A fire safety integrated management system and method based on cloud platform

Abstract

The present invention discloses a fire safety integrated management system and method based on a cloud platform, which belongs to the field of fire safety technology, and includes a comprehensive information acquisition module for acquiring monitoring data of preset catering stores, and the monitoring data acquisition date is counted from the opening date, and the acquired monitoring data is generated into a cloud platform library, and the cloud database is updated daily, and a city data acquisition module is used to obtain the fire command and dispatch resource information data of the city where the preset catering store is located, upload the acquired information data to the cloud platform library, and divide the data in the cloud platform library. The present invention uses deep learning analysis technology to collect and analyze the monitoring data of preset catering stores in different time periods in the city, and discriminates and processes the collected and analyzed monitoring data based on the fire safety data of the fire safety management department, so as to obtain the safety status of the preset catering store, and then judge whether it has a fire hazard.

Description

Technical Field

[0001] The present invention relates to the field of fire safety technology, and in particular to a cloud platform-based fire safety integrated management system and method. Background Art

[0002] With the rapid development of social economy and the continuous emergence and use of various application materials, the probability of fire has increased in some cases, making urban firefighting work face great challenges. Among them, there are many urban restaurants, and the business hours of restaurants are long, more than 12 hours a day. The refrigeration and insulation equipment is under load for a long time, which is easy to heat up and cause failures. Electricity is a vital infrastructure in catering stores. A reasonable, safe and efficient power design specification is essential for the normal operation and customer experience of catering stores. In real life, if the current in the same line passing through a shop is too large, it will cause the voltage drop of other shops connected to the shop line to increase sharply. In this case, the voltage on the load will suddenly drop, causing the voltage of other electrical appliances to drop sharply, which will seriously affect the operation. At the same time, whether it is data statistics or news reports, the probability of fire in catering places is greater than that in other industries. Traditional fire management and rescue have increased the pressure of fire prevention and control and the risk of fire fighting and rescue. The management situation of the troops is becoming more and more complicated. The contradiction between traditional working methods and new situations and new tasks is becoming increasingly prominent.

[0003] Traditional methods and means face prominent contradictions such as difficulty in dynamically perceiving fire management objects, difficulty in real-time control of fire hazards, and lack of intelligent support for command and dispatch. Under the realistic conditions of limited fire police force and unlimited responsibility, traditional methods cannot fully utilize big data to conduct in-depth mining and application of existing data resources, find potential patterns, and predict risks in advance, which hinders early warning of fire hazards. Summary of the invention

[0004] In view of the above problems existing in the existing fire safety management, the present invention is proposed.

[0005] Therefore, one of the objects of the present invention is to provide a cloud platform-based integrated fire safety management system and method, which uses deep learning analysis technology to collect and analyze monitoring data of preset catering stores in the city at different time periods, and performs judgment processing on the collected and analyzed monitoring data based on the fire safety data of the fire safety management department to obtain the safety status of the preset catering stores, and then determine whether there are fire hazards. It can also obtain real-time road conditions, fire trucks, video surveillance three-dimensional maps and street view maps of the urban area where the preset catering stores are located, so as to conveniently deploy fire police forces when necessary, and then participate in fire fighting and rescue as soon as possible. It can not only predict the possible safety hazards of the preset catering stores in a more comprehensive and in-depth manner, but also better control possible fires.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] On the one hand, the present invention provides a cloud platform-based fire safety integrated management system, comprising:

[0008] A comprehensive information acquisition module is used to acquire monitoring data of preset catering stores, and the monitoring data acquisition date is counted from the opening date, and the acquired monitoring data is generated into a cloud platform database, and the cloud database is updated daily;

[0009] The city data collection module is used to obtain the fire command and dispatch resource information data of the city where the preset catering store is located, upload the obtained information data to the cloud platform library, divide the data in the cloud platform library, and update the cloud platform library at the same time;

[0010] A data analysis module is used to analyze the monitoring data of the preset catering stores, including analyzing the peak power consumption change data of the preset catering stores, and analyzing the change rules of the current value data and the voltage value data corresponding to the peak power consumption change data, and also includes analyzing the kitchen humidity and heat data;

[0011] A data processing system, the data processing system responds to the data analysis module, the data processing system includes a determination module and a storage module;

[0012] The discrimination module is used to discriminate the data analysis results, including processing and discriminating the peak power consumption change data, and processing and discriminating the change rules of the current value data and voltage value data corresponding to the change data, and also includes discriminating the kitchen humidity and heat data;

[0013] The storage module is based on the discrimination module and is used to record and store data values ​​corresponding to each analysis result, wherein the data values ​​include data values ​​of the analyzed objects, and a safety threshold is preset based on the analyzed objects. When the data processing and discrimination result of any analyzed object exceeds the safety threshold, the system determines that the preset catering store has a fire hazard and issues an early warning. Otherwise, no determination is made.

[0014] A data fusion sharing unit, which is based on the analysis results obtained by the data processing system and is used to upload the analysis results to the fire safety management department including the cloud platform library, and the data fusion sharing unit includes a communication module;

[0015] The communication module includes a module for communicating with the fire safety management department of the area where the preset catering store is located by using a wireless networking communication method;

[0016] The monitoring terminal is used to obtain the fire safety management department that matches the jurisdiction of the urban area where the preset catering store is located, and receive the on-site data uploaded by the collection monitoring point, and update the background data at the same time. When the data processing and judgment result of the preset catering store exceeds the safety threshold, an information request for the hidden danger warning status of the preset catering store is initiated to the fire safety management department that matches the jurisdiction of the urban area.

[0017] As a preferred solution of the present invention, the monitoring data of catering stores around a preset catering store is obtained, the surrounding range includes 1 to 1.5 kilometers from the preset catering store, and the change pattern of the monitoring data of the surrounding catering stores is collected and obtained, and a prediction model is generated at the same time.

[0018] As a preferred solution of the present invention, wherein: in the cloud platform library, the data is divided according to the fire command and dispatch resource information data obtained by the city data acquisition module and the monitoring data obtained by the comprehensive information acquisition module, wherein the fire command and dispatch resource information data includes fire trucks, personnel, water sources, pre-dispatch command cases, key units, real-time road conditions, population information, fire-fighting facilities, video surveillance three-dimensional maps and street view maps of the city where the preset catering store is located; when the fire safety management department that matches the jurisdiction of the urban area where the preset catering store is located receives a hidden danger warning status information request initiated by the preset catering store, the fire safety management department analyzes the fire command and dispatch resource information data and dispatches fire police force.

[0019] As a preferred solution of the present invention, based on the change characteristics of the monitoring data of the surrounding catering stores, the surrounding catering stores are divided into low change level, medium change level and high change level in a step-by-step manner, wherein the low change level, medium change level and high change level respectively correspond to small, medium and large fluctuations in the change of the monitoring data of the catering stores, and based on the medium change level and the high change level, the corresponding catering stores are monitored at a level higher than that of the low change level catering stores.

[0020] As a preferred solution of the present invention, the fluctuation range of the medium change level and the high change level catering stores is divided into the first operating time period, the second operating time period and the third operating time period based on which the first operating time period, the second operating time period and the third operating time period respectively represent the breakfast time period, the lunch time period and the dinner time period of the catering stores, and the change law of the monitoring data of the catering stores in the three operating time periods is analyzed, the change law is uploaded to the prediction model, and the prediction model is updated, and at the same time, the proportion of the change of the monitoring data in the second operating time period to the change of the monitoring data of the catering store throughout the day is calculated based on the second operating time period, and the calculation is obtained according to the following formula:

[0021] y t =g(w hi *t+b i ), where y t represents the maximum change data collected by the catering store at the t-th time point in the second operating time period;

[0022] Where g represents the initial data collected on the monitoring day corresponding to the maximum change data, and w hi represents the i-th factor corresponding to the maximum change data collected for the h-th time at the restaurant, b i represents the number of occurrences of the i-th factor.

[0023] As a preferred solution of the present invention, among the surrounding catering stores, based on the changing pattern of the monitoring data in the second operating time period, the changing characteristics of the monitoring data of the preset catering stores are collected, wherein the data changes of the current and voltage of the preset catering stores are analyzed based on the changing characteristics of the preset catering stores at peak power consumption and non-peak power consumption. When the data change values ​​of the current and voltage exceed the safety threshold, the system determines that the preset catering store has a fire hazard and issues a warning. Otherwise, no determination is made.

[0024] As a preferred solution of the present invention, based on the peak power consumption period, the ranking of factors causing the monitoring data change to exceed the safety threshold during the period is counted, and the factors are ranked according to θ1, θ2, ..., θ n Sorting is performed, where n represents the nth factor, the correlation between the factor sorting and the change of the monitoring data is analyzed, and the correlation rule is uploaded to the prediction model.

[0025] As a preferred solution of the present invention, the Monte Carlo method is used to sample the current data, voltage data and line heat data of the preset catering stores at peak power consumption and non-peak power consumption, determine the current output, voltage and line heat status and calculate the circuit reliability, and establish a maintenance and analysis and judgment method for peak power consumption and non-peak power consumption circuit failures based on the sample values ​​obtained by the Monte Carlo method, and at the same time establish the impact of weather conditions in different months on the circuit and reliability evaluation.

[0026] As a preferred solution of the present invention, the sampling data is divided into several evaluation indicators, the evaluation indicator data of the preset catering store and its surrounding catering stores are statistically obtained, and the data is normalized to calculate the weight of each evaluation indicator.

[0027] In another aspect, the present invention provides a method for a fire safety integrated management system based on a cloud platform, comprising the following steps:

[0028] Acquire monitoring data of preset catering stores, and the monitoring data acquisition date is counted from the opening date, and generate a cloud platform database with the acquired monitoring data, and update the cloud database daily;

[0029] Obtaining fire command and dispatch resource information data of the city where the preset catering store is located, uploading the obtained information data to the cloud platform library, dividing the data in the cloud platform library, and updating the cloud platform library at the same time;

[0030] Analyze the monitoring data of the preset catering stores, and perform judgment processing on the analysis results, record the data value corresponding to each analysis result, and preset a safety threshold based on the data value of the analyzed object. When the data processing judgment result of any analyzed object exceeds the safety threshold, the system determines that the preset catering store has a fire hazard and issues an early warning. Otherwise, no judgment is made;

[0031] Obtain the fire safety management department that matches the jurisdiction of the city area where the preset catering store is located, upload the analysis results to the fire safety management department including the cloud platform library, receive the field data uploaded by the collection monitoring point, and update the background data at the same time. When the data processing and judgment result of the preset catering store exceeds the safety threshold, initiate an information request for the hidden danger warning status of the preset catering store to the fire safety management department that matches the jurisdiction of the city area;

[0032] Obtain monitoring data of catering stores around the preset catering store, collect and obtain the changing rules of the monitoring data of the surrounding catering stores, and generate a prediction model at the same time;

[0033] In the cloud platform library, the data is divided according to the fire command and dispatch resource information data obtained by the city data collection module and the monitoring data obtained by the comprehensive information acquisition module. When the fire safety management department that matches the urban area where the preset catering store is located receives a hidden danger warning status information request initiated by the preset catering store, the fire safety management department analyzes the fire command and dispatch resource information data and dispatches fire police force.

[0034] Beneficial effects:

[0035] The present invention utilizes deep learning analysis technology to collect and analyze monitoring data of preset catering stores in the city at different time periods, and performs discrimination processing on the collected and analyzed monitoring data based on the fire safety data of the fire safety management department, so as to obtain the safety status of the preset catering stores, and then judge whether there are fire hazards. In this process, it is also possible to obtain real-time road conditions, fire trucks, video surveillance three-dimensional maps and street view maps of the urban area where the preset catering stores are located, so as to conveniently deploy fire police forces when necessary, and then participate in fire fighting and rescue as soon as possible. At the same time, it is also possible to collect monitoring data of other catering stores around the preset catering stores within a certain distance range to comprehensively judge the changes in monitoring data and safety status of the preset catering stores, analyze the changes in monitoring data of surrounding catering stores in different operating periods to the changing rules of monitoring data of the preset catering stores, and then predict the potential safety hazards of the preset catering stores in a more comprehensive and in-depth manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0037] Figure 1 Schematic diagram of the modular structure of the cloud platform-based fire safety integrated management system in an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of a method flow of an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of the process structure of an embodiment of the present invention;

[0040] Numbers in the figure: 110 - comprehensive information acquisition module; 120 - urban data collection module; 130 - data analysis module; 140 - data processing system; 1401 - identification module; 1402 - storage module; 150 - data fusion sharing unit; 1501 - communication module; 160 - monitoring terminal. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0042] Due to traditional methods and means, there are prominent contradictions such as difficulty in dynamically perceiving fire management objects, difficulty in real-time control of fire hazards, and lack of intelligent support for command and dispatch. Under the realistic conditions of limited fire police force and unlimited responsibility, traditional methods cannot fully utilize big data means to conduct in-depth mining and application of existing data resources, find potential patterns, and predict risks in advance, which poses certain obstacles to early warning of fire hazards.

[0043] Based on this, the present invention proposes a cloud platform-based fire safety integrated management system and method, which uses deep learning analysis technology to collect and analyze monitoring data of preset catering stores in the city at different time periods, and judges and processes the collected and analyzed monitoring data based on the fire safety data of the fire safety management department to obtain the safety status of the preset catering stores, and then judge whether there are fire hazards. It can also obtain real-time road conditions, fire trucks, video surveillance three-dimensional maps and street view maps of the urban area where the preset catering stores are located, so as to conveniently deploy fire police forces when necessary, and then participate in fire fighting and rescue as soon as possible. It can not only predict the possible safety hazards of the preset catering stores in a more comprehensive and in-depth manner, but also better control possible fires.

[0044] The present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0045] Embodiment 1

[0046] Reference Figures 1 to 3 , is an embodiment of the present invention, which provides a fire safety integrated management system based on a cloud platform, including:

[0047] The comprehensive information acquisition module 110 is used to acquire the monitoring data of the preset catering stores, and the monitoring data acquisition date is counted from the opening date, and the acquired monitoring data is generated into a cloud platform database, and the cloud database is updated daily;

[0048] It should be noted that the monitoring data of this embodiment includes the power consumption of the preset catering store, the power consumption includes the peak power consumption, and the current and voltage under the peak power consumption, and the store temperature value corresponding to the peak power consumption period, and the store temperature value includes the temperature value of the entire area of ​​the preset catering store;

[0049] It also includes the data on the thickness of oil stains on the fume ducts of the preset catering stores, the humidity of the kitchen, the aging data of the electrical lines, the number of flue building materials and fire-fighting facilities and equipment placed in the preset catering stores;

[0050] Among them, the comprehensive information acquisition module includes a CCD camera, a temperature sensor and a humidity sensor. The monitoring data also includes kitchen humidity data and grease thickness data when fires occur in other catering stores in the city where the preset catering store is located, as well as current and voltage under peak power consumption conditions, and store temperature values ​​corresponding to the peak power consumption period;

[0051] In this embodiment, the monitoring data further includes obtaining monitoring data of catering stores around the preset catering store, the surrounding range includes 1 to 1.5 kilometers from the preset catering store, and collecting and obtaining the change pattern of the monitoring data of the surrounding catering stores, and generating a prediction model at the same time;

[0052] On the basis of the above, this embodiment divides the surrounding catering stores into low change level, medium change level and high change level in a step-by-step manner based on the change characteristics of the monitoring data of the surrounding catering stores, wherein the low change level, medium change level and high change level correspond to small, medium and large fluctuations in the change of the monitoring data of the catering stores, respectively, and the corresponding catering stores are monitored at a higher level than the low change level catering stores based on the medium change level and the high change level;

[0053] At the same time, on the basis of the above, based on the catering stores with medium change level and high change level, the fluctuation range of the change is divided into the first operating time period, the second operating time period and the third operating time period, where the first operating time period, the second operating time period and the third operating time period represent the breakfast time period, the lunch time period and the dinner time period of the catering stores respectively, and the change pattern of the monitoring data of the catering stores in the three operating time periods is analyzed, the change pattern is uploaded to the prediction model, and the prediction model is updated. At the same time, based on the second operating time period, the proportion of the change of the monitoring data in this period to the change of the monitoring data of the catering stores throughout the day is calculated, and it is calculated according to the following formula:

[0054] yt =g(w hi *t+b i ), where y t Indicates the maximum change data collected by the catering store at the tth time point in the second operating period;

[0055] In the formula, g represents the initial data collected on the monitoring day corresponding to the maximum change data, and w hi represents the i-th factor corresponding to the maximum change data collected from the restaurant store for the hth time, b i represents the number of occurrences of the i-th factor, wherein the factors include aging of electrical circuits, humidity, and overload, short circuit and / or leakage caused from time to time by aging of electrical circuits;

[0056] It should be noted that in this embodiment, based on common sense, the highest temperature period in a day is noon. Therefore, in this embodiment, the proportion of the change of the monitoring data in this period to the change of the monitoring data of the catering store throughout the day is calculated based on the second operating time period, which is more meaningful for reference.

[0057] Based on the above, this embodiment takes the peak power consumption period as the basis, and sorts the factors that cause the monitoring data change to exceed the safety threshold during the statistical time period, and sorts the factors according to θ1, θ2, ..., θ n Sorting, where n represents the nth factor, analyzing the correlation between the factor sorting and the monitoring data changes, and uploading the correlation rules to the prediction model;

[0058] It should be noted that in this embodiment, the ranking of factors includes ranking the number of occurrences of each factor;

[0059] At the same time, this embodiment further collects the change characteristics of the monitoring data of the preset catering stores based on the change law of the monitoring data in the second operating time period in the surrounding catering stores, wherein the data changes of the current and voltage of the preset catering stores are analyzed based on the change characteristics of the preset catering stores at peak power consumption and non-peak power consumption. When the data change values ​​of the current and voltage exceed the safety threshold, the system determines that the preset catering store has a fire hazard and issues an early warning. Otherwise, no determination is made.

[0060] The city data collection module 120 is used to obtain the fire command and dispatch resource information data of the city where the preset catering store is located, upload the obtained information data to the cloud platform library, divide the data in the cloud platform library, and update the cloud platform library at the same time;

[0061] It should be noted that in the present embodiment, in the cloud platform library, data is divided according to the fire command and dispatch resource information data obtained by the city data acquisition module 120 and the monitoring data obtained by the comprehensive information acquisition module 110, wherein the fire command and dispatch resource information data includes fire vehicles, personnel, water sources, pre-dispatch command cases, key units, real-time road conditions, population information, fire facilities, video surveillance three-dimensional maps and street view maps of the city where the preset catering store is located. When the fire safety management department that matches the jurisdiction of the city where the preset catering store is located receives the hidden danger warning status information request initiated by the preset catering store, the fire safety management department analyzes the fire command and dispatch resource information data and dispatches fire police force;

[0062] The data analysis module 130 is used to analyze the monitoring data of the preset catering stores, including analyzing the peak power consumption change data of the preset catering stores, analyzing the change rules of the current value data and the voltage value data corresponding to the peak power consumption change data, and also analyzing the kitchen humidity and heat data;

[0063] In this embodiment, the analysis of the monitoring data further includes analyzing the change data of the preset catering store during the non-peak power consumption, and also includes analyzing the change rules of the current value data and the voltage value data corresponding to the non-peak power consumption change data;

[0064] The data processing system 140 responds to the data analysis module 130 , and the data processing system 140 includes a determination module 1401 and a storage module 1402 ;

[0065] The determination module 1401 is used to determine the data analysis results, including processing and determining the peak power consumption change data, and processing and determining the change rules of the current value data and voltage value data corresponding to the change data, and also including determining the kitchen humidity and heat data;

[0066] The storage module 1402 is based on the discrimination module 1401 and is used to record and store data values ​​corresponding to each analysis result, the data values ​​include data values ​​of the analyzed objects, and a safety threshold is preset based on the analyzed objects. When the data processing discrimination result of any analyzed object exceeds the safety threshold, the system determines that the preset catering store has a fire hazard and issues an early warning. Otherwise, no determination is made.

[0067] It should be noted that in this embodiment, the safety threshold is based on the kitchen humidity, calorific value and peak power consumption changes set by the fire safety management department, as well as the changes in current value data and voltage value data corresponding to the peak power consumption changes;

[0068] The data fusion sharing unit 150 is used to upload the analysis results obtained by the data processing system 140 to the fire safety management department including the cloud platform library, and the data fusion sharing unit includes a communication module 1501;

[0069] The communication module 1501 includes a communication connection with the fire safety management department of the area where the preset catering store is located by using a wireless networking communication method;

[0070] The monitoring terminal 160 is used to obtain the fire safety management department that matches the jurisdiction of the urban area where the preset catering store is located, and receive the on-site data uploaded by the collection monitoring point, and update the background data at the same time. When the data processing and judgment result of the preset catering store exceeds the safety threshold, an information request for the hidden danger warning status of the preset catering store is initiated to the fire safety management department that matches the jurisdiction of the urban area.

[0071] Monte Carlo method is used to sample the current data, voltage data and line heat data of the preset catering stores at peak power consumption and non-peak power consumption, determine the current output, voltage and line heat status and calculate the circuit reliability, and establish the maintenance and analysis and judgment method of peak power consumption and non-peak power consumption circuit faults based on the sample values ​​obtained by Monte Carlo method, and at the same time establish the impact of weather conditions in different months on the circuit and reliability evaluation;

[0072] It should be further explained in this embodiment that different months include months corresponding to winter and summer, and the impact and reliability evaluation are performed in summer and winter. Since the temperatures in summer and winter and the temperature difference between the two seasons are the highest in the year, the temperature difference and temperature change in summer and winter have a more valuable reference for the impact on the circuit than other months and / or seasons with relatively stable temperature changes;

[0073] The sampled data is divided into several evaluation indicators, and the evaluation indicator data of the preset catering stores and their surrounding catering stores are statistically obtained. The data is normalized and the weight of each evaluation indicator is calculated. The data is normalized using the Min-Max normalization method, and the values ​​in the evaluation indicator data set are mapped to the range of [0, 1] to obtain the standard value of the evaluation indicator data. According to the standard value of the evaluation indicator data, the proportion of the evaluation indicator data is analyzed.

[0074] Based on the above, the present invention uses deep learning analysis technology to collect and analyze monitoring data of preset catering stores in the city at different time periods, and performs judgment processing on the collected and analyzed monitoring data based on the fire safety data of the fire safety management department to obtain the safety status of the preset catering stores, and then determine whether there are fire hazards.

[0075] Embodiment 2

[0076] Reference Figure 2 , is an embodiment of the present invention. This embodiment combines the above-mentioned cloud platform-based fire safety integrated management system to propose a cloud platform-based fire safety integrated management method as follows:

[0077] Obtain monitoring data of preset catering stores, and the monitoring data acquisition date is counted from the opening date, and generate a cloud platform database with the acquired monitoring data, and update the cloud database daily;

[0078] Obtain the fire command and dispatch resource information data of the city where the preset catering store is located, upload the obtained information data to the cloud platform library, divide the data in the cloud platform library, and update the cloud platform library at the same time;

[0079] Analyze the monitoring data of the preset catering stores, and perform judgment processing on the analysis results, record the data value corresponding to each analysis result, and preset a safety threshold based on the data value of the analyzed object. When the data processing judgment result of any analyzed object exceeds the safety threshold, the system determines that the preset catering store has a fire hazard and issues an early warning. Otherwise, no judgment is made;

[0080] Obtain the fire safety management department that matches the jurisdiction of the city where the preset catering store is located, upload the analysis results to the fire safety management department including the cloud platform library, receive the on-site data uploaded by the collection monitoring point, and update the background data at the same time. When the data processing and judgment result of the preset catering store exceeds the safety threshold, initiate an information request for the hidden danger warning status of the preset catering store to the fire safety management department that matches the jurisdiction of the city area;

[0081] Obtain monitoring data of catering stores around the preset catering stores, collect and obtain the changing patterns of the monitoring data of the surrounding catering stores, and generate a prediction model at the same time;

[0082] In the cloud platform library, the data is divided according to the fire command and dispatch resource information data obtained by the urban data collection module and the monitoring data obtained by the comprehensive information acquisition module. When the fire safety management department that matches the urban area where the preset catering store is located receives a hidden danger warning status information request initiated by the preset catering store, the fire safety management department analyzes the fire command and dispatch resource information data and dispatches fire police force.

[0083] Embodiment 3

[0084] Reference Figure 3, which is an embodiment of the present invention, provides an integrated fire safety management method based on a cloud platform. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0085] In this embodiment, a specific usage experiment is carried out on the method of the present invention. Based on the capacitance data of surrounding catering stores, under a preset identical experimental environment, this embodiment conducts two groups of experiments on the existing traditional method and the method of this embodiment, respectively. The specific experimental results are shown in Tables 1 and 2.

[0086] Working conditions:

[0087] The circuit status of other catering stores collected around the preset catering stores, including current capacity: 10~30kVA; power: 0~80kW;

[0088] Distance between designated restaurants: 1 to 1.5 km;

[0089] Number of catering stores collected: 1 to 5.

[0090] Table 1 Capacitance increase state impact test evaluation comparison table

[0091]

[0092] Table 2 Comparison table of capacitance reduction state impact test evaluation

[0093]

[0094]

[0095] Through the above comparative experiments, it can be determined that the simulation test method provided by the present invention can form a fusion analysis of the data, and can predict the safety status of the preset catering stores from multiple angles. Compared with the existing technology, it can identify the fire hazards existing in the preset catering stores at different time periods to a higher degree, which not only improves the prediction efficiency, but also has real-time performance and can conduct a more comprehensive hazard assessment of the preset catering stores.

[0096] In summary, the present invention utilizes deep learning analysis technology to collect and analyze monitoring data of preset catering stores in the city at different time periods, and performs discrimination processing on the collected and analyzed monitoring data based on the fire safety data of the fire safety management department, so as to obtain the safety status of the preset catering stores, and then determine whether there are any fire hazards. In this process, it is also possible to obtain real-time road conditions, fire trucks, video surveillance three-dimensional maps and street view maps of the urban area where the preset catering stores are located, so as to conveniently deploy fire police forces when necessary, and then participate in fire fighting and rescue as soon as possible. At the same time, it is also possible to collect monitoring data of other catering stores around the preset catering stores within a certain distance range to comprehensively judge the changes in monitoring data and safety status of the preset catering stores, analyze the changes in monitoring data of surrounding catering stores in different operating periods to the changing rules of monitoring data of the preset catering stores, and then predict the potential safety hazards of the preset catering stores in a more comprehensive and in-depth manner.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A fire safety integrated management system based on a cloud platform, characterized in that: include: A comprehensive information acquisition module is used to acquire monitoring data of preset catering stores, and the monitoring data acquisition date is counted from the opening date, and the acquired monitoring data is generated into a cloud platform database, and the cloud database is updated daily; The city data collection module is used to obtain the fire command and dispatch resource information data of the city where the preset catering store is located, upload the obtained information data to the cloud platform library, divide the data in the cloud platform library, and update the cloud platform library at the same time; A data analysis module is used to analyze the monitoring data of the preset catering stores, including analyzing the peak power consumption change data of the preset catering stores, and analyzing the change rules of the current value data and the voltage value data corresponding to the peak power consumption change data, and also includes analyzing the kitchen humidity and heat data; A data processing system, the data processing system responds to the data analysis module, the data processing system includes a determination module and a storage module; The discrimination module is used to discriminate the data analysis results, including processing and discriminating the peak power consumption change data, and processing and discriminating the change rules of the current value data and voltage value data corresponding to the change data, and also includes discriminating the kitchen humidity and heat data; The storage module is based on the discrimination module and is used to record and store data values ​​corresponding to each analysis result, wherein the data values ​​include data values ​​of the analyzed objects, and a safety threshold is preset based on the analyzed objects. When the data processing and discrimination result of any analyzed object exceeds the safety threshold, the system determines that the preset catering store has a fire hazard and issues an early warning. Otherwise, no determination is made. A data fusion sharing unit, which is based on the analysis results obtained by the data processing system and is used to upload the analysis results to the fire safety management department including the cloud platform library, and the data fusion sharing unit includes a communication module; The communication module includes a module for communicating with the fire safety management department of the area where the preset catering store is located by using a wireless networking communication method; The monitoring terminal is used to obtain the fire safety management department that matches the jurisdiction of the urban area where the preset catering store is located, and receive the on-site data uploaded by the collection monitoring point, and update the background data at the same time. When the data processing and judgment result of the preset catering store exceeds the safety threshold, an information request for the hidden danger warning status of the preset catering store is initiated to the fire safety management department that matches the jurisdiction of the urban area.

2. A cloud platform-based fire safety integrated management system as claimed in claim 1, characterized in that: The monitoring data of catering stores around the preset catering store is obtained, and the surrounding range includes 1 to 1.5 kilometers from the preset catering store. The changing patterns of the monitoring data of the surrounding catering stores are collected and obtained, and a prediction model is generated at the same time.

3. The cloud platform-based fire safety integrated management system according to claim 1, characterized in that: In the cloud platform library, the data is divided according to the fire command and dispatch resource information data obtained by the city data acquisition module and the monitoring data obtained by the comprehensive information acquisition module, wherein the fire command and dispatch resource information data includes fire trucks, personnel, water sources, pre-dispatch command plans, key units, real-time traffic conditions, population information, fire-fighting facilities, video surveillance three-dimensional maps and street view maps of the city where the preset catering store is located. When the fire safety management department that matches the jurisdiction of the urban area where the preset catering store is located receives a hidden danger warning status information request initiated by the preset catering store, the fire safety management department analyzes the fire command and dispatch resource information data and dispatches fire police force.

4. The cloud platform-based fire safety integrated management system according to claim 2, characterized in that: Based on the change characteristics of the monitoring data of the surrounding catering stores, the surrounding catering stores are divided into low change level, medium change level and high change level in a step-by-step manner, wherein the low change level, medium change level and high change level respectively correspond to small, medium and large fluctuations in the change of the monitoring data of the catering stores, and based on the medium change level and high change level, the corresponding catering stores are monitored at a level higher than that of the low change level catering stores.

5. The cloud platform-based fire safety integrated management system according to claim 4, characterized in that: Based on the catering stores with medium change levels and high change levels, the fluctuation ranges are divided into the first operating time period, the second operating time period and the third operating time period, wherein the first operating time period, the second operating time period and the third operating time period respectively represent the breakfast time period, the lunch time period and the dinner time period of the catering stores, and the change rules of the monitoring data of the catering stores in the three operating time periods are analyzed, the change rules are uploaded to the prediction model, and the prediction model is updated, and at the same time, based on the second operating time period, the proportion of the change in the monitoring data of the time period to the change in the monitoring data of the catering store throughout the day is calculated, and the calculation is based on the following formula: y t =g(w hi *t+b i ), where y t represents the maximum change data collected by the catering store at the t-th time point in the second operating time period; Where g represents the initial data collected on the monitoring day corresponding to the maximum change data, and w hi represents the i-th factor corresponding to the maximum change data collected for the h-th time at the restaurant, b i represents the number of occurrences of the i-th factor.

6. A cloud platform-based fire safety integrated management system as claimed in claim 5, characterized in that: Among the surrounding catering stores, based on the changing pattern of the monitoring data in the second operating time period, the changing characteristics of the monitoring data of the preset catering stores are collected, wherein the data changes of the current and voltage of the preset catering stores are analyzed based on the changing characteristics of the preset catering stores at peak power consumption and non-peak power consumption. When the data change values ​​of the current and voltage exceed the safety threshold, the system determines that the preset catering store has a fire hazard and issues a warning. Otherwise, no determination is made.

7. The cloud platform-based fire safety integrated management system according to claim 5, characterized in that: Based on the peak power consumption period, the ranking of factors that cause the monitoring data change to exceed the safety threshold during the period is counted, and the factors are ranked according to θ1, θ2, ..., θ n Sorting is performed, where n represents the nth factor, the correlation between the factor sorting and the change of the monitoring data is analyzed, and the correlation rule is uploaded to the prediction model.

8. The cloud platform-based integrated fire safety management system according to claim 7, characterized in that: The Monte Carlo method is used to sample the current data, voltage data and line heat data of the preset catering stores at peak power consumption and non-peak power consumption, determine the current output, voltage and line heat status, and calculate the circuit reliability. Based on the sampling values ​​obtained by the Monte Carlo method, a maintenance and analysis and judgment method for peak power consumption and non-peak power consumption circuit faults is established. At the same time, the impact of weather conditions in different months on the circuit and reliability evaluation are established.

9. The cloud platform-based fire safety integrated management system according to claim 8, characterized in that: The sampled data is divided into a number of evaluation indicators, the evaluation indicator data of the preset catering store and its surrounding catering stores are statistically calculated, the data is normalized, and the weight of each evaluation indicator is calculated.

10. The method of a fire safety integrated management system based on a cloud platform according to claim 1, characterized in that: The following steps are involved: Acquire monitoring data of preset catering stores, and the monitoring data acquisition date is counted from the opening date, and generate a cloud platform database with the acquired monitoring data, and update the cloud database daily; Obtaining fire command and dispatch resource information data of the city where the preset catering store is located, uploading the obtained information data to the cloud platform library, dividing the data in the cloud platform library, and updating the cloud platform library at the same time; Analyze the monitoring data of the preset catering stores, and perform judgment processing on the analysis results, record the data value corresponding to each analysis result, and preset a safety threshold based on the data value of the analyzed object. When the data processing judgment result of any analyzed object exceeds the safety threshold, the system determines that the preset catering store has a fire hazard and issues an early warning. Otherwise, no judgment is made; Obtain the fire safety management department that matches the jurisdiction of the city area where the preset catering store is located, upload the analysis results to the fire safety management department including the cloud platform library, receive the field data uploaded by the collection monitoring point, and update the background data at the same time. When the data processing and judgment result of the preset catering store exceeds the safety threshold, initiate an information request for the hidden danger warning status of the preset catering store to the fire safety management department that matches the jurisdiction of the city area; Obtain monitoring data of catering stores around the preset catering store, collect and obtain the changing rules of the monitoring data of the surrounding catering stores, and generate a prediction model at the same time; In the cloud platform library, the data is divided according to the fire command and dispatch resource information data obtained by the city data collection module and the monitoring data obtained by the comprehensive information acquisition module. When the fire safety management department that matches the urban area where the preset catering store is located receives a hidden danger warning status information request initiated by the preset catering store, the fire safety management department analyzes the fire command and dispatch resource information data and dispatches fire police force.

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