Fire-fighting communication quality monitoring system and method based on big data

Abstract

This invention discloses a fire communication quality monitoring system and method based on big data, belonging to the field of fire communication quality monitoring technology. This invention combines on-site combat status, building space blockage, historical loss of communication events, real-time environmental status, and real-time communication quality for joint processing. It can identify key communication links under different fire-fighting tasks, dynamically assess link risks, and output communication quality trend results. Compared with existing static monitoring methods, this invention can not only reflect the current communication status, but also predict the risk of loss of communication in advance and output support and adjustment signals, thereby improving the continuity of command and communication and the reliability of on-site coordination in complex fire scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fire-fighting communication quality monitoring, and in particular to a fire-fighting communication quality monitoring system and method based on big data. BACKGROUND

[0002] In the process of fire rescue, the communication system is an important support foundation for on-site command, operational coordination, personnel scheduling and risk disposal. In complex scenes such as large commercial complexes, underground garages, subway station halls, high-rise building interiors and chemical plant areas, the building structure is complex, the degree of space closure is high, the wall blocking is strong, there are many corner nodes, and factors such as smoke diffusion, temperature change, door opening and closing, and personnel advancing path change will continuously affect the communication link quality. Especially in the stages of internal attack fire extinguishing, personnel search and rescue, water supply coordination, dangerous area disposal and on-site transfer, the dependence of different operational tasks on voice instruction transmission, positioning feedback, state feedback and coordination contact is not the same, and once the key link deteriorates or loses connection, it will directly affect the on-site command efficiency and disposal safety.

[0003] Existing fire-fighting communication monitoring technologies mostly focus on static collection and abnormal alarm of communication indicators such as signal strength, time delay and packet loss rate, although they can reflect the communication state at a certain moment, they usually only monitor the surface of the communication result, lacking joint analysis of fire scene task state, operational grouping relationship, advancing position change and building blocking factors. For the same communication fluctuation, the actual impact caused by different fire stages is not consistent, and the existing technology often cannot identify this difference, and it is also difficult to judge which link belongs to the most critical operational communication link at present.

[0004] In addition, in underground spaces, high-rise interiors and large complexes, communication failure is often not a sudden isolated event, but is formed by the joint accumulation of space blocking, environmental deterioration and advancing path change, and has obvious evolution characteristics. The existing technology can only alarm after the communication quality has decreased significantly, it is difficult to make risk judgments before the loss of connection area really forms, it is also difficult to give targeted relay adjustment, priority protection or link optimization suggestions according to the current operational state, and therefore it is difficult to meet the requirements of communication continuity, key link protection capability and early intervention capability in complex fire fighting. SUMMARY

[0005] The technical problem solved by the present application is that the existing technology is difficult to continuously monitor, trend predict and output executable protection adjustment signals for communication quality under the condition of dynamic evolution of complex fire scenes, in combination with specific fire fighting operational tasks, space blocking state and key link value.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] A fire communication quality monitoring system based on big data includes a task profile creation module, a spatial obstruction analysis module, a critical link identification module, a quality trend assessment module, and a result output module.

[0008] The task profiling module is used to collect on-site combat status data and historical combat status data, establish a task communication profiling mechanism, and obtain an initial evaluation model.

[0009] The spatial blocking analysis module is used to collect building space data, historical disconnection event data and real-time environmental status data, establish a spatial blocking mechanism, embed the spatial blocking mechanism into the initial evaluation model, and obtain the link evaluation model.

[0010] The critical link identification module is used to collect real-time communication quality data, establish a link value mechanism, input the real-time communication quality data into the link evaluation model, and obtain the link risk results.

[0011] The quality trend assessment module is used to establish a trend assessment mechanism based on the link risk results and obtain communication quality trend results.

[0012] The result output module is used to output an adjustment signal based on the communication quality trend result and send it to the notification end.

[0013] Preferably, the mission profile building module includes a combat data acquisition unit and a profile mechanism building unit;

[0014] The combat data acquisition unit is used to collect on-site combat status data and historical combat status data. The on-site combat status data includes alarm type data, combat phase data, combat formation data, and advance position data. The historical combat status data includes historical alarm type data, historical combat phase data, historical combat formation data, and historical advance position data.

[0015] The profiling mechanism establishment unit is used to establish a mission communication profiling mechanism based on on-site combat status data and historical combat status data.

[0016] Preferably, the logic of the task communication profiling mechanism is as follows:

[0017] Based on historical alarm type data and historical combat phase data, the system is divided into three states: high command dependence, high data feedback dependence, and continuous coordination. Values ​​are assigned to these states based on historical combat formation data and historical advance position data, and the output is a mission profile value.

[0018] The alarm type data, operational phase data, operational group data, and advance position data are input into the mission communication profiling mechanism to obtain the current mission profile value, and the current mission profile value is written into the initial evaluation model.

[0019] Preferably, the space blocking analysis module includes a blocking data acquisition unit and a blocking mechanism establishment unit;

[0020] The data acquisition unit is used to collect building space data, historical disconnection event data, and real-time environmental status data. The building space data includes floor structure data, wall distribution data, passage node data, and fixed communication equipment location data. The historical disconnection event data includes historical disconnection location data and historical disconnection time period data. The real-time environmental status data includes real-time smoke status data, real-time temperature status data, and real-time door opening and closing status data.

[0021] The blocking mechanism establishment unit is used to establish a spatial blocking mechanism based on building space data, historical loss of contact event data, and real-time environmental status data.

[0022] Preferably, the logic of the spatial blocking mechanism is as follows:

[0023] The basic blocking zone is divided based on floor structure data, wall distribution data, and passage node data. The historical high disconnection zone is extracted based on historical disconnection location data and historical disconnection time data. The basic blocking zone and historical high disconnection zone are corrected based on real-time smoke status data, real-time temperature status data, and real-time door opening and closing status data. The output is the real-time blocking zone.

[0024] The location data of fixed communication equipment is mapped to the real-time blocking zone to obtain the spatial blocking value, and the spatial blocking value is embedded into the initial evaluation model to obtain the link evaluation model.

[0025] Preferably, the critical link identification module includes a communication data acquisition unit and a link value establishment unit;

[0026] The communication data acquisition unit is used to acquire real-time communication quality data, which includes real-time latency data, real-time packet loss data, real-time signal strength data, and real-time voice interruption count data.

[0027] The link value establishment unit is used to establish a link value mechanism based on the current mission profile value, advance position data, combat formation data and real-time communication quality data, and to obtain link risk results.

[0028] Preferably, the logic of the link value mechanism is as follows:

[0029] The link quality value is obtained by weighting the real-time latency data, real-time packet loss data, real-time signal strength data, and real-time voice interruption count data according to the current task profile value.

[0030] Based on advance location data and combat formation data, command transmission links, backhaul transmission links, and coordination transmission links are identified, and the link quality values ​​are corrected by combining spatial blocking values, outputting the link risk results.

[0031] Preferably, the quality trend assessment module includes a trend mechanism establishment unit and a trend result acquisition unit;

[0032] The trend mechanism establishment unit is used to establish a trend assessment mechanism based on link risk results, real-time environmental status data, and advancement location data.

[0033] The trend result acquisition unit is used to input the link risk result, real-time environmental status data and advance position data into the trend evaluation mechanism to obtain the communication quality trend result, which includes stable state, deterioration state and disconnection warning state.

[0034] Preferably, the result output module includes a result response unit and a signal transmission unit;

[0035] The result response unit is used to obtain the communication quality trend result. If the communication quality trend result is in a stable state, a hold signal is output.

[0036] If the communication quality trend result is a deterioration state, then output a relay adjustment signal;

[0037] If the communication quality trend result indicates a loss of connection warning, then a priority protection signal will be output.

[0038] The signal transmitting unit is used to send hold signals, relay adjustment signals, or priority protection signals to the notification end.

[0039] A big data-based method for monitoring the quality of fire communication includes the following steps:

[0040] Step S1: Collect on-site combat status data and historical combat status data, establish a mission communication profile mechanism, and obtain an initial assessment model;

[0041] Step S2: Collect building space data, historical disconnection event data and real-time environmental status data, establish a spatial blocking mechanism, embed the spatial blocking mechanism into the initial assessment model, and obtain the link assessment model;

[0042] Step S3: Collect real-time communication quality data, establish a link value mechanism, input the real-time communication quality data into the link evaluation model, and obtain the link risk results;

[0043] Step S4: Establish a trend assessment mechanism based on the link risk results to obtain communication quality trend results;

[0044] Step S5: Output an adjustment signal based on the communication quality trend result and send it to the notification end.

[0045] The beneficial effects of this invention are as follows: This invention combines on-site combat status, building space blockage, historical loss of contact events, real-time environmental status, and real-time communication quality for joint processing. It can identify key communication links under different fire-fighting tasks, dynamically assess link risks, and output communication quality trend results. Compared with existing static monitoring methods, this invention can not only reflect the current communication status, but also predict the risk of loss of contact in advance and output support and adjustment signals, thereby improving the continuity of command and communication and the reliability of on-site coordination in complex fire scenes. Attached Figure Description

[0046] Figure 1 A basic flowchart of a fire communication quality monitoring system based on big data is provided as an embodiment of the present invention;

[0047] Figure 2 The present invention provides a flowchart of a fire communication quality monitoring method based on big data, which is an embodiment of the present invention. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0049] Example 1, refer to Figure 1 This paper presents a big data-based fire communication quality monitoring system, deployed between a fire command terminal, a vehicle-mounted edge computing terminal, and a back-end monitoring server. The notification end includes a command terminal and a communication support terminal. During system operation, the task profile creation module first processes on-site and historical operational status data to obtain the current task profile value. Then, the spatial obstruction analysis module processes building space data, historical disconnection event data, and real-time environmental status data to obtain spatial obstruction values. The current task profile value and spatial obstruction value are then jointly written into the link evaluation model. Next, the critical link identification module calculates the link value from the real-time communication quality data to obtain the link risk result. Finally, the quality trend evaluation module, based on the link risk result, real-time environmental status data, and advance position data, obtains the communication quality trend result. Finally, the result output module sends hold signals, relay adjustment signals, or priority protection signals to the notification end.

[0050] This invention integrates on-site combat status, building space disruption, historical loss of communication events, real-time environmental status, and real-time communication quality for joint processing. It can identify critical communication links under different firefighting tasks, dynamically assess link risks, and output communication quality trend results. Compared with existing static monitoring methods, this invention can not only reflect the current communication status but also predict the risk of loss of communication in advance and output support and adjustment signals, thereby improving the continuity of command and communication and the reliability of on-site coordination in complex fire scenes.

[0051] The operational data acquisition unit is used to collect on-site operational status data and historical operational status data. On-site operational status data includes incident type data, operational phase data, operational formation data, and advance position data. Historical operational status data includes historical incident type data, historical operational phase data, historical operational formation data, and historical advance position data. Incident type data can be coded according to shopping mall fires, high-rise fires, underground space fires, and warehouse fires, and denoted as follows: Operational phase data is coded according to the deployment phase, interior attack phase, search and rescue phase, smoke removal phase, and transfer phase, respectively denoted as: Operational grouping data is used to characterize the communication dependencies between the current command group, internal attack group, search and rescue group, water supply group, and communications support group, denoted as... The positioning data is uploaded by firefighters' positioning terminals and is represented using the building's internal coordinate system, denoted as... .

[0052] The profiling mechanism establishment unit is used to establish a mission communication profiling mechanism based on on-site combat status data and historical combat status data. For any historical sample... First, historical alarm type data, historical combat phase data, historical combat formation data, and historical advance position data are used to construct a mission state vector, which serves as the unified input for subsequent dependency extraction.

[0053] ;

[0054] Based on the aforementioned task state vector and combined with historical communication impact records, instruction dependency, return dependency, and cooperation dependency are extracted respectively, denoted as... , and Command dependency reflects the impact of voice command interruptions on combat operations; feedback dependency reflects the impact of location feedback or status feedback interruptions on combat operations; and coordination dependency reflects the degree of multi-group synchronous communication requirements. All three values ​​range from 0 to 1. Subsequently, a mission communication profiling function is established:

[0055] ;

[0056] In the formula, , and This is used for profile weighting. After the current on-site sample is input into the task communication profile mechanism, the current task profile value is obtained. The higher the current task profile value, the greater the dependence of the current scenario on communication continuity and the arrival of key instructions. This current task profile value is written into the initial evaluation model, forming the first input to the subsequent link evaluation model.

[0057] The data acquisition unit for blocking access is used to collect building space data, historical data on lost communication events, and real-time environmental status data. Building space data includes floor structure data, wall distribution data, passageway node data, and fixed communication equipment location data; historical data on lost communication events includes historical location data and historical time periods of lost communication; real-time environmental status data includes real-time smoke status data, real-time temperature status data, and real-time door opening / closing status data. The blocking mechanism establishment unit first calculates the basic blocking quantity for each space unit:

[0058] ;

[0059] ;

[0060] ;

[0061] in, Representing spatial units The basic blocking amount, Indicates the degree of floor obstruction. Indicates the degree of wall obstruction. Indicates the degree of channel turning. , , These represent the basic obstruction weights corresponding to the degree of floor obstruction, wall obstruction, and passageway turning, respectively. All basic obstruction weights are non-negative and are obtained through training or calibration using the correspondence between historical disconnection event data and building space data. During training or calibration, the weights are determined by whether the historical disconnection location data falls within a spatial unit. As a benchmark, if the spatial unit corresponding to a certain type of building structure appears frequently in historical loss-of-connection events, the foundation blocking weight corresponding to that type of structural element is increased; if it appears infrequently, the corresponding foundation blocking weight is decreased. Indicates the frequency of historical loss of contact. Representing spatial units Real-time blocking volume, This represents the quantized value of real-time flue gas status data. This represents the quantized value of real-time temperature status data. This represents the quantized value of the real-time door opening and closing status data. , , , These represent the dynamic correction weights corresponding to historical frequency of communication loss, real-time flue gas status data, real-time temperature status data, and real-time door opening / closing status data, respectively. Each dynamic correction weight is a non-negative value and is trained or calibrated based on the correspondence between environmental state changes and communication quality degradation in historical communication loss events. The quantized value of the real-time flue gas status data... According to spatial units The quantized value of real-time temperature status data is obtained from the values ​​collected by the internal flue gas concentration sensor or from the on-site flue gas level determination. According to spatial units The quantized value of the real-time door opening and closing status data is obtained from the internal temperature sensor or infrared temperature measurement results. This can be obtained based on the opening and closing status of fire doors, roller shutters, or passageway doors. Through the above processing, the system can incorporate both fixed building blocking factors and dynamic fire scene environmental factors into the calculation of spatial blocking values. Representing spatial units Historical frequency of missing persons This indicates the total number of historical missing persons events. The reciprocal of the term, This indicates that historical data on lost locations has fallen into spatial units. The cumulative number of times, by spatial units The ratio of the cumulative number of lost contacts to the total number of historical lost contacts events can be used to calculate the spatial unit. The relative frequency of occurrence among all historical communication loss events is used to characterize the historical tendency of communication loss in similar fire scenarios for this space unit. The real-time blocking amount of each space unit is mapped with the location data of fixed communication equipment to obtain the spatial blocking value of the area corresponding to the advance position data. The larger the spatial blocking value, the more likely the current location and its surrounding area are to form a high communication attenuation zone or a disconnection zone. This spatial blocking value is embedded into the initial evaluation model to obtain the link evaluation model.

[0062] In this embodiment, the interior of the building is divided into several spatial units. This can be a room, a section of corridor, a stair landing, or a facility room area. The degree of floor partitioning... Degree of wall obstruction and the degree of channel turning All data are derived from architectural space data and used to characterize spatial units. Basic communication disruptions between the location of fixed communication equipment and the location of the equipment.

[0063] Among them, the degree of floor barrier Calculated from floor structure data, used to characterize spatial units Communication disruptions between the location of fixed communication equipment and the actual location can be caused by differences in floor levels, floor structure, and vertical connectivity. Specifically, when a spatial unit... When the fixed communication equipment is located on the same floor, the floor barrier value is lower; when there is cross-floor propagation, the floor barrier value is increased based on the number of floors crossed, floor slab thickness, fire compartment relationship, and whether the stairwell or shaft is connected. Its quantification method can be expressed as:

[0064] ;

[0065] In the formula, Representing spatial units The number of floors between the location of the fixed communication equipment and the location of the fixed communication equipment. Indicates the barrier level of the corresponding floor slab structure. Representing spatial units Are there vertical connecting passages such as stairwells, pipe shafts, or elevator shafts between the location of the fixed communication equipment and the location of the equipment? This represents the floor barrier quantification function. Through this process, the degree of floor barrier is determined. It can be calculated directly from the floor structure data, rather than being arbitrarily specified by humans.

[0066] wall barrier Calculated from wall distribution data, used to characterize spatial units Signal attenuation caused by solid walls, firewalls, metal partitions, or fire doors between the signal source and the location of fixed communication equipment. Specifically, the degree of wall obstruction is determined based on the number of walls along the propagation path, the wall material's barrier level, and the door's opening / closing status. This can be quantified as follows:

[0067] ;

[0068] In the formula, Representing spatial units The number of walls between the location of the fixed communication equipment and the location of the equipment. Indicates the barrier rating of the wall material. Indicates the opening and closing state of the gate along the propagation path. This represents the quantification function of wall barrier properties. Through this process, the degree of wall barrier property is determined. It is directly related to the number of walls, wall materials, and door conditions, and can reflect the actual impact of complex building interior wall structures on fire communication.

[0069] Channel turning degree Calculated from channel node data, used to characterize spatial units The complexity of the propagation path between the fixed communication equipment location and the location is determined by factors such as corridor corners, stairwell turns, equipment room detours, and narrow passages. Specifically, the degree of passageway complexity is determined based on the number of corners, the angle of the corners, and the width of the passage. This complexity can be quantified as follows:

[0070] ;

[0071] In the formula, Representing spatial units The number of channel turns between the location of the fixed communication equipment and the location of the fixed communication equipment. This represents a combined value indicating the turning angle of the path. Indicates the corresponding channel width level. This represents the channel inflection quantization function. Through this processing, the degree of channel inflection is determined. It can be quantified from channel node data and is used to reflect the impact of corners, narrow passages and detours on communication propagation in fire scenes.

[0072] The communication data acquisition unit is used to collect real-time communication quality data. Real-time communication quality data includes real-time latency data, real-time packet loss data, real-time signal strength data, and real-time voice interruption count data, denoted as follows: , , and The link value establishment unit identifies command transmission links, feedback transmission links, and collaborative transmission links based on operational group data, and then assigns value weights to each type of link according to the current mission profile value. For any link First, calculate the link quality value:

[0073] ;

[0074] ;

[0075] in, , , and These represent the normalized results of real-time latency data, real-time packet loss data, real-time signal strength data, and real-time voice interruption count data, respectively. Indicates the link quality value. Indicates the link risk result, , , , These represent the link quality weights corresponding to real-time latency data, real-time packet loss data, real-time signal strength data, and real-time voice interruption count data, respectively. Since a higher real-time signal strength value is generally better, while higher values ​​for the other metrics are generally worse, the real-time signal strength data is processed in reverse during normalization to ensure that all four metrics maintain the consistent meaning that higher values ​​indicate worse quality. Then, the current task profile value is... and spatial blocking value By introducing this information, the link risk results can be obtained. This represents the risk amplification factor corresponding to the spatial blocking value. This represents the risk amplification factor corresponding to the current mission profile value. The purpose of setting the exponential amplification term is that when the spatial blocking value or the current mission profile value is high, even a moderate degradation in the link quality value may still pose a high risk to operational communications in the fire scene. Therefore, it needs to be non-linearly amplified to reflect the amplifying effect of spatial blocking and mission dependence on communication risks in complex fire scenes. After calculating the risk of each link separately, the highest link risk result can be selected, or the total link risk result can be obtained by aggregating the results by operational group. .

[0076] The trend mechanism establishment unit is used to establish a trend assessment mechanism based on link risk results, real-time environmental status data, and propulsion location data. The system does not merely perform a threshold judgment on the current link risk result, but further predicts its changing trend. Let the link risk results at three consecutive sampling times be... , and The risk change rate, risk acceleration, propulsion sensitivity, and environmental degradation degree are calculated using the following formulas:

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] in, Indicates the rate of change of risk. Indicates an acceleration of risk. Indicates sensitivity to advancement, Indicates the degree of environmental degradation. Indicates trend score, This indicates the overall risk outcome of the on-site data link at the current moment. Indicates the rate of change of risk. Indicates an acceleration of risk. Indicates sensitivity to advancement, Indicates the degree of environmental degradation. , , , , These represent the trend weights of the five data categories mentioned above in the prediction of communication quality trend results. Each trend weight is a non-negative value and is obtained through training or calibration using the correspondence between link risk evolution and the final communication quality trend result in historical samples. Advance sensitivity reflects whether the current advance direction is heading towards a higher spatial blocking value region, while environmental degradation is obtained jointly from real-time flue gas status data, real-time temperature status data, and real-time door opening / closing status data. The communication quality trend result is obtained based on the comparison between the trend score and a preset threshold. When the trend score is less than the first threshold, the communication quality trend result is in a stable state; when the trend score is between the first and second thresholds, the communication quality trend result is in a deteriorated state; and when the trend score is greater than the second threshold, the communication quality trend result is in a disconnection warning state.

[0083] The result response unit is used to acquire communication quality trend results. If the communication quality trend result is stable, a hold signal is output; if the communication quality trend result is deteriorating, a relay adjustment signal is output; if the communication quality trend result is a loss-of-connection warning state, a priority guarantee signal is output. The signal transmission unit is used to send the hold signal, relay adjustment signal, or priority guarantee signal to the notification end. After receiving the relay adjustment signal, the notification end displays the spatial blocking value distribution around the advance position data and provides adjustment suggestions between the fixed communication equipment position data and the current advance path; after receiving the priority guarantee signal, it performs priority bandwidth guarantee and critical voice guarantee for the formation containing high-value links in the current combat formation data.

[0084] Example 2, refer to Figure 2 This paper provides a method for monitoring the quality of fire communication based on big data, including the following steps:

[0085] Step S1: Collect on-site combat status data and historical combat status data, establish a mission communication profile mechanism, and obtain an initial assessment model.

[0086] Step S2: Collect building space data, historical disconnection event data, and real-time environmental status data; establish a spatial blocking mechanism; embed the spatial blocking mechanism into the initial assessment model; and obtain the link assessment model.

[0087] Step S3: Collect real-time communication quality data, establish a link value mechanism, input the real-time communication quality data into the link evaluation model, and obtain the link risk results.

[0088] Step S4: Establish a trend assessment mechanism based on the link risk results to obtain communication quality trend results.

[0089] Step S5: Output an adjustment signal based on the communication quality trend result and send it to the notification end.

[0090] This invention first processes on-site and historical operational status data to construct a current mission profile, enabling the system to differentiate communication dependencies across different alarm types, operational phases, operational groups, and advance positions. Compared to methods that only uniformly evaluate network performance, this invention ensures that communication quality assessment results correspond to the actual needs of the current firefighting mission. This avoids using the same static judgment criteria in high command dependence, high feedback dependence, and continuous coordination states, thus improving the adaptability of monitoring results to actual firefighting command.

[0091] This invention further integrates building space data, historical disconnection event data, and real-time environmental status data to establish a spatial blocking mechanism. It not only considers the impact of floor structure, wall distribution, passageway nodes, and the location of fixed communication equipment on communication propagation, but also dynamically corrects the spatial blocking value based on historical disconnection locations and real-time smoke, temperature, and door opening / closing status. This allows the system to identify potential communication risk areas from the perspective of fire scene spatial structure and environmental evolution. Compared to passively issuing alarms only after link quality deteriorates, this invention can detect high-blockage areas and areas prone to disconnection in advance, providing stronger data support for subsequent risk prediction.

[0092] This invention also jointly processes current mission profile values, advance position data, combat formation data, and real-time communication quality data to establish a link value mechanism. This mechanism identifies key links among command transmission links, feedback transmission links, and coordination transmission links, and generates link risk results. Since different links play different roles in a fire, this invention does not monitor all links equally. Instead, it assigns value weights based on the current mission's dependence on each type of link, allowing for the priority identification of risk changes in critical links. This better meets the actual needs of firefighters to ensure that critical commands are not interrupted, critical feedback is not lost, and critical coordination is not disrupted.

[0093] This invention establishes a trend assessment mechanism by continuously analyzing link risk results, real-time environmental status data, and advance position data, outputting communication quality trend results. This enables the system not only to identify the current communication quality status but also its subsequent evolution direction. When the link risk results continuously increase with changes in advance position and environmental deterioration, the system can output a deterioration status or a loss-of-connection warning status before a true loss of contact occurs, thereby improving the foresight of communication risk identification. Especially in space-constrained scenarios such as underground parking garages, subway station halls, and high-rise interiors, this forward-looking capability helps to adjust on-site communication support methods in advance, reducing the probability of command and control loss in critical areas.

[0094] This invention also transforms communication quality trend results into hold signals, relay adjustment signals, and priority protection signals, and sends them to the notification end. This allows monitoring results to directly impact communication support actions, rather than merely remaining at the status display level. In other words, this invention can not only identify problems but also provide actionable responses to the on-site commander and communication support personnel, such as maintaining the current communication strategy, prompting adjustments to relay deployment, and upgrading the protection level of critical links. This helps enhance the closed-loop nature and response efficiency of on-site communication support.

[0095] Therefore, this invention can improve the task-oriented nature, spatial awareness, critical link identification, and trend early warning capabilities of fire communication quality monitoring in complex fire environments, making the communication monitoring results closer to real combat needs and improving the initiative and reliability of on-site communication support.

[0096] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A fire communication quality monitoring system based on big data, characterized in that, It includes a task profile creation module, a spatial obstruction analysis module, a critical link identification module, a quality trend assessment module, and a results output module; The task profiling module is used to collect on-site combat status data and historical combat status data, establish a task communication profiling mechanism, and obtain an initial evaluation model. The spatial blocking analysis module is used to collect building space data, historical disconnection event data and real-time environmental status data, establish a spatial blocking mechanism, embed the spatial blocking mechanism into the initial evaluation model, and obtain the link evaluation model. The critical link identification module is used to collect real-time communication quality data, establish a link value mechanism, input the real-time communication quality data into the link evaluation model, and obtain the link risk results. The quality trend assessment module is used to establish a trend assessment mechanism based on the link risk results and obtain communication quality trend results. The result output module is used to output an adjustment signal based on the communication quality trend result and send it to the notification end.

2. The fire communication quality monitoring system based on big data as described in claim 1, characterized in that, The mission profile creation module includes a combat data acquisition unit and a profile mechanism creation unit; The combat data acquisition unit is used to collect on-site combat status data and historical combat status data. The on-site combat status data includes alarm type data, combat phase data, combat formation data, and advance position data. The historical combat status data includes historical alarm type data, historical combat phase data, historical combat formation data, and historical advance position data. The profiling mechanism establishment unit is used to establish a mission communication profiling mechanism based on on-site combat status data and historical combat status data.

3. The fire communication quality monitoring system based on big data as described in claim 2, characterized in that, The logic of the task communication profiling mechanism is as follows: Based on historical alarm type data and historical combat phase data, the system is divided into three states: high command dependence, high data feedback dependence, and continuous coordination. Values ​​are assigned to these states based on historical combat formation data and historical advance position data, and the output is a mission profile value. The alarm type data, operational phase data, operational group data, and advance position data are input into the mission communication profiling mechanism to obtain the current mission profile value, and the current mission profile value is written into the initial evaluation model.

4. The fire communication quality monitoring system based on big data as described in claim 3, characterized in that, The space blocking analysis module includes a blocking data acquisition unit and a blocking mechanism establishment unit; The data acquisition unit is used to collect building space data, historical disconnection event data, and real-time environmental status data. The building space data includes floor structure data, wall distribution data, passage node data, and fixed communication equipment location data. The historical disconnection event data includes historical disconnection location data and historical disconnection time period data. The real-time environmental status data includes real-time smoke status data, real-time temperature status data, and real-time door opening and closing status data. The blocking mechanism establishment unit is used to establish a spatial blocking mechanism based on building space data, historical loss of contact event data, and real-time environmental status data.

5. The fire communication quality monitoring system based on big data as described in claim 4, characterized in that, The logic of the space blocking mechanism is as follows: The basic blocking zone is divided based on floor structure data, wall distribution data, and passage node data. The historical high disconnection zone is extracted based on historical disconnection location data and historical disconnection time data. The basic blocking zone and historical high disconnection zone are corrected based on real-time smoke status data, real-time temperature status data, and real-time door opening and closing status data. The output is the real-time blocking zone. The location data of fixed communication equipment is mapped to the real-time blocking zone to obtain the spatial blocking value, and the spatial blocking value is embedded into the initial evaluation model to obtain the link evaluation model.

6. The fire communication quality monitoring system based on big data as described in claim 5, characterized in that, The critical link identification module includes a communication data acquisition unit and a link value establishment unit; The communication data acquisition unit is used to acquire real-time communication quality data, which includes real-time latency data, real-time packet loss data, real-time signal strength data, and real-time voice interruption count data. The link value establishment unit is used to establish a link value mechanism based on the current mission profile value, advance position data, combat formation data and real-time communication quality data, and to obtain link risk results.

7. The fire communication quality monitoring system based on big data as described in claim 6, characterized in that, The logic of the link value mechanism is as follows: The link quality value is obtained by weighting the real-time latency data, real-time packet loss data, real-time signal strength data, and real-time voice interruption count data according to the current task profile value. Based on advance location data and combat formation data, command transmission links, backhaul transmission links, and coordination transmission links are identified, and the link quality values ​​are corrected by combining spatial blocking values, outputting the link risk results.

8. The fire communication quality monitoring system based on big data as described in claim 7, characterized in that, The quality trend assessment module includes a trend mechanism establishment unit and a trend result acquisition unit; The trend mechanism establishment unit is used to establish a trend assessment mechanism based on link risk results, real-time environmental status data, and advancement location data. The trend result acquisition unit is used to input the link risk result, real-time environmental status data and advance position data into the trend evaluation mechanism to obtain the communication quality trend result, which includes stable state, deterioration state and disconnection warning state.

9. A fire communication quality monitoring system based on big data as described in claim 8, characterized in that, The result output module includes a result response unit and a signal transmission unit; The result response unit is used to obtain the communication quality trend result. If the communication quality trend result is in a stable state, a hold signal is output. If the communication quality trend result is a deterioration state, then output a relay adjustment signal; If the communication quality trend result indicates a loss of connection warning, then a priority protection signal will be output. The signal transmitting unit is used to send hold signals, relay adjustment signals, or priority protection signals to the notification end.

10. A method for monitoring the quality of fire communication based on big data, applied to a fire communication quality monitoring system based on big data as described in any one of claims 1-9, characterized in that, Includes the following steps: Step S1: Collect on-site combat status data and historical combat status data, establish a mission communication profile mechanism, and obtain an initial assessment model; Step S2: Collect building space data, historical disconnection event data and real-time environmental status data, establish a spatial blocking mechanism, embed the spatial blocking mechanism into the initial assessment model, and obtain the link assessment model; Step S3: Collect real-time communication quality data, establish a link value mechanism, input the real-time communication quality data into the link evaluation model, and obtain the link risk results; Step S4: Establish a trend assessment mechanism based on the link risk results to obtain communication quality trend results; Step S5: Output an adjustment signal based on the communication quality trend result and send it to the notification end.

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