Intelligent fire-fighting big data supervision method and system

Abstract

The present application provides a kind of wisdom fire-fighting big data supervision method and system, comprising the following steps: receiving the alarm information of fire-fighting system, determine alarm occurrence position, obtain the monitoring video data of alarm occurrence position;Call the video monitoring data of alarm occurrence position in two hours before alarm occurrence, call the real-time video monitoring data of alarm occurrence position, simultaneously to the correlation processing of two, obtain alarm occurrence reason and solution traceability dataset;Analysis alarm occurrence reason and solution traceability dataset, obtain alarm occurrence reason and best solution, store and record alarm occurrence reason, apply best solution to solve fire alarm;Alarm occurrence reason and best solution are associated, form alarm occurrence solution event group, store to cloud server, synchronize to each video monitoring equipment.The analysis of fire alarm cause is realized at the time of the incident, the best solution of fire alarm is ensured in the first time, and the efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer technology, and in particular to a smart fire-fighting big data supervision method and system. BACKGROUND

[0002] The smart fire-fighting management system is a system that applies GIS technology, wireless communication technology, GPS positioning technology, and Internet of Things terminal technology to the management, supervision, collection, and monitoring services of fire-fighting work. In the fire-fighting management work, the system reflects the real-time updating and dynamic monitoring of various data, the closed-loop management of patrol inspection and operation and maintenance work, the online display of personnel work conditions, and other data platform display. By linking various elements and links in fire-fighting work together and breaking the barriers of information transmission, the system combines, matches, and optimizes online monitoring data and fire-fighting work content, and makes the process of each link of fire-fighting management work clear.

[0003] Chinese patent CN116028671A "Fire-fighting service database management method and system" discloses an intelligent fire-fighting management system based on the Internet of Things, which includes a fire-fighting water source monitoring module, a fire-fighting patrol inspection module, a video monitoring module, and a fire alarm management module. The patent realizes the daily inspection of the distributed detection sensors through the point inspection management module and the test terminal, that is, the daily detection of the sensors. However, when a fire alarm occurs, the patent only stores the fire-fighting data at that time, and then correlates and extracts the data for post-event analysis of the alarm cause. This method has a certain delay and is not timely enough. SUMMARY

[0004] Therefore, the present application provides a smart fire-fighting big data supervision method and system. When a fire alarm occurs, the video monitoring data of the fire alarm are processed in real time, the cause of the fire alarm is analyzed at the time of the incident, and the best solution is obtained according to the analysis of the alarm cause. This not only ensures the analysis of the cause of the fire alarm and facilitates the response next time, but also ensures that the best solution for the fire alarm is obtained at the first time, improving the efficiency.

[0005] The technical solution of the present application is as follows: on the one hand, the present application provides a smart fire-fighting big data supervision method, which includes the following steps:

[0006] S1, receiving the alarm information of the fire-fighting system, determining the alarm occurrence position, and obtaining the monitoring video data of the alarm occurrence position;

[0007] S2, calling the video monitoring data of the alarm occurrence position two hours before the alarm, calling the real-time video monitoring data of the alarm occurrence position, and simultaneously correlating and processing the two data sets to obtain the alarm cause and solution traceability data set;

[0008] S3, analyzing the alarm occurrence reason and the solution trace data set, obtaining the alarm occurrence reason and the best solution, storing and recording the alarm occurrence reason, and applying the best solution to solve the fire alarm;

[0009] S4, associating the alarm occurrence reason and the best solution, forming an alarm occurrence solution event group, storing to the cloud server, and synchronizing to each video monitoring device.

[0010] Preferably, step S1 comprises:

[0011] The fire-fighting system is monitored by the video monitoring device. When an alarm event occurs, the fire-fighting system receives the alarm information sent from the outside, and determines the alarm occurrence position through the position information of the alarm information and the positioning function of the video monitoring device.

[0012] Preferably, step S2 comprises:

[0013] Firstly, the video monitoring data of the alarm occurrence position within two hours before the alarm occurrence is called, and the real-time video monitoring data of the alarm occurrence position at the time of the alarm occurrence is called, the two video monitoring data are associated into a data set, and the alarm occurrence reason and the solution trace data set are obtained.

[0014] Preferably, step S3 comprises:

[0015] S31, analyzing the alarm occurrence reason and the solution trace data set Ω = {A, B},

[0016] Wherein, A is the video monitoring data of the alarm occurrence position within two hours before the alarm occurrence, and B is the real-time video monitoring data of the alarm occurrence position at the time of the alarm occurrence,

[0017] Analyzing the video monitoring data A, obtaining the alarm occurrence reason, storing and recording the alarm occurrence reason;

[0018] S32, analyzing the video monitoring data B, combining the alarm occurrence reason, obtaining the best solution, and applying the best solution to solve the fire alarm.

[0019] Preferably, step S31 comprises:

[0020] S311, analyzing the video monitoring data A, viewing the events occurring within two hours before the alarm occurrence, thereby inferring the alarm occurrence reason, recording the events that can cause the fire alarm in the video monitoring, and then analyzing the alarm occurrence reason from all the events occurring in the video monitoring data A,

[0021] A = {a1, a2, …, a n},

[0022] Wherein, a nA represents events occurred in the video monitoring data A, n represents the number of events occurred in the video monitoring data A, and n is a positive integer not less than 3.

[0023] Preferably, the step S32 comprises:

[0024] S321, analyzing the video monitoring data B to view the events occurred when the alarm occurs, combining the alarm occurrence reason, comprehensively analyzing the alarm occurrence reason and the real-time fire-fighting situation, and then obtaining the best solution from the events occurred in the video monitoring data A and B,

[0025] AB={a1b1, a2b2, …, anbn, …, a n b n},

[0026] Wherein, AB represents a set of all events of the video monitoring data A and B used to obtain the best solution, a n represents events occurred in the video monitoring data A, b n represents events occurred in the video monitoring data B, n represents the number of events occurred in the video monitoring data A, and n is a positive integer not less than 3.

[0027] Preferably, the step S4 comprises:

[0028] The fire-fighting system associates the alarm occurrence reason and the best solution, forms a set of alarm occurrence solution event groups, stores to the cloud server through the network, and synchronizes to each video monitoring device, wherein the video monitoring device stores a plurality of best solutions to cope with different alarm information.

[0029] On the other hand, the present application also provides a smart fire-fighting big data supervision system, which comprises:

[0030] An alarm positioning module, configured to receive alarm information of the fire-fighting system, determine the alarm occurrence position, and obtain monitoring video data of the alarm occurrence position;

[0031] A monitoring data module, configured to call video monitoring data of the alarm occurrence position in two hours before the alarm occurrence, call real-time video monitoring data of the alarm occurrence position, and simultaneously perform association processing on the two to obtain an alarm occurrence reason and a solution traceability data set;

[0032] A reason analysis and solution module, configured to analyze the alarm occurrence reason and the solution traceability data set, obtain the alarm occurrence reason and a best solution, store and record the alarm occurrence reason, and apply the best solution to solve the fire-fighting alarm;

[0033] A record synchronization module, configured to associate the alarm occurrence reason and the best solution, form an alarm occurrence solution event group, store to the cloud server, and synchronize to each video monitoring device.

[0034] In another aspect, the embodiments of the present application also provide a device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program implements the steps of the method for supervising intelligent fire-fighting big data when executed by the processor.

[0035] In another aspect, the embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method for supervising intelligent fire-fighting big data when executed by a processor.

[0036] The method and system for supervising intelligent fire-fighting big data have the following advantages over the prior art:

[0037] (1) The causes of alarms and the best solutions are analyzed one by one by simultaneously extracting video monitoring data two hours before and at the time of the occurrence of the alarms, and the video monitoring data of fire alarms are processed in real time to analyze the causes of the fire alarms at the time of the occurrence, so that the best solutions for the fire alarms are obtained in the first time, and the efficiency is improved.

[0038] (2) The causes of alarms are analyzed from a set of all events of video monitoring data A, and the best solutions for the fire alarms are analyzed from the set of all events of video monitoring data A and corresponding events of video monitoring data B.

[0039] (3) The alarm occurrence solution event group is obtained from video monitoring data A and B, the data of the alarm occurrence solution event group is synchronized to a cloud server through a network, and multiple best solutions are stored in all video monitoring devices to cope with different alarm information by adopting different best solutions. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0041] Figure 1 The flow chart of the method for supervising intelligent fire-fighting big data is provided.

[0042] Figure 2 The structural diagram of the system for supervising intelligent fire-fighting big data is provided. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0044] A smart fire-fighting big data supervision method is provided, as shown in the accompanying drawings, comprising the following steps: Figure 1

[0045] S1, receiving alarm information of a fire-fighting system, determining the alarm occurrence position, and obtaining monitoring video data of the alarm occurrence position;

[0046] S2, calling video monitoring data of the alarm occurrence position two hours before the alarm occurrence, calling real-time video monitoring data of the alarm occurrence position, and simultaneously performing correlation processing on the two to obtain an alarm occurrence reason and a solution traceability data set;

[0047] S3, analyzing the alarm occurrence reason and the solution traceability data set to obtain an alarm occurrence reason and a best solution, storing and recording the alarm occurrence reason, and applying the best solution to solve the fire-fighting alarm;

[0048] S4, correlating the alarm occurrence reason and the best solution to form an alarm occurrence solution event group, storing the alarm occurrence solution event group to a cloud server, and synchronizing the alarm occurrence solution event group to each video monitoring device.

[0049] It should be noted that, in order to realize smart fire-fighting management, the existing smart fire-fighting management system usually stores various types of data in a fire-fighting business database, and after a fire-fighting alarm occurs, the data in the fire-fighting business database is manually reviewed.

[0050] However, the current smart fire-fighting management is mostly after the fire-fighting alarm information is processed. Some problems occurring in the process of processing the fire-fighting alarm information are analyzed after the fact, and then added to the database, so that when the same fire-fighting alarm information is encountered again next time, the processing speed can be accelerated. However, such processing has a problem. If the data processing and analysis are performed after the fact, it is too delayed. The data processing and analysis need to be performed at the same time as the alarm information arrives, highlighting the immediacy, and realizing the analysis of the fire-fighting alarm reason at the time of the incident, ensuring that the best solution to the fire-fighting alarm is obtained at the first time, and improving the efficiency.

[0051] Step S1 comprises:

[0052] ​The fire-fighting system is monitored by a video monitoring device, when an alarm event occurs, the fire-fighting system receives an alarm information sent from outside, and the alarm occurrence position is determined by the position information of the alarm information and the positioning function of the video monitoring device.

[0053] It should be noted that when an alarm event occurs, the alarm information is sent to the fire-fighting system, and the alarm information has position information when the alarm information is sent, and then the nearest video monitoring device to the alarm information sending place is selected through the positioning function of the video monitoring device. The nearest video monitoring device can be one or more, and in the embodiment of the present application, we assume that it is one, but it is also similar in the case of multiple.

[0054] Step S2 includes:

[0055] First, the video monitoring data of the alarm occurrence position two hours before the alarm occurs is called, and the real-time video monitoring data of the alarm occurrence position at the time of the alarm occurs is also called, and the two video monitoring data are associated into a data set to obtain the alarm occurrence reason and the solution tracing data set.

[0056] It should be noted that when analyzing the alarm occurrence reason, the current real-time video data or the video data before the alarm should not be analyzed alone, and the alarm occurrence before and at the time of the alarm should be combined to judge; similarly, the best solution should also be combined with the two video data before and at the time of the alarm, and the best solution is designed according to the alarm occurrence reason.

[0057] Step S3 includes:

[0058] S31, analyze the alarm occurrence reason and the solution tracing data set Ω={A, B},

[0059] Wherein, A is the video monitoring data of the alarm occurrence position two hours before the alarm occurs, and B is the real-time video monitoring data of the alarm occurrence position at the time of the alarm occurs,

[0060] Analyze the video monitoring data A to obtain the alarm occurrence reason, store and record the alarm occurrence reason;

[0061] S32, analyze the video monitoring data B, combine the alarm occurrence reason, obtain the best solution, and apply the best solution to solve the fire alarm.

[0062] It should be noted that the alarm occurrence reason and the solution tracing data set are composed of the video monitoring data A and B, and the alarm occurrence reason can be analyzed from A, and the best solution for the alarm occurrence reason is obtained on the basis of the alarm occurrence reason by combining A and B.

[0063] Step S31 includes:

[0064] S311, analyze the video monitoring data A to see what happened in the two hours before the alarm occurred, so as to deduce the alarm occurrence reason, record the events seen in the video monitoring that will cause the fire alarm, and then analyze the alarm occurrence reason from all the events occurred in the video monitoring data A,

[0065] A = {a1, a2, …, an}, n},

[0066] wherein a n is the event occurred in the video monitoring data A, n is the number of events occurred in the video monitoring data A, and n is a positive integer not less than 3.

[0067] It should be noted that there are many events recorded in the video monitoring data A, and all the events in A are separated and analyzed to deduce the alarm occurrence reason, such as the leakage of flammable gas (liquid), the unextinguished cigarette end, the open fire, etc. The events are comprehensively analyzed to deduce the alarm occurrence reason, which is relatively more accurate.

[0068] Step S32 includes:

[0069] S321, analyze the video monitoring data B to see what happened at the time of the alarm, combine the alarm occurrence reason, comprehensively analyze the alarm occurrence reason and the real-time fire situation, and then get the best solution from the events occurred in the video monitoring data A and B,

[0070] AB = {a1b1, a2b2, …, anbn}, n b n},

[0071] wherein AB represents the set of all events of the video monitoring data A and B used to obtain the best solution, a n is the event occurred in the video monitoring data A, b n is the event occurred in the video monitoring data B, n is the number of events occurred in the video monitoring data A, and n is a positive integer not less than 3.

[0072] It should be noted that the video monitoring data B corresponds to the video monitoring data A, which is the image two hours after the video monitoring data A, so you can find the corresponding event in B for each event in A, and combine the two, see what has happened after two hours, and then make a judgment again, eliminate those events that may not be the cause of the fire alarm information and those events that are not important. Finally, take measures for the most important event or several events, i.e. the best solution.

[0073] Step S4 includes:

[0074] The fire-fighting system associates the alarm occurrence reason and the optimal solution to form a set of alarm occurrence and solution event groups, stores to the cloud server through the network, synchronizes to each video monitoring device, and the video monitoring device stores multiple optimal solutions to cope with different alarm information.

[0075] It should be noted that after analyzing the alarm occurrence reason and obtaining the optimal solution, the two are associated into a set and stored to the cloud server through the network, each video monitoring device is connected to the network, each video monitoring device is synchronized through the network, and each video monitoring device stores all optimal solutions, so that the same or similar alarm information can be handled quickly by using the recorded optimal solution in the future, thereby greatly improving the efficiency.

[0076] In order to realize the above-mentioned intelligent fire-fighting big data supervision method, Figure 2 The system includes:

[0077] The alarm positioning module is used for receiving alarm information of the fire-fighting system, determining the alarm occurrence position, and obtaining monitoring video data of the alarm occurrence position.

[0078] The monitoring data module is used for calling video monitoring data of the alarm occurrence position two hours before the alarm occurrence, calling real-time video monitoring data of the alarm occurrence position, and associating the two to obtain alarm occurrence reason and solution traceability data set.

[0079] The reason analysis and solution module is used for analyzing the alarm occurrence reason and the solution traceability data set, obtaining the alarm occurrence reason and the optimal solution, storing and recording the alarm occurrence reason, and applying the optimal solution to solve the fire-fighting alarm.

[0080] The record synchronization module is used for associating the alarm occurrence reason and the optimal solution to form an alarm occurrence and solution event group, storing to the cloud server, and synchronizing to each video monitoring device.

[0081] It should be noted that in order to realize intelligent fire-fighting management, the existing intelligent fire-fighting management system usually uses a fire-fighting business database to store various data, and after a fire-fighting alarm occurs, the data in the fire-fighting business database is manually reviewed.

[0082] And the current wisdom fire management is mostly in the fire alarm information processing after, for some problems in the fire alarm information processing process, in the after analysis, and then join the database, in order to when the next time again encounter the same fire alarm information, can speed up the processing speed. But such processing will exist a problem, if all in the after data processing analysis, too late, need to alarm information to the data processing analysis at the same time, highlight the instantaneity, in the event of the same time to realize the analysis of the cause of the fire alarm, ensure the first time to get the best solution of fire alarm, improve the efficiency.

[0083] The alarm positioning module is used for:

[0084] The fire system is monitored by video monitoring equipment, when the alarm event occurs, the fire system receives the alarm information sent from the outside, and determines the alarm occurrence position through the position information of the alarm information and the positioning function of the video monitoring equipment.

[0085] It should be noted that: when the alarm event occurs, the alarm information is sent to the fire system, and the alarm information has the position information when the alarm information is sent. Then the positioning function of the video monitoring equipment is selected to select the nearest video monitoring equipment from the alarm information sending place. The nearest video monitoring equipment can be one or more, in the embodiment of the present application we assume that it is one, but in the case of multiple, it is also similar.

[0086] The monitoring data module is used for:

[0087] Firstly, the video monitoring data of the alarm occurrence position two hours before the alarm occurrence is called, and the real-time video monitoring data of the alarm occurrence position at the time of the alarm occurrence is called, the two video monitoring data are associated into a data set, and the alarm occurrence reason and the solution traceability data set are obtained.

[0088] It should be noted that: when analyzing the alarm occurrence reason, the current real-time video data or the video data before the alarm should not be analyzed alone, and the alarm occurrence before and at the time of the alarm should be combined to judge; similarly, the best solution should also be combined with the two video data before and at the time of the alarm, and the best solution is designed according to the alarm occurrence reason.

[0089] The reason analysis and solution module is used for:

[0090] The alarm occurrence reason unit is used for analyzing the alarm occurrence reason and the solution traceability data set Omega={A,B},

[0091] Wherein, A is the video monitoring data of the alarm occurrence position two hours before the alarm occurrence, and B is the real-time video monitoring data of the alarm occurrence position at the time of the alarm occurrence,

[0092] The alarm cause analysis unit is configured to analyze the video monitoring data A to obtain the alarm cause, and store and record the alarm cause.

[0093] The best solution unit is configured to analyze the video monitoring data B, combine the alarm cause, and obtain the best solution to the fire alarm.

[0094] It should be noted that the alarm cause and the solution tracing data set are composed of the video monitoring data A and B, the alarm cause can be analyzed from the A, and the best solution to the alarm cause is obtained on the basis of the alarm cause by combining the A and the B.

[0095] The alarm cause unit is configured to:

[0096] The alarm cause analysis unit is configured to analyze the video monitoring data A to obtain the alarm cause, and store and record the alarm cause.

[0097] A = {a1, a2, …, an}, n},

[0098] wherein a n is an event occurring in the video monitoring data A, n is the number of events occurring in the video monitoring data A, and n is a positive integer not less than 3.

[0099] It should be noted that there are many events recorded in the video monitoring data A, and all the events in A are separately divided, and the alarm cause is analyzed by n events, such as flammable gas (liquid) leakage, unextinguished cigarette, open fire, etc. The alarm cause is analyzed by comprehensively analyzing these events, which is relatively more accurate.

[0100] The best solution unit is configured to:

[0101] The best solution unit is configured to analyze the video monitoring data B, combine the alarm cause, and obtain the best solution to the fire alarm.

[0102] AB = {a1b1, a2b2, …, anbn}, n b n},

[0103] wherein AB represents a set of all events of the video monitoring data A and B used to obtain the best solution, a n is an event occurring in the video monitoring data A, and b nFor the event occurred in the video monitoring data B, the number of events occurred in the video monitoring data A is n, and n is a positive integer not less than 3.

[0104] It should be noted that: corresponding to the video monitoring data A, the video monitoring data B is the image two hours after the video monitoring data A, so you can find the corresponding event of each event in A in B, and combine the two, after two hours, what changes have occurred, and then make a judgment again, exclude those events that may not be the cause of the fire alarm information and those events that are not important. Finally, take measures for the most important event or several events, that is, the best solution.

[0105] The recording synchronization module is used to:

[0106] The fire-fighting system associates the alarm occurrence reason and the best solution to form a set of alarm occurrence and solution event groups, and stores the set to the cloud server through the network and synchronizes to each video monitoring device, wherein the video monitoring device stores a plurality of best solutions to cope with different alarm information.

[0107] It should be noted that: after analyzing the alarm occurrence reason and obtaining the best solution, the two are associated into a set and stored to the cloud server through the network, and each video monitoring device is connected to the network and synchronized to each video monitoring device through the network, and each video monitoring device stores all the best solutions, so that when encountering the same or similar alarm information in the future, the best solution recorded can be taken to quickly process, greatly improving the efficiency.

[0108] On the other hand, the embodiment of the present application also provides a device, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program is executed by the processor to realize the steps of the intelligent fire-fighting big data supervision method.

[0109] On the other hand, the embodiment of the present application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the intelligent fire-fighting big data supervision method.

[0110] The above only describes the preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A smart fire-fighting big data supervision method, characterized in that, The method comprises the following steps: S1, receiving alarm information of a fire-fighting system, determining an alarm occurrence position, and obtaining monitoring video data of the alarm occurrence position; S2, calling video monitoring data of the alarm occurrence position two hours before the alarm occurrence, calling real-time video monitoring data of the alarm occurrence position, simultaneously performing association processing on the two, obtaining an alarm occurrence cause and a solution trace data set; S3, analyzing the alarm occurrence cause and the solution trace data set, obtaining an alarm occurrence cause and a best solution, storing and recording the alarm occurrence cause, and applying the best solution to solve the fire-fighting alarm; The step S3 comprises: S31, analyzing the alarm occurrence cause and the solution trace data set Ω={A, B}, Wherein, A is the video monitoring data of the alarm occurrence position two hours before the alarm occurrence, and B is the real-time video monitoring data of the alarm occurrence position at the time of the alarm occurrence, Analyzing the video monitoring data A, obtaining an alarm occurrence cause, storing and recording the alarm occurrence cause; S32, analyzing the video monitoring data B, combining the alarm occurrence cause, obtaining a best solution, and applying the best solution to solve the fire-fighting alarm; The step S31 comprises: S311, analyzing the video monitoring data A, viewing events occurring within two hours before the alarm occurrence, thereby inferring an alarm occurrence cause, recording events that can cause a fire-fighting alarm seen in the video monitoring, and then analyzing the alarm occurrence cause from all events occurring in the video monitoring data A, A = {a1, a2,..., a n}, wherein a n is an event occurring in the video monitoring data A, n is the number of events occurring in the video monitoring data A, and n is a positive integer not less than 3; The step S32 comprises: S321, analyzing the video monitoring data B, viewing events occurring at the time of the alarm occurrence, combining the alarm occurrence cause, comprehensively analyzing the alarm occurrence cause and real-time fire-fighting situation, and then obtaining a best solution from events occurring in the video monitoring data A and B, AB = {a1b1, a2b2,..., anbn}, n = 1, 2, 3,..., n b n}, wherein AB represents a set of events of all video monitoring data A and B used to obtain an optimal solution, a n for an event occurring in video monitoring data A, b n for an event occurring in video monitoring data B, n is a number of events occurring in video monitoring data A, and n is a positive integer not less than 3; S4, associating the alarm occurrence cause and the best solution to form an alarm occurrence solution event group, storing to a cloud server, and synchronizing to each video monitoring device; The step S4 comprises: The fire-fighting system associates the alarm occurrence cause and the best solution to form a set form of an alarm occurrence solution event group, stores to a cloud server through a network, and synchronizes to each video monitoring device, wherein the video monitoring device stores a plurality of best solutions to cope with different alarm information.

2. The intelligent fire-fighting big data supervision method of claim 1, wherein, The step S1 comprises: The fire-fighting system monitors through a video monitoring device, when an alarm event occurs, receives alarm information sent from outside, and determines an alarm occurrence position through position information of the alarm information and a positioning function of the video monitoring device.

3. The intelligent fire-fighting big data supervision method of claim 1, wherein, The step S2 comprises: First, call the video monitoring data of the alarm occurrence position two hours before the alarm occurrence, simultaneously call the real-time video monitoring data of the alarm occurrence position at the time of the alarm occurrence, associate the two video monitoring data into a data set, and obtain an alarm occurrence cause and a solution trace data set.

4. A smart fire-fighting big data supervision system for performing the smart fire-fighting big data supervision method according to any one of claims 1-3, characterized in that, The system comprises: An alarm positioning module, configured to receive alarm information of a fire-fighting system, determine an alarm occurrence position, and obtain monitoring video data of the alarm occurrence position; The monitoring data module is used to call video monitoring data of the alarm occurrence position in two hours before the alarm occurrence, call real-time video monitoring data of the alarm occurrence position, and perform correlation processing on the two to obtain alarm occurrence reason and solution trace data sets; The reason analysis and solution module is used to analyze the alarm occurrence reason and solution trace data sets, obtain the alarm occurrence reason and the best solution, store and record the alarm occurrence reason, and apply the best solution to solve the fire alarm; The record synchronization module is used to correlate the alarm occurrence reason and the best solution, form an alarm occurrence solution event group, store the alarm occurrence solution event group to a cloud server, and synchronize to each video monitoring device.

5. An electronic device, comprising: The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program implements the steps of the method of any one of claims 1 to 3 when executed by the processor.

6. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable storage medium, and the computer program implements the steps of the method of any one of claims 1 to 3 when executed by the processor.

Images