CN113869562A - Abnormal event response level determining method, device, equipment and readable storage medium - Google Patents
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Abstract
The application discloses a method, a device, equipment and a readable storage medium for determining the response level of an abnormal event, wherein the method comprises the following steps: acquiring a first prediction model; inputting a first parameter of the urban comprehensive pipe gallery to a first prediction model to obtain a first prediction result; acquiring a second prediction result corresponding to a second parameter of the urban comprehensive pipe gallery based on a preset mapping relation; if the first prediction result and the second prediction result meet preset requirements, improving the response level of the abnormal event corresponding to the first prediction result; and/or improving the response level of the abnormal event corresponding to the second prediction result. According to the method and the device, the response grade of the corresponding abnormal event is improved by combining the first prediction result with the second prediction result, the problem that the response grade of the abnormal event determined by a single prediction result is not enough, so that the abnormal event is not processed timely is avoided, and the reasonability of the corresponding grade given by the abnormal event is improved.
Description
Technical Field
The application relates to the field of urban comprehensive pipe galleries, in particular to a method, a device and equipment for determining abnormal event response levels and a readable storage medium.
Background
With the rapid development of urban economy and the continuous improvement of urban functions, urban underground pipelines serving as key public infrastructures are more and more in variety, wider in laying region range and higher in density; the aging, capacity increasing and capacity expanding of the underground pipelines not only cause urban congestion, but also cause frequent pipeline accidents, so that the state vigorously pushes the underground pipelines to enter the comprehensive pipe gallery.
The urban comprehensive pipe gallery is an urban underground pipeline comprehensive corridor, namely a tunnel space is built underground in a city, various engineering pipelines such as electric power, communication, gas, heat supply, water supply and drainage and the like are integrated, a special overhaul port, a lifting port and a monitoring system are arranged, unified planning, unified design, unified construction and unified management are implemented, and the urban comprehensive pipe gallery is an important infrastructure and a 'lifeline' for guaranteeing urban operation.
The current city utility tunnel has the ability of reminding abnormal events intelligently, however, some abnormal events need emergency treatment at present, but because the response grade given is not enough, the processing of the abnormal events is not enough in time, thereby causing irrecoverable consequences.
That is, the level of response currently given to an exceptional event is not reasonable.
Disclosure of Invention
The application mainly aims to provide an abnormal event response level determining method, an abnormal event response level determining device and a readable storage medium, and aims to solve the technical problem of how to improve the reasonability of the response level given to an abnormal event.
In order to achieve the above object, the present application provides an abnormal event response level determining method, including the steps of:
acquiring a first prediction model;
inputting a first parameter of the urban comprehensive pipe gallery to a first prediction model to obtain a first prediction result;
acquiring a second prediction result corresponding to a second parameter of the urban comprehensive pipe gallery based on a preset mapping relation;
if the first prediction result and the second prediction result meet preset requirements, improving the response level of the abnormal event corresponding to the first prediction result; and/or the presence of a gas in the gas,
and improving the response level of the abnormal event corresponding to the second prediction result.
Optionally, the obtaining a second prediction result corresponding to a second parameter of the city utility tunnel based on a preset mapping relationship includes:
acquiring a second prediction model;
and inputting a second parameter of the urban comprehensive pipe gallery to the second prediction model to obtain a second prediction result.
Optionally, before the obtaining the first prediction model, the method includes:
determining an abnormal event corresponding to the urban comprehensive pipe rack;
and determining an occurrence factor of the abnormal event, and training a model to be trained based on the occurrence factor to obtain a first prediction model.
Optionally, the training a model to be trained based on the occurrence factor to obtain a first prediction model includes:
acquiring a training data set and a model to be trained corresponding to the occurrence factor;
performing iterative training on the model to be trained based on the training data set to obtain an updated model to be trained, and determining whether the updated model to be trained meets a preset iteration ending condition;
if the updated model to be trained meets the preset iteration end condition, taking the updated model to be trained as the first prediction model;
and if the updated model to be trained does not meet the iteration ending condition, returning to the step of performing the iterative training on the model to be trained based on the training data set until the updated model to be trained meets the iteration ending condition.
Optionally, if the first prediction result and the second prediction result meet a preset requirement, the response level of the abnormal event corresponding to the first prediction result is improved; and/or the presence of a gas in the gas,
improving the response level of the abnormal event corresponding to the second prediction result, including:
and if the first prediction result and the second prediction result are both potential hazards of abnormal events, improving the response level of the abnormal events corresponding to the first prediction result and/or the second prediction result.
Optionally, the method further comprises:
acquiring real-time Internet of things data of the urban comprehensive pipe gallery;
constructing a digital twin model based on the real-time internet of things data;
and outputting prompt information corresponding to the response grade based on the digital twin model.
Optionally, the method further comprises:
generating a response instruction of the abnormal event;
and outputting the response instruction to an execution module corresponding to the abnormal event, and solving the abnormal event through the execution module.
In order to achieve the above object, the present application also provides an abnormal event response level determining apparatus including:
a first obtaining module for obtaining a first prediction model;
the input module is used for inputting a first parameter of the urban comprehensive pipe gallery to the first prediction model to obtain a first prediction result;
the second obtaining module is used for obtaining a second prediction result corresponding to a second parameter of the urban comprehensive pipe gallery based on a preset mapping relation;
the improving module is used for improving the response level of the abnormal event corresponding to the first prediction result if the first prediction result and the second prediction result meet the preset requirement; and/or the presence of a gas in the gas,
and improving the response level of the abnormal event corresponding to the second prediction result.
Further, to achieve the above object, the present application also provides an exceptional response level determining device comprising a memory, a processor and an exceptional response level determining program stored on the memory and executable on the processor, wherein the exceptional response level determining program, when executed by the processor, implements the steps of the exceptional response level determining method as described above.
Further, to achieve the above object, the present application also provides a computer-readable storage medium having stored thereon an abnormal event response level determination program which, when executed by a processor, implements the steps of the abnormal event response level determination method as described above.
The method comprises the steps of obtaining a first prediction model; inputting a first parameter of the urban comprehensive pipe gallery to a first prediction model to obtain a first prediction result; acquiring a second prediction result corresponding to a second parameter of the urban comprehensive pipe gallery based on a preset mapping relation; if the first prediction result and the second prediction result meet preset requirements, improving the response level of the abnormal event corresponding to the first prediction result; and/or improving the response level of the abnormal event corresponding to the second prediction result. According to the method and the device, the response grade of the corresponding abnormal event is improved by combining the first prediction result with the second prediction result, the problem that the response grade of the abnormal event determined by a single prediction result is not enough, so that the abnormal event is not processed timely is avoided, and the reasonability of the corresponding grade given by the abnormal event is improved.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating a first embodiment of an abnormal event response level determination method according to the present application;
FIG. 2 is a functional block diagram of a preferred embodiment of an abnormal event response level determination apparatus according to the present application;
fig. 3 is a schematic structural diagram of a hardware operating environment according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Referring to fig. 1, fig. 1 is a schematic flowchart of a first embodiment of an abnormal event response level determination method according to the present application.
While a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in an order different from that shown or described herein. The abnormal event response level determination method may be applied to a terminal or a server. For convenience of description, the execution subject describes each step of the abnormal event response level determination method, which is omitted below. The abnormal event response level determining method comprises the following steps:
step S110, a first prediction model is obtained.
In this embodiment, the first prediction model is a machine learning model or a deep learning model, and is used for predicting an abnormal event that may occur in the urban comprehensive pipe gallery. Wherein, the unusual incident includes that the gas is revealed, the conflagration, the pipeline is revealed, outside invasion of piping lane and piping lane body structure damage etc. can understand, and the purpose of predicting the unusual incident is in the bud for avoiding the emergence of unusual incident, accomplishes to prevent and meets an emergency.
Further, before the obtaining the first prediction model, the method includes:
step a, determining an abnormal event corresponding to the urban comprehensive pipe gallery;
and b, determining an occurrence factor of the abnormal event, and training a model to be trained based on the occurrence factor to obtain a first prediction model.
In this embodiment, because the number of devices in the urban comprehensive pipe rack is large and the environment is complex, there are various abnormal events that may occur in the urban comprehensive pipe rack, that is, there are multiple abnormal events, and if all the abnormal events are predicted by one prediction model, the problems of locally optimal solutions (for example, the accuracy of predicting the abnormal event a is high, and the accuracy of predicting the abnormal event B is low) and low prediction accuracy are inevitably caused.
It should be noted that different abnormal events occur under different conditions, for example, gas leakage may be caused by aging of a seal ring or cracking of a gas pipeline, and structural damage of a pipe gallery body may be caused by stress concentration. Therefore, by acquiring occurrence factors which may cause the occurrence of an abnormal event and predicting whether the abnormal event will occur through the occurrence factors, it can be understood that different abnormal events generally have different occurrence factors, the model to be trained is trained through the occurrence factors of one abnormal event, and the obtained first prediction model is only used for predicting the abnormal event corresponding to the occurrence factor and is not used for predicting other abnormal events. It can be understood that the occurrence factor is an occurrence condition of the abnormal event, and is specifically embodied as relevant parameters such as a device self parameter, a pipe gallery environment parameter and the like before the abnormal event occurs.
Further, the training of the model to be trained based on the occurrence factor to obtain the first prediction model includes:
b1, acquiring a training data set and a model to be trained corresponding to the occurrence factor;
b2, performing iterative training on the model to be trained based on the training data set to obtain an updated model to be trained, and determining whether the updated model to be trained meets a preset iteration ending condition;
b3, if the updated model to be trained meets the preset iteration end condition, taking the updated model to be trained as the first prediction model;
step b4, if the updated model to be trained does not meet the iteration end condition, returning to the step of performing the iteration training on the model to be trained based on the training data set until the updated model to be trained meets the iteration end condition.
In this embodiment, iterative training is performed on a model to be trained based on a training data set to obtain an updated model to be trained, and it is determined whether the updated model to be trained satisfies a preset iteration end condition; if the updated model to be trained meets the preset iteration end condition, taking the updated model to be trained as a first prediction model; and if the updated model to be trained does not meet the iteration ending condition, returning to the step of performing the iterative training on the model to be trained based on the training data set until the updated model to be trained meets the iteration ending condition. The training data set is a data set related to the occurrence factor, for example, the occurrence factor is a device parameter, and the training data set related to the device parameter includes the device parameter when the abnormal event occurs and the device parameter when the abnormal event does not occur.
Specifically, iterative training is performed on the model to be trained through the occurrence factor to obtain an updated model to be trained. After obtaining the updated model to be trained each time, determining whether the updated model to be trained meets a preset iteration ending condition, if so, ending the iteration, and taking the last updated model to be trained as a first prediction model; and if the updated model to be trained does not meet the iteration ending condition, the updated model to be trained does not meet the use condition, and the step of performing iterative training on the model to be trained based on the training data set is returned until the updated model to be trained meets the iteration ending condition.
It should be noted that the iterative training is a process of training a model to be trained through the occurrence factor for multiple times, and generally, the first prediction model obtained from the model to be trained needs to be updated through multiple rounds of training. It should be noted that, when the preset iteration end condition is that the model to be trained is input or the updated model to be trained is used as the model, the prediction accuracy reaches the preset accuracy threshold, the iteration is ended.
And step S120, inputting a first parameter of the urban comprehensive pipe gallery to the first prediction model to obtain a first prediction result.
In this embodiment, the first parameter is a parameter corresponding to the first prediction model, and it should be noted that the first prediction model is used to predict whether an abnormal event will occur, and accordingly, the first parameter is a factor causing the abnormal event to occur, the factor includes an equipment parameter (for example, equipment state information) of the city utility tunnel, a sensor parameter (for example, environment sensing information) and/or an image parameter (for example, equipment image information and environment image information), and it can be understood that the equipment state information is equipment sensing information, that is, sensing information obtained by a sensor disposed on the equipment. The first prediction result is that an abnormal event may occur or may not occur, and accordingly, the first prediction model may specifically be a two-classification model, such as a logistic regression model, a random forest, a support vector machine, a decision tree, and the like.
And S130, acquiring a second prediction result corresponding to a second parameter of the urban comprehensive pipe gallery based on a preset mapping relation.
In this embodiment, the second parameter is related to the first parameter, and there is a correlation (mutual influence) between a first abnormal event corresponding to the first parameter and a second abnormal event corresponding to the second parameter, where the correlation is recorded by a preset mapping relationship, specifically, after the first abnormal event is determined, the second abnormal event is determined by the preset mapping relationship, and after the second abnormal event is determined, a second prediction result of the second abnormal event is obtained, where the second abnormal event may be one or more.
It should be noted that the correlation between the first abnormal event and the second abnormal event is specifically embodied in that the occurrence of the second abnormal event amplifies the harm brought by the first abnormal event or the occurrence of the first abnormal event amplifies the harm brought by the second abnormal event, that is, the first abnormal event and the second abnormal event occur independently from each other with less harm than when the first abnormal event and the second abnormal event occur simultaneously, and therefore, when the first abnormal event and the second abnormal event may occur simultaneously, it is necessary to increase the response level of the first abnormal event and/or the second abnormal event, i.e., giving preference to the first exception event and/or the second exception event in the limited exception event handling capability, therefore, the huge harm caused by the simultaneous occurrence of the first abnormal event and the second abnormal event is avoided.
For example, the first abnormal event is gas leakage, and the second abnormal event is cable leakage, so that it can be understood that when gas leakage or cable leakage occurs independently, the hazard is far lower than that caused by simultaneous occurrence of gas leakage and cable leakage, because the gas leakage and the cable leakage occur simultaneously, which may cause explosion accidents; for another example, the first abnormal event is water supply pipeline breakage, and the second abnormal event is water discharge pipeline breakage, so that when the water supply pipeline breakage and the water discharge pipeline breakage respectively occur independently, only water leakage is caused, and once the water supply pipeline breakage and the water discharge pipeline breakage occur simultaneously, sewage in the water discharge pipeline flows into the water supply pipeline, so that water in the water supply pipeline is polluted, the risk of causing diseases is caused, and the damage caused by the abnormal events is also obviously improved.
It should be noted that the preset mapping relationship may be updated in real time, and the updating process is implemented based on an undiscovered association relationship between the abnormal events, where the undiscovered association relationship is embodied in an abnormal event association relationship that is not recorded in the preset mapping relationship, specifically, when an abnormal event occurs, there is another abnormal event that affects the abnormal event, where in the preset mapping relationship, the abnormal event has no association relationship with the other abnormal event, that is, there is an undiscovered association relationship between the abnormal event and the other abnormal event.
Further, the obtaining of the second prediction result corresponding to the second parameter of the urban comprehensive pipe gallery based on the preset mapping relationship includes:
step c, acquiring a second prediction model;
and d, inputting a second parameter of the urban comprehensive pipe gallery to a second prediction model to obtain a second prediction result.
In this embodiment, it can be understood that the second prediction result is obtained by predicting through the second prediction model, the prediction process of the second prediction result is similar to the prediction process of the first prediction result, and accordingly, the specific implementation manner of obtaining the second prediction result is substantially the same as the embodiment of obtaining the first prediction result, and is not described herein again.
Step S140, if the first prediction result and the second prediction result meet preset requirements, improving the response level of the abnormal event corresponding to the first prediction result; and/or the presence of a gas in the gas,
and improving the response level of the abnormal event corresponding to the second prediction result.
Further, if the first prediction result and the second prediction result meet preset requirements, the response level of the abnormal event corresponding to the first prediction result is improved; and/or the presence of a gas in the gas,
improving the response level of the abnormal event corresponding to the second prediction result, including:
and e, if the first prediction result and the second prediction result are both potential hazards of abnormal events, improving the response level of the abnormal events corresponding to the first prediction result and/or the second prediction result.
In this embodiment, the first prediction result and the second prediction result respectively represent whether a first abnormal event and a second abnormal event occur, and when the first abnormal event and the second abnormal event both have hidden dangers, the response level of the abnormal event is improved; when the first abnormal event and the second abnormal event do not have the hidden danger at the same time (for example, the first abnormal event has the hidden danger and the second abnormal event does not have the hidden danger), the response level of the abnormal event does not need to be improved. When the response level of the abnormal event is increased, the response level of the first abnormal event and the response level of the second abnormal event may be increased at the same time, or only the response level of the first abnormal event or only the response level of the second abnormal event may be increased, which may be specifically determined by the controllable degree of the first abnormal event and the second abnormal event. For example, if the first abnormal event is gas leakage and the second abnormal event is cable leakage, the problem of gas leakage is easy to solve and the problem of cable leakage is not easy to solve, the response level of the first abnormal event can be improved, but the response level of the second abnormal event is not improved; for another example, the first abnormal event is the water supply pipeline breakage, the second abnormal event is the water discharge pipeline breakage, and at the moment, the water supply pipeline breakage and the water discharge pipeline breakage are both easy to solve, so that the response level of the first abnormal event and the response level of the second abnormal event can be improved simultaneously.
Further, the method further comprises:
step f, acquiring real-time Internet of things data of the urban comprehensive pipe gallery;
step g, constructing a digital twin model based on the real-time Internet of things data;
and h, outputting prompt information corresponding to the response grade based on the digital twin model.
In this embodiment, a digital twin technology is used for analog simulation of the entity city utility tunnel, and the real-time internet of things data acquisition of the entity city utility tunnel is performed by using the internet of things technology, specifically, the simulation model is constructed by a GIS (Geographic Information System) and a BIM (Building Information model), after the construction of the digital twin model is completed, the position or the equipment where the first abnormal event and/or the second abnormal event may occur is determined on the digital twin model, and prompt Information corresponding to the response grade is output, so as to quickly and accurately remind relevant personnel (for example, operation and maintenance personnel) of the position or the equipment where the first abnormal event and/or the second abnormal event may occur.
Further, the method further comprises:
step i, generating a response instruction of the abnormal event;
and j, outputting the response instruction to an execution module corresponding to the abnormal event, and solving the abnormal event through the execution module.
In this embodiment, in order to avoid the occurrence of an abnormal event, it is necessary to take corresponding measures immediately when a seedling end of the abnormal event occurs, and it can be understood that a certain time is required for a relevant person (e.g., a maintenance person) to arrive at the scene, and the best time for avoiding the abnormal event may be missed within the time from the finding of the seedling end to the time when the relevant person arrives at the scene of the abnormal event, so that a quick response to the abnormal event is required. Specifically, the seedling head of the abnormal event is cut off in a mode of remotely controlling the execution module. For example, if the abnormal event is cable leakage, the power supply of the area where the cable is located is cut off; and if the abnormal event is the rupture of the water supply pipeline, closing a water valve in the area where the water supply pipeline is located.
The method comprises the steps of obtaining a first prediction model; inputting a first parameter of the urban comprehensive pipe gallery to a first prediction model to obtain a first prediction result; acquiring a second prediction result corresponding to a second parameter of the urban comprehensive pipe gallery based on a preset mapping relation; if the first prediction result and the second prediction result meet preset requirements, improving the response level of the abnormal event corresponding to the first prediction result; and/or improving the response level of the abnormal event corresponding to the second prediction result. According to the method and the device, the response grade of the corresponding abnormal event is improved by combining the first prediction result with the second prediction result, the problem that the response grade of the abnormal event determined by a single prediction result is not enough, so that the abnormal event is not processed timely is avoided, and the reasonability of the corresponding grade given by the abnormal event is improved.
In addition, the present application also provides an abnormal event response level determining apparatus, as shown in fig. 2, the abnormal event response level determining apparatus includes:
a first obtaining module 10, configured to obtain a first prediction model;
the input module 20 is used for inputting a first parameter of the urban comprehensive pipe gallery to the first prediction model to obtain a first prediction result;
the second obtaining module 30 is configured to obtain a second prediction result corresponding to a second parameter of the urban comprehensive pipe gallery based on a preset mapping relationship;
the improving module 40 is configured to improve a response level of an abnormal event corresponding to the first prediction result if the first prediction result and the second prediction result meet a preset requirement; and/or the presence of a gas in the gas,
and improving the response level of the abnormal event corresponding to the second prediction result.
Further, the second obtaining module 30 is further configured to:
acquiring a second prediction model;
and inputting a second parameter of the urban comprehensive pipe gallery to the second prediction model to obtain a second prediction result.
Further, the abnormal event response level determining apparatus further includes:
the first determining module is used for determining an abnormal event corresponding to the urban comprehensive pipe gallery;
and the second determining module is used for determining the occurrence factor of the abnormal event and training the model to be trained based on the occurrence factor to obtain the first prediction model.
Further, the second determining module is further configured to:
acquiring a training data set and a model to be trained corresponding to the occurrence factor;
performing iterative training on the model to be trained based on the training data set to obtain an updated model to be trained, and determining whether the updated model to be trained meets a preset iteration ending condition;
if the updated model to be trained meets the preset iteration end condition, taking the updated model to be trained as the first prediction model;
and if the updated model to be trained does not meet the iteration ending condition, returning to the step of performing the iterative training on the model to be trained based on the training data set until the updated model to be trained meets the iteration ending condition.
Further, the increasing module 40 is further configured to:
and if the first prediction result and the second prediction result are both potential hazards of abnormal events, improving the response level of the abnormal events corresponding to the first prediction result and/or the second prediction result.
Further, the abnormal event response level determining apparatus further includes:
the third acquisition module is used for acquiring real-time Internet of things data of the urban comprehensive pipe gallery;
the construction module is used for constructing a digital twin model based on the real-time Internet of things data;
and the first output module is used for outputting prompt information corresponding to the response grade based on the digital twin model.
Further, the abnormal event response level determining apparatus further includes:
the generating module is used for generating a response instruction of the abnormal event;
and the second output module is used for outputting the response instruction to the execution module corresponding to the abnormal event and solving the abnormal event through the execution module.
The specific implementation of the abnormal event response level determining apparatus of the present application is substantially the same as the embodiments of the abnormal event response level determining method described above, and details thereof are not repeated herein.
In addition, the application also provides abnormal event response level determining equipment. As shown in fig. 3, fig. 3 is a schematic structural diagram of a hardware operating environment according to an embodiment of the present application.
It should be noted that fig. 3 is a schematic structural diagram of a hardware operating environment of the abnormal event response level determination device.
As shown in fig. 3, the abnormal event response level determining apparatus may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the abnormal event response level determining apparatus may further include an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like.
Those skilled in the art will appreciate that the exceptional response level determining apparatus configuration shown in fig. 3 does not constitute a limitation of the exceptional response level determining apparatus and may include more or less components than those shown, or combine some components, or a different arrangement of components.
As shown in fig. 3, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an abnormal event response level determining program. The operating system is a program for managing and controlling hardware and software resources of the abnormal event response level determination device, and supports the operation of the abnormal event response level determination program and other software or programs.
In the abnormal event response level determining apparatus shown in fig. 3, the user interface 1003 is mainly used for connecting a terminal, and performing data communication with the terminal, such as receiving an alarm signal sent by the terminal; the network interface 1004 is mainly used for the background server and performs data communication with the background server; the processor 1001 may be configured to invoke an exceptional response level determining program stored in the memory 1005 and perform the steps of the exceptional response level determining method as described above.
The specific implementation of the abnormal event response level determining apparatus of the present application is substantially the same as the embodiments of the abnormal event response level determining method described above, and details thereof are not repeated herein.
Furthermore, an embodiment of the present application also provides a computer-readable storage medium, where an abnormal event response level determining program is stored on the computer-readable storage medium, and when executed by a processor, the abnormal event response level determining program implements the steps of the abnormal event response level determining method described above.
The specific implementation of the computer-readable storage medium of the present application is substantially the same as the embodiments of the above abnormal event response level determining method, and is not described herein again.
Furthermore, a computer program product is proposed in an embodiment of the present application, which includes a computer program that, when being executed by a processor, implements the steps of the abnormal event response level determination method as described above.
The specific implementation of the computer program product of the present application is substantially the same as the embodiments of the abnormal event response level determination method, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, a device, or a network device) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. An abnormal event response level determination method, characterized by comprising the steps of:
acquiring a first prediction model;
inputting a first parameter of the urban comprehensive pipe gallery to a first prediction model to obtain a first prediction result;
acquiring a second prediction result corresponding to a second parameter of the urban comprehensive pipe gallery based on a preset mapping relation;
if the first prediction result and the second prediction result meet preset requirements, improving the response level of the abnormal event corresponding to the first prediction result; and/or the presence of a gas in the gas,
and improving the response level of the abnormal event corresponding to the second prediction result.
2. The method of claim 1, wherein the obtaining a second prediction result corresponding to a second parameter of the urban utility tunnel based on a preset mapping relationship comprises:
acquiring a second prediction model;
and inputting a second parameter of the urban comprehensive pipe gallery to the second prediction model to obtain a second prediction result.
3. The method of claim 1, wherein said obtaining the first predictive model is preceded by:
determining an abnormal event corresponding to the urban comprehensive pipe rack;
and determining an occurrence factor of the abnormal event, and training a model to be trained based on the occurrence factor to obtain a first prediction model.
4. The method of claim 3, wherein training the model to be trained based on the occurrence factor to obtain a first predictive model comprises:
acquiring a training data set and a model to be trained corresponding to the occurrence factor;
performing iterative training on the model to be trained based on the training data set to obtain an updated model to be trained, and determining whether the updated model to be trained meets a preset iteration ending condition;
if the updated model to be trained meets the preset iteration end condition, taking the updated model to be trained as the first prediction model;
and if the updated model to be trained does not meet the iteration ending condition, returning to the step of performing the iterative training on the model to be trained based on the training data set until the updated model to be trained meets the iteration ending condition.
5. The method according to claim 1, wherein if the first predicted result and the second predicted result satisfy a preset requirement, the response level of the abnormal event corresponding to the first predicted result is increased; and/or the presence of a gas in the gas,
improving the response level of the abnormal event corresponding to the second prediction result, including:
and if the first prediction result and the second prediction result are both potential hazards of abnormal events, improving the response level of the abnormal events corresponding to the first prediction result and/or the second prediction result.
6. The method of claim 1, wherein the method further comprises:
acquiring real-time Internet of things data of the urban comprehensive pipe gallery;
constructing a digital twin model based on the real-time internet of things data;
and outputting prompt information corresponding to the response grade based on the digital twin model.
7. The method of claim 1, wherein the method further comprises:
generating a response instruction of the abnormal event;
and outputting the response instruction to an execution module corresponding to the abnormal event, and solving the abnormal event through the execution module.
8. An abnormal event response level determination apparatus, characterized in that the abnormal event response level determination apparatus comprises:
a first obtaining module for obtaining a first prediction model;
the input module is used for inputting a first parameter of the urban comprehensive pipe gallery to the first prediction model to obtain a first prediction result;
the second obtaining module is used for obtaining a second prediction result corresponding to a second parameter of the urban comprehensive pipe gallery based on a preset mapping relation;
the improving module is used for improving the response level of the abnormal event corresponding to the first prediction result if the first prediction result and the second prediction result meet the preset requirement; and/or the presence of a gas in the gas,
and improving the response level of the abnormal event corresponding to the second prediction result.
9. An exceptional response level determination device, characterized in that said exceptional response level determination device comprises a memory, a processor and an exceptional response level determination program stored on said memory and executable on said processor, said exceptional response level determination program when executed by said processor implementing the steps of the exceptional response level determination method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that an exceptional response level determining program is stored on the computer-readable storage medium, which when executed by a processor implements the steps of the exceptional response level determining method according to any one of claims 1 to 7.
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