CN113610309B - Fire station site selection method and device based on big data and artificial intelligence - Google Patents
Fire station site selection method and device based on big data and artificial intelligence Download PDFInfo
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Abstract
The invention discloses a fire station site selection method and a fire station site selection device based on big data and artificial intelligence, wherein the method comprises the following steps: acquiring fire-fighting alarm condition data, meteorological data and social data of an area to be addressed, which comprises a plurality of subareas; performing multi-source data fusion and correlation analysis on fire fighting alarm data and meteorological data and social data respectively to establish a time series model and a geographic space regression model; coupling and superposing meteorological factors and social factors according to the model, and establishing a safety risk matrix of each subarea in the area to be addressed; and segmenting time, predicting the risk of the safety risk matrix and each segment of time based on the Bayesian network to determine the safety risk probability of each partition, and comprehensively determining the optimal fire station address based on the safety risk probability, the real-time road condition and the boundary which can be reached by fire fighting force in the preset time. The method can use big data and artificial intelligence to enable fire-fighting site selection decision, and improve the rationality of site selection of the fire-fighting station.
Description
Technical Field
The invention relates to the technical field of big data service, in particular to a fire station site selection method and device based on big data and artificial intelligence.
Background
With the rapid development of social economy, enterprises producing new energy, new materials, new processes and the like are increased continuously, so that economic benefits are brought, meanwhile, products produced by the enterprises bring more fire hazards, the occurrence frequency of urban fires is increased continuously, losses caused by the fires are increased continuously, and in order to prevent and reduce the urban fires and protect urban fire safety, some fire stations are required to be built in cities.
However, with the diversification of fire fighting alarms, fire suppression often only occupies 1/3 or even less in different urban police missions, and along with the development of economic society, the proportion of emergency rescue and social rescue increases day by day, so the fire station based on fire suppression construction can not satisfy diversified fire fighting alarm requirements, the rationality of fire station site selection is poor, and the overall situation, the scientificity and the practicability of the site selection layout of the fire station need to be improved urgently.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the invention aims to provide a fire station site selection method based on big data and artificial intelligence, which can utilize the big data and the artificial intelligence to enable fire station site selection decisions, meet diversified fire alarm requirements, improve the rationality of site selection and effectively protect the life and property safety of people.
The invention also aims to provide a fire station site selection device based on big data and artificial intelligence.
In order to achieve the above purpose, an embodiment of the invention provides a fire station site selection method based on big data and artificial intelligence, which comprises the following steps: acquiring fire alarm condition data, meteorological data and social data of an area to be addressed, wherein the area to be addressed comprises a plurality of subareas; performing multi-source time data fusion on the fire-fighting alarm condition data and the meteorological data, and performing correlation analysis on the fire-fighting alarm condition and meteorological factors to establish a time series model; performing multi-source spatial data fusion on the fire fighting alarm data and the social data, and performing correlation analysis on the fire fighting alarm and social factors to establish a geographic spatial regression model; coupling and superposing the meteorological factors and the social factors according to the time sequence model and the geographic space regression model, and establishing a safety risk matrix of each subarea in the area to be addressed; and segmenting time, predicting the risk of the safety risk matrix and each segment of time based on a Bayesian network to determine the safety risk probability of each subarea, determining the subarea with the safety risk probability greater than the preset probability, and determining the optimal fire station address according to the real-time road condition and the reachable boundary of the fire fighting force in the preset time.
According to the fire station site selection method based on big data and artificial intelligence, fire fighting alarm situation data including fire suppression, rescue and social aid are obtained through the big data, multi-source data fusion is carried out based on the artificial intelligence, the fire fighting, the rescue and the aid serve as integral dependent variables, site selection of the fire station is comprehensively considered in combination with road condition time, and therefore the big data and the artificial intelligence are used for enabling decisions for fire fighting site selection, diversified fire fighting alarm situation requirements are met, rationality of site selection is improved, and life and property safety of people is effectively protected.
In addition, the fire station site selection method based on big data and artificial intelligence according to the above embodiment of the present invention may further have the following additional technical features:
further, the multi-source time data fusion is performed on the fire alarm data and the meteorological data, and the correlation analysis of the fire alarm and the meteorological factors is performed to establish a time series model, which includes: carrying out variance analysis and multi-source regression analysis on the fusion data to establish a machine learning model; the monthly degrees of the four seasons of the area to be addressed are subdivided according to a fire-fighting alarm receiving rule, the hysteresis influence of the meteorological factors on the fire-fighting alarm condition is analyzed, and the machine learning model is adjusted and optimized according to daily degree, weekly degree and monthly degree data; and performing ten-fold cross validation based on the adjusted and optimized machine learning model, and establishing the time series model according to the selected optimal solution.
Further, the multi-source spatial data fusion is performed on the fire alarm data and the social data, and the correlation analysis between the fire alarm and the social factors is performed to establish a geospatial regression model, which includes: carrying out normalization processing on the fire alarm situation data and the social data so as to carry out multi-source spatial data fusion; and performing correlation analysis and prediction on the fused multi-source spatial data to obtain a spatial autocorrelation evolution rule, and establishing a geospatial regression model according to the spatial autocorrelation evolution rule.
Further, the coupling and superposition of the meteorological factors and the social factors according to the time series model and the geospatial regression model to establish a security risk matrix of each partition in the to-be-addressed area includes: fusing the fire alarm data, the meteorological data and the social data into each subarea; and normalizing the data in each partition to establish a security risk matrix.
Further, the fire fighting alarm data comprises fire fighting data, emergency rescue data and social rescue data; the meteorological data comprises one or more items of associated data of temperature, relative humidity, rainfall, average wind speed, instantaneous maximum wind speed and sunshine duration; the social data comprises one or more items of associated data of resident population, floating population, economic development level, power consumption, road network road condition, traffic flow, population density, income condition and education degree.
In order to achieve the above object, an embodiment of another aspect of the present invention provides a fire station site selection device based on big data and artificial intelligence, including: the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring fire alarm condition data, meteorological data and social data of an area to be addressed, and the area to be addressed comprises a plurality of subareas; the first modeling module is used for carrying out multi-source time data fusion on the fire-fighting alarm condition data and the meteorological data and carrying out correlation analysis on the fire-fighting alarm condition and the meteorological factors so as to establish a time series model; the second modeling module is used for carrying out multi-source spatial data fusion on the fire fighting alarm data and the social data and carrying out correlation analysis on the fire fighting alarm and social factors so as to establish a geographic spatial regression model; the processing module is used for coupling and superposing the meteorological factors and the social factors according to the time sequence model and the geographic space regression model, and establishing a safety risk matrix of each subarea in the to-be-addressed area; and the optimization module is used for segmenting time, predicting the risk of the safety risk matrix and each segment of time based on the Bayesian network so as to determine the safety risk probability of each subarea, determining the subarea with the safety risk probability greater than the preset probability, and determining the optimal fire station address based on the real-time road condition and the reachable boundary of the fire fighting force in the preset time.
According to the fire station site selection device based on the big data and the artificial intelligence, fire fighting alarm situation data including fire suppression, rescue and social aid are obtained through the big data, multi-source data fusion is carried out based on the artificial intelligence, the fire fighting, the rescue and the aid are taken as integral dependent variables, and site selection of the fire station is comprehensively considered in combination with road condition time, so that the big data and the artificial intelligence are used for enabling decisions for fire fighting site selection, diversified fire fighting alarm situation requirements are met, the rationality of site selection is improved, and the life and property safety of people is effectively protected.
In addition, the fire station site selection device based on big data and artificial intelligence according to the above embodiment of the present invention may also have the following additional technical features:
further, the first modeling module is further used for performing variance analysis and multi-source regression analysis on the fusion data to establish a machine learning model; the monthly degrees of the four seasons of the area to be addressed are subdivided according to a fire-fighting alarm receiving rule, the hysteresis influence of the meteorological factors on the fire-fighting alarm condition is analyzed, and the machine learning model is adjusted and optimized according to daily degree, weekly degree and monthly degree data; and performing ten-fold cross validation based on the adjusted and optimized machine learning model, and establishing the time series model according to the selected optimal solution.
Further, the second modeling module is further used for carrying out normalization processing on the fire-fighting alarm data and the social data so as to carry out multi-source spatial data fusion; and performing correlation analysis and prediction on the fused multi-source spatial data to obtain a spatial autocorrelation evolution rule, and establishing a geospatial regression model according to the spatial autocorrelation evolution rule.
Further, the processing module further fuses the fire alarm data, the meteorological data and the social data into each zone; and normalizing the data in each partition to establish a security risk matrix.
Further, the fire fighting alarm data comprises fire fighting data, emergency rescue data and social rescue data; the meteorological data comprises one or more items of associated data of temperature, relative humidity, rainfall, average wind speed, instantaneous maximum wind speed and sunshine duration; the social data comprises one or more items of associated data of resident population, floating population, economic development level, power consumption, road network road conditions, traffic flow, population density, income condition and education degree.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a big data and artificial intelligence based fire station addressing method according to an embodiment of the present invention;
FIG. 2 is a flow diagram of a big data and artificial intelligence based fire station addressing method according to one embodiment of the present invention;
FIG. 3 is a block diagram of a big data and artificial intelligence based fire station addressing device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The present invention is based on the recognition and discovery by the inventors of the following problems:
fire fighting and police situations are mainly divided into fire suppression, emergency rescue and social rescue. At present, the main object of fire fighting research is still fire, and the research on emergency rescue and social rescue is less. Related researches mainly focus on individual scenes in emergency rescue and social rescue, such as fire-fighting social rescue warnings of traffic accident rescue and suicide. From the related studies, there are mainly the following problems:
(1) insufficient knowledge of evolution law of rescue and rescue demands
From the current research, the influence of meteorological and social factors on fire is researched more, but the influence is generally researched based on statistical data, and no fine granularity analysis is carried out on day or accurate police-out time data. Under the condition that emergency rescue and social rescue occupy fire-fighting mainstream alarm receiving work, the influence rule of meteorological and social factors on the fire-fighting mainstream alarm receiving work is lacked to carry out overall or classified research.
(2) Quantitative influence of environmental and social factors on fire fighting conditions awaits research
The related research results exist in the same research direction, and the opposite conclusion is drawn. Among the complex factors affecting the occurrence of fire, the social and economic development and climate change have great influence on the fire. Family income, building characteristics, population quality, urbanization rate, poor phenomenon, private enterprise proportion, education degree and the like all can generate obvious influence on the fire.
In general, although some progress has been made in the research related to urban fire-fighting and police, there still exists a certain gap, which is as follows:
first, the above studies cover social factors or climatic factors. However, although the fire is closely related to the time-space factor, documents for applying the time-space analysis technology to research the fire change rule are not available. Social and economic conditions are different at different times and different regions, climate change has obvious regional difference, the relation between climate change and fire risks is possibly different, and the fire needs to be analyzed by combining space-time elements. The urban fire fighting face has wide warning coverage, is influenced by social, economic and climatic factors, and has randomness and uncertainty in disaster occurrence. With the rapid development of economy and global climate change, academic research should consider the composite impact of social development and climate change on urban disaster accidents.
Secondly, most of the existing research on the influence of the spatio-temporal factors on the city is based on the analysis on a certain city or region, the overall statistical data of the fire fighting yearbook is used, the granularity of the data is coarse, no daily, weekly and monthly data exist, the data does not have detailed time, address and event type for receiving the alarm, the specific evolution mechanism of the fire fighting alarm cannot be quantitatively analyzed and verified, and little research is carried out on the spatial metering analysis of the influence of the large-scale spatio-temporal factors on the city alarm.
Third, the literature has many researches on fire suppression in fire fighting and police situations, but fire suppression only accounts for 1/3 in different urban police missions, and with the development of economic society, the proportion of emergency rescue and social rescue is increasing day by day, and there is a need to consider fire suppression, emergency rescue and social rescue in academic research.
In the embodiment of the invention, the influence of the space-time factors on the urban fire under the influence of rapid economic development and climate change is analyzed by using a statistical method.
The method and the device for locating the fire station based on big data and artificial intelligence according to the embodiment of the invention will be described with reference to the accompanying drawings, and firstly, the method for locating the fire station based on big data and artificial intelligence according to the embodiment of the invention will be described with reference to the accompanying drawings.
FIG. 1 is a flow chart of a big data and artificial intelligence based fire station addressing method according to an embodiment of the invention.
As shown in FIG. 1, the fire station site selection method based on big data and artificial intelligence comprises the following steps:
in step S101, fire alarm data, meteorological data, and social data of an area to be addressed are obtained, where the area to be addressed includes a plurality of partitions.
It can be understood that the embodiment of the invention can obtain historical data of the area to be addressed in the past period of time based on big data, for example, fire alarm data, meteorological data, social data and the like in the last 5 years can be selected.
Wherein, the area to be selected is an urban area.
The fire fighting and warning data comprises fire fighting and rescue data, emergency rescue data and social rescue data; the meteorological data comprises one or more items of associated data of temperature, relative humidity, rainfall, average wind speed, instantaneous maximum wind speed and sunshine duration; the social data comprises one or more items of associated data of resident population, floating population, economic development level, power consumption, road network road conditions, traffic flow, population density, income condition and education degree.
In the embodiment of the present invention, different attributes of the area to be addressed may be subdivided into grids of a preset size, for example, the area may be subdivided into grids of 300-500m, so as to ensure that each grid can distinguish different attributes. Wherein each grid is a partition; the size of each partition may be the same or different.
In step S102, multi-source time data fusion is performed on fire fighting alarm data and meteorological data, and correlation analysis of fire fighting alarm and meteorological factors is performed to establish a time series model.
In the embodiment of the present invention, step S102 includes: carrying out variance analysis and multiple regression analysis on the fusion data to establish a machine learning model; the monthly degrees of the four seasons of the area to be addressed are subdivided according to the fire-fighting alarm receiving rule, the hysteresis influence of meteorological factors on the fire-fighting alarm condition is analyzed, and the machine learning model is adjusted and optimized according to the daily degree, the week degree and the monthly data; and performing ten-fold cross validation based on the adjusted and optimized machine learning model, and establishing a time series model according to the selected optimal solution.
Specifically, in the research of meteorological factors and fire fighting conditions, (1) multi-source data fusion is carried out on factors such as temperature, relative humidity, rainfall, average wind speed, instantaneous maximum wind speed, sunshine duration and the like and alarm receiving data of fire suppression, rescue and social aid; (2) analyzing the correlation, analyzing the possible hysteresis influence of the weather on fire fighting and police situations, and performing variance analysis and multi-source regression analysis modeling on the fusion data; (3) adjusting and optimizing the model according to daily, weekly and monthly data, repartitioning the monthly data of the four seasons according to the law of receiving fire alarms, using machine learning ten-fold cross validation to establish the model, comparing decision trees, bagging, random forests, support vector machines, neural networks and other methods, selecting an optimal solution, and using a time sequence model to predict the future trend.
In step S103, multi-source spatial data fusion is performed on fire fighting alarm data and social data, and correlation analysis between fire fighting alarm and social factors is performed to establish a geospatial regression model.
In the embodiment of the present invention, step S103 includes: carrying out normalization processing on fire alarm situation data and social data to carry out multi-source spatial data fusion; and performing correlation analysis and prediction on the fused multi-source spatial data to obtain a spatial autocorrelation evolution rule, and establishing a geospatial regression model according to the spatial autocorrelation evolution rule.
Specifically, in the research of social factors and fire-fighting alarms, the embodiment of the invention can combine the map-related data to obtain the social factor indexes of a certain city, such as: the method comprises the steps of fusing data such as resident population, floating population, economic development level, power consumption, road network road conditions and traffic flow, population density, income condition, education degree and the like with fire-fighting alarm receiving and processing data, carrying out normalization processing on the data, carrying out correlation analysis and prediction by using a Bayes or neural network algorithm, analyzing a spatial autocorrelation evolution rule, establishing a geospatial regression model, and carrying out causal reasoning on the relation between fire-fighting alarm conditions and social factors.
In step S104, coupling and superimposing the meteorological factors and the social factors according to the time series model and the geospatial regression model, and establishing a security risk matrix of each partition in the area to be addressed.
In the embodiment of the present invention, step S104 includes: fusing fire-fighting alarm data, meteorological data and social data into each subarea; and normalizing the data in each partition to establish a security risk matrix.
Specifically, the embodiment of the invention can dynamically research the influence of social factors on fire-fighting warning situations and the comprehensive effect of meteorological factors and social factors on fire-fighting warning situations from a microscopic level. Selecting a large node province city in the middle of the city, fusing multi-source data such as population flow data, temperature, humidity, wind speed and the like into a grid, carrying out normalization processing on the data, and establishing a security risk matrix.
In step S105, time is segmented, the safety risk matrix and the risk prediction are performed for each segment of time based on the bayesian network to determine the safety risk probability of each segment, the segment with the safety risk probability greater than the preset probability is determined, and the optimal fire station address is determined based on the real-time road condition and the reachable boundary of the fire fighting power within the preset time.
The preset time is, for example, 5 minutes or 6 minutes, and the example of this embodiment and the following embodiments takes 5 minutes as an example. For example, the embodiment of the invention can comprehensively determine the optimal fire station address according to the subarea with the safety risk probability larger than the preset probability, the real-time road condition as the basis and the boundary criterion that the 5-minute fire fighting force can reach.
It is understood that the time may be segmented according to actual requirements, for example, 24 hours per day may be divided into one time segment every two hours, and the like. After segmentation, the embodiment of the invention can calculate the probability of the security risk from two dimensions of time and space based on the Bayesian network, take the security risk as an input item, then select some specific grids, verify the grids by using real alarm receiving data, and modify the model; and selecting another representative city, verifying the model again, and searching a general regional model.
The selection of some specific grids may be determined based on the safety risk probability, for example, the safety risk probability is greater than the preset probability, and the preset probability may be specifically set according to the actual selection requirement.
Furthermore, the embodiment of the invention can also research the spatial autocorrelation and the spatial diversity, the multivariable is uniformly distributed in a two-dimensional space, the causal relationship between the dependent variable and the independent variable is obtained, and the causal relationship test is carried out by using the Granger causal relationship test to carry out causal analysis and determine the hysteresis order. Simulating a position model by using a genetic algorithm, and solving a path selection problem of fire fighting team police-out by using a developed path optimization algorithm; analyzing the management strategy of site selection of the fire station and the fire truck police-out path planning by applying the proposed model; the method comprises the steps of performing simulation of an addressing model by using a genetic algorithm, solving the problem of fire fighting team police-out path selection by using a path optimization algorithm, performing modeling analysis on fire fighting safety risks by using methods such as GTWR (general transient wave response) and GWR (global wire-weighted response) and the like, selecting an optimal solution to participate in a geographical weighted regression model, and finally constructing a disaster risk situation evaluation model by using a risk evaluation index system and a design of a hierarchical quantitative standard, a risk dynamic evaluation model and a safety situation index model.
The fire station site selection method based on big data and artificial intelligence will be explained by a specific embodiment, as shown in fig. 2, specifically as follows:
firstly, fire fighting alarm data such as fire suppression, emergency rescue, social rescue and the like are obtained, and the data are cleaned and mined. Because the existing fire fighting data is generally based on the annual identification of fire fighting, the granularity is relatively coarse, only summary statistical data divided into provinces and cities are available, and the time, the place, the specific event condition, the police-out mid-team, the disposal type and the like which are necessary for each fire fighting team to take the police are not available; the data of the fire-fighting alarm receiving system can provide the real-time alarm receiving condition, and can be fused with other data to carry out data mining; the economic development is changed from low-speed growth to high-speed growth to a high-quality development stage, the economic speed-increasing and gear-shifting fall-back, the high-speed growth of about 10 percent in the past is changed into the medium-high speed growth of 7 to 8 percent, which is the most basic characteristic of a new normal state, and the development of the two-three-line city economic society in the last 5 years enters a related stable development stage; therefore, the embodiment of the invention can select the fire-fighting alarm receiving data of about 5 years to be fused with the meteorological data, and can avoid the influence of social factors on the alarm receiving data to the maximum extent.
Secondly, researching from a macroscopic level, in the research of meteorological factors and fire fighting conditions, carrying out multisource data fusion on factors such as temperature, relative humidity, rainfall, average wind speed, instantaneous maximum wind speed, sunshine duration and the like and fire fighting data of fire suppression, rescue and social assistance, analyzing correlation, analyzing possible hysteresis influence of the meteorological conditions on the fire fighting conditions, carrying out variance analysis and multiple regression analysis modeling on the fused data, adjusting and optimizing the model according to day and week month data, subdividing the month of the local four seasons according to the fire fighting receiving rule, using a machine to learn ten-fold cross validation, establishing the model, comparing decision trees, bagging, random forests, support vector machines, neural networks and other methods, selecting an optimal solution, and using a time sequence model to predict future trends. In the research of social factors and fire fighting alarms, data such as floating population, population density, generated energy and the like are normalized, multi-source time-space data fusion is carried out, a spatial autocorrelation evolution law is analyzed, a geographic space regression model is established, and causal reasoning is carried out on the relationship between the fire fighting alarms and the social factors.
And finally, dynamically researching the influence of social factors on the fire-fighting warning condition and the coupling effect of the meteorological factors and the social factors on the fire-fighting warning condition on a microscopic level. Selecting a large node province city in the middle of the city, subdividing the city into 300-plus-500 m grids according to different attributes to ensure that each grid can distinguish different attributes, fusing multi-source data such as population flow data, temperature, humidity, wind speed and the like into the grids, carrying out normalization processing on the data, establishing a safety risk matrix, dividing 24 hours per day into two time intervals every two hours, calculating safety risk probability from two dimensions of time and space based on a Bayesian network, taking the safety risk as an input item, then selecting some specific grids, verifying the grids by using real alarm receiving data, and correcting the model. And selecting another representative city, verifying the model again, and searching the general regional model. And optimizing the site selection of the fire station and the police resource allocation by using research results, analyzing the difference trend of the alarm situations among cities, and providing an improvement suggestion for fire-fighting alarm receiving and processing decisions.
In summary, the current research of fire-fighting alarm conditions and the site selection of the fire station based on the fire-fighting alarm conditions are mainly based on fire data, and from the fire-fighting alarm condition, the fire only accounts for 1/3 of all the alarm conditions, and from the fire-fighting development rule, the proportion of the fire will be gradually reduced to about 10%, so that the research of the evolution rule is carried out by taking the fire as the reference, and the site selection of the fire station is determined to be not in accordance with the era development. Meanwhile, with the continuous acceleration of the urbanization process, the method for selecting the site by taking the fire station as the center and the protection radius of the square circle and the 7KM cannot meet the requirement, the development of big data and artificial intelligence can enable the decision of selecting the site for fire fighting, and the site selection optimization method based on the fire fighting power alarm receiving arrival in 5 minutes based on the real-time road condition data can greatly improve the embarrassment situation faced by the existing fire fighting team, and greatly improve and protect the safety of lives and properties of people.
According to the fire station site selection method based on big data and artificial intelligence provided by the embodiment of the invention, fire fighting alarm situation data including fire suppression, rescue and social aid are obtained by using the big data, multi-source data fusion is carried out based on the artificial intelligence, the fire fighting, the rescue and the aid are taken as integral dependent variables, and site selection of the fire station is comprehensively considered by combining road condition time, so that diversified fire fighting alarm situation requirements are met for fire fighting site selection decision by using the big data and the artificial intelligence, the rationality of site selection is improved, and energized life and property safety of people is effectively protected.
Next, a fire station site selection device based on big data and artificial intelligence according to an embodiment of the invention will be described with reference to the accompanying drawings.
FIG. 3 is a block diagram of a big data and artificial intelligence based fire station addressing device according to an embodiment of the present invention.
As shown in fig. 3, the fire station site selection device 10 based on big data and artificial intelligence comprises: an acquisition module 100, a first modeling module 200, a second modeling module 300, a processing module 400, and an optimization module 500.
The acquisition module 100 is used for acquiring fire alarm data, meteorological data and social data of an area to be addressed, wherein the area to be addressed comprises a plurality of subareas; the first modeling module 200 is used for performing multi-source time data fusion on fire-fighting alarm condition data and meteorological data, and performing correlation analysis on the fire-fighting alarm condition and meteorological factors to establish a time series model; the second modeling module 300 is used for performing multi-source spatial data fusion on the fire-fighting alarm condition data and the social data, and performing correlation analysis on the fire-fighting alarm condition and the social factors to establish a geospatial regression model; the processing module 400 is configured to perform coupling superposition on meteorological factors and social factors according to the time series model and the geospatial regression model, and establish a security risk matrix of each partition in the area to be addressed; the optimization module 500 is configured to segment time, perform risk prediction on the security risk matrix and each segment of time based on the bayesian network to determine the security risk probability of each partition, determine the partition with the security risk probability greater than the preset probability, and determine the optimal fire station address based on the real-time road condition and the reachable boundary of the fire protection strength within the preset time.
Further, the first modeling module 200 is further configured to perform variance analysis and multi-source regression analysis on the fusion data to build a machine learning model; the monthly degrees of the four seasons of the area to be addressed are subdivided according to the fire-fighting alarm receiving rule, the hysteresis influence of meteorological factors on the fire-fighting alarm condition is analyzed, and the machine learning model is adjusted and optimized according to the daily degree, the week degree and the monthly data; and performing ten-fold cross validation based on the adjusted and optimized machine learning model, and establishing a time series model according to the selected optimal solution.
Further, the second modeling module 300 is further configured to perform normalization processing on the fire alarm data and the social data to perform multi-source spatial data fusion; and performing correlation analysis and prediction on the fused multi-source spatial data to obtain a spatial autocorrelation evolution rule, and establishing a geospatial regression model according to the spatial autocorrelation evolution rule.
Further, the processing module 400 further fuses fire alarm data, meteorological data, and social data into each partition; and normalizing the data in each partition to establish a security risk matrix.
Further, the fire fighting alarm data comprises fire fighting data, emergency rescue data and social rescue data; the meteorological data comprises one or more items of associated data of temperature, relative humidity, rainfall, average wind speed, instantaneous maximum wind speed and sunshine duration; the social data comprises one or more items of associated data of resident population, floating population, economic development level, power consumption, road network road conditions, traffic flow, population density, income condition and education degree.
It should be noted that the foregoing explanation on the embodiment of the fire station site selection method based on big data and artificial intelligence is also applicable to the fire station site selection device based on big data and artificial intelligence of this embodiment, and details are not described here.
According to the fire station site selection device based on big data and artificial intelligence provided by the embodiment of the invention, fire fighting alarm situation data including fire suppression, rescue and social aid are obtained by using the big data, multi-source data fusion is carried out based on the artificial intelligence, the fire fighting, the rescue and the aid are taken as integral dependent variables, and site selection of the fire station is comprehensively considered by combining road condition time, so that diversified fire fighting alarm situation requirements are met for fire fighting site selection decision by using the big data and the artificial intelligence, the rationality of site selection is improved, and energized life and property safety of people is effectively protected.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (4)
1. A fire station site selection method based on big data and artificial intelligence is characterized by comprising the following steps:
acquiring fire alarm condition data, meteorological data and social factor data of an area to be addressed, wherein the area to be addressed comprises a plurality of grids with different attributes and preset sizes;
performing multi-source time data fusion on the fire fighting alarm condition data and the meteorological data, and establishing a machine learning model; the method comprises the steps of subdividing monthly degrees according to a fire-fighting alarm receiving rule, analyzing the hysteresis influence of meteorological factors on fire-fighting alarm conditions, adjusting and optimizing a machine learning model according to daily degree, weekly degree and monthly degree data, establishing a time sequence model, introducing fire fighting alarm conditions such as fire suppression, emergency rescue and social rescue into fire-fighting alarm condition prediction, and predicting a future trend;
multi-source spatial data fusion is carried out on the fire fighting alarm condition data and the social factor data, and a spatial evolution rule is researched;
establishing a security risk matrix of each grid in the to-be-addressed area in a space-time dimension according to the time sequence model and the spatial evolution rule; and
segmenting time, carrying out dynamic risk assessment on the safety risk matrix and each segment of time based on a Bayesian network to determine the safety risk probability of each grid, taking the safety risk as an input item, selecting some specific grids, verifying the grids by using real alarm receiving data, modifying a model, simultaneously selecting a representative city to verify the model again, searching a regional general model, taking the dynamic safety risk probability, real-time road conditions and fire fighting force reachable boundaries in preset time as comprehensive judgment bases, thereby integrating meteorological factors, social factors and dynamic data of road network road conditions, comprehensively considering three types of alarms of fire, emergency rescue and social rescue, optimizing fire stations by using the general model, analyzing the alarm condition difference trend among cities and jointly determining the optimal fire station address.
2. The method of claim 1, wherein,
the fire fighting alarm data comprises fire suppression data, emergency rescue data and social rescue data;
the meteorological data comprises one or more items of associated data of temperature, relative humidity, rainfall, average wind speed, instantaneous maximum wind speed, sunshine duration and average water vapor pressure;
the social factor data comprises one or more items of associated data of the standing population, the floating population, the economic development level, the power consumption, the road network road condition, the traffic flow and the population density.
3. The utility model provides a fire station site selection device based on big data and artificial intelligence which characterized in that includes:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring fire alarm condition data, meteorological data and social factor data of an area to be addressed, and the area to be addressed comprises a plurality of grids with different attributes and preset sizes;
the first modeling module is used for carrying out multi-source time data fusion on the fire-fighting alarm data and the meteorological data and establishing a machine learning model; the method comprises the steps of subdividing monthly degrees according to a fire-fighting alarm receiving rule, analyzing the hysteresis influence of meteorological factors on fire-fighting alarm conditions, adjusting and optimizing a machine learning model according to daily degree, weekly degree and monthly degree data, establishing a time sequence model, introducing fire fighting alarm conditions such as fire suppression, emergency rescue and social rescue into fire-fighting alarm condition prediction, and predicting a future trend;
the second modeling module is used for carrying out multi-source spatial data fusion on the fire-fighting alarm data and the social factor data and researching a spatial evolution rule;
the processing module is used for establishing a safety risk matrix of each grid in the to-be-addressed area in a space-time dimension according to the time sequence model and the spatial evolution rule; and
an optimization module for segmenting time, performing dynamic risk assessment on the security risk matrix and each segment of time based on a Bayesian network, determining the safety risk probability of each grid, taking the safety risk as an input item, selecting some specific grids, verifying by using real alarm receiving data, correcting the model, meanwhile, a representative city is selected to carry out verification again on the model, a regional general model is searched, the dynamic safety risk probability, the real-time road condition and the reachable boundary of the fire fighting force within the preset time are taken as the comprehensive judgment basis, thereby integrating the dynamic data of meteorological factors, social factors and road conditions of road networks, comprehensively considering three types of alarms of fire, emergency rescue and social rescue, and optimizing the fire station site selection by using the general model, analyzing the alarm condition difference trend among cities, and jointly determining the optimal fire station address.
4. The apparatus of claim 3, wherein,
the fire fighting alarm data comprises fire suppression data, emergency rescue data and social rescue data;
the meteorological data comprises one or more items of associated data of temperature, relative humidity, rainfall, average wind speed, instantaneous maximum wind speed, sunshine duration and average water vapor pressure;
the social factor data comprises one or more items of associated data of a standing population, a floating population, an economic development level, power consumption, road network conditions, traffic flow and population density.
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