Smoke detection early warning system and method suitable for data machine room, product and medium

Abstract

The invention discloses a smoke detection early warning system and method suitable for a data room, a product and a medium, and relates to the technical field of smoke detection. The sampling pipe network subsystem can cover the whole machine room area through a plurality of sampling pipes and sampling holes, and sampling comprehensiveness is ensured. The air suction pump sucks air into the system, and dust particles are removed through the filter, so that the detection accuracy is improved. The laser emitter and the scattered light receiver in the laser cavity detect smoke particles according to the light scattering principle, the control unit converts scattered light signals into smoke concentration values, the smoke concentration values and output signals are associated with time, and detailed event records are generated and stored in a database. Therefore, the change trend of the smoke concentration is continuously monitored and recorded in detail, the continuous recording mechanism enables managers to know the complete process of fire development, valuable data support is provided for postmortem analysis and prevention measure formulation, and the safety management level of a data machine room is greatly improved.

Description

Technical Field

[0001] The present application relates to the field of smoke detection technology, and in particular to a smoke detection and early warning system, method, product and medium suitable for a data room. Background Art

[0002] With the rapid development of information technology, data centers play an increasingly important role in modern society. These centers store and process massive amounts of critical data, and their security is directly related to the normal operation of enterprises and organizations. Among the many security threats, fire is undoubtedly one of the most serious. Therefore, an efficient and reliable smoke detection and early warning system is crucial to ensure the safety of data centers.

[0003] At present, common data center smoke detection and early warning systems mainly use point smoke detectors or linear beam smoke detectors. Point smoke detectors detect smoke particles in the air to determine whether there is a fire hazard, while linear beam smoke detectors detect the attenuation of the light beam by smoke. These systems usually set a fixed alarm threshold, and when the detected smoke concentration exceeds the preset threshold, the system will send an alarm signal to the control panel. The control panel will then trigger the sound and light alarm device and indicate the alarm status through a simple digital display or indicator light.

[0004] However, these systems have obvious deficiencies in data recording. They can only provide instantaneous information when the alarm is triggered, and lack the ability to continuously monitor and record the trend of smoke concentration changes in detail. They cannot provide managers with complete process information on the development of the fire, making it difficult to conduct in-depth post-analysis, which in turn affects the accurate judgment of the cause of the fire and the formulation of effective preventive measures. Summary of the invention

[0005] The present application provides a smoke detection and warning system, method, product and medium suitable for a data center, which is used for continuous monitoring and detailed recording of smoke concentration change trends. This continuous recording mechanism enables management personnel to understand the complete process of fire development.

[0006] In a first aspect, the present application provides a smoke detection and warning system suitable for a data room, comprising: a detection subsystem and a sampling pipe network subsystem, the detection subsystem comprising an air suction pump, a filter, a laser cavity, and a control unit; the sampling pipe network subsystem comprising a plurality of sampling pipes, each of which is provided with a plurality of sampling holes; the input end of the air suction pump is connected to the sampling pipe network system, and the output end is connected to the filter, so as to draw the air in the data room area through the sampling holes and the sampling pipes in turn and send it into the filter; the output end of the filter is connected to the laser cavity, so as to filter the dust particles in the air and deliver the filtered air to the The laser cavity; a laser transmitter and a scattered light receiver are arranged in the laser cavity, wherein: the laser transmitter is used to transmit a laser beam to the filtered air; the scattered light receiver is arranged at an angle different from the propagation direction of the laser beam, and is used to receive scattered light of the filtered air and convert the scattered light into an electrical signal; the input end of the control unit is connected to the output end of the scattered light receiver, and is used to convert the electrical signal into a smoke concentration value, and generate an output signal according to the smoke concentration value and a preset alarm threshold; the smoke concentration value and the output signal are respectively associated with corresponding times, an event record is generated, and the event record is stored in a database.

[0007] By adopting the above technical solutions, the sampling pipe network subsystem can cover the entire computer room area through multiple sampling pipes and sampling holes to ensure the comprehensiveness of sampling. The suction pump draws air into the system and removes dust particles through the filter, which improves the accuracy of detection. The laser transmitter and scattered light receiver in the laser cavity detect smoke particles through the principle of light scattering, and the control unit converts the scattered light signal into a smoke concentration value. The system can not only detect smoke in time, but also monitor the changes in smoke concentration in real time. In particular, the system associates the smoke concentration value and output signal with time, generates detailed event records and stores them in the database, so the continuous monitoring and detailed recording of the trend of smoke concentration changes, this continuous recording mechanism enables managers to understand the complete process of fire development, provides valuable data support for post-analysis and preventive measures, and greatly improves the safety management level of the data room.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the method includes: obtaining air from a plurality of sampling points and filtering the air; emitting a laser beam to the filtered air; receiving scattered light obtained by refraction of the laser beam by the filtered air; converting the scattered light into an electrical signal; converting the electrical signal into a smoke concentration value, and generating an output signal according to the smoke concentration value and a preset alarm threshold; associating the smoke concentration value and the output signal with corresponding times, respectively, generating an event record, and storing the event record in a database.

[0009] By adopting the above technical solutions, the system obtains air samples from multiple sampling points to ensure the wide range of monitoring. The air is filtered to effectively remove impurities that may affect the detection accuracy. The emission of laser beams and the reception of scattered light constitute a highly sensitive smoke detection mechanism. The process of converting scattered light into electrical signals and then into smoke concentration values ​​realizes the conversion of physical signals into digital information. The system generates output signals based on preset thresholds to ensure the timeliness of alarms. The system associates smoke concentration values ​​and output signals with time, generates detailed event records and stores them in the database. This comprehensive data collection and storage mechanism provides managers with rich historical data, making fire risk analysis and prevention strategy formulation more accurate and effective.

[0010] In combination with some embodiments of the first aspect, in some embodiments: after the step of converting the electrical signal into a smoke concentration value and generating an output signal according to the smoke concentration value and a preset alarm threshold, the method further includes: determining the time difference between each pair of detection subsystems according to the time when different detection subsystems first output the electrical signal; establishing a corresponding hyperbolic equation for each pair of detection subsystems based on the time difference; calculating the intersection of all the hyperbolic equations; and determining the intersection as the smoke source position.

[0011] By adopting the above technical solution, the system works through multiple detection subsystems simultaneously and records the time when each system first detects smoke. By calculating the time difference between different detection subsystems, the system is able to establish a set of hyperbolic equations. The intersection of these equations is the location of the smoke source. This positioning method takes advantage of the time difference of smoke propagation and achieves high-precision positioning through mathematical modeling. The ability to accurately locate the source of smoke is crucial for rapid response and precise fire extinguishing. It not only helps staff quickly find the source of the fire, but also provides key information for post-event analysis.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments: the hyperbola equation is: In the formula, x1 is the coordinate of the horizontal axis of one detection subsystem, y1 is the coordinate of the vertical axis of one detection subsystem, x2 is the coordinate of the horizontal axis of another detection subsystem, y2 is the coordinate of the vertical axis of another detection subsystem, v is the propagation speed of smoke in the sampling tube of the detection subsystem, and Δt is the time difference between each pair of detection subsystems.

[0013] By adopting the above technical solutions, the hyperbolic equation takes into account the spatial position of the detection subsystem and the smoke propagation speed, as well as the detection time difference. This mathematical model enables the system to accurately locate the smoke source in a two-dimensional plane. The design of the equation takes into account the physical characteristics of smoke propagation, making the positioning result more accurate. By solving this set of equations, the system can quickly calculate the exact coordinates of the smoke source. This precise positioning not only helps to quickly locate the fire source, but also provides accurate spatial information for firefighting deployment.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the hyperbola equation is: In the formula, x1 is the coordinate of the horizontal axis of one detection subsystem, y1 is the coordinate of the vertical axis of one detection subsystem, x2 is the coordinate of the horizontal axis of another detection subsystem, y2 is the coordinate of the vertical axis of another detection subsystem, s1 is the path parameter from one detection subsystem to the smoke source; s2 is the path parameter from another detection subsystem to the smoke source, v1(s1,t) is the propagation speed of the smoke source in the sampling tube of one detection subsystem, v2(s2,t) is the propagation speed of the smoke source in the sampling tube of another detection subsystem, and Δt is the time difference between each pair of detection subsystems.

[0015] By adopting the above technical solutions, the improved hyperbolic equation takes into account more practical factors, such as the speed difference of the pipelines where different detection subsystems are located, and the non-linear characteristics of the pipelines. This optimization enables the system to adapt to more complex actual environments, such as curved or zigzag pipelines. By introducing path parameters, the system can more accurately simulate the propagation path of smoke in complex pipelines. This improvement not only improves the accuracy of positioning, but also enhances the adaptability of the system in various complex environments.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments: In the formula, v1(s1,t) is the propagation speed of the smoke source in the sampling tube of a detection subsystem, Q is the suction rate of the suction pump of the detection subsystem, A is the cross-sectional area of ​​the sampling tube of the detection subsystem, and k is i is the local loss coefficient of the ith bend or branch, δ(xx i ) is the Dirac function, x is the position coordinate, x i is the position coordinate of the i-th bend or branch.

[0017] By adopting the above technical solutions, the effects of pipe bends and branches on smoke propagation speed are further considered, and these effects are accurately described by introducing local loss coefficients and Dirac functions. This enables the system to more accurately predict the propagation behavior of smoke in complex pipe systems. By considering these detailed factors, the system not only improves the accuracy of smoke source positioning, but also better adapts to different pipe layouts and environmental conditions.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the intersection as the location of the smoke source, the method also includes: determining the area to which the smoke source belongs based on the location of the smoke source; determining a first weight based on the area to which it belongs; calculating the distance between the smoke source and the detection subsystem based on the location of the smoke source; determining a second weight based on the distance; calculating a comprehensive weight coefficient based on the first weight and the second weight; and adjusting the alarm threshold using the comprehensive weight coefficient.

[0019] By adopting the above technical solution, the system determines the area to which the smoke source belongs according to its location and assigns the corresponding first weight, taking into account the characteristics and importance of equipment in different areas. Secondly, by calculating the distance between the smoke source and the detection subsystem, the second weight is determined, reflecting the impact of distance on detection sensitivity. Combining these two weights, the system calculates a comprehensive weight coefficient for dynamically adjusting the alarm threshold. This intelligent adjustment mechanism enables the system to flexibly adjust the alarm sensitivity according to specific circumstances. For example, for important equipment areas or locations far away from the detector, the system can lower the alarm threshold and increase the detection sensitivity. This dynamic adjustment not only improves the accuracy and reliability of the system, but also reduces the probability of false alarms and missed alarms, greatly improving the safety management level of the data room.

[0020] In a second aspect, the present application provides a smoke detection and warning system suitable for a data room, the smoke detection and warning system suitable for a data room comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, the one or more processors call the computer instructions to enable the smoke detection and warning system suitable for a data room to execute the method as claimed in any one of claims 2-7.

[0021] In a third aspect, the present application provides a computer program product comprising instructions, which, when run on a smoke detection and warning system suitable for a data room, enables the smoke detection and warning system suitable for a data room to execute the method as claimed in any one of claims 2 to 7.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a smoke detection and warning system applicable to a data room, enables the smoke detection and warning system applicable to a data room to execute the method as claimed in any one of claims 2 to 7.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The sampling pipe network subsystem can cover the entire room area through multiple sampling pipes and sampling holes to ensure the comprehensiveness of sampling. The suction pump draws air into the system and removes dust particles through the filter, which improves the accuracy of detection. The laser transmitter and scattered light receiver in the laser cavity detect smoke particles through the principle of light scattering, and the control unit converts the scattered light signal into a smoke concentration value. The system can not only detect smoke in time, but also monitor the changes in smoke concentration in real time. In particular, the system associates the smoke concentration value and output signal with time, generates detailed event records and stores them in the database, so the continuous monitoring and detailed recording of the trend of smoke concentration changes, this continuous recording mechanism enables managers to understand the complete process of fire development, provides valuable data support for post-analysis and preventive measures, and greatly improves the safety management level of the data room.

[0024] 2. The improved hyperbolic equation takes into account more practical factors, such as the speed difference of the pipes where different detection subsystems are located, and the non-linear characteristics of the pipes. This optimization enables the system to adapt to more complex actual environments, such as curved or zigzag pipes. By introducing path parameters, the system can more accurately simulate the propagation path of smoke in complex pipes. This improvement not only improves the accuracy of positioning, but also enhances the adaptability of the system in various complex environments.

[0025] 3. The system determines the area to which the smoke source belongs based on its location and assigns the corresponding first weight, taking into account the characteristics and importance of equipment in different areas. Secondly, the second weight is determined by calculating the distance between the smoke source and the detection subsystem, reflecting the impact of distance on detection sensitivity. Combining these two weights, the system calculates a comprehensive weight coefficient for dynamically adjusting the alarm threshold. This intelligent adjustment mechanism enables the system to flexibly adjust the alarm sensitivity according to specific circumstances. For example, for important equipment areas or locations far away from the detector, the system can lower the alarm threshold and increase the detection sensitivity. This dynamic adjustment not only improves the accuracy and reliability of the system, but also reduces the probability of false alarms and missed alarms, greatly improving the safety management level of the data center. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic diagram of a smoke detection and early warning system applicable to a data center in an embodiment of the present application; Figure 2 is a schematic diagram of a laser cavity in a smoke detection and warning system suitable for a data room in an embodiment of the present application; Figure 3 It is a flow chart of a smoke detection and early warning method applicable to a data center in an embodiment of the present application; Figure 4 is another flow chart of a smoke detection and early warning method applicable to a data center in an embodiment of the present application; Figure 5 It is an exemplary hardware structure diagram of a smoke detection and warning system suitable for a data room in an embodiment of the present application.

[0027] Description of the drawings: 101, suction pump; 102, filter; 103, laser cavity; 1031, laser transmitter; 1032, filtered air; 1033, scattered light receiver; 104, control unit; 201, end cap; 202, sampling tube; 203, sampling hole. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] Figure 1 It is a schematic diagram of a smoke detection and warning system suitable for a data room in an embodiment of the present application.

[0031] See also Figure 1 , a smoke detection and early warning system suitable for a data room, comprising: a detection subsystem and a sampling pipe network subsystem, the detection subsystem comprising an air pump 101, a filter 102, a laser cavity 103, and a control unit 104; The sampling pipe network system refers to a pipe network distributed throughout the data center for collecting air samples, including a sampling pipe 202 and a sampling hole 203 . The end cap 201 is used to seal the end of the sampling pipe 202 .

[0032] The sampling pipe network subsystem includes a plurality of sampling pipes 202, each of which is provided with a plurality of sampling holes 203; In some embodiments, the sampling holes 203 are arranged at equal intervals.

[0033] The input end of the air pump 101 is connected to the sampling pipe network system, and the output end is connected to the filter, and is used to draw the air in the data room area through the sampling holes and the sampling pipes in sequence and send it to the filter; The air pump 101 represents a device for extracting and delivering gas, and is used to extract air samples from the sampling pipe network, and these samples come from different areas of the data room. The extracted air then passes through the filter 102 to remove impurities such as dust that may affect the detection accuracy.

[0034] The output end of the filter is connected to the laser cavity to filter dust particles in the air and transport the filtered air to the laser cavity; The filter 102 is a device for removing impurities from the air and is used to clean the air drawn from the sampling pipe network.

[0035] See also Figure 2 , Figure 2 is a schematic diagram of a laser cavity in a smoke detection and warning system suitable for a data room in an embodiment of the present application; The laser cavity 103 is provided with a laser transmitter 1031 and a scattered light receiver 1033, wherein: The laser transmitter 1031 is used to transmit a laser beam to the filtered air 1032; The scattered light receiver 1033 is arranged at an angle different from the propagation direction of the laser beam, and is used to receive the scattered light of the filtered air and convert the scattered light into an electrical signal; In the laser cavity 103, the laser transmitter 1031 emits a laser beam to the filtered air 1032. The laser transmitter 1031 emits a highly focused laser beam to the air sample. This laser beam is usually visible light or near-infrared light, and the wavelength is carefully selected to achieve the best smoke particle scattering effect. If there are smoke particles in the air, these tiny particles will cause the laser to undergo Mie scattering. The intensity and direction of the scattered light are related to the size, concentration and refractive index of the smoke particles. The received scattered light is converted into an electrical signal of corresponding intensity by the photoelectric conversion element (such as a photodiode or a photomultiplier tube) inside the scattered light receiver 1033. The intensity of this electrical signal is proportional to the concentration of smoke particles in the air.

[0036] The input end of the control unit 104 is connected to the output end of the scattered light receiver, and is used to convert the electrical signal into a smoke concentration value, generate an output signal according to the smoke concentration value and a preset alarm threshold; associate the smoke concentration value and the output signal with the corresponding time respectively, generate an event record, and store the event record in a database.

[0037] In some embodiments, in addition to recording conventional smoke concentration data, the control unit 104 also records the operating behaviors of the system administrator or maintenance personnel, such as parameter adjustment, equipment maintenance, etc. At the same time, it will automatically record the operating status and possible fault information of each component in the system (such as the suction pump 101, the filter 102, the laser cavity 103, etc.). These records will be saved in real time, and even if the system suddenly loses power, the saved records will not be lost. This comprehensive event recording function provides a detailed timeline and environmental data for subsequent fire accident analysis, which helps to determine the cause and development process of the fire and the effectiveness of the system response.

[0038] It can be seen that the sampling pipe network subsystem can cover the entire computer room area through multiple sampling pipes and sampling holes to ensure the comprehensiveness of sampling. The suction pump draws air into the system and removes dust particles through the filter, which improves the accuracy of detection. The laser transmitter and scattered light receiver in the laser cavity detect smoke particles through the principle of light scattering, and the control unit converts the scattered light signal into a smoke concentration value. The system can not only detect smoke in time, but also monitor the changes in smoke concentration in real time. In particular, the system associates the smoke concentration value and output signal with time, generates detailed event records and stores them in the database, so the continuous monitoring and detailed recording of the trend of smoke concentration changes, this continuous recording mechanism enables managers to understand the complete process of fire development, provides valuable data support for post-analysis and preventive measures, and greatly improves the safety management level of the data room.

[0039] The smoke detection and warning method applicable to a data center in an embodiment of the present application is described below in conjunction with the above-mentioned example diagram: See also Figure 3 , which is a flow chart of a smoke detection and early warning method applicable to a data center in an embodiment of the present application.

[0040] S301, obtaining air from several sampling points and filtering it; The sampling points represent specific locations in the monitoring area for collecting air samples, which usually correspond to the sampling holes 203. Filtration refers to the process of removing dust, fibers and other impurities from the air using the filter 102 to improve the accuracy of subsequent detection. The number of sampling points is usually determined according to the size and complexity of the monitoring area.

[0041] In a specific embodiment, the air pump 101 extracts air samples through a sampling pipe network system distributed throughout the monitoring area. Multiple sampling holes 203 on the sampling pipe 202 correspond to different sampling points to ensure that the entire monitoring area can be fully covered. The extracted air sample then passes through the filter 102 to remove impurities that may affect the laser scattering detection.

[0042] In some embodiments, the system dynamically adjusts the sampling frequency of each sampling point based on historical data and environmental factors.

[0043] S302, emitting a laser beam to the filtered air; The filtered air 1032 refers to a clean air sample after being processed by the filter 102, and does not contain large particles of impurities. The laser beam refers to a highly focused, monochromatic light beam generated by the laser emitter 1031, which is used to detect smoke particles in the air. Emission refers to the action of the laser emitter 1031 projecting laser light into the air sample. The laser emitter 1031 refers to a device that can generate laser light of a specific wavelength, and is usually installed in the laser cavity 103.

[0044] In a specific embodiment, after the filtered air sample 1032 is delivered to the laser cavity 103, the laser emitter 1031 emits a precisely controlled laser beam to the air sample. The laser beam is usually selected to have a wavelength in the visible or near-infrared spectrum because these wavelengths have a good scattering effect on smoke particles. The power, wavelength and focus of the laser beam are carefully adjusted to obtain the best detection sensitivity and reliability.

[0045] S303, receiving scattered light obtained by refraction of the laser beam by the filtered air; Among them, scattered light refers to the reflected light generated by the interaction between the laser beam and the smoke particles in the air. Refraction refers to the phenomenon that the propagation direction of light changes at the interface of different media, and here specifically refers to the laser beam being changed in direction by tiny smoke particles. Reception refers to the process of scattered light being captured by the scattered light receiver 1033. The scattered light receiver 1033 refers to a device that can detect and collect scattered light, and is usually installed in the laser cavity 103 at a specific angle to the laser emission direction.

[0046] In other embodiments, the scattered light receiver 1033 is typically installed at a certain angle (such as 90 degrees) to the incident laser beam to capture the scattered light.

[0047] S304, converting the scattered light into an electrical signal; In some embodiments, after receiving the scattered light, the photoelectric conversion element (such as a photodiode or a photomultiplier tube) inside the scattered light receiver 1033 generates a photocurrent proportional to the light intensity. This weak photocurrent is then converted into a voltage signal and initially amplified by a preamplifier. The amplified electrical signal is filtered and further processed to improve the signal-to-noise ratio and signal quality. Finally, this processed electrical signal is sent to the control unit 104 for subsequent analysis.

[0048] S305, converting the electrical signal into a smoke concentration value, and generating an output signal according to the smoke concentration value and a preset alarm threshold; The smoke concentration value indicates the density of smoke particles in the air, usually expressed as the number of particles per cubic meter or the mass per cubic meter. The alarm threshold refers to the critical smoke concentration value that triggers the alarm, which is pre-set by the system administrator based on environmental characteristics and safety requirements. The output signal represents the alarm or status information generated by the system based on the smoke concentration value, which may include normal, warning or alarm levels.

[0049] In some embodiments, the control unit 104 first receives an electrical signal from the scattered light receiver 1033. Through a pre-calibrated algorithm, the control unit 104 converts the intensity of the electrical signal into a corresponding smoke concentration value. After obtaining the smoke concentration value, the control unit 104 compares it with a preset alarm threshold. Based on the comparison result, the system generates a corresponding output signal, such as a normal operating state, a warning prompt, or an emergency alarm, etc., which is not limited here.

[0050] S306: Associating the smoke density value and the output signal with corresponding time respectively, generating an event record, and storing the event record in a database.

[0051] Among them, time correlation refers to the process of matching smoke concentration values ​​and output signals with the exact time points at which they were generated. Event records represent data entries containing smoke concentration values, output signals, and corresponding timestamps, which are used to record the complete history of system operation.

[0052] In some embodiments, the control unit 104 first obtains the current precise time, usually accurate to the millisecond level. Then, it associates this timestamp with the smoke concentration value and output signal just generated to form a complete event record. This record may also include other relevant information, such as the sampling point location (which can be obtained in the following embodiments), environmental parameters, etc. Subsequently, the control unit 104 sends this event record to the system's database for storage. The database organizes and indexes these records in an optimized manner for quick query and analysis.

[0053] It can be seen that the system obtains air samples from multiple sampling points to ensure the wide range of monitoring. The air is filtered to effectively remove impurities that may affect the detection accuracy. The emission of laser beams and the reception of scattered light constitute a highly sensitive smoke detection mechanism. The process of converting scattered light into electrical signals and then into smoke concentration values ​​realizes the conversion of physical signals into digital information. The system generates output signals based on preset thresholds to ensure the timeliness of alarms. The system associates smoke concentration values ​​and output signals with time, generates detailed event records and stores them in the database. This comprehensive data collection and storage mechanism provides managers with rich historical data, making fire risk analysis and prevention strategy formulation more accurate and effective.

[0054] In actual use, there is a technical problem that accurate smoke source location information can help emergency personnel take action more quickly and accurately, and improve the efficiency of fire fighting and evacuation. In summary, it is necessary to find the smoke source location information based on the smoke detection and early warning system in the data center; See also Figure 4 , is another flow chart of the smoke detection and early warning method applicable to a data center in an embodiment of the present application.

[0055] In some specific embodiments, after step S305, the following steps are further included: S307, determine the time difference between each pair of detection subsystems according to the time when different detection subsystems first output the electrical signal; the first output time refers to the moment when each detection subsystem first detects smoke and generates an electrical signal. The time difference refers to the time interval between the first electrical signal outputs of two detection subsystems. Each pair of detection subsystems refers to a combination of any two detection subsystems in the system.

[0056] In some embodiments, the system first records the precise time when each detection subsystem first outputs an electrical signal, usually with millisecond accuracy. Then, the system calculates the time difference between all possible pairs of detection subsystems. For example, if there are n detection subsystems, C(n, 2) time difference values ​​will be calculated. These time difference values ​​contain important information about smoke propagation and provide basic data for subsequent positioning calculations.

[0057] In actual use, for cost considerations, only 2 or 3 detection subsystems are needed.

[0058] S308, establishing a corresponding hyperbolic equation for each pair of detection subsystems based on the time difference; The hyperbola equation represents a geometric model that describes a trajectory where the difference in distance from two foci to any point on the curve is a constant.

[0059] In a specific embodiment, the hyperbola equation is: In the formula, x1 is the coordinate of the horizontal axis of one detection subsystem, y1 is the coordinate of the vertical axis of one detection subsystem, x2 is the coordinate of the horizontal axis of another detection subsystem, y2 is the coordinate of the vertical axis of another detection subsystem, v is the propagation speed of smoke in the sampling tube of the detection subsystem, and Δt is the time difference between each pair of detection subsystems.

[0060] As can be seen, the hyperbolic equation takes into account the spatial position of the detection subsystem and the smoke propagation speed, as well as the detection time difference. This mathematical model enables the system to accurately locate the smoke source in a two-dimensional plane. The design of the equation takes into account the physical characteristics of smoke propagation, making the positioning result more accurate. By solving this set of equations, the system can quickly calculate the exact coordinates of the smoke source. This precise positioning not only helps to quickly locate the fire source, but also provides accurate spatial information for firefighting deployment.

[0061] However, in actual use, there may be significant differences in airflow speeds in the pipes where different detection subsystems are located, which will affect the smoke propagation time and concentration distribution. At the same time, the actual pipeline system is usually not an ideal straight line structure, but contains complex geometric shapes such as bends and broken lines. This nonlinear path will have an important impact on the smoke propagation pattern. In summary, the actual distance and speed of smoke movement in different detection subsystems are not the same. The above embodiment is too idealized. Therefore, in other embodiments, the hyperbola equation is: In the formula, x1 is the coordinate of the horizontal axis of one detection subsystem, y1 is the coordinate of the vertical axis of one detection subsystem, x2 is the coordinate of the horizontal axis of another detection subsystem, y2 is the coordinate of the vertical axis of another detection subsystem, s1 is the path parameter from one detection subsystem to the smoke source; s2 is the path parameter from another detection subsystem to the smoke source, v1(s1,t) is the propagation speed of the smoke source in the sampling tube of one detection subsystem, v2(s2,t) is the propagation speed of the smoke source in the sampling tube of another detection subsystem, and Δt is the time difference between each pair of detection subsystems.

[0062] This hyperbolic equation describes the difference in time required for smoke to propagate from the source point (x, y) to two different detectors (x1, y1), (x2, y2). Δt is the actual measured time difference; is the time it takes for smoke to propagate from the source point (x, y) to the detector (x1, y1), is the time it takes for smoke to propagate from the source point (x, y) to the detector (x2, y2).

[0063] s1 and s2 are variables used to describe the position of a point on a curve or path. They are used to represent the position on the path from the smoke source to the detector. s is usually used to represent the path parameter, which can be thought of as the "distance" along the path. For a path from point A to point B, s∈[0,L] is usually defined, where L is the total length of the path. By changing the value of s, you can "move" to different positions on the path.

[0064] Represents the time required for smoke to travel through an infinitesimal path segment.

[0065] Add up the time required for all infinitesimal segments ds1 to pass.

[0066] It can be seen that the improved hyperbolic equation takes into account more practical factors, such as the speed difference of the pipelines where different detection subsystems are located, and the non-linear characteristics of the pipelines. This optimization enables the system to adapt to more complex actual environments, such as curved or broken-line pipelines. By introducing path parameters, the system can more accurately simulate the propagation path of smoke in complex pipelines. This improvement not only improves the accuracy of positioning, but also enhances the adaptability of the system in various complex environments.

[0067] However, in actual use, bends, branch structures and other nonlinear geometric shapes in the piping system will significantly affect the speed of the internal airflow.

[0068] Therefore, in some embodiments, In the formula, v1(s1,t) is the propagation speed of the smoke source in the sampling tube of a detection subsystem, Q is the suction rate of the suction pump of the detection subsystem, A is the cross-sectional area of ​​the sampling tube of the detection subsystem, and k is i is the local loss coefficient of the ith bend or branch, δ(xx i ) is the Dirac function, x is the position coordinate, x i is the position coordinate of the i-th bend or branch.

[0069] The principle of v2(s2,t) is the same as that of v1(s1,t), and can be referred to v1(s1,t), and is not limited here.

[0070] It first assumes a base speed This is the average flow velocity under ideal conditions. This base velocity is then corrected by subtracting a series of local loss terms from 1. Each local loss term is determined by the loss coefficient k i and position function δ(xx i ) indicates a reduction in speed at a specific location (such as a bend).

[0071] It can be seen that the influence of pipe bending and branching on smoke propagation speed is further considered, and these influences are accurately described by introducing local loss coefficients and Dirac functions. This enables the system to more accurately predict the propagation behavior of smoke in complex pipe systems. By considering these detailed factors, the system not only improves the accuracy of smoke source positioning, but also better adapts to different pipe layouts and environmental conditions.

[0072] S309, calculating all the intersection points of the hyperbola equations; The intersection point represents the location where two or more hyperbolas intersect, and ideally represents the possible location of the smoke source.

[0073] S310: Determine the intersection as the smoke source location.

[0074] As can be seen, the system works simultaneously through multiple detection subsystems, recording the time when each system first detects smoke. By calculating the time difference between different detection subsystems, the system is able to establish a set of hyperbolic equations. The intersection of these equations is the location of the smoke source. This positioning method takes advantage of the time difference of smoke propagation and achieves high-precision positioning through mathematical modeling. The ability to accurately locate the source of smoke is crucial for rapid response and accurate fire extinguishing. It not only helps staff quickly find the source of the fire, but also provides key information for post-event analysis.

[0075] In actual use, due to differences in equipment distribution, importance and environmental conditions in different areas, as well as different distances from detection equipment to potential smoke sources, the requirements for alarm thresholds at each detection point are different. This difference makes it impractical to set a unified alarm threshold, and a more flexible and personalized threshold adjustment strategy is needed.

[0076] Therefore, in some embodiments, after step S310, the method further includes: S311, determining the area to which the smoke source belongs according to the location of the smoke source; The area to which it belongs refers to the spatial scope divided by specific functions or management where the smoke source is located.

[0077] In some embodiments, the system first receives the precise location information of the smoke source, which may be a three-dimensional coordinate or a specific location description. Then, the system compares this location with a predefined area map. This area map is usually a detailed layout of a building or facility, containing boundary information of different areas. Through spatial analysis algorithms, the system can quickly determine which predefined area the smoke source falls within, thereby completing the determination of area ownership.

[0078] S312, determining a first weight based on the region to which it belongs; In some embodiments, the system first accesses a pre-set regional weight database. This database contains weight values ​​for all possible regions, which are usually pre-set by safety experts based on factors such as the importance, flammability, and density of people in each region. The system extracts the corresponding weight value from the database based on the region determined in step S311.

[0079] In some embodiments, if a region does not have a preset weight, the system may use a default value or interpolate based on weights of similar regions.

[0080] S313, calculating the distance between the smoke source and the detection subsystem according to the position of the smoke source; S314, determining a second weight based on the distance; In some embodiments, a distance-weight mapping function needs to be defined. This function is usually a decreasing function, and the weight gradually decreases as the distance increases. Common function forms include linear decreasing, exponential decreasing, or piecewise functions. The system substitutes each distance value calculated in step S313 into this function to obtain the corresponding second weight. For extremely long distances, the system may set a minimum weight threshold to ensure that even very far detection subsystems can play a role in the overall judgment.

[0081] S315, calculating a comprehensive weight coefficient according to the first weight and the second weight; In some embodiments, the comprehensive weight coefficient is the product of the first weight and the second weight.

[0082] S316: Use the comprehensive weight coefficient to adjust the alarm threshold.

[0083] In some embodiments, the new alarm threshold is the product of the original alarm threshold and the comprehensive weight coefficient.

[0084] It can be seen that the system determines the area to which the smoke source belongs based on its location and assigns the corresponding first weight, taking into account the characteristics and importance of equipment in different areas. Secondly, by calculating the distance between the smoke source and the detection subsystem, the second weight is determined, reflecting the impact of distance on detection sensitivity. Combining these two weights, the system calculates a comprehensive weight coefficient for dynamically adjusting the alarm threshold. This intelligent adjustment mechanism enables the system to flexibly adjust the alarm sensitivity according to specific circumstances. For example, for important equipment areas or locations far away from the detector, the system can lower the alarm threshold and increase the detection sensitivity. This dynamic adjustment not only improves the accuracy and reliability of the system, but also reduces the probability of false alarms and missed alarms, greatly improving the safety management level of the data room.

[0085] The following is an introduction to an exemplary smoke detection and warning system 500 applicable to a data center provided in an embodiment of the present application. Figure 5It is a schematic diagram of an exemplary hardware structure of a smoke detection and warning system 500 suitable for a data room provided in an embodiment of the present application.

[0086] In some embodiments, the smoke detection and warning system 500 suitable for a data room is a computer device or the smoke detection and warning system 500 suitable for a data room includes a computer device. The computer device includes a processor, a memory and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, the method in the embodiment of the present application is implemented.

[0087] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0088] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0089] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0090] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0091] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A smoke detection and early warning system suitable for a data center, characterized in that: include: A detection subsystem and a sampling pipe network subsystem, wherein the detection subsystem includes an air pump, a filter, a laser cavity, and a control unit; The sampling pipe network subsystem includes a plurality of sampling pipes, each of which is provided with a plurality of sampling holes; The input end of the suction pump is connected to the sampling pipe network system, and the output end is connected to the filter, so as to draw the air in the data room area through the sampling holes and the sampling pipes in sequence and send the air into the filter; The output end of the filter is connected to the laser cavity, and is used to filter dust particles in the air and transport the filtered air to the laser cavity; The laser cavity is provided with a laser transmitter and a scattered light receiver, wherein: The laser transmitter is used to emit a laser beam to the filtered air; The scattered light receiver is arranged at an angle different from the propagation direction of the laser beam, and is used to receive scattered light of the filtered air and convert the scattered light into an electrical signal; The input end of the control unit is connected to the output end of the scattered light receiver, and is used to convert the electrical signal into a smoke concentration value, and generate an output signal according to the smoke concentration value and a preset alarm threshold; The smoke density value and the output signal are respectively associated with corresponding times, an event record is generated, and the event record is stored in a database.

2. A smoke detection and early warning method suitable for a data center, characterized in that: include: Obtain air from several sampling points and filter it; firing a laser beam at the filtered air; receiving scattered light obtained by refraction of the laser beam by filtered air; converting the scattered light into an electrical signal; Converting the electrical signal into a smoke concentration value, and generating an output signal according to the smoke concentration value and a preset alarm threshold; The smoke density value and the output signal are respectively associated with corresponding times, an event record is generated, and the event record is stored in a database.

3. The method according to claim 2, characterized in that: After the step of converting the electrical signal into a smoke concentration value and generating an output signal according to the smoke concentration value and a preset alarm threshold, the method further comprises: Determine the time difference between each pair of detection subsystems according to the time when different detection subsystems first output the electrical signal; Establishing a corresponding hyperbolic equation for each pair of detection subsystems based on the time difference; Calculating the intersection points of all the hyperbolic equations; The intersection point is determined as the smoke source position.

4. The method according to claim 3, characterized in that: The hyperbola equation is: In the formula, x1 is the coordinate of the horizontal axis of one detection subsystem, y1 is the coordinate of the vertical axis of one detection subsystem, x2 is the coordinate of the horizontal axis of another detection subsystem, y2 is the coordinate of the vertical axis of another detection subsystem, v is the propagation speed of smoke in the sampling tube of the detection subsystem, and Δt is the time difference between each pair of detection subsystems.

5. The method according to claim 3, characterized in that: The hyperbola equation is: In the formula, x1 is the coordinate of the horizontal axis of one detection subsystem, y1 is the coordinate of the vertical axis of one detection subsystem, x2 is the coordinate of the horizontal axis of another detection subsystem, y2 is the coordinate of the vertical axis of another detection subsystem, s1 is the path parameter from one detection subsystem to the smoke source; s2 is the path parameter from another detection subsystem to the smoke source, v1(s1,t) is the propagation speed of the smoke source in the sampling tube of one detection subsystem, v2(s2,t) is the propagation speed of the smoke source in the sampling tube of another detection subsystem, and Δt is the time difference between each pair of detection subsystems.

6. The method according to claim 5, characterized in that: In the formula, v1(s1,t) is the propagation speed of the smoke source in the sampling tube of a detection subsystem, Q is the suction rate of the suction pump of the detection subsystem, A is the cross-sectional area of ​​the sampling tube of the detection subsystem, and k is i is the local loss coefficient of the ith bend or branch, δ(xx i ) is the Dirac function, x is the position coordinate, x i is the position coordinate of the i-th bend or branch.

7. The method according to claim 3, characterized in that: After the step of determining the intersection point as the smoke source position, the method further comprises: Determine the area to which the smoke source belongs according to the location of the smoke source; Determine a first weight based on the region to which it belongs; Calculate the distance between the smoke source and the detection subsystem according to the smoke source position; determining a second weight based on the distance; Calculating a comprehensive weight coefficient according to the first weight and the second weight; The comprehensive weight coefficient is used to adjust the alarm threshold.

8. A smoke detection and early warning system suitable for a data center, characterized in that: The smoke detection and warning system suitable for a data room includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the smoke detection and warning system suitable for a data room to execute the method described in any one of claims 2-7.

9. A computer program product comprising instructions, characterized in that When the computer program product is run on a smoke detection and warning system applicable to a data room, the smoke detection and warning system applicable to a data room executes the method according to any one of claims 2 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a smoke detection and warning system applicable to a data room, the smoke detection and warning system applicable to a data room executes the method according to any one of claims 2 to 7.

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