Fire-fighting hidden danger identification method and system based on smart city, medium and equipment

Abstract

The invention provides a fire-fighting hidden danger identification method and system based on a smart city, a medium and equipment, and the method comprises the steps: selecting a key node, and obtaining the completion condition of a sub-event; obtaining the influence of the historical data of the sub-events at the key nodes on the fire-fighting hidden danger, so as to obtain key values of the sub-events; obtaining the influence of the historical data of the sub-events at the different key nodes on the fire-fighting hidden danger, so as to obtain the weight values of the sub-events; fusing the key value and the weight value of the sub-event to obtain an importance degree; obtaining sub-scores based on the importance degree, and fusing all the sub-scores at the key node to obtain a comprehensive score at the key node; based on the comprehensive scores of all the key nodes, a fire safety statistical chart is constructed, the correlation degree between any place of the city and the fire hazard is obtained, the fire hazard degree of one city is comprehensively integrated from the angle of digitization, and then the area with the fire hazard can be safely deployed before the danger occurs.

Description

Technical Field

[0001] The present invention relates to the field of smart fire protection technology, and in particular to a fire hazard identification method, system, medium and equipment based on a smart city. Background Art

[0002] Fires are characterized by high frequency and large time and space spans, and the losses they cause are also very serious. Therefore, fire monitoring and timely alarm of fire incidents are particularly important in people's daily lives.

[0003] Among the existing technologies, smart fire protection has been popularized. Through cameras in various key areas in the city, objects or behaviors with fire hazards are detected, and then the monitoring data is uploaded to the terminal to monitor the hazards. At present, the monitoring of fire hazards is often carried out through visual solutions. This method often takes measures simultaneously after the hazards are detected. It does not make full use of historical data that affects fire hazards to prevent fire hazards in advance, which is not conducive to the development of digital smart fire protection. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a fire hazard identification method based on a smart city, aiming to solve the technical problems mentioned in the background technology.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions: A fire hazard identification method based on smart city, comprising the following steps: Select key nodes in the city and obtain the completion status of fire-related sub-events at key nodes based on the smart city monitoring system; Based on the monitoring system of the smart city, the impact of the historical data of the sub-events at each of the key nodes on the fire hazards is obtained, and the completion status of the sub-events is integrated to obtain the key values ​​of the sub-events at each of the key nodes; Obtaining the impact of historical data of sub-events at different key nodes on the fire hazards to obtain the weight value of each sub-event itself; The key value of the sub-event at each of the key nodes is combined with the weight value of the sub-event itself to obtain the importance of the sub-event at each of the key nodes; Based on the importance of the sub-events at each of the key nodes, a sub-score of each of the sub-events at the key node is obtained, and all the sub-scores at the key node are integrated to obtain a comprehensive score at the key node; Based on the comprehensive scores of all the key nodes, a fire safety statistical map of the city is constructed, and based on the fire safety statistical map, the correlation between any place in the city and the fire hazard is obtained.

[0006] According to one aspect of the above technical solution, the monitoring system based on the smart city obtains the impact of the historical data of the sub-events at each of the key nodes on the fire hazards, and integrates the completion status of the sub-events to obtain the key values ​​of the sub-events at each of the key nodes, specifically including: Define multiple intermediate events under fire hazards as (Q1, Q2, ..., Q n ), and define multiple sub-events as (q1, q2, ..., q m ); wherein n represents the number of the intermediate events, and m represents the number of the sub-events; Obtain each of the sub-events q from the monitoring system of the smart city j The historical data for the corresponding intermediate event Q i The historical estimated probability ζ ij ;i=(1,2,…,n), j=(1,2,…,m); According to the completion of the sub-event, the historical estimated probability ζ ij Optimize to get the current estimated probability Y of the intermediate event ij ; Based on the current estimated probability Y of each intermediate event ij , calculate the probability of occurrence of the fire hazard: ; Among them, θ = (1, 2, ..., v), v represents the total number of key nodes, P (θ) represents the probability of occurrence of fire hazards in the θth key node, q j ∈Q i represents the sub-event j under the intermediate event i, k∈[1,n], q j ∈Q i UQ k represents the sub-event j under the intermediate event i or k; According to the occurrence probability P of the fire hazard, the sub-event q under each intermediate event ij Calculate partial derivatives to obtain the sub-event q under each intermediate event ij The key value E ij ; .

[0007] According to one aspect of the above technical solution, obtaining the influence of the historical data of sub-events at different key nodes on the fire hazards to obtain the weight value of each sub-event itself specifically includes: Obtain historical data on the impact of sub-events at different key nodes on intermediate events from the monitoring system of the smart city, and construct a standardized matrix ζ(θ); ; Among them, nmv represents the historical estimated probability of the intermediate event n caused by the influence of the m sub-event at the key node θ; Calculate the historical estimated probability ζ of each sub-event at different key nodes for different intermediate events nmv The weight of ; Among them, H j represents the entropy of the j-th sub-event, represents the weight of the jth sub-event under the i-th intermediate event in the key node θ; The weight of the corresponding sub-event in each key node Multiply them to obtain the weight value W of each sub-event's influence on each intermediate event. ij .

[0008] According to one aspect of the above technical solution, the formula for fusing the key value of the sub-event at each key node with the weight value of the sub-event itself to obtain the importance of the sub-event at each key node is as follows: W ij ·E ij =G ij ; Among them, G represents importance.

[0009] According to one aspect of the above technical solution, the importance of each sub-event at the key node is used to obtain a sub-score of each sub-event at the key node, and all sub-scores at the key node are integrated to obtain a comprehensive score at the key node, specifically including: Defining a sub-scoring interval O for the importance, and calculating the expected value, entropy value and super entropy value of the sub-event at each of the key nodes according to the sub-scoring interval O; ; ; ; in, Indicates the expected value, represents the entropy value, represents the super entropy value, φ=0.02, and They represent the upper and lower limits of the scoring interval o respectively. represents the order of the normal function in the scoring interval O; Calculate the matching degree ψ of the sub-event in the key node in the sub-scoring interval; ; Merging the matching degrees of all sub-events of the key node in the sub-scoring interval to obtain a total matching degree K; Based on the total matching degree K, a quantitative comprehensive score η is given to the key node; ; Among them, β represents the level of the total matching degree K.

[0010] According to one aspect of the above technical solution, based on the comprehensive scores of all the key nodes, a fire safety statistical map of the city is constructed, and the correlation between any place in the city and the fire hazard is obtained according to the fire safety statistical map, specifically including: According to the city's floor plan, construct the city's kernel density map; ; ; Where f(x) represents the kernel density at position x, represents the position of the θth key node on the kernel density map, T represents the spatial dimension, α represents the bandwidth, and L represents the spatial weight function; Based on the comprehensive scores of all the key nodes, a bandwidth α in the kernel density map is calculated; ; ; Among them, VD represents the weighted standard distance, represents the comprehensive score at the θth key node, { , , } respectively represent the three-axis coordinates of the θth key node, { , , } respectively represent the weighted average center of the three axes after considering the comprehensive score, the kernel density map has multiple field values ​​that convert non-quantitative features into quantitative features, μ represents the sum of all field values ​​in the kernel density map, D m represents the median distance of the weighted average center of the three axes; Substituting the bandwidth α into the kernel density map to obtain a fire safety statistics map of the city; The kernel density of each area in the fire safety statistical diagram is analyzed based on the Moran's index analysis method to obtain the Moran's index of each area. The area where the Moran's index is greater than the preset index is regarded as an area with a high correlation with the fire hazard and is recorded.

[0011] The present invention also provides a fire hazard identification system based on a smart city, comprising: The first acquisition module is used to select key nodes in the city and obtain the completion status of sub-events related to fire protection at the key nodes based on the monitoring system of the smart city; The second acquisition module is used to acquire the impact of the historical data of the sub-events at each of the key nodes on the fire hazard based on the monitoring system of the smart city, and to obtain the key value of the sub-event at each of the key nodes by integrating the completion status of the sub-events; The second acquisition module is specifically used for: Define multiple intermediate events under fire hazards as (Q1, Q2, ..., Q n ), and define multiple sub-events as (q1, q2, ..., q m ); wherein n represents the number of the intermediate events, and m represents the number of the sub-events; Obtain each of the sub-events q from the monitoring system of the smart city j The historical data for the corresponding intermediate event Q i The historical estimated probability ζ ij ;i=(1,2,…,n), j=(1,2,…,m); According to the completion of the sub-event, the historical estimated probability ζ ij Optimize to get the current estimated probability Y of the intermediate event ij ; Based on the current estimated probability Y of each intermediate event ij , calculate the probability of occurrence of the fire hazard: ; Among them, θ = (1, 2, ..., v), v represents the total number of key nodes, P (θ) represents the probability of occurrence of fire hazards in the θth key node, q j ∈Q i represents the sub-event j under the intermediate event i, k∈[1,n], q j ∈Q i UQ k represents the sub-event j under the intermediate event i or k; According to the occurrence probability P of the fire hazard, the sub-event q under each intermediate event ij Calculate partial derivatives to obtain the sub-event q under each intermediate eventij The key value E ij ; ; The third acquisition module is used to acquire the impact of the historical data of sub-events at different key nodes on the fire hazards, so as to obtain the weight value of each sub-event itself; The third acquisition module is specifically used for: Obtain historical data on the impact of sub-events at different key nodes on intermediate events from the monitoring system of the smart city, and construct a standardized matrix ζ(θ); ; Among them, nmv represents the historical estimated probability of the intermediate event n caused by the influence of the m sub-event at the key node θ; Calculate the historical estimated probability ζ of each sub-event at different key nodes for different intermediate events nmv The weight of ; Among them, H j represents the entropy of the j-th sub-event, represents the weight of the jth sub-event under the i-th intermediate event in the key node θ; The weight of the corresponding sub-event in each key node Multiply them to obtain the weight value W of each sub-event's influence on each intermediate event. ij ; Fusion module: used to fuse the key value of the sub-event at each key node with the weight value of the sub-event itself to obtain the importance of the sub-event at each key node; Scoring module: used to obtain the sub-score of each sub-event at the key node based on the importance of the sub-event at each key node, and to integrate all the sub-scores at the key node to obtain a comprehensive score at the key node; The scoring module is specifically used for: Defining a sub-scoring interval O for the importance, and calculating the expected value, entropy value and super entropy value of the sub-event at each of the key nodes according to the sub-scoring interval O; ; ; ; in, Indicates the expected value, represents the entropy value, represents the super entropy value, φ=0.02, and They represent the upper and lower limits of the scoring interval o respectively. represents the order of the normal function in the scoring interval O; Calculate the matching degree ψ of the sub-event in the key node in the sub-scoring interval; ; Merging the matching degrees of all sub-events of the key node in the sub-scoring interval to obtain a total matching degree K; Based on the total matching degree K, a quantitative comprehensive score η is given to the key node; ; Among them, β represents the level of the total matching degree K; Statistics module: used to construct a fire safety statistics map of the city based on the comprehensive scores of all the key nodes, and obtain the correlation between any place in the city and the fire hazards according to the fire safety statistics map; The statistical module is specifically used for: According to the city's floor plan, construct the city's kernel density map; ; ; Where f(x) represents the kernel density at position x, represents the position of the θth key node on the kernel density map, T represents the spatial dimension, α represents the bandwidth, and L represents the spatial weight function; Based on the comprehensive scores of all the key nodes, a bandwidth α in the kernel density map is calculated; ; ; Among them, VD represents the weighted standard distance, represents the comprehensive score at the θth key node, { , , } respectively represent the three-axis coordinates of the θth key node, { , , } respectively represent the weighted average center of the three axes after considering the comprehensive score, the kernel density map has multiple field values ​​that convert non-quantitative features into quantitative features, μ represents the sum of all field values ​​in the kernel density map, D m represents the median distance of the weighted average center of the three axes; Substituting the bandwidth α into the kernel density map to obtain a fire safety statistics map of the city; The kernel density of each area in the fire safety statistical diagram is analyzed based on the Moran's index analysis method to obtain the Moran's index of each area. The area where the Moran's index is greater than the preset index is regarded as an area with a high correlation with the fire hazard and is recorded.

[0012] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the fire hazard identification method based on smart city as described above is implemented.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the fire hazard identification method based on smart city as described above is implemented.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By distinguishing multiple key nodes in the city, regional monitoring is carried out. First, the historical data of the impact of sub-events at key nodes on fire hazards in the area are collected through the monitoring system of the smart city to obtain the key value. Then, the historical data of the impact of sub-events at other key nodes on fire hazards are collected to obtain the weight value. The key value and the weight value are integrated to obtain the importance of the sub-event at the key node. This method takes into account the impact of sub-events in other key nodes on fire hazards, which can avoid the sub-event in a key node from being trapped in the excellence of its own historical data, and more comprehensively reflects the comprehensive importance of the sub-event; then, a sub-score is given to the sub-event in the key node, and then all the sub-scores are integrated to obtain the comprehensive score in the key node area. The comprehensive score reflects the probability of fire hazards in the area. The higher the comprehensive score, the higher the probability of fire hazards; finally, considering the fire hazards of each key node, a fire safety statistical chart is constructed to comprehensively reflect the safety level of each area in a city, that is, the correlation between any place in the city and fire hazards, and then carry out corresponding sub-event rectification according to the safety level of each area; The present invention comprehensively summarizes the degree of fire hazards in a city from a digital perspective, and can then carry out safety deployment in areas with fire hazards before danger occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a fire hazard identification method based on smart city in the first embodiment of the present invention; Figure 2 It is a structural block diagram of a fire hazard identification system based on smart city in the second embodiment of the present invention; Figure 3 is a structural block diagram of an electronic device in a third embodiment of the present invention; The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0016] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0017] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0019] See also Figure 1 , shown is a fire hazard identification method based on smart city in the first embodiment of the present invention, comprising the following steps: S10, selecting key nodes in the city, and obtaining the completion status of sub-events related to fire protection at the key nodes based on the monitoring system of the smart city; S20, based on the monitoring system of the smart city, obtaining the impact of the historical data of the sub-events at each of the key nodes on the fire hazard, and comprehensively considering the completion status of the sub-events to obtain the key value of the sub-event at each of the key nodes; S30, obtaining the impact of historical data of sub-events at different key nodes on the fire hazard to obtain the weight value of each sub-event itself; S40, fusing the key value of the sub-event at each of the key nodes with the weight value of the sub-event itself to obtain the importance of the sub-event at each of the key nodes; S50, based on the importance of each sub-event at the key node, obtaining a sub-score of each sub-event at the key node, and integrating all sub-scores at the key node to obtain a comprehensive score at the key node; S60, constructing a fire safety statistical map of the city based on the comprehensive scores of all the key nodes, and obtaining the correlation between any place in the city and the fire hazard according to the fire safety statistical map.

[0020] It can be understood that the present invention performs zoning monitoring by distinguishing multiple key nodes in the city. First, the monitoring system of the smart city is used to collect historical data on the impact of sub-events at key nodes on fire hazards in the area to obtain key values, and then the historical data on the impact of sub-events at other key nodes on fire hazards are collected to obtain weight values. The key values ​​and weight values ​​are integrated to obtain the importance of sub-events at key nodes. This method takes into account the impact of sub-events in other key nodes on fire hazards, which can avoid that a sub-event in a key node is only trapped in the excellence of its own historical data, and more comprehensively reflects the comprehensive importance of sub-events; then, a sub-score is given to the sub-event in the key node, and then all sub-scores are integrated to obtain a comprehensive score in the key node area. The comprehensive score reflects the probability of fire hazards in the area. The higher the comprehensive score, the higher the probability of fire hazards; finally, a fire safety statistical chart is constructed by considering the fire hazards of each key node to comprehensively reflect the safety level of each area in a city, that is, the correlation between any place in the city and fire hazards, and then the corresponding sub-event rectification is carried out according to the safety level of each area; The present invention comprehensively summarizes the degree of fire hazards in a city from a digital perspective, and can then carry out safety deployment in areas with fire hazards before danger occurs.

[0021] Among them, key nodes include places prone to fire, such as schools, factories, and hospitals. The completion status of sub-events includes the number of flammable materials, the inspection of fire-fighting facilities, the use of fire-proof materials at key nodes, the coverage of safety publicity, etc. With the popularization of smart fire protection, the completion status of these sub-events can be obtained from the monitoring system of smart cities; Furthermore, the specific steps of step S20 include: Define multiple intermediate events under fire hazards as (Q1, Q2, ..., Q n ), and define multiple sub-events as (q1, q2, ..., q m ); wherein n represents the number of the intermediate events, and m represents the number of the sub-events; Obtain each of the sub-events q from the monitoring system of the smart city j The historical data for the corresponding intermediate event Q i The historical estimated probability ζ ij ;i=(1,2,…,n), j=(1,2,…,m); According to the completion of the sub-event, the historical estimated probability ζ ij Optimize to get the current estimated probability Y of the intermediate event ij ; Based on the current estimated probability Y of each intermediate event ij , calculate the probability of occurrence of the fire hazard: ; Among them, θ = (1, 2, ..., v), v represents the total number of key nodes, P (θ) represents the probability of occurrence of fire hazards in the θth key node, q j ∈Q i represents the sub-event j under the intermediate event i, k∈[1,n], q j ∈Q i UQ k represents the sub-event j under the intermediate event i or k; According to the occurrence probability P of the fire hazard, the sub-event q under each intermediate event ij Calculate partial derivatives to obtain the sub-event q under each intermediate event ij The key value E ij ; .

[0022] It can be understood that the intermediate events are the causes of fire hazards, including fire in the data room, casualties, and house damage. The degree of influence of each sub-event on the intermediate events is different according to historical data. For example, whether to set fireproof materials for the item cabinet will greatly affect the probability of fire in the data room, but the direct impact on casualties is relatively small. The present invention discusses the direct impact. The historical data derives the impact of each sub-event q j For the corresponding intermediate event Q i The historical estimated probability ζ ij Then, the historical estimated probability ζ is updated according to the completion of the sub-events ij For example, in the historical data, when the number of fire-fighting facilities in the data room is 2, the probability of causing the data room to catch fire is 0.5. When it is reduced to 1, we optimize the probability to 1%, and then get the current estimated probability Y ij ; Then we can calculate the probability of fire hazards after integrating all intermediate events. In the formula, the first term represents the sum of the probability of each sub-event in each intermediate event. Since some sub-events are repeatedly counted for intermediate events, the second term is expressed as the probability of any two intermediate events occurring at the same time minus the probability of the same sub-event, and then the probability of fire hazards P is calculated. Since the P(θ) function is a multivariate function, the partial derivative of the sub-event can be obtained to obtain the sub-event q under each intermediate event.ij The key value E ij , key value E ij It reflects the impact of sub-events revealed by historical data on fire hazards at the current key node.

[0023] Furthermore, the specific steps of step S30 include: Obtain historical data on the impact of sub-events at different key nodes on intermediate events from the monitoring system of the smart city, and construct a standardized matrix ζ(θ); ; Among them, nmv represents the historical estimated probability of the intermediate event n caused by the influence of the m sub-event at the key node θ; Calculate the historical estimated probability ζ of each sub-event at different key nodes for different intermediate events nmv The weight of ; Among them, H j represents the entropy of the j-th sub-event, represents the weight of the jth sub-event under the i-th intermediate event in the key node θ; The weight of the corresponding sub-event in each key node Multiply them to obtain the weight value W of each sub-event's influence on each intermediate event. ij .

[0024] It is understandable that in order to avoid the local optimum or the local worst of the impact of sub-events in one area on fire hazards, it is necessary to consider the impact of sub-events in other areas on fire hazards. First, a standardized matrix is ​​constructed for each key node θ. The values ​​in the standardized matrix reflect the impact of sub-events on intermediate events in different key areas. Here we use the entropy calculation method to measure the impact of sub-events on fire hazards. First, the entropy of the sub-event is calculated, and then the weight of the sub-event for different intermediate events at different key nodes is calculated. Then, the weight of the sub-event for the same intermediate event in each key node is multiplied to obtain the weight value W of the sub-event itself on each intermediate event. ij .

[0025] Furthermore, in step S40, the formula for obtaining the importance of the sub-events at each of the key nodes is as follows: W ij ·E ij =G ij ; Among them, G represents importance.

[0026] It can be understood that the weight value W of the sub-event itself on each intermediate event is obtained ij Then, its value is compared with the key value E of the sub-event in each key node. ij Multiply them to get the importance, which more accurately reflects the impact of the sub-event on the fire hazard at each key node.

[0027] Furthermore, the specific steps of step S50 include: Defining a sub-scoring interval O for the importance, and calculating the expected value, entropy value and super entropy value of the sub-event at each of the key nodes according to the sub-scoring interval O; ; ; ; in, Indicates the expected value, represents the entropy value, represents the super entropy value, φ=0.02, and They represent the upper and lower limits of the scoring interval o respectively. represents the order of the normal function in the scoring interval O; Calculate the matching degree ψ of the sub-event in the key node in the sub-scoring interval; ; Merging the matching degrees of all sub-events of the key node in the sub-scoring interval to obtain a total matching degree K; Based on the total matching degree K, a quantitative comprehensive score η is given to the key node; ; Among them, β represents the level of the total matching degree K.

[0028] It can be understood that after obtaining the impact of each sub-event on the fire hazard in the key area, the matching degree of the sub-event in the sub-scoring interval defined by oneself can be calculated according to the above algorithm. The matching degree can more intuitively measure the impact of the sub-event on the fire hazard. The algorithm is based on the cloud model, which has been widely used in the field of safety assessment. The idea is to convert the importance of qualitative analysis into a quantitative concept. The expected value, entropy value and super entropy value are the characteristics of the cloud model. Then, the matching degree is obtained based on the expected value, entropy value and super entropy value, and the importance of qualitative analysis is mapped to the matching degree; then the matching degrees of all sub-events in a key node are added to obtain the total matching degree K, and then the comprehensive scoring formula is used to obtain the comprehensive score of the entire key area. The comprehensive score reflects the safety situation of an area. Since the sub-events in this scheme are all analyzed based on events that have an adverse impact on the fire situation, the higher the score, the higher the fire hazard, the more unsafe it is, and the lower the score, the lower the fire hazard, the safer it is.

[0029] Furthermore, the specific steps of step S60 include: Based on the comprehensive scores of all the key nodes, a fire safety statistical map of the city is constructed. According to the fire safety statistical map, the correlation between any place in the city and the fire hazards is obtained, including: According to the city's floor plan, construct the city's kernel density map; ; ; Where f(x) represents the kernel density at position x, represents the position of the θth key node on the kernel density map, T represents the spatial dimension, α represents the bandwidth, and L represents the spatial weight function; Based on the comprehensive scores of all the key nodes, a bandwidth α in the kernel density map is calculated; ; ; Among them, VD represents the weighted standard distance, represents the comprehensive score at the θth key node, { , , } respectively represent the three-axis coordinates of the θth key node, { , , } respectively represent the weighted average center of the three axes after considering the comprehensive score, the kernel density map has multiple field values ​​that convert non-quantitative features into quantitative features, μ represents the sum of all field values ​​in the kernel density map, D m represents the median distance of the weighted average center of the three axes; Substituting the bandwidth α into the kernel density map to obtain a fire safety statistics map of the city; The kernel density of each area in the fire safety statistical diagram is analyzed based on the Moran's index analysis method to obtain the Moran's index of each area. The area where the Moran's index is greater than the preset index is regarded as an area with a high correlation with the fire hazard and is recorded.

[0030] It can be understood that after obtaining the comprehensive scores of each key node, the comprehensive scores of the key nodes can be embedded in the map. Specifically, a kernel density map is constructed, and then the most critical parameter in the kernel density map, bandwidth, is reasonably calculated through the comprehensive scores of each key node. By using the comprehensive scores as weights, the points on the three axes of each key node { , , }Weighted to get the weighted average center , , }, the weighted distance VD can be calculated, and then the bandwidth is calculated through the weighted distance VD. The bandwidth obtained by this algorithm is more reasonable. When the bandwidth is too large, the conclusion reflected by the kernel density map is more rough. When the bandwidth is too small, it cannot reflect the overall safety level of the city. Then the kernel density map is calculated through the bandwidth to obtain the kernel density value of each location in the city. Then, the fire hazards in different regions can be intuitively felt in the kernel density map. Then, the Moran index is used to evaluate the autocorrelation degree of each location in the kernel density map. The autocorrelation degree can reflect the spatial correlation degree between a region and the whole, and then the correlation between the fire hazard degree of each location and other locations is obtained. Therefore, whether the Moran index of the area exceeds the preset Moran index threshold can be used to judge whether the area is in a dangerous state, and then fire hazards in various regions can be prevented in advance from the big data level.

[0031] In summary, the fire hazard identification method based on smart city in the above-mentioned embodiment of the present invention comprehensively integrates the degree of fire hazards in a city from a digital perspective, and then safely deploys areas with fire hazards before the danger occurs. Please refer to Figure 2 , which is a fire hazard identification system based on smart city in the second embodiment of the present invention, comprising: The first acquisition module 11 is used to select key nodes in the city and obtain the completion status of sub-events related to fire protection at the key nodes based on the monitoring system of the smart city; The second acquisition module 12 is used to acquire the impact of the historical data of the sub-events at each of the key nodes on the fire hazard based on the monitoring system of the smart city, and to obtain the key value of the sub-event at each of the key nodes by integrating the completion status of the sub-events; The second acquisition module 12 is specifically used for: Define multiple intermediate events under fire hazards as (Q1, Q2, ..., Q n ), and define multiple sub-events as (q1, q2, ..., q m ); wherein n represents the number of the intermediate events, and m represents the number of the sub-events; Obtain each of the sub-events q from the monitoring system of the smart city j The historical data for the corresponding intermediate event Q i The historical estimated probability ζ ij ;i=(1,2,…,n), j=(1,2,…,m); According to the completion of the sub-event, the historical estimated probability ζ ij Optimize to get the current estimated probability Y of the intermediate event ij ; Based on the current estimated probability Y of each intermediate event ij , calculate the probability of occurrence of the fire hazard: ; Among them, θ = (1, 2, ..., v), v represents the total number of key nodes, P (θ) represents the probability of occurrence of fire hazards in the θth key node, q j ∈Q i represents the sub-event j under the intermediate event i, k∈[1,n], q j ∈Q i UQ k represents the sub-event j under the intermediate event i or k; According to the occurrence probability P of the fire hazard, the sub-event q under each intermediate event ij Calculate partial derivatives to obtain the sub-event q under each intermediate event ij The key value E ij ; ; The third acquisition module 13 is used to acquire the impact of the historical data of sub-events at different key nodes on the fire hazard, so as to obtain the weight value of each sub-event itself; The third acquisition module 13 is specifically used for: Obtain historical data on the impact of sub-events at different key nodes on intermediate events from the monitoring system of the smart city, and construct a standardized matrix ζ(θ); ; Among them, nmv represents the historical estimated probability of the intermediate event n caused by the influence of the m sub-event at the key node θ; Calculate the historical estimated probability ζ of each sub-event at different key nodes for different intermediate events nmv The weight of ; Among them, H j represents the entropy of the j-th sub-event, represents the weight of the jth sub-event under the i-th intermediate event in the key node θ; The weight of the corresponding sub-event in each key node Multiply them to obtain the weight value W of each sub-event's influence on each intermediate event. ij ; Fusion module 14: used to fuse the key value of the sub-event at each key node with the weight value of the sub-event itself to obtain the importance of the sub-event at each key node; Scoring module 15: used to obtain a sub-score of each sub-event at the key node based on the importance of each sub-event at the key node, and integrate all sub-scores at the key node to obtain a comprehensive score at the key node; The scoring module 15 is specifically used for: Defining a sub-scoring interval O for the importance, and calculating the expected value, entropy value and super entropy value of the sub-event at each of the key nodes according to the sub-scoring interval O; ; ; ; in, Indicates the expected value, represents the entropy value, represents the super entropy value, φ=0.02, and They represent the upper and lower limits of the scoring interval o respectively. represents the order of the normal function in the scoring interval O; Calculate the matching degree ψ of the sub-event in the key node in the sub-scoring interval; ; Merging the matching degrees of all sub-events of the key node in the sub-scoring interval to obtain a total matching degree K; Based on the total matching degree K, a quantitative comprehensive score η is given to the key node; ; Among them, β represents the level of the total matching degree K; Statistics module 16: used to construct a fire safety statistics map of the city based on the comprehensive scores of all the key nodes, and obtain the correlation between any place in the city and the fire hazards according to the fire safety statistics map; The statistical module 16 is specifically used for: According to the city's floor plan, construct the city's kernel density map; ; ; Where f(x) represents the kernel density at position x, represents the position of the θth key node on the kernel density map, T represents the spatial dimension, α represents the bandwidth, and L represents the spatial weight function; Based on the comprehensive scores of all the key nodes, a bandwidth α in the kernel density map is calculated; ; ; Among them, VD represents the weighted standard distance, represents the comprehensive score at the θth key node, { , , } respectively represent the three-axis coordinates of the θth key node, { , , } respectively represent the weighted average center of the three axes after considering the comprehensive score, the kernel density map has multiple field values ​​that convert non-quantitative features into quantitative features, μ represents the sum of all field values ​​in the kernel density map, D m represents the median distance of the weighted average center of the three axes; Substituting the bandwidth α into the kernel density map to obtain a fire safety statistics map of the city; The kernel density of each area in the fire safety statistical diagram is analyzed based on the Moran's index analysis method to obtain the Moran's index of each area. The area where the Moran's index is greater than the preset index is regarded as an area with a high correlation with the fire hazard and is recorded.

[0032] The present invention also provides an electronic device, see Figure 3, shown is an electronic device in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned fire hazard identification method based on smart city is implemented.

[0033] Among them, the memory 10 includes at least one type of storage medium, and the storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10 can be an internal storage unit of an electronic device, such as a hard disk of the electronic device. In other embodiments, the memory 10 can also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 10 can also include both an internal storage unit of the electronic device and an external storage device. The memory 10 can not only be used to store application software and various types of data installed in the electronic device, but also can be used to temporarily store data that has been output or is to be output.

[0034] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs, etc.

[0035] It should be pointed out that Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than those shown in the figure, or combine certain components, or arrange the components differently.

[0036] An embodiment of the present invention further provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the fire hazard identification method based on a smart city as described above.

[0037] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0038] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0039] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0040] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0041] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0042] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0043] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A fire hazard identification method based on smart city, characterized in that: The steps include: Select key nodes in the city and obtain the completion status of fire-related sub-events at key nodes based on the smart city monitoring system; Based on the monitoring system of the smart city, the impact of the historical data of the sub-events at each of the key nodes on the fire hazards is obtained, and the completion status of the sub-events is integrated to obtain the key values ​​of the sub-events at each of the key nodes; Obtaining the impact of historical data of sub-events at different key nodes on the fire hazards to obtain the weight value of each sub-event itself; The key value of the sub-event at each of the key nodes is combined with the weight value of the sub-event itself to obtain the importance of the sub-event at each of the key nodes; Based on the importance of the sub-events at each of the key nodes, a sub-score of each of the sub-events at the key node is obtained, and all the sub-scores at the key node are integrated to obtain a comprehensive score at the key node; Based on the comprehensive scores of all the key nodes, a fire safety statistical map of the city is constructed, and based on the fire safety statistical map, the correlation between any place in the city and the fire hazard is obtained.

2. The fire hazard identification method based on smart city according to claim 1 is characterized in that: The monitoring system based on the smart city obtains the impact of the historical data of the sub-events at each of the key nodes on the fire hazards, and integrates the completion status of the sub-events to obtain the key values ​​of the sub-events at each of the key nodes, specifically including: Define multiple intermediate events under fire hazards as (Q1, Q2, ..., Q n ), and define multiple sub-events as (q1, q2, ..., q m ); wherein n represents the number of the intermediate events, and m represents the number of the sub-events; Obtain each of the sub-events q from the monitoring system of the smart city j The historical data for the corresponding intermediate event Q i The historical estimated probability ζ ij ;i=(1,2,…,n), j=(1,2,…,m); According to the completion of the sub-event, the historical estimated probability ζ ij Optimize to get the current estimated probability Y of the intermediate event ij ; Based on the current estimated probability Y of each intermediate event ij , calculate the probability of occurrence of the fire hazard: ； Among them, θ = (1, 2, ..., v), v represents the total number of key nodes, P (θ) represents the probability of occurrence of fire hazards in the θth key node, q j ∈Q i represents the sub-event j under the intermediate event i, k∈[1,n], q j ∈Q i UQ k represents the sub-event j under the intermediate event i or k; According to the occurrence probability P of the fire hazard, the sub-event q under each intermediate event ij Calculate partial derivatives to obtain the sub-event q under each intermediate event ij The key value E ij ; 。 3. The fire hazard identification method based on smart city according to claim 2 is characterized in that: The acquisition of the influence of the historical data of sub-events at different key nodes on the fire hazards to obtain the weight value of each sub-event itself specifically includes: Obtain historical data on the impact of sub-events at different key nodes on intermediate events from the monitoring system of the smart city, and construct a standardized matrix ζ(θ); ； Among them, nmv represents the historical estimated probability of the intermediate event n caused by the influence of the m sub-event at the key node θ; Calculate the historical estimated probability ζ of each sub-event at different key nodes for different intermediate events nmv The weight of ； Among them, H j represents the entropy of the j-th sub-event, represents the weight of the jth sub-event under the i-th intermediate event in the key node θ; The weight of the corresponding sub-event in each key node Multiply them to obtain the weight value W of each sub-event's influence on each intermediate event. ij .

4. The fire hazard identification method based on smart city according to claim 3 is characterized in that: The formula for fusing the key value of the sub-event at each key node with the weight value of the sub-event itself to obtain the importance of the sub-event at each key node is as follows: W ij ·HAVE BEEN ij =G ij ; Among them, G represents importance.

5. The fire hazard identification method based on smart city according to claim 4 is characterized in that: The importance of each sub-event at each key node is used to obtain a sub-score of each sub-event at the key node, and all sub-scores at the key node are integrated to obtain a comprehensive score at the key node, specifically including: Defining a sub-scoring interval O for the importance, and calculating the expected value, entropy value and super entropy value of the sub-event at each of the key nodes according to the sub-scoring interval O; ； ； ； in, Indicates the expected value, represents the entropy value, represents the super entropy value, φ=0.02, and They represent the upper and lower limits of the scoring interval o respectively. represents the order of the normal function in the scoring interval O; Calculate the matching degree ψ of the sub-event in the key node in the sub-scoring interval; ； Merging the matching degrees of all sub-events of the key node in the sub-scoring interval to obtain a total matching degree K; Based on the total matching degree K, a quantitative comprehensive score η is given to the key node; ； Among them, β represents the level of the total matching degree K.

6. The fire hazard identification method based on smart city according to claim 5 is characterized in that: Based on the comprehensive scores of all the key nodes, a fire safety statistical map of the city is constructed. According to the fire safety statistical map, the correlation between any place in the city and the fire hazards is obtained, including: According to the city's floor plan, construct the city's kernel density map; ； ； Where f(x) represents the kernel density at position x, represents the position of the θth key node on the kernel density map, T represents the spatial dimension, α represents the bandwidth, and L represents the spatial weight function; Based on the comprehensive scores of all the key nodes, a bandwidth α in the kernel density map is calculated; ； ； Among them, VD represents the weighted standard distance, represents the comprehensive score at the θth key node, { , , } respectively represent the three-axis coordinates of the θth key node, { , , } respectively represent the weighted average center of the three axes after considering the comprehensive score, the kernel density map has multiple field values ​​that convert non-quantitative features into quantitative features, μ represents the sum of all field values ​​in the kernel density map, D m represents the median distance of the weighted average center of the three axes; Substituting the bandwidth α into the kernel density map to obtain a fire safety statistics map of the city; The kernel density of each area in the fire safety statistical diagram is analyzed based on the Moran's index analysis method to obtain the Moran's index of each area. The area where the Moran's index is greater than the preset index is regarded as an area with a high correlation with the fire hazard and is recorded.

7. A fire hazard identification system based on smart city, characterized in that: include: The first acquisition module is used to select key nodes in the city and obtain the completion status of sub-events related to fire protection at the key nodes based on the monitoring system of the smart city; The second acquisition module is used to acquire the impact of the historical data of the sub-events at each of the key nodes on the fire hazard based on the monitoring system of the smart city, and to obtain the key value of the sub-event at each of the key nodes by integrating the completion status of the sub-events; The third acquisition module is used to acquire the impact of the historical data of sub-events at different key nodes on the fire hazards, so as to obtain the weight value of each sub-event itself; Fusion module: used to fuse the key value of the sub-event at each key node with the weight value of the sub-event itself to obtain the importance of the sub-event at each key node; Scoring module: used to obtain the sub-score of each sub-event at the key node based on the importance of the sub-event at each key node, and integrate all the sub-scores at the key node to obtain the comprehensive score at the key node; Statistics module: used to construct a fire safety statistics map of the city based on the comprehensive scores of all the key nodes, and obtain the correlation between any place in the city and the fire hazards based on the fire safety statistics map.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the fire hazard identification method based on a smart city as described in any one of claims 1 to 6 is implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the fire hazard identification method based on smart city is implemented as described in any one of claims 1-6.

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