CN114186747A - Big data based accident analysis decision method and system - Google Patents
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Abstract
The embodiment of the invention provides a big data-based accident analysis decision method and system. Belonging to the technical field of data accident analysis. The big data-based accident analysis decision method comprises the following steps: determining a one-to-one mapping relation between various traffic accidents and various influence factor data aiming at the pre-analyzed road section; acquiring current influence factor data of a road section, determining a traffic accident corresponding to the current influence factor data as a predicted accident according to the mapping relation, and determining the probability of the predicted accident corresponding to the current influence factor data based on a preset probability prediction model; and determining and executing the current alarm-giving strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the probability of the predicted accident and the alarm-giving strategy. The accident analysis and decision method and system based on the big data can automatically estimate the accident reason and realize the prevention of the accident based on the reason analysis and decision.
Description
Technical Field
The invention relates to the technical field of big data accident analysis, in particular to a big data accident analysis-based decision-making method and system.
Background
Along with the development of traffic, how to deal with the traffic accident after the occurrence of the traffic accident can avoid the expansion of the traffic accident. At present, the reason of the accident is mainly identified manually, the reason of the accident is judged manually according to the actual situation of the accident site, and prevention and control are realized according to the reason of the accident. The method for artificially predicting the accident reason has low efficiency and low accuracy, has great dependence on artificial factors, and is inconvenient to popularize and use.
Disclosure of Invention
The invention aims to provide a big data-based accident analysis decision method and a big data-based accident analysis decision system, which can automatically estimate the accident reason and realize the accident prevention based on the reason analysis decision.
In order to achieve the above object, an embodiment of the present invention provides a big data based incident analysis decision method, where the big data based incident analysis decision method includes:
determining a one-to-one mapping relation between various traffic accidents and various influence factor data aiming at the pre-analyzed road section;
acquiring current influence factor data of a road section, determining a traffic accident corresponding to the current influence factor data as a predicted accident according to the mapping relation, and determining the probability of the predicted accident corresponding to the current influence factor data based on a preset probability prediction model; and
and determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the probability of the predicted accident and the alarm strategy.
Preferably, the probabilistic predictive model is determined by:
establishing a probability prediction initial model, wherein the probability prediction initial model takes influence factor data as input and takes the probability of an accident as output; and
acquiring historical data containing historical influence factor data and the probability of the historical accident, and training the probability prediction initial model based on the acquired historical data to obtain a trained probability prediction model.
Preferably, the determining and executing the current alarm policy corresponding to the predicted accident according to the preset accident and the corresponding relationship between the probability of the predicted accident and the alarm policy includes:
and when the probability of the predicted accident is greater than a preset probability threshold, calculating the difference between the probability of the predicted accident and the preset probability threshold, and determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the difference and the alarm strategy.
Preferably, the alarm policy is configured to be related to the number of configured alarm staff;
the accident is configured to be associated with an casualty condition of the accident;
and, the determining and executing the current alarm strategy corresponding to the predicted accident and the difference value according to the preset corresponding relationship between the accident and the difference value and the alarm strategy comprises:
determining a current accident weight value reflecting the accident and the difference value; and
and determining and executing the current alarm strategy corresponding to the current accident weight according to the corresponding relation between the preset accident weight and the alarm strategy.
Preferably, the influencing factor data comprises: and environmental influence factor data and time influence factors are acquired through a preset environmental data sensor.
In addition, the invention also provides a big data-based accident analysis and decision system, which comprises:
the relation determining unit is used for determining a one-to-one mapping relation between various traffic accidents and various influence factor data aiming at the pre-analyzed road section;
the probability accident determining unit is used for acquiring current influence factor data of a road section, determining a traffic accident corresponding to the current influence factor data as a predicted accident according to the mapping relation, and determining the probability of the predicted accident corresponding to the current influence factor data based on a preset probability prediction model; and
and the strategy determining unit is used for determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the probability of the predicted accident and the alarm strategy.
Preferably, the probabilistic predictive model is determined by:
the model establishing module is used for establishing a probability prediction initial model, wherein the probability prediction initial model takes influence factor data as input and takes the probability of an accident as output; and
and the model determining module is used for acquiring historical data containing historical influence factor data and the probability of the historical accidents, and training the probability prediction initial model based on the acquired historical data to obtain a trained probability prediction model.
Preferably, the policy determining unit is configured to, when the probability of the predicted accident is greater than a preset probability threshold, calculate a difference between the probability of the predicted accident and the preset probability threshold, and determine and execute a current alarm policy corresponding to the predicted accident according to a preset accident and a corresponding relationship between the difference and the alarm policy.
In addition, the invention also provides a machine-readable storage medium, which stores instructions for causing a machine to execute the big data accident analysis decision-making method.
In addition, the present invention also provides a processor for executing a program, wherein the program is executed to perform: the big data accident analysis decision-making method is described above.
By the technical scheme, various theoretical factors of the accident can be determined, the predicted accident is corresponding to the actual factors, and the alarm strategy is determined according to the predicted accident and the corresponding probability, namely, the accident prevention is realized based on the reason analysis decision.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart illustrating a big data incident analysis based decision method of the present invention; and
FIG. 2 is a block diagram of a big data based incident analysis decision making system illustrating the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart of a big data accident analysis-based decision method according to the present invention, and as shown in fig. 1, the big data accident analysis-based decision method includes:
s101, determining a one-to-one mapping relation between various traffic accidents and various influence factor data according to the pre-analyzed road sections. The mapping relation can be that the traffic accident is large, the fog day, the rain day, the air temperature is low, and the time is the evening time and the night peak time. Each traffic accident (accident size division) corresponds to an overall set of multiple factors, which is determined from the correspondence between the traffic accident and the factors.
S102, obtaining current influence factor data of a road section, determining a traffic accident corresponding to the current influence factor data as a predicted accident according to the mapping relation, and determining the probability of the predicted accident corresponding to the current influence factor data based on a preset probability prediction model. The data of the influence factors comprise temperature, weather, time, date and the like, different influence factors correspond to the traffic accident, although the influence factors correspond to the traffic accident, the influence factors can be mapped to the corresponding traffic accident, and actually, the influence privacy is in an and relation, not in an or relation. The step mainly comprises the steps of determining a traffic accident (predicted accident) according to relevant information such as environment, time and the like, wherein the accident can be predicted according to data because the environment and the time can be predicted through a sensor or weather forecast and the like, and can be predicted according to current data, the prediction result is to reflect the type of the accident, and the probability of the accident can be determined through a probability prediction model.
S103, determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the probability of the predicted accident and the alarm strategy. The police-out strategy is how many people need to go to the road section for duty, for example, when the predicted accident is large, more staff need to be on duty, the police-out strategy is that most people go to duty, and conversely, when the accident is small, less people need to be on duty.
Preferably, the probabilistic predictive model may be determined by:
establishing a probability prediction initial model, wherein the probability prediction initial model takes influence factor data as input and takes the probability of an accident as output; and acquiring historical data containing historical influence factor data and the probability of the historical accident, and training the probability prediction initial model based on the acquired historical data to obtain a trained probability prediction model.
Preferably, the determining and executing the current alarm policy corresponding to the predicted accident according to the preset accident and the corresponding relationship between the probability of the predicted accident and the alarm policy may include:
and when the probability of the predicted accident is more than 20% of a preset probability threshold, calculating the difference between the probability of the predicted accident and the preset probability threshold, and determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the difference and the alarm strategy. When the probability of the accident occurrence of the road section predicted by the big data exceeds 20%, the proportion of the probability exceeding a preset probability threshold value needs to be calculated, and a final strategy is determined according to the corresponding relation between the numerical value, the accident size and the alarm-giving strategy.
Preferably, the alarm policy is configured to be related to the number of configured alarm staff; wherein, different polices are corresponded to different strategies by different alarm output numbers, and can be preset.
The accident is configured to be associated with an casualty condition of the accident;
and, the determining and executing the current alarm strategy corresponding to the predicted accident and the difference value according to the preset corresponding relationship between the accident and the difference value and the alarm strategy comprises:
determining a current incident weight value reflecting the incident and the difference value, e.g. a weight value of 1, 2 or 3; and
and determining and executing the current alarm strategy corresponding to the current accident weight according to the corresponding relation between the preset accident weight and the alarm strategy. If the weight is 1, the alarm strategy is 5 people, the weight is 2, the alarm strategy is 10 people, the weight is 3, and the alarm strategy is 15 people.
Preferably, the influencing factor data may include: and environmental influence factor data and time influence factors are acquired through a preset environmental data sensor.
In addition, as shown in fig. 2, the present invention further provides a big data based incident analysis decision system, which may include:
the relation determining unit is used for determining a one-to-one mapping relation between various traffic accidents and various influence factor data aiming at the pre-analyzed road section;
the probability accident determining unit is used for acquiring current influence factor data of a road section, determining a traffic accident corresponding to the current influence factor data as a predicted accident according to the mapping relation, and determining the probability of the predicted accident corresponding to the current influence factor data based on a preset probability prediction model; and
and the strategy determining unit is used for determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the probability of the predicted accident and the alarm strategy.
Preferably, the probabilistic predictive model is determined by:
the model establishing module is used for establishing a probability prediction initial model, wherein the probability prediction initial model takes influence factor data as input and takes the probability of an accident as output; and
and the model determining module is used for acquiring historical data containing historical influence factor data and the probability of the historical accidents, and training the probability prediction initial model based on the acquired historical data to obtain a trained probability prediction model.
Preferably, the policy determining unit is configured to, when the probability of the predicted accident is greater than a preset probability threshold, calculate a difference between the probability of the predicted accident and the preset probability threshold, and determine and execute a current alarm policy corresponding to the predicted accident according to a preset accident and a corresponding relationship between the difference and the alarm policy.
In addition, the invention also provides a machine-readable storage medium, which stores instructions for causing a machine to execute the big data accident analysis decision-making method.
In addition, the present invention also provides a processor for executing a program, wherein the program is executed to perform: the big data accident analysis decision-making method is described above.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that 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 identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A big data based accident analysis decision method is characterized by comprising the following steps:
determining a one-to-one mapping relation between various traffic accidents and various influence factor data aiming at the pre-analyzed road section;
acquiring current influence factor data of a road section, determining a traffic accident corresponding to the current influence factor data as a predicted accident according to the mapping relation, and determining the probability of the predicted accident corresponding to the current influence factor data based on a preset probability prediction model; and
and determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the probability of the predicted accident and the alarm strategy.
2. The big-data-based incident analysis decision method according to claim 1, wherein the probabilistic predictive model is determined by:
establishing a probability prediction initial model, wherein the probability prediction initial model takes influence factor data as input and takes the probability of an accident as output; and
acquiring historical data containing historical influence factor data and the probability of the historical accident, and training the probability prediction initial model based on the acquired historical data to obtain a trained probability prediction model.
3. The big data accident analysis and decision method according to claim 1, wherein the determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relationship between the probability of the predicted accident and the alarm strategy comprises:
and when the probability of the predicted accident is greater than a preset probability threshold, calculating the difference between the probability of the predicted accident and the preset probability threshold, and determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the difference and the alarm strategy.
4. The big-data-based incident analysis decision method according to claim 3,
the alarm policy is configured to correlate to a number of police officers of the configured alarm;
the accident is configured to be associated with an casualty condition of the accident;
and, the determining and executing the current alarm strategy corresponding to the predicted accident and the difference value according to the preset corresponding relationship between the accident and the difference value and the alarm strategy comprises:
determining a current accident weight value reflecting the accident and the difference value; and
and determining and executing the current alarm strategy corresponding to the current accident weight according to the corresponding relation between the preset accident weight and the alarm strategy.
5. The big-data-based incident analysis decision method according to claim 1, wherein the influencing factor data comprises: and environmental influence factor data and time influence factors are acquired through a preset environmental data sensor.
6. A big-data-based incident analysis decision system, comprising:
the relation determining unit is used for determining a one-to-one mapping relation between various traffic accidents and various influence factor data aiming at the pre-analyzed road section;
the probability accident determining unit is used for acquiring current influence factor data of a road section, determining a traffic accident corresponding to the current influence factor data as a predicted accident according to the mapping relation, and determining the probability of the predicted accident corresponding to the current influence factor data based on a preset probability prediction model; and
and the strategy determining unit is used for determining and executing the current alarm strategy corresponding to the predicted accident according to the preset accident and the corresponding relation between the probability of the predicted accident and the alarm strategy.
7. The big-data-based incident analysis decision system according to claim 6, wherein the probabilistic predictive model is determined by:
the model establishing module is used for establishing a probability prediction initial model, wherein the probability prediction initial model takes influence factor data as input and takes the probability of an accident as output; and
and the model determining module is used for acquiring historical data containing historical influence factor data and the probability of the historical accidents, and training the probability prediction initial model based on the acquired historical data to obtain a trained probability prediction model.
8. The big data accident analysis and decision system according to claim 6, wherein the policy determination unit is configured to calculate a difference between the probability of the predicted accident and a preset probability threshold when the probability of the predicted accident is greater than the preset probability threshold, and determine and execute a current alarm policy corresponding to the predicted accident according to the preset accident and a corresponding relationship between the difference and the alarm policy.
9. A machine-readable storage medium having stored thereon instructions for causing a machine to perform the big data incident analysis decision-making method of any of claims 1-5.
10. A processor configured to execute a program, wherein the program is configured to perform: a big data incident analysis decision-making method as claimed in any one of claims 1 to 5.
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