neural network model can rapidly position the accident position, reduce the time and cost of accident investigation and avoid unnecessary loss and delay. The system and the method have the advantages that the emergency repair resources are accurately scheduled to be used as a bedding, the resource waste and unnecessary manpower and material resource cost are avoided, the emergency repair efficiency can be improved through reasonable resource utilization, the maintenance cost is reduced, the downtime of a gas system is reduced, and the production efficiency and the user satisfaction are improved.

S2, intelligent scheduling and reasonable distribution are carried out on emergency repair personnel and equipment by utilizing a Monte Carlo tree algorithm according to the accident type, the geographical position and the availability of emergency repair resources, so that a command decision plan is obtained, and the emergency repair efficiency is improved to the greatest extent.

The Monte Carlo tree is utilized to simultaneously meet the restrictions of resources and personnel in the process from the occurrence of the gas accident to the completion of the repair, so that the gas emergency repair plan with minimized repair time and work complexity is realized.

The following definitions are provided: t represents time; a represents the position of the gas accident;indicating the position of the ith emergency repair person,；/>representing the total number of emergency repair personnel; />Representing the location of the jth emergency resource, +.>；/>Representing the total number of emergency resources; and initializing the root node, and setting the current state as the root node to represent the initial gas accident state.

The search process of the Monte Carlo tree is specifically as follows:

(1) Selecting actions, starting from the root node, evaluating the value of each child node according to UCB strategies, and selecting the child node with the largest UCB value for expansion; when selecting the child node, calculating an upper bound confidence interval of each node, and then selecting the node with the highest upper bound, wherein the specific process is as follows:

aiming at minimizing the time from the occurrence of the gas accident to the completion of the rush repair, a score V of a node is defined,

；

wherein,the number of personnel actually participating in the rush repair is represented; />Representing a maximum number of people that can participate in the rush repair;indicating the number of emergency resources required; d represents the total available gas emergency repair working time; />Representing the corresponding parameter weight value.

(2) Expanding, namely expanding the selected child nodes and adding the child nodes into a tree;

；

wherein,representing the best decision; v represents the average benefit of the node; c represents an adjustment parameter for balance exploration and utilization; />Representing the access times of the child nodes; n represents the number of accesses by the parent node.

(3) Simulation, namely performing simulation on the newly expanded nodes, adopting a random strategy or heuristic strategy, performing random selection on possible actions generated by the nodes, and simulating a feasible solution;

the constraint conditions are specifically as follows:

；

wherein,the time for realizing the rush repair and the minimization of the complicated degree of the work are represented; />Indicates the completion time of the gas emergency repair, +.>Indicating the occurrence time of the gas accident; />The time for the rush-repair personnel to arrive at the rush-repair site is represented; />Representing the analog regulation parameters;

；

i.e. each rush-repair person can only be at one placeThe time points are at one location;

i.e. each emergency resource can only be in one location at one point in time.

(4) And backtracking, and updating node information on a path from the extension node to the root node according to the simulation result. For example, the number of access times of the update node, the accumulated total time consumption and the number of personnel involved in the rush repair are as follows:

where Q represents the value of the current state.

Finally, according to the access times of the child nodes of the root node, the action with the highest access times is selected as the optimal solution.

According to the invention, factors such as accident types, geographic positions, availability of rush-repair resources and the like can be comprehensively considered through a Monte Carlo tree algorithm, and an optimal resource allocation scheme is found through simulating and evaluating different decision paths, so that the existing gas rush-repair personnel and equipment resources are utilized to the greatest extent, the waste and repeated scheduling of the resources are avoided, and the resource utilization rate is improved; through an intelligent decision plan, emergency repair personnel and equipment can be effectively and optimally distributed to the accident scene, the emergency repair time and response delay are reduced, and the emergency repair efficiency is improved; the decision basis based on the gas data is provided, subjectivity and randomness of the decision are reduced, and scientificity and accuracy of the decision are enhanced. Through intelligent scheduling and reasonable distribution, human misjudgment and decision errors can be reduced, and the quality of command decisions is improved.

S3, monitoring emergency repair personnel and emergency resources in real time in the emergency repair process, receiving tasks and instructions by the emergency repair personnel through mobile terminal equipment, and reporting the emergency repair progress and results in time; the risk level of the gas accident is preset according to the prediction of the gas accident and the positioning judgment of the accident, the gas emergency repair budget is set according to the risk level, the safety prevention and control range index of the gas accident is obtained through calculation and analysis, and the gas emergency repair effect is judged.

Further, according to the prediction of the gas accident and the positioning judgment of the accident, the risk level of the gas accident is preset, the gas emergency repair budget is set according to the risk level, and meanwhile, the emergency resources and personnel allocation are reasonably integrated.

The risk grade of the gas accident is divided into general risk, medium risk and high risk, and then the gas emergency repair budget is set according to the risk grade, wherein the initial gas emergency repair budget is thatThe specific process is as follows:

if the risk level of the gas accident is a general risk, the gas accident has some simple problems, and is easy to control and manage;setting the gas emergency repair budget as +.f for the initial adjustment parameters of the corresponding gas emergency repair>，；

If the gas accident risk level is medium risk, the gas system has obvious risk, and certain rush repair control and management measures are needed;setting the gas emergency repair budget as +.f for the initial adjustment parameters of the corresponding gas emergency repair>，/>；

If the gas accident risk level is high, which means that serious risks exist in the gas system, serious injuries can be caused to personnel and environment, and emergency control and emergency needs to be adoptedRush repair measures;setting the gas emergency repair budget as +.f for the initial adjustment parameters of the corresponding gas emergency repair>，/>The method comprises the steps of carrying out a first treatment on the surface of the Wherein, setting。

When the gas accident risk level is higher, the emergency repair budget needed by the gas accident place is more, so that enough manpower, material resources and financial resources can be quickly mobilized to carry out the emergency repair work when the gas accident occurs, and meanwhile, the resource waste and unnecessary cost expenditure are reduced.

The gas flow of the gas accident site is obtained through a sensor and other proper instruments, and then the gas emergency repair budget correction parameter U is calculated, wherein the specific process is as follows:

wherein,representing the total number of gas pipelines; />Representing an expected fuel gas flow value; />Indicating the total time when the gas flow is measured; />Representing a correction factor; />Indicating that obtained at j and kThe gas flow rate obtained is equal to the gas flow rate,；/>indicating the number of times the gas flow is obtained, +.>Representing a total number of locations at which the gas flow is obtained; />Representing the bias.

According to the invention, the gas emergency repair budget is preset according to the risk level of the gas accident, and the emergency repair personnel and equipment resources can be reasonably allocated according to the severity and the emergency degree of the accident, so that under the conditions of high risk and emergency, enough resources are input for emergency repair, and the accident treatment efficiency and success rate are improved; by presetting the gas emergency repair budget, resource allocation and repair work can be performed within the budget range, so that the cost is effectively controlled, and the waste of resources and unnecessary expense are avoided. Meanwhile, budget correction parameters are calculated according to gas flow data of a gas accident scene, budget can be corrected according to actual conditions, and cost is controlled more accurately.

Presetting a gas emergency repair budget correction parameter, comparing the gas emergency repair budget correction parameter U with the preset gas emergency repair budget correction parameter, then adjusting the gas emergency repair budget according to a comparison result, and defining a first preset gas emergency repair budget comparison quantity as followsThe method comprises the steps of carrying out a first treatment on the surface of the Then through the gas flow Q and the expected gas flow +.>Judging whether to optimally support the adjusted fuel gas emergency repair budget or not according to the comparison result of the fuel gas emergency repair budget, wherein the specific process is as follows:

if it isThe adjusted gas emergency repair budget is +.>，/>；

If it isThe adjusted gas emergency repair budget is +.>，/>；

If it isThe adjusted gas emergency repair budget is +.>，/>；

The accuracy and the rationality of the gas emergency repair budget are further determined through the obtained gas emergency repair budget correction parameters, and effective information data are provided for subsequent gas emergency repair work.

Gas flow Q value and expected gas flowThe measurement of the value is carried out in the same time interval and position ifWherein->Representing the judgment coefficient; the matching degree of the gas flow and the gas emergency repair budget is qualified,the adjusted emergency repair budget does not need to be optimally supported; if it isThe gas flow and the gas emergency repair budget are unqualified, and the gas emergency repair budget needs to be optimally supported, and the specific process is as follows:

defining a first expected fuel gas flow value asThe second expected fuel gas flow value is +.>If (3)The optimized gas emergency repair budget is +.>，/>The method comprises the steps of carrying out a first treatment on the surface of the If it isThe optimized gas emergency repair budget is +.>，/>Wherein->、/>Respectively represent the corresponding optimization factors.

The gas emergency repair budget correction parameters are compared with preset gas emergency repair budget correction parameters, the gas emergency repair budget is adjusted according to the comparison result, and the matching of the budget and the actual correction parameters is ensured, so that the emergency repair budget is accurately controlled and managed, the occurrence of the situation of excessive budget or insufficient budget is avoided, and the resource utilization efficiency is improved; the comparison result of the gas flow and the expected gas flow is used for judging whether the regulated gas emergency repair budget is optimally supported, so that the scientificity and adaptability of the budget can be ensured, and the actual repair requirement can be better met; through optimizing support, reasonable utilization of the rush-repair resources can be ensured, the working efficiency is improved, the overall rush-repair cost is reduced, the accident handling time is reduced, the loss is reduced, and the coping capability to gas accidents is enhanced.

Determining a safety prevention and control range of the gas accident according to the gas emergency repair budget obtained after the optimization support, and passing through the safety prevention and control range indexJudging the gas emergency repair effect, wherein the specific process is as follows:

；

wherein,representing the estimated expenditure of the gas emergency repair according to past experience; watch->The benefit brought by perfect safety prevention and control measures can be set in advance by experts and combining historical data; />The input of safety prevention and control to the gas system is shown; />Representing the constant adjustment coefficient.

If it isAbove 0, the current emergency repair scheme for fuel gas is active and effective;

if it isLess than or equal to 0, it indicates that the current emergency repair scheme for fuel gas is poor in effect and even possibly causes larger adverse effects, or the effect of the safety prevention and control measures is not as expected, and an alarm is sent to remind workers of making policy changes.

According to the invention, through judging the gas emergency repair effect, real-time monitoring and improvement can be performed, if the defect or the unsatisfactory emergency repair effect is found, measures can be taken in time to adjust and improve, the emergency repair capability of gas accidents is improved, the emergency repair budget and the prevention and control strategy are continuously optimized and perfected, and the overall safety and efficiency are improved; and the rush repair budget and prevention and control measures are improved and optimized in time, so that the capability of preventing and coping with gas accidents is improved.

Referring to fig. 2, the embodiment provides an intelligent command management system based on gas emergency repair, which comprises the following contents:

the system comprises a data acquisition module, an accident prediction module, a scheduling distribution module, a real-time monitoring module and an early warning management module;

the data acquisition module acquires real-time monitoring data information of the gas pipeline and gas accident recording condition information through a sensor, monitoring equipment and the like, and transmits the acquired data to the accident prediction module after integration;

the accident prediction module is used for carrying out data analysis according to the acquired data information, analyzing the running state of the gas system, predicting potential accident risks, positioning the accident position when the gas accident happens so as to take corresponding solving measures in time, and sending the analysis result to the scheduling distribution module, the real-time monitoring module and the early warning management module;

the dispatching distribution module is used for optimally dispatching and distributing the gas emergency repair tasks according to the result of the accident prediction module and the real-time monitoring data so as to improve the repair efficiency and response speed to the greatest extent;

the real-time monitoring module is used for obtaining an index of a prevention and control range according to an accident prediction result of the gas system and by combining the running state of the gas pipeline and the equipment performance, so that problems can be found conveniently and timely, and corresponding measures can be taken;

and the early warning management module monitors emergency conditions in the gas system through data interaction with the real-time monitoring module and the accident prediction module and timely sends out early warning notification.

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. Intelligent command management system based on gas emergency repair, which is characterized by comprising the following parts:

the system comprises a data acquisition module, an accident prediction module, a scheduling distribution module, a real-time monitoring module and an early warning management module;

the data acquisition module acquires real-time monitoring data information of the gas pipeline and gas accident recording condition information through a sensor, monitoring equipment and the like, and transmits the acquired data to the accident prediction module after integration;

the accident prediction module is used for carrying out data analysis according to the acquired data information, analyzing the running state of the gas system, predicting potential accident risks, positioning accident positions when the gas is in an accident, and sending analysis results to the dispatching distribution module, the real-time monitoring module and the early warning management module;

the dispatching distribution module is used for optimally dispatching and distributing the gas emergency repair tasks according to the result of the accident prediction module and the real-time monitoring data;

the real-time monitoring module obtains an index of a prevention and control range according to an accident prediction result of the gas system and by combining the running state and the equipment performance of the gas pipeline, so that problems can be found conveniently and timely, and corresponding measures can be taken;

the early warning management module monitors emergency in the gas system through data interaction with the real-time monitoring module and the accident prediction module and timely sends out early warning notification.

2. The intelligent command management method based on the gas emergency repair is characterized by comprising the following steps of:

s1, monitoring gas pipeline equipment in real time by using a sensor and monitoring equipment, extracting training data from acquired historical monitoring data and combining with gas accident recording conditions, and establishing a gas accident prediction neural network model;

s2, intelligent scheduling and reasonable distribution are carried out on emergency repair personnel and equipment by utilizing a Monte Carlo tree algorithm according to the accident type, the geographical position and the availability of emergency repair resources, so as to obtain a command decision plan;

s3, monitoring emergency repair personnel and emergency resources in real time in the emergency repair process, presetting the risk level of the gas accident according to the prediction of the gas accident and the positioning judgment of the accident, setting the gas emergency repair budget according to the risk level, and judging the gas emergency repair effect by obtaining the safety prevention and control range index of the gas accident through calculation and analysis.

3. The intelligent command management method based on gas emergency repair according to claim 2, wherein the step S1 specifically includes:

the gas accident prediction neural network model is characterized in that training data is firstly divided into a training set and a testing set, the training set is the acquired historical data of a gas pipeline and accident label information, the gas accident prediction neural network model is fitted through the training set, for each training sample data, a predicted value of the model is calculated through forward propagation, loss between the predicted value and a real label is calculated, then the Adam algorithm is used for calculating the current gradient, and weight parameters of the model are updated according to the gradient.

4. The intelligent command management method based on gas emergency repair according to claim 3, wherein a gating function is introduced into a gas accident prediction neural network model, and the specific process is as follows:

will beInput deviceIn the gas accident prediction neural network model, the initial state is expressed as +.>，

；

Wherein,representing acquired gas pipeline related data information, < >>Representing the number of elements in the gas pipeline related data information; />A weight value representing an initial state; />Representing the bias at the initial state;

the updating door determines the degree to which the gas state information at the previous moment is brought into the current state to judge whether a potential accident occurs or not; the forgetting door help model ignores information irrelevant or unimportant to the current state so as to reduce the influence of interference and noise on accident prediction; the reset gate determines the information fusion degree of the input at the current moment and the hidden state at the previous moment.

5. The intelligent command management method based on gas emergency repair according to claim 2, wherein the step S2 specifically includes:

the searching process of the Monte Carlo tree starts from a root node, evaluates the value of each child node according to UCB strategy, and selects the child node with the largest UCB value for expansion; when selecting the child nodes, calculating an upper bound confidence interval of each node, and then selecting the node with the highest upper bound; expanding the selected child node and adding the child node into a tree; simulation is carried out on the newly expanded nodes, and random selection is carried out on possible actions generated by the nodes by adopting a random strategy or a heuristic strategy; and updating node information on a path from the extension node to the root node according to the simulation result.

6. The intelligent command management method based on gas emergency repair according to claim 2, wherein the step S3 specifically includes:

presetting the risk level of the gas accident according to the prediction of the gas accident and the positioning judgment of the accident, and setting the gas emergency repair budget according to the risk level; and acquiring the gas flow of the gas accident site through a sensor, and then calculating the budget correction parameter of the gas emergency repair.

7. The intelligent command management method based on gas emergency repair according to claim 6, wherein the gas emergency repair budget correction parameters are preset, the gas emergency repair budget correction parameters are compared with the preset gas emergency repair budget correction parameters, and then the gas emergency repair budget is adjusted according to the comparison result; and judging whether to optimally support the regulated gas emergency repair budget according to a comparison result of the gas flow and the expected gas flow.

8. The intelligent command management method based on gas emergency repair according to claim 7, wherein the safety prevention and control range of the gas accident is determined according to the gas emergency repair budget obtained after optimization support, and the gas emergency repair effect is judged through the safety prevention and control range index.