A converged communication intelligent scheduling system

Abstract

The present invention relates to the field of converged communication technologies, and specifically discloses a converged communication intelligent scheduling system, including a target fire scene image acquisition module, a fire spread risk analysis module, a firefighter location information analysis module, a monitoring screen quality assessment module, a communication link status monitoring module, and a converged communication scheduling decision module; The present invention calculates a risk coefficient based on image features and meteorological data to generate a first scheduling instruction, and calculates a concentration density coefficient based on the location information of firefighters to generate a second scheduling instruction; The communication link is adaptively adjusted based on the image quality assessment result, and intelligent scheduling optimization is performed based on the stability assessment result of the communication link, and the first scheduling instruction and the second scheduling instruction are integrated to execute communication resource allocation adjustment; By introducing the converged communication technology, the present invention realizes the instant transmission and efficient integration of information, provides accurate decision-making support for commanders, and overall improves the efficiency and success rate of fire rescue.

Description

Technical Field

[0001] The present invention relates to the technical field of converged communications, and in particular to a converged communications intelligent scheduling system. Background Art

[0002] In emergency response scenarios such as fire rescue, rapid information transmission and accurate analysis are the key to ensuring the success of rescue operations. Converged communication technology, as an outstanding representative of modern information technology, integrates multiple communication methods, such as satellite communication, mobile communication, wireless intercom, etc., to achieve seamless transmission and efficient integration of information, providing strong technical support for real-time information acquisition and command and dispatch at the fire scene. Traditional fire dispatch systems have obvious deficiencies in dealing with complex fire situations and are difficult to meet the needs of modern fire rescue.

[0003] Traditional dispatch systems mainly rely on manual experience to judge fire conditions and dispatch resources, and lack the ability to scientifically analyze and predict the risk of fire spread. In a complex fire environment, it is difficult to accurately assess the development trend of the fire based on experience alone, which can easily lead to improper resource allocation. Although some systems have introduced simple fire analysis models, they cannot effectively combine real-time meteorological data and on-site image features, making it difficult to accurately predict the risk of fire spread.

[0004] In terms of personnel management, the existing system can only provide rough location information and cannot accurately locate and analyze the concentration of firefighters, resulting in unreasonable personnel distribution, scattered rescue forces, and other problems, increasing safety risks. It also lacks the ability to automatically generate dispatch instructions based on the distribution status of personnel, and cannot achieve intelligent personnel deployment.

[0005] In terms of communication support, traditional systems often use a single communication method and lack a real-time monitoring and evaluation mechanism for communication quality. In the case of complex fire environments and blocked signals, communication interruptions or quality degradation are prone to occur, affecting the timeliness and accuracy of command and dispatch. In particular, the existing system lacks the ability to automatically evaluate and adaptively adjust the quality of monitoring images. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a fusion communication intelligent scheduling system to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: a fusion communication intelligent dispatching system, comprising a target fire scene image acquisition module, a fire spread risk analysis module, a firefighter location information analysis module, a monitoring picture quality assessment module, a communication link status monitoring module and a fusion communication dispatching decision module;

[0008] Target fire scene image acquisition module: Based on the real-time image of the target fire scene monitored and displayed by the integrated communication dispatching console, extract the image features of the target fire scene through image analysis technology;

[0009] Fire spread risk analysis module: Based on the image features and meteorological data of the target fire scene, analyze the spread risk of the target fire scene, obtain the fire spread risk coefficient of the target fire scene, and automatically generate the corresponding first dispatch instruction according to the fire spread risk status;

[0010] Firefighter location information analysis module: Used to obtain the real-time location data of firefighters through GPS integration technology, analyze the concentration density coefficient of firefighters, and automatically generate the corresponding second dispatch instruction according to the concentration status of firefighters;

[0011] Monitoring screen quality evaluation module: Used to analyze the resolution, color saturation and packet loss rate of the monitoring screen of the target fire scene, obtain the image quality evaluation coefficient of the target fire scene, and conduct quality evaluation on it, and adaptively adjust the communication link according to the image quality evaluation result;

[0012] Communication link status monitoring module: Used to obtain the communication link status data, analyze the stability evaluation coefficient of the communication link, and conduct stability evaluation on it, and conduct intelligent scheduling optimization on the data with unstable evaluation results;

[0013] Integrated communication scheduling decision-making module: Based on the first dispatch instruction and the second dispatch instruction, execute the allocation and adjustment of communication resources, and output the intelligent scheduling decision result of integrated communication in real time.

[0014] Preferably, the execution method of the target fire scene image acquisition module is as follows:

[0015] Based on the real-time image of the target fire scene monitored and displayed by the integrated communication dispatching console, extract the image features of the target fire scene through image analysis technology. The image features of the target fire scene include the flame spread speed, flame area and flame brightness.

[0016] Preferably, the execution method of the fire spread risk analysis module is as follows:

[0017] Extract the image features of the target fire scene, analyze the image feature risk factors, and the specific calculation formula is as follows:

[0018] , where, IRF represents the image feature risk factor, Fv represents the flame spread speed, Fa represents the flame area, Fb represents the flame brightness, respectively represent a preset maximum flame spread speed threshold, a maximum flame area threshold, and a maximum flame brightness threshold;

[0019] Obtain meteorological data, where the meteorological data includes ambient temperature, wind speed, and air humidity. Analyze the meteorological data and calculate a meteorological data risk factor. The specific calculation formula is as follows:

[0020] , where, MRF represents the meteorological data risk factor, Te represents the ambient temperature, W represents the wind speed, H represents the air humidity, respectively represent a preset standard ambient temperature, standard wind speed, and standard air humidity;

[0021] Read the image feature risk factor IRF and the meteorological data risk factor MRF , analyze the fire spread risk of the target fire ground, and obtain the fire spread risk coefficient of the target fire ground. The specific calculation formula is as follows:

[0022] , where, SRC represents the fire spread risk coefficient of the target fire ground, respectively represent the weight coefficients of the image feature risk factor and the meteorological data risk factor, and ;

[0023] Based on the fire spread risk coefficient of the target fire ground, classify the fire spread risk of the target fire ground. According to the classification result of the fire spread risk level, analyze the corresponding fire spread risk status and automatically generate a corresponding first dispatch instruction.

[0024] Preferably, the specific content of classifying the fire spread risk of the target fire ground is as follows:

[0025] Read the fire spread risk coefficient of the target fire ground and conduct low-risk, medium-risk, and high-risk level classifications. When the fire spread risk coefficient , it indicates that the fire situation of the target fire ground develops slowly, the spread trend is controllable, and the threat to the surrounding environment is small. It is determined that the fire spread risk coefficient level of the target fire ground is low risk at this time; when , it indicates that the fire situation of the target fire ground shows an expanding trend, intervention is required for control, and there are risks in some surrounding areas. It is determined that the fire spread risk coefficient level of the target fire ground is medium risk at this time; when the fire spread risk coefficient , it indicates that the fire situation of the target fire ground is fierce, the spread speed is fast, and it poses a serious threat to the surrounding environment. A full-scale emergency response needs to be launched. It is determined that the fire spread risk coefficient level of the target fire ground is high risk at this time; among them, K1 takes a value of 0.4 and K2 takes a value of 0.7;

[0026] According to the results of the fire spread risk level division, analyze the corresponding fire spread risk status, and automatically generate the corresponding first dispatching instruction.

[0027] Preferably, the execution mode of the firefighter position information analysis module is specifically as follows:

[0028] Obtain the real-time GPS position coordinates of each firefighter through GPS fusion technology , where i represents the number of each firefighter;

[0029] From the formula , , calculate the centroid coordinates of the real-time position of the firefighters ;

[0030] Calculate the distance from each firefighter to the centroid. The specific calculation formula is: , where d i represents the distance from the i-th firefighter to the centroid, i = 1, 2, 3,..., n, and n represents the total number of firefighters;

[0031] Determine the maximum distance from all firefighters to the centroid , and the specific calculation formula is: ;

[0032] From the formula , calculate the concentration density coefficient of the firefighters DCD .

[0033] Preferably, the specific content of the automatically generated corresponding second dispatching instruction is as follows:

[0034] Based on the concentration density coefficient DCD of the firefighters, establish a second dispatching instruction model, specifically as follows:

[0035] , where DCD 1 represents the sparse warning line threshold, DCD 2 represents the dense warning line threshold;

[0036] When , that is, when the concentration density coefficient of the firefighters is less than the sparse warning line threshold, trigger the formula: , and automatically generate an area reinforcement instruction;

[0037] When , that is, when the concentration density coefficient of the firefighters is between the sparse warning line threshold and the dense warning line threshold, trigger the formula: , and automatically generate a dynamic adjustment instruction;

[0038] When When the concentration density coefficient of firefighters is greater than the dense warning line threshold, the formula is triggered: An instruction for personnel dispersion is automatically generated.

[0039] Preferably, the execution mode of the monitoring screen quality evaluation module is as follows:

[0040] Obtain the resolution of the monitoring screen of the target fire scene Rs and color saturation Cs as well as packet loss rate Pl , and calculate the image quality evaluation coefficient QEC of the target fire scene. The specific calculation formula is as follows:

[0041] , where QEC represents the image quality evaluation coefficient of the target fire scene, represents the maximum resolution allowed by the monitoring device, represents the preset maximum allowed saturation, exp represents the exponential function;

[0042] Read the image quality evaluation coefficient of the target fire scene, and compare it with the preset image quality evaluation coefficient threshold. If the image quality evaluation coefficient of the target fire scene is greater than the preset image quality evaluation coefficient threshold, it is determined that the image quality of the target fire scene is normal. If the image quality evaluation coefficient of the target fire scene is less than or equal to the preset image quality evaluation coefficient threshold, it is determined that the image quality of the target fire scene is abnormal, and the communication link is adaptively adjusted according to the image quality evaluation result.

[0043] Preferably, the specific content of adaptively adjusting the communication link according to the image quality evaluation result is as follows:

[0044] When the image quality of the target fire scene is normal, keep the existing acquisition parameters for the target fire scene image acquisition module, and transmit the image through the current communication link;

[0045] When the image quality of the target fire scene is abnormal, trigger the communication link status monitoring module, automatically switch to the standby communication link, and send an image quality abnormality warning to the operation and maintenance terminal through the integrated communication scheduling decision module.

[0046] Preferably, the execution mode of the communication link status monitoring module is as follows:

[0047] Obtain the communication link status data. The communication link status data includes bit error rate Er, transmission delay duration Du, and retransmission rate Rr, and calculate the stability evaluation coefficient of the communication link. The specific calculation formula is: , where SEC represents the stability evaluation coefficient of the communication link, are respectively represented as a preset reference bit error rate, a reference transmission delay duration, and a reference retransmission rate weight coefficients respectively represented as a bit error rate, a transmission delay duration, and a retransmission rate, and ;

[0048] Compare the stability evaluation coefficient of the communication link with a preset stability evaluation coefficient threshold. If the stability evaluation coefficient of the communication link is less than the preset stability evaluation coefficient threshold, it is determined that the stability of the communication link state is normal. If the stability evaluation coefficient of the communication link is greater than or equal to the preset stability evaluation coefficient threshold, it is determined that the stability of the communication link state is abnormal, and intelligent scheduling optimization adjustment is performed on the abnormal data of the communication link state stability.

[0049] As described above, a converged communication intelligent scheduling system provided by the present invention has at least the following beneficial effects:

[0050] A converged communication intelligent scheduling system provided by the present invention obtains real-time images of a target fire scene, extracts image features of the target fire scene through image analysis technology, and accurately calculates a risk coefficient and generates a first scheduling instruction based on the image features and meteorological data, effectively improving the emergency response speed and decision-making accuracy at the fire scene. At the same time, the system can calculate the concentration density coefficient in real time according to the position information of firefighters and generate a second scheduling instruction, ensuring the reasonable distribution and efficient utilization of rescue forces. In addition, the system has the ability to adaptively adjust the communication link based on the image quality evaluation result, ensuring the stability and reliability of information transmission. Combined with the intelligent scheduling optimization of communication link stability, the system can further optimize resource allocation and reduce communication delay. Finally, by integrating the first scheduling instruction and the second scheduling instruction, the system performs precise communication resource allocation adjustment, overall improving the efficiency of fire rescue and ensuring the high efficiency and safety of rescue operations; by introducing converged communication technology, instant transmission and efficient integration of information are realized, providing accurate decision-making support for commanders, and overall improving the efficiency and success rate of fire rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the following drawings without creative efforts.

[0052] Figure 1 It is a schematic structural diagram of a converged communication intelligent scheduling system of the present invention.

[0053] Figure 2 It is a schematic structural diagram of an electronic device of a converged communication intelligent scheduling system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0055] Embodiment 1

[0056] Please refer to Figure 1 As shown, the present invention provides an integrated communication intelligent scheduling system, including a target fire scene image acquisition module, a fire spread risk analysis module, a fireman position information analysis module, a monitoring screen quality evaluation module, a communication link status monitoring module, and an integrated communication scheduling decision module;

[0057] Target fire scene image acquisition module: Based on the real-time image of the target fire scene monitored and displayed on the integrated communication scheduling console, extract the image features of the target fire scene through image analysis technology;

[0058] In this embodiment, it should be specifically noted that the execution method of the target fire scene image acquisition module is as follows:

[0059] Based on the real-time image of the target fire scene monitored and displayed on the integrated communication scheduling console, extract the image features of the target fire scene through image analysis technology. The image features of the target fire scene include the flame spread speed, flame area, and flame brightness; the real-time image of the target fire scene monitored and displayed on the integrated communication scheduling console is accessed through RTSP / RTMP protocol to heterogeneous video sources including but not limited to drones, satellites, and ground cameras, and supports hardware decoding acceleration of H.264 / H.265 encoding format; dock with the scheduling console video platform through GB / T 28181 or ONVIF standard protocol to realize target fire scene image capture and metadata injection.

[0060] Fire spread risk analysis module: Based on the image features of the target fire scene and meteorological data, analyze the spread risk of the target fire scene to obtain the fire spread risk coefficient of the target fire scene, and automatically generate a corresponding first scheduling instruction according to the fire spread risk status;

[0061] In this embodiment, it should be specifically noted that the execution method of the fire spread risk analysis module is as follows:

[0062] Extract the image features of the target fire scene and analyze the image feature risk factors. The specific calculation formula is as follows:

[0063] , where IRF represents the image feature risk factor, Fv represents the flame spread speed,Fa represents the flame area, Fb represents the flame brightness, respectively represent the preset maximum flame spread speed threshold, maximum flame area threshold, and maximum flame brightness threshold;

[0064] It should be specifically noted that in the formula, the larger the flame spread speed Fv, the larger the flame area Fa, and the larger the flame brightness Fb, the larger the image feature risk factor IRF, indicating that the faster the flame spread speed, the larger the flame area, and the higher the flame brightness, the higher the fire spread risk of the target fire scene.

[0065] Obtain meteorological data, where the meteorological data includes environmental temperature, wind speed, and air humidity. Analyze the meteorological data and calculate the meteorological data risk factor. The specific calculation formula is as follows:

[0066] , where, MRF represents the meteorological data risk factor, Te represents the environmental temperature, W represents the wind speed, H represents the air humidity, respectively represent the preset standard environmental temperature, standard wind speed, and standard air humidity;

[0067] It should be specifically noted that in the formula, the environmental temperature Te is larger, the wind speed W is larger, and the air humidity H is smaller, then the meteorological data risk factor MRF is larger, indicating that the higher the environmental temperature, the larger the wind speed, and the lower the air humidity, the higher the fire spread risk of the target fire scene.

[0068] It should be specifically noted that the meteorological data is subscribed to the meteorological bureau API through the MQTT protocol, and QoS2 is set to ensure reliable transmission (such as "GB / T 33670 - 2017 Meteorological Data Communication Protocol").

[0069] Read the image feature risk factor IRF and the meteorological data risk factor MRF , analyze the fire spread risk of the target fire scene, and obtain the fire spread risk coefficient of the target fire scene. The specific calculation formula is as follows:

[0070] , where, SRC represents the fire spread risk coefficient of the target fire scene, respectively represent the weight coefficients of the image feature risk factor and the meteorological data risk factor, and ;

[0071] Based on the fire spread risk coefficient of the target fire scene, grade the fire spread risk of the target fire scene. According to the results of the fire spread risk grade division, analyze the corresponding fire spread risk status, and automatically generate the corresponding first dispatch instruction;

[0072] It should be specifically noted that in a specific embodiment, can be set to 0.6, can be set to 0.4. The image feature risk factor directly reflects the dynamic information of the fire scene such as flame area, spread speed, and brightness, and is an intuitive representation of the fire spread situation, playing a leading role in risk assessment. Therefore, a higher weight of 0.6 is given; while the meteorological data risk factor reflects the influence of external conditions such as environmental temperature, wind speed, and humidity on the fire situation. Although it is an important driving factor for the development of the fire, it belongs to the auxiliary support dimension, so 0.4 is given.

[0073] In this embodiment, it should be specifically noted that the specific content of grading the fire spread risk of the target fire scene is as follows:

[0074] Read the fire spread risk coefficient of the target fire scene and conduct low-risk, medium-risk, and high-risk grade divisions. When the fire spread risk coefficient , it indicates that the fire situation of the target fire scene develops slowly, the spread trend is controllable, and the threat to the surrounding environment is small. It is determined that the fire spread risk coefficient level of the target fire scene is low risk at this time; when the fire spread risk coefficient , it indicates that the fire situation of the target fire scene shows an expanding trend, intervention is required for control, and there are risks in some surrounding areas. It is determined that the fire spread risk coefficient level of the target fire scene is medium risk at this time; when the fire spread risk coefficient , it indicates that the fire situation of the target fire scene is fierce, the spread speed is fast, and it poses a serious threat to the surrounding environment. A full-scale emergency response needs to be initiated. It is determined that the fire spread risk coefficient level of the target fire scene is high risk at this time; among them, the value of K1 is 0.4 and the value of K2 is 0.7;

[0075] According to the results of the fire spread risk grade division, analyze the corresponding fire spread risk status, and automatically generate the corresponding first dispatch instruction;

[0076] When the fire spread risk coefficient level of the target fire scene is low risk, automatically generate the corresponding first dispatch instruction. The specific content of the first dispatch instruction is:

[0077] Dispatch the drone to take pictures of the target fire scene every 30 minutes, and dispatch the meteorological department to synchronize the meteorological data every 2 hours. Through the 5G / self-organizing network link of the communication transmission layer, the image feature data and meteorological data are transmitted back to the fusion communication dispatching desk in real time;

[0078] Activate the fire spread risk analysis module to update the fire spread risk coefficient in real time; and dispatch one fire rescue team to the area within 3 kilometers around the fire scene to set up a physical isolation belt relying on the terrain.

[0079] When the fire spread risk coefficient level of the target fire scene is medium risk, automatically generate the corresponding first dispatch instruction, and the specific content of the first dispatch instruction is as follows:

[0080] Activate the communication resource scheduling function of the system communication transmission layer, prioritize the allocation of communication bandwidth for the area around the fire scene to ensure the stable backhaul of data from the integrated communication dispatching console; deploy portable communication base stations to ensure that the communication coverage rate around the fire scene reaches more than 90%.

[0081] Dispatch the drone to take pictures of the target fire scene every 20 minutes, and dispatch the meteorological department to synchronize meteorological data every 1 hour. Through the 5G / self-organizing network link of the communication transmission layer, the image feature data and meteorological data are transmitted back to the integrated communication dispatching console in real time.

[0082] Activate the fire spread risk analysis module to update the fire spread risk coefficient in real time, and dispatch two fire rescue teams to the area within 2 kilometers around the fire scene to set up a physical isolation belt relying on the terrain.

[0083] When the fire spread risk coefficient level of the target fire scene is high risk, automatically generate the corresponding first dispatch instruction, and the specific content of the first dispatch instruction is as follows:

[0084] Enable the satellite communication link to ensure the transmission of core command information and establish a dual-channel communication between the core command post at the fire scene and the rear command center.

[0085] Dispatch the fire-fighting helicopter associated with the system to perform high-altitude water spraying operations.

[0086] Use the personnel deployment instruction device of the execution dispatching layer to guide the surrounding residents to evacuate along the safety route preset by the data middle platform layer through the integrated communication terminal.

[0087] Coordinate the medical emergency team docked with the system to maintain real-time communication through the integrated communication module in the safe area and prepare for the treatment of the wounded.

[0088] Dispatch the drone to take pictures of the target fire scene every 10 minutes, and dispatch the meteorological department to synchronize meteorological data every 30 minutes. Through the 5G / self-organizing network link of the communication transmission layer, the image feature data and meteorological data are transmitted back to the integrated communication dispatching console in real time.

[0089] Activate the fire spread risk analysis module to update the fire spread risk coefficient in real time, and dispatch all fire rescue forces within 5 kilometers to the scene.

[0090] Firefighter Location Information Analysis Module: Used to obtain real-time location data of firefighters through GPS fusion technology, analyze and obtain the concentration density coefficient of firefighters, and automatically generate corresponding second dispatch instructions according to the concentration status of firefighters;

[0091] In this embodiment, it should be specifically noted that the execution method of the Firefighter Location Information Analysis Module is as follows:

[0092] Obtain the real-time GPS location coordinates of each firefighter through GPS fusion technology (such as Beidou + 5G positioning) , where i represents the number of each firefighter;

[0093] From the formula , , calculate the centroid coordinates of the real-time location of firefighters ;

[0094] It should be specifically noted that the centroid is the geometric center of the firefighter group and represents the average position of the personnel distribution.

[0095] Calculate the distance from each firefighter to the centroid. The specific calculation formula is: , where d i represents the distance from the i-th firefighter to the centroid, i = 1, 2, 3,..., n, and n represents the total number of firefighters;

[0096] Determine the maximum distance from all firefighters to the centroid , and the specific calculation formula is: ;

[0097] From the formula , calculate the concentration density coefficient of firefighters DCD ;

[0098] It should be specifically noted that the unit of the concentration density coefficient formula for firefighters is: people / 100m 2 , divide the total number of firefighters n by the coverage area A, where , to obtain the number of firefighters per unit area. Multiply by 100 to convert to people / 100m 2 , which conforms to the density measurement habit of the fire rescue scenario.

[0099] In this embodiment, it should be specifically noted that the specific content of the automatically generated corresponding second dispatch instructions is as follows:

[0100] Based on the concentration density coefficient of firefighters DCD , establish a second dispatch instruction model, which is specifically as follows:

[0101] , where DCD1 is represented as the sparse warning line threshold, DCD 2 is represented as the dense warning line threshold, DCD The value of 1 is 5 persons / 100m 2 , DCD The value of 2 is 15 persons / 100m 2 ;

[0102] When i.e., the concentrated density coefficient of firefighters is less than the sparse warning line threshold, the trigger formula is: , an area reinforcement instruction is automatically generated, and the second dispatching instruction rule is generated: reinforce according to the difference of 5 persons / 100m 2 ;

[0103] In a specific embodiment, if DCD = 3, the second dispatching instruction rule is generated: 2 persons need to be reinforced to the sparse area, and the specific dispatching instruction is: dispatch 2 firefighters to carry mobile water cannons to the area with coordinates (15, 20), and form a standard formation of 5 persons / 100m after arrival. 2 standard formation.

[0104] When i.e., the concentrated density coefficient of firefighters is between the sparse warning line threshold and the dense warning line threshold, the trigger formula is: , a dynamic adjustment instruction is automatically generated, and the second dispatching instruction rule is generated: adjust the spacing inversely according to the concentrated density coefficient, where the recommended spacing = DCD , ensuring that the spacing is greater than or equal to 10m; where

[0105] In a specific embodiment, if DCD = 10, the second dispatching instruction rule is generated: the recommended spacing = , and the specific dispatching instruction is: each fire brigade advances at an interval of 30m, focuses on investigating combustibles in the middle area, and the thermal imaging images are transmitted back through the communication link.

[0106] When i.e., the concentrated density coefficient of firefighters is greater than the dense warning line threshold, the trigger formula is: , a personnel dispersion instruction is automatically generated, and the second dispatching instruction rule is generated: evacuate according to the difference of 15 persons / 100m 2 ;

[0107] In a specific embodiment, if DCD = 20, the second dispatching instruction rule is generated: 5 persons need to be evacuated to the periphery, and the specific dispatching instruction is: density exceeded, 5 firefighters immediately evacuate 50m to the northeast / southwest directions respectively to form a double-ring defense line, and the evacuation path has avoided the flame spread prediction area.

[0108] Monitoring video quality evaluation module: It is used to analyze the resolution, color saturation, and packet loss rate of the monitoring video of the target fire scene, obtain the image quality evaluation coefficient of the target fire scene, conduct quality evaluation on it, and adaptively adjust the communication link according to the image quality evaluation result;

[0109] In this embodiment, it should be specifically noted that the execution method of the monitoring video quality evaluation module is as follows:

[0110] Obtain the resolution of the monitoring video of the target fire scene Rs , color saturation Cs and packet loss rate Pl , calculate the image quality evaluation coefficient QEC of the target fire scene, and the specific calculation formula is as follows:

[0111] , where QEC represents the image quality evaluation coefficient of the target fire scene, represents the maximum resolution allowed by the monitoring device, represents the preset maximum allowed saturation, and exp represents the exponential function;

[0112] Read the image quality evaluation coefficient of the target fire scene, compare it with the preset image quality evaluation coefficient threshold. If the image quality evaluation coefficient of the target fire scene is greater than the preset image quality evaluation coefficient threshold, it is determined that the image quality of the target fire scene is normal. If the image quality evaluation coefficient of the target fire scene is less than or equal to the preset image quality evaluation coefficient threshold, it is determined that the image quality of the target fire scene is abnormal, and the communication link is adaptively adjusted according to the image quality evaluation result;

[0113] It should be specifically noted that the specific content of adaptively adjusting the communication link according to the image quality evaluation result is as follows:

[0114] When the image quality of the target fire scene is normal, maintain the existing acquisition parameters (such as resolution level, acquisition frequency) for the target fire scene image acquisition module, and stably transmit the image through the current communication link (such as 5G network);

[0115] When the image quality of the target fire scene is abnormal, trigger the communication link status monitoring module, automatically switch to the backup communication link (such as satellite communication or Mesh ad hoc network), prioritize ensuring the transmission stability of image data, reduce the packet loss rate, and send an "image quality abnormal" warning to the operation and maintenance terminal through the integrated communication scheduling decision-making module.

[0116] Communication link status monitoring module: It is used to obtain communication link status data, analyze and obtain the stability evaluation coefficient of the communication link, conduct stability evaluation on it, and perform intelligent scheduling optimization on the data with unstable evaluation results;

[0117] In this embodiment, it should be specifically noted that the execution method of the communication link status monitoring module is as follows:

[0118] Obtain communication link status data, where the communication link status data includes bit error rate Er, transmission delay duration Du, and retransmission rate Rr, and calculate the stability evaluation coefficient of the communication link. The specific calculation formula is as follows:

[0119] , where SEC represents the stability evaluation coefficient of the communication link, respectively represent the preset reference bit error rate, reference transmission delay duration, and reference retransmission rate, respectively represent the weight coefficients of the bit error rate, transmission delay duration, and retransmission rate, and ;

[0120] Compare the stability evaluation coefficient of the communication link with the preset stability evaluation coefficient threshold. If the stability evaluation coefficient of the communication link is less than the preset stability evaluation coefficient threshold, it is determined that the communication link status stability is normal. If the stability evaluation coefficient of the communication link is greater than or equal to the preset stability evaluation coefficient threshold, it is determined that the communication link status stability is abnormal, and intelligent scheduling optimization adjustment is performed on the communication link status stability abnormal data.

[0121] Fusion communication scheduling decision-making module: Based on the first scheduling instruction and the second scheduling instruction, perform the allocation and adjustment of communication resources, and output the intelligent scheduling decision result of the fusion communication in real time.

[0122] Embodiment 2

[0123] An electronic device according to an exemplary embodiment includes: a processor and a memory, where a computer program that can be called by the processor is stored in the memory;

[0124] The processor executes the above-mentioned fusion communication intelligent scheduling system by calling the computer program stored in the memory.

[0125] Figure 2 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present application. This electronic device may vary greatly due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) and one or more memories. Among them, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement a fusion communication intelligent scheduling system provided by each of the above method embodiments.

[0126] The electronic device can also include other components for implementing the functions of the device. For example, the electronic device can also have components such as a wired or wireless network interface and an input / output interface for input and output. Details thereof are not described in the embodiments of the present application.

[0127] This embodiment also provides a computer program product stored on a computer-readable medium, including a computer-readable program, which when executed on an electronic device, provides a user input interface to implement the integrated communication intelligent scheduling system described above.

[0128] It should be understood that in various embodiments of the present application, the order numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0129] It should be understood that determining B based on A does not mean determining B only based on A, and B can also be determined based on A and / or other information.

[0130] Finally, the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0131] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An integrated communication intelligent scheduling system, characterized in that, Including: Target fire scene image acquisition module: Based on the real-time image of the target fire scene monitored and displayed by the integrated communication dispatching console, extract the image features of the target fire scene through image analysis technology; The execution method of the target fire scene image acquisition module is specifically as follows: Based on the real-time image of the target fire scene monitored and displayed by the integrated communication dispatching console, extract the image features of the target fire scene through image analysis technology. The image features of the target fire scene include the flame spread speed, flame area, and flame brightness; Fire spread risk analysis module: Based on the image features of the target fire scene and meteorological data, analyze the spread risk of the target fire scene, obtain the fire spread risk coefficient of the target fire scene, and automatically generate the corresponding first dispatching instruction according to the fire spread risk status; The execution method of the fire spread risk analysis module is specifically as follows: Extract the image features of the target fire scene, analyze the image feature risk factors, and the specific calculation formula is as follows: , where IRF represents the image feature risk factor, Fv represents the flame spread speed, Fa represents the flame area, Fb represents the flame brightness, respectively represent the preset maximum flame spread speed threshold, maximum flame area threshold, and maximum flame brightness threshold; Obtain meteorological data, where the meteorological data includes ambient temperature, wind speed, and air humidity, analyze the meteorological data, and calculate the meteorological data risk factor. The specific calculation formula is as follows: , where MRF represents the meteorological data risk factor, Te represents the ambient temperature, W represents the wind speed, H represents the air humidity, respectively represent the preset standard ambient temperature, standard wind speed, and standard air humidity; Read image feature risk factors IRF and meteorological data risk factors MRF , analyze the spreading risk of the target fire area to obtain the fire spreading risk coefficient of the target fire area. The specific calculation formula is as follows: , where SRC represents the fire spread risk coefficient of the target fire ground, respectively represent the weight coefficients of the image feature risk factor and the meteorological data risk factor, and ; Based on the fire spread risk coefficient of the target fire scene, classify the fire spread risk of the target fire scene. According to the fire spread risk level classification result, analyze the corresponding fire spread risk status, and automatically generate the corresponding first dispatching instruction; Firefighter location information analysis module: Used to obtain the real-time location data of firefighters through GPS fusion technology, analyze the concentration density coefficient of firefighters, and automatically generate the corresponding second dispatching instruction according to the concentration status of firefighters; The execution method of the firefighter location information analysis module is specifically as follows: Obtain the real-time GPS position coordinates of each firefighter through GPS fusion technology , where i represents the number of each firefighter; From the formula , , the centroid coordinates of the real-time position of the firefighter are calculated ; Calculate the distance from each firefighter to the centroid. The specific calculation formula is as follows: , where d i represents the distance from the \(i\)-th firefighter to the centroid, \(i = 1, 2, 3,\cdots, n\), and \(n\) represents the total number of firefighters; Determine the maximum distance from all firefighters to the centroid , and the specific calculation formula is as follows: ; From the formula , the concentrated density coefficient of firefighters is calculated as DCD ; The specific content of the automatically generated corresponding second dispatching instruction is as follows: Based on the centralized density coefficient of firefighters DCD , a second dispatching instruction model is established as follows: , where DCD 1 represents the sparse warning line threshold, DCD 2 represents the dense warning line threshold; When That is, when the concentration density coefficient of firefighters is less than the sparse warning line threshold, the trigger formula: is used to automatically generate an area reinforcement instruction; When that is, when the centralized density coefficient of firefighters is between the sparse warning line threshold and the dense warning line threshold, the trigger formula: is used to automatically generate dynamic adjustment instructions; When That is, when the concentration density coefficient of firefighters is greater than the dense warning line threshold, the formula is triggered: , and a personnel dispersion instruction is automatically generated; Monitoring screen quality evaluation module: Used to analyze the resolution, color saturation, and packet loss rate of the target fire scene monitoring screen, obtain the image quality evaluation coefficient of the target fire scene, and perform quality evaluation on it. Adaptively adjust the communication link according to the image quality evaluation result; The execution method of the monitoring screen quality evaluation module is specifically as follows: Obtain the resolution of the target fire scene monitoring screen Rs , color saturation Cs and packet loss rate Pl , and calculate the image quality evaluation coefficient QEC of the target fire scene. The specific calculation formula is as follows: , where QEC represents the image quality assessment coefficient of the target fire scene, represents the maximum resolution allowed by the monitoring device, represents the preset maximum allowable saturation, exp represents the exponential function; Read the image quality evaluation coefficient of the target fire scene, compare it with the preset image quality evaluation coefficient threshold. If the image quality evaluation coefficient of the target fire scene is greater than the preset image quality evaluation coefficient threshold, it is judged that the image quality of the target fire scene is normal. If the image quality evaluation coefficient of the target fire scene is less than or equal to the preset image quality evaluation coefficient threshold, it is judged that the image quality of the target fire scene is abnormal. Adaptively adjust the communication link according to the image quality evaluation result; Communication link status monitoring module: Used to obtain the communication link status data, analyze the stability evaluation coefficient of the communication link, and perform stability evaluation on it. Perform intelligent scheduling optimization on the data with unstable evaluation results; Integrated communication dispatching decision-making module: Based on the first dispatching instruction and the second dispatching instruction, execute the allocation and adjustment of communication resources, and output the intelligent dispatching decision-making result of integrated communication in real time.

2. The integrated communication intelligent scheduling system according to claim 1, characterized in that: The specific content of classifying the fire spread risk of the target fire scene is as follows: Read the fire spread risk coefficient of the target fire scene, and conduct low-risk, medium-risk, and high-risk level classifications. When the fire spread risk coefficient , it indicates that the fire situation in the target fire scene develops slowly, the spread trend is controllable, and the threat to the surrounding environment is small. It is determined that the fire spread risk coefficient level of the target fire scene is low risk at this time; when , it indicates that the fire situation in the target fire scene shows an expanding trend, intervention is required for control, and there are risks in some surrounding areas. It is determined that the fire spread risk coefficient level of the target fire scene is medium risk at this time; when the fire spread risk coefficient , it indicates that the fire situation in the target fire scene is fierce, the spread speed is fast, and it poses a serious threat to the surrounding environment. A full-scale emergency response needs to be initiated. It is determined that the fire spread risk coefficient level of the target fire scene is high risk at this time; among them, the value of K1 is 0.4, and the value of K2 is 0.7; According to the classification result of the fire spread risk level, analyze the corresponding fire spread risk status, and automatically generate the corresponding first dispatch instruction.

3. The integrated communication intelligent scheduling system according to claim 1, characterized in that: The specific content of adaptively adjusting the communication link according to the image quality evaluation result is as follows: When the image quality of the target fire scene is normal, maintain the existing acquisition parameters for the target fire scene image acquisition module, and transmit the image through the current communication link; When the image quality of the target fire scene is abnormal, trigger the communication link status monitoring module, automatically switch to the standby communication link, and send an image quality abnormality warning to the operation and maintenance terminal through the integrated communication scheduling decision-making module.

4. The integrated communication intelligent scheduling system according to claim 1, characterized in that: The execution method of the communication link status monitoring module is specifically as follows: Obtain communication link status data, where the communication link status data includes bit error rate Er, transmission delay duration Du, and retransmission rate Rr, and calculate the stability evaluation coefficient of the communication link. The specific calculation formula is: , where SEC represents the stability evaluation coefficient of the communication link, respectively represent the preset reference bit error rate, reference transmission delay duration, and reference retransmission rate, respectively represent the weight coefficients of the bit error rate, transmission delay duration, and retransmission rate, and ; Compare the stability evaluation coefficient of the communication link with the preset stability evaluation coefficient threshold. If the stability evaluation coefficient of the communication link is less than the preset stability evaluation coefficient threshold, it is judged that the communication link status stability is normal. If the stability evaluation coefficient of the communication link is greater than or equal to the preset stability evaluation coefficient threshold, it is judged that the communication link status stability is abnormal, and intelligent scheduling optimization adjustment is performed on the communication link status stability abnormal data.

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