Condensed aerosol fire extinguishing device integrated with environment perception function

Abstract

The invention relates to the technical field of environmental data monitoring, in particular to a condensed aerosol fire extinguishing device integrated with an environmental perception function, which comprises the following control processes: acquiring environmental data of monitoring points, and according to the change trend of the same type of environmental data of each monitoring point at each moment, determining the environmental data of each monitoring point; obtaining an environmental anomaly index in combination with the environmental data increase amplitude; dividing all the monitoring points to obtain different anomaly monitoring groups according to the environment anomaly indexes and the distance distribution among the different monitoring points; analyzing the diffusion trend at the current moment, and judging whether a fire occurs at the current moment by combining the difference between the diffusion trends at the current moment and the adjacent historical moments and the environmental anomaly index; and if yes, determining a fire extinguishing device control scheme corresponding to each abnormity monitoring group at the current moment according to the environment abnormity index growth trend, the diffusion trend distribution characteristics and the position characteristics. According to the invention, a misjudgment condition possibly existing due to the change of an actual working environment is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental data monitoring, and particularly to a thermo-aerosol fire extinguishing device integrating environmental perception function. Background Art

[0002] In the field of intelligent fire protection technology, constructing a dynamic regulation system for fire extinguishing devices based on environmental perception is the core technical path to achieve early fire warning and efficient disposal. As a typical representative of special fire protection equipment, the thermo-aerosol fire extinguishing device has become the core protection device for data center server clusters, power transmission and distribution systems, and high-precision electronic equipment rooms due to its non-toxic and harmless environmental protection characteristics and rapid response ability. Especially in the substation scenario, for the fire protection of equipment such as transformers and distribution cabinets, this device can effectively contain the spread of incipient fires and provide an active safety barrier for power infrastructure with extremely high value density.

[0003] The existing method is to monitor the environmental changes in real time through sensors. For example, temperature sensors are set to monitor the real-time collected environmental data. When the perceived environmental data exceeds the normal temperature fluctuation range, an alarm device or a fire extinguishing device is activated. However, in actual work, due to objective influencing factors such as load fluctuations, continuous high loads, and indoor environmental heat dissipation effects in scenarios such as substations, there may be a situation where the temperature in a local area rises abnormally but there is no fire, making it possible for the fixed-range environmental monitoring method to have misjudgments. Summary of the Invention

[0004] In order to solve the technical problem that the existing fixed-range environmental monitoring method has the possibility of misjudgment, the purpose of the present invention is to provide a thermo-aerosol fire extinguishing device integrating environmental perception function, and the specific technical solution adopted is as follows: A thermo-aerosol fire extinguishing device integrating environmental perception function includes a processor and a memory. The processor is used to process the instructions stored in the memory to implement the following control process: Obtain different types of environmental data at each monitoring point in the environment to be monitored at each moment, where the moment includes the current moment and historical moments; According to the similarity of the change trends between the same types of environmental data at each monitoring point at each moment, combined with the growth rate of the environmental data, obtain the environmental anomaly index at each monitoring point at each moment; According to the environmental anomaly index at each monitoring point at the current moment and the distance distribution between different monitoring points, divide all monitoring points to obtain different anomaly monitoring groups; Analyze the diffusion trend at the current moment based on the differences between the environmental anomaly indicators of each monitoring point in each anomaly monitoring group at the current moment. Combine the difference in the diffusion trend between the current moment and the adjacent historical moments, as well as the environmental anomaly indicators of the monitoring points in each anomaly monitoring group at the current moment, to determine whether a fire occurs at the current moment; If so, determine the fire extinguishing device control plan corresponding to each anomaly monitoring group at the current moment according to the growth trend of the environmental anomaly indicators of each monitoring point in each anomaly monitoring group at the current moment, the distribution characteristics of the diffusion trend, and the location characteristics.

[0005] Preferably, obtaining the environmental anomaly indicator of each monitoring point at each moment according to the similarity of the change trends between the environmental data of the same type at each moment of each monitoring point and combining the growth amplitude of the environmental data specifically includes: Based on the difference between the environmental data of the same type between each monitoring point at each moment and the adjacent moment, determine the environmental difference of each type at each moment of each monitoring point; the environmental data includes temperature data and light extinction rate data; The continuous moments when the environmental difference of the temperature data at each moment of each monitoring point belongs to the growth trend form a growth time period; Based on the cumulative result of the difference between the environmental difference of the temperature data and the environmental difference of the light extinction rate data at the same moment in the growth time period where each moment of each monitoring point is located, determine the data increase amplitude difference indicator of each monitoring point at each moment; Obtain the temperature growth index of each monitoring point at each moment according to the temperature data of each monitoring point at each moment and the temperature data at the same moment in the historical data; Based on the ratio between the temperature growth index and the data increase amplitude difference indicator of each monitoring point at each moment, determine the environmental anomaly indicator of each monitoring point at each moment.

[0006] Preferably, obtaining the temperature growth index of each monitoring point at each moment according to the temperature data of each monitoring point at each moment and the temperature data at the same moment in the historical data specifically includes: Take the ratio between the average value of the temperature data of each monitoring point at each moment and the temperature data at the same moment in the historical data as the temperature growth index of each monitoring point at each moment.

[0007] Preferably, dividing all monitoring points into different anomaly monitoring groups according to the environmental anomaly indicators of each monitoring point at the current moment and the distance distribution between different monitoring points specifically includes: Sequentially screen the monitoring points as the monitoring points to be analyzed in the order of the environmental anomaly indicators of each monitoring point at the current moment from large to small; For each monitoring point to be analyzed, based on the difference in environmental anomaly indicators between the monitoring point to be analyzed and adjacent monitoring points at the current moment, and combining the positional distribution between the monitoring point to be analyzed and other monitoring points, the similarity degree of the state between each monitoring point to be analyzed and adjacent monitoring points is obtained; The adjacent monitoring points corresponding to the similarity degree greater than the preset similarity threshold of the monitoring point to be analyzed are classified into the abnormal monitoring group where the monitoring point to be analyzed is located.

[0008] Preferably, the obtaining of the similarity degree of the state between each monitoring point to be analyzed and adjacent monitoring points according to the difference in environmental anomaly indicators between the monitoring point to be analyzed and adjacent monitoring points at the current moment, and combining the positional distribution between the monitoring point to be analyzed and other monitoring points specifically includes: Based on the spatial distance between the monitoring point to be analyzed and each adjacent monitoring point, and the absolute value of the difference in environmental anomaly indicators between the monitoring point to be analyzed and each adjacent monitoring point at the current moment, a difference characteristic coefficient is determined; The normalized value of the ratio of the environmental anomaly indicator of the monitoring point to be analyzed at the current moment to the difference characteristic coefficient is determined as the similarity degree of the state between the monitoring point to be analyzed and each adjacent monitoring point.

[0009] Preferably, the analyzing of the diffusion trend at the current moment according to the difference in environmental anomaly indicators between each monitoring point in each abnormal monitoring group at the current moment, combining the difference in the diffusion trend between the current moment and adjacent historical moments and the environmental anomaly indicators of the monitoring points in each abnormal monitoring group at the current moment, and judging whether a fire occurs at the current moment specifically includes: The abnormal monitoring group corresponding to the maximum value of the mean of the environmental anomaly indicators of all monitoring points in each abnormal monitoring group at the current moment is recorded as the first attention monitoring group, and the abnormal monitoring group corresponding to the minimum value of the mean of the environmental anomaly indicators of all monitoring points in each abnormal monitoring group at the current moment is recorded as the second attention monitoring group; The spatial distribution direction between the first attention monitoring group and the second attention monitoring group is determined as the diffusion direction at the current moment; Based on the ratio of the environmental anomaly indicators between each monitoring point at the current moment and adjacent historical moments, the abnormal change index of each monitoring point at the current moment is determined; According to the quantity distribution of all abnormal monitoring groups, combining the difference in the diffusion direction between the current moment and adjacent historical moments and the abnormal change index, the possibility of a suspected fire at the current moment is obtained; According to the possibility of a suspected fire at the current moment, it is judged whether a fire occurs at the current moment.

[0010] Preferably, based on the quantity distribution of all abnormal monitoring groups, combining the difference in the diffusion direction between the current moment and the adjacent historical moment, and the abnormal change index, the likelihood of a suspected fire at the current moment is obtained, specifically including: Determining a first characteristic coefficient based on the proportion of the number of abnormal monitoring groups; determining a second characteristic coefficient based on the average value of the abnormal change indexes of all monitoring points in all abnormal monitoring groups at the current moment; determining a third characteristic coefficient based on the cumulative result of the included angles of the diffusion directions between every two adjacent moments within the growth time period where the current moment is located; Obtaining the likelihood of a suspected fire at the current moment according to the first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient. Both the first characteristic coefficient and the second characteristic coefficient are positively correlated with the likelihood of a suspected fire, and the third characteristic coefficient is negatively correlated with the likelihood of a suspected fire.

[0011] Preferably, based on the likelihood of a suspected fire at the current moment, it is determined whether a fire has occurred at the current moment, specifically including: If the likelihood of a suspected fire at the current moment is greater than a preset abnormal threshold, then a fire has occurred at the current moment; If the likelihood of a suspected fire at the current moment is less than or equal to the preset abnormal threshold, then no fire has occurred at the current moment.

[0012] Preferably, based on the growth trend of the environmental abnormal index, the distribution characteristics of the diffusion trend, and the location characteristics of each monitoring point in each abnormal monitoring group at the current moment, the fire extinguishing device control plan corresponding to each abnormal monitoring group at the current moment is determined, specifically including: Obtaining the abnormal severity index of each abnormal monitoring group according to the abnormal change indexes of all monitoring points in each abnormal monitoring group at the current moment, the difference between the diffusion situation within the abnormal monitoring group and the diffusion direction at the current moment, and the location distribution of each abnormal monitoring group; When the abnormal severity index of the abnormal monitoring group is greater than the preset activation threshold, activate the fire extinguishing device within the abnormal monitoring group.

[0013] Preferably, obtaining the abnormal severity index of each abnormal monitoring group according to the abnormal change indexes of all monitoring points in each abnormal monitoring group at the current moment, the difference between the diffusion situation within the abnormal monitoring group and the diffusion direction at the current moment, and the location distribution of each abnormal monitoring group, specifically including: For any one abnormal monitoring group, calculating the average value of the abnormal change indexes of all monitoring points in the abnormal monitoring group at the current moment to obtain a first coefficient; Obtain the spatial distribution direction from the maximum value to the minimum value of the environmental anomaly indicators of all monitoring points within the anomaly monitoring group at the current moment, and use it as the anomaly diffusion direction of the anomaly monitoring group at the current moment; calculate the cosine function value of the angle between the anomaly diffusion direction of the anomaly monitoring group at the current moment and the diffusion direction at the current moment to obtain the second coefficient; Take the spatial distance between the center point position of the anomaly monitoring group and the center point position of the first concerned monitoring group as the third coefficient; calculate the product of the second coefficient and the third coefficient, and take the result of normalizing the ratio of the first coefficient to the product as the anomaly severity index of the anomaly monitoring group.

[0014] The embodiments of the present invention have at least the following beneficial effects: The present invention first obtains various environmental data, providing a data basis for multi-dimensional joint analysis of environmental changes in the current scenario. Then, it fuses the perception of environmental changes, conducts a preliminary analysis of environmental anomalies for the change trend, quantifies the environmental anomaly indicators of a single environmental monitoring point, and further divides different anomaly monitoring groups based on the characteristics of distance distribution on this basis, capable of screening out parts with relatively similar environmental anomaly states and higher anomaly degrees to further compare the degree of coincidence with the characteristics during a fire. Further, based on the environmental anomaly indicators, the fire source and the fire spread trend are determined according to the possible distribution differences of the fire at different positions, specifically judging whether a fire occurs at the current moment, effectively avoiding misjudgment situations that may exist due to changes in the actual working environment. If a fire occurs, it is necessary to further specifically judge whether the fire extinguishing device needs to be controlled and activated within the anomaly monitoring group in the current scenario, improving the accuracy of fire prevention and control. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of the method steps for the execution control process of a thermo-aerosol fire extinguishing device integrating environmental perception functions provided by the present invention; Figure 2 It is a flowchart of the method steps for obtaining environmental anomaly indicators provided by the present invention; Figure 3 It is a flowchart of the method steps for obtaining different anomaly monitoring groups provided by the present invention; Figure 4 It is a flowchart of the method steps for judging whether a fire occurs at the current moment provided by the present invention. Detailed Implementation Modes

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following specifically describes, with reference to the accompanying drawings and preferred embodiments, a thermo-aerosol fire extinguishing device integrating an environmental perception function according to the present invention, including its specific implementation modes, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0019] The following specifically describes, with reference to the accompanying drawings, the specific solution of a thermo-aerosol fire extinguishing device integrating an environmental perception function provided by the present invention.

[0020] The implementation scenario targeted by the present invention is specifically as follows: Taking the substation, a common scenario of thermo-aerosol fire extinguishing equipment, as an example, analyze the change trends of environmental data at different positions in the scenario, judge the possibility of a fire occurring in different local area ranges at the current moment, and adaptively adjust the startup plan of the thermo-aerosol fire extinguishing equipment.

[0021] Please refer to Figure 1 , which shows a flowchart of the method steps for a thermo-aerosol fire extinguishing device integrating an environmental perception function provided by an embodiment of the present invention to execute a control process. The method includes the following steps: Step S100, obtain environmental data of different types at each monitoring point in the environment to be monitored at each moment, where the moments include the current moment and historical moments.

[0022] In the substation scenario, sensors are set at the positions of each monitoring point to monitor the environmental data of each monitoring point at each moment in real time. Specifically, different types of environmental data at the current moment and each historical moment within a preset time period before the current moment are collected.

[0023] In some embodiments, the substation is divided into grid areas, monitoring points are set within the grid areas, a temperature sensor and a line-type beam smoke detector are respectively set at the position of each monitoring point. The temperature sensor is used to collect the temperature in real time, and the line-type beam smoke detector is used to collect the light extinction rate in real time. The larger the value, the greater the attenuation degree of the beam and the greater the smoke concentration, indicating a greater possibility of a fire occurring.

[0024] More specifically, the environmental data mainly includes two categories, namely temperature data and light reduction rate data. To avoid the influence of dimensions on the results of subsequent data analysis, the collected temperature and light reduction rate are standardized. The standardization method can be the Z-score standardization method, and the process will not be introduced here.

[0025] In some embodiments, the time length of the preset time period can be set to 3 hours, that is, the historical moments within 3 hours before the current moment are collected, and the time intervals between adjacent moments are the same. For example, it can be set to 1 minute, etc. The implementer can adjust according to the specific implementation scenario and environmental changes.

[0026] It can be understood that considering that there may be certain variation rules in the environmental changes at different time points within a day, to provide a data basis for subsequent data analysis, historical moments at the same time as the current moment on different days can be determined in the historical data.

[0027] Step S200, according to the similarity of the change trends between the environmental data of the same type at each monitoring point at each moment, combined with the growth rate of the environmental data, obtain the environmental anomaly index of each monitoring point at each moment.

[0028] At the same data monitoring location, a single type of environmental data may have monitoring errors in the fire monitoring process. For example, for temperature data, due to factors such as continuous high load of electrical equipment such as distribution cabinets in the substation, the local environmental temperature rises, resulting in an abnormal increase in the temperature data monitored by the sensor, which affects the judgment result of the fire situation.

[0029] Therefore, by analyzing the multiple types of environmental data collected by multiple sensors and combining historical data, the monitoring anomalies of different states with fires are evaluated. Specifically, when the temperature is in a continuous growth state, the greater the possibility of environmental anomalies. At the same time, analyze whether the temperature and the light reduction rate belong to the same change trend in this growth state. If so, it indicates that the possibility of a fire in the current environmental anomaly is greater. If not, it indicates that the current environmental anomaly may be only a fluctuation anomaly caused by temperature changes, and no fire has occurred, so the smoke concentration has not changed significantly abnormally. At this time, the possibility of a fire in the current environmental anomaly state is smaller.

[0030] In some embodiments, as Figure 2 shown, the method for obtaining the environmental anomaly index of each monitoring point at each moment can be implemented by steps S201 to S205.

[0031] Step S201, based on the difference between the environmental data of the same type between each monitoring point at each moment and the adjacent moment, determine the environmental difference of each type at each monitoring point at each moment.

[0032] To analyze the variation of environmental data over time, calculate the difference between the environmental data of the same type at each monitoring point at each moment and the adjacent previous historical moment, which is used as the environmental difference of each type at each monitoring point at each moment.

[0033] Considering that the data analysis process at each monitoring point at each moment is the same, take any moment as an example for illustration. Denote any moment as the selected moment. For any monitoring point, calculate the difference between the temperature data at the selected moment at the current monitoring point and the temperature data at the adjacent previous moment to obtain the environmental difference at the selected moment at the current monitoring point.

[0034] Step S202: The consecutive moments when the environmental difference of the temperature data at each monitoring point at each moment is in an increasing trend form an increasing time period.

[0035] When the value of the environmental difference is greater than 0, and the larger the value, it indicates that the environmental data at the current monitoring point at the selected moment has an increasing trend compared to the historical moment, and the increasing trend is greater. When the value of the environmental difference is less than 0, and the smaller the value, it indicates that the environmental data at the current monitoring point at the selected moment has a decreasing trend compared to the historical moment, and the decreasing trend is also greater. In this embodiment, only the environmental data with an increasing trend is analyzed for characteristics, that is, the longer the environmental data continuously increases and the greater the degree of increase, the greater the possibility of environmental anomalies at the location of the current monitoring point.

[0036] As a specific example, for any monitoring point, the moments when the environmental difference of the temperature data at each moment is greater than 0 are taken as the increasing moments, and then the consecutive increasing moments form an increasing time period.

[0037] It should be noted that during the implementation of monitoring the environmental anomalies in the current scenario, when there is an increasing trend in the temperature data at multiple consecutive moments, it indicates that there is a greater possibility of environmental anomalies at the current moment, and further differentiation and analysis are required. Therefore, based on this feature, in some embodiments, when the time length of the increasing time period exceeds the preset length, the subsequent analysis process can be carried out, that is, the analysis process of the environmental anomalies in the current scenario. Among them, the preset length can be set by the implementer according to the specific implementation scenario.

[0038] It should be understood that during the real-time monitoring of the current substation scene, regarding whether to activate the fire extinguishing device at the current moment, subsequent steps should be analyzed for the current moment after consecutive growth moments. Otherwise, it indicates that the environmental anomaly degree at the current moment is small, and there will be no fire situation, and thus there is no need to control the activation of the fire extinguishing device. Therefore, in this embodiment, further feature judgment is only performed when the current moment and multiple historical moments before it belong to the same growth time period.

[0039] Step S203: Based on the cumulative result of the difference between the environmental difference of temperature data and the environmental difference of light extinction rate data at the same moment within the growth time period of each moment of each monitoring point, determine the data increase difference index of each monitoring point at each moment.

[0040] During the growth time period, the temperature data at each moment has a certain growth trend. At this time, it is necessary to jointly analyze the change of the smoke concentration in the current scene with the light extinction rate data. If the change trends between the two are relatively similar, it indicates that the possibility of environmental anomaly during the growth time period is greater.

[0041] As a specific example, taking any growth moment as an example for illustration, for the data increase difference index of the i-th growth moment of any monitoring point can be expressed as , where represents the number of moments included in the growth time period where the i-th growth moment of the monitoring point is located, represents the environmental difference of temperature data at the n-th moment within the growth time period where the i-th growth moment of the monitoring point is located, represents the environmental difference of light extinction rate data at the n-th moment within the growth time period where the i-th growth moment of the monitoring point is located.

[0042] For any monitoring point, when the ratio of the data change situations of the temperature data and the light extinction rate data at the same moment is closer to 1, it indicates that the data change difference between the two is smaller, that is, reflects the difference situation of the environmental differences of the temperature data and the light extinction rate data at the same moment. The larger the cumulative result of this difference, the greater the data difference in the change situations of the temperature data and the light extinction rate data within the growth time period where the current i-th growth moment is located.

[0043] In some embodiments, taking any growth moment as an example for illustration, for any monitoring point, the environmental differences of the temperature data at all moments within the growth time period where the i-th growth moment of the monitoring point is located can be obtained to form a temperature difference sequence, and the environmental differences of the light reduction rate data at all moments within the growth time period where the i-th growth moment of the monitoring point is located can be obtained to form a light reduction rate difference sequence. Further, the DTW distance between the temperature difference sequence and the light reduction rate difference sequence is calculated to reflect the difference between the two difference sequences. The greater the difference, that is, there is a large data difference in the change of the temperature data and the light reduction rate data within the growth time period where the i-th growth moment is located.

[0044] Step S204: Obtain the temperature growth index of each monitoring point at each moment based on the temperature data of each monitoring point at each moment and the temperature data at the same moment in the historical data.

[0045] Considering that there is a certain regularity in the change trend of the environmental data corresponding to the position of each monitoring point in the historical data over time. For example, during the day, the environmental temperature will relatively increase at noon and in the afternoon. Therefore, to avoid the possibility of misjudgment in only analyzing the current temperature data change, the data distribution situation of the same moment in the historical data can also be combined to analyze the change of the data growth trend at each growth moment relative to the historical data.

[0046] Specifically, the ratio between the temperature data of each monitoring point at each moment and the average value of the temperature data at the same moment in the historical data is used as the temperature growth index of each monitoring point at each moment.

[0047] It can be understood that there is the same moment every day in the historical data for each moment. For example, assuming the current moment is 15:00, then the average value of the temperature data at the same moment of the same monitoring point at 15:00 every day in the historical data is obtained. To avoid the excessive data volume affecting the data calculation result, the temperature data at the same moment within 15 days before the current moment of the same monitoring point is selected to calculate the average value, which reflects the balanced distribution of the temperature data at the moment in the historical data that is the same as the current moment.

[0048] For any monitoring point, if the value of the temperature data at any moment is larger compared to the balanced distribution of the temperature data in the historical data, the corresponding temperature growth index value is larger, indicating that the degree of growth of the temperature data of the monitoring point at this moment is greater compared to the historical normal situation. At this time, from the perspective of the temperature data dimension, the possibility of environmental abnormality at the location of the monitoring point is greater.

[0049] Step S205: Determine the environmental abnormality index of each monitoring point at each moment based on the ratio between the temperature growth index of each monitoring point at each moment and the data amplitude difference index.

[0050] Specifically, for any monitoring point, the ratio between the temperature growth index and the data increase difference index at each moment is normalized to obtain the environmental anomaly index of the monitoring point at each moment. Among them, the normalization method is a well-known technology and will not be introduced in detail here.

[0051] The temperature growth index characterizes the difference between the temperature data of the monitoring point at the corresponding moment and the historical data. The data increase difference index characterizes the difference in the change trends of different environmental data at the same moment under the same monitoring point. The larger the value of the temperature growth index, the greater the difference between the temperature change of the monitoring point at the corresponding moment and the history, and thus the greater the possibility of environmental anomaly at the monitoring point at the corresponding moment. The larger the value of the data increase difference index, the greater the difference in the change trends of different environmental data at the same moment of the monitoring point, and thus the smaller the possibility that the current temperature anomaly is caused by the environmental anomaly due to a fire. At this time, the value of the environmental anomaly index is smaller. The environmental anomaly index characterizes the possibility of the existence of a fire environmental anomaly at the monitoring point at the corresponding moment.

[0052] It should be noted that the characteristic analysis process of the environmental anomaly situation of each monitoring point is the same. In this embodiment, any one monitoring point is taken as an example for illustration. It should be understood that the said moment includes the current moment and the historical moment. In this embodiment, only any one moment is taken as an example for a detailed description of the data quantification process. For the part that does not belong to the growth moment, this step of analysis is not carried out, that is, there is no corresponding growth time period for the part that does not belong to the growth moment, and the parameter acquisition process of the environmental anomaly index is not carried out either.

[0053] Step S300, according to the environmental anomaly index of each monitoring point at the current moment and the distance distribution between different monitoring points, all monitoring points are divided to obtain different abnormal monitoring groups.

[0054] In the initial stage of a fire, the degree of abnormal monitoring of each monitoring point fluctuates to some extent with the change of time sequence. When local monitoring anomalies are caused by the load change or heat dissipation difference of each electrical equipment such as the internal power distribution cabinet in the substation, the degree of anomaly of each monitoring point is relatively stable. At the same time, considering that a fire has a certain spreadability and radiates at the fire source point, there are certain fluctuations in the corresponding environmental data. By analyzing the similarity of the degree of environmental anomaly, monitoring points with relatively close distances can be divided into the same area or the same group for comprehensive analysis.

[0055] In some embodiments, as Figure 3 shown, the acquisition method of different abnormal monitoring groups can be realized by steps S301 to S303.

[0056] Step S301: Screen the monitoring points as the to-be-analyzed monitoring points in sequence according to the order of the environmental anomaly indicators of each monitoring point from large to small at the current moment.

[0057] The larger the value of the environmental anomaly indicator, the greater the possibility that there is an environmental anomaly caused by a fire at the monitoring point at the current moment. Therefore, analyze the situation of similar environmental anomaly distributions at the positions of each monitoring point in sequence according to the order of the environmental anomaly indicators from large to small. At the position of the monitoring point corresponding to the maximum value of the environmental anomaly indicators of all monitoring points at the current moment, the possibility of belonging to the source point position where a fire occurred at the current moment is greater. So, conduct sequential analysis according to the order from large to small.

[0058] Step S302: For each to-be-analyzed monitoring point, obtain the similarity degree of the state between each to-be-analyzed monitoring point and its adjacent monitoring points according to the difference situation of the environmental anomaly indicators between the to-be-analyzed monitoring point and its adjacent monitoring points at the current moment, and combine the position distribution between the to-be-analyzed monitoring point and other monitoring points.

[0059] Specifically, take any to-be-analyzed monitoring point as an example to illustrate. Based on the spatial distance between the to-be-analyzed monitoring point and each adjacent monitoring point, and the absolute value of the difference in the environmental anomaly indicators between the to-be-analyzed monitoring point and each adjacent monitoring point at the current moment, determine the difference characteristic coefficient; determine the normalized value of the ratio of the environmental anomaly indicator of the to-be-analyzed monitoring point at the current moment to the difference characteristic coefficient as the similarity degree of the state between the to-be-analyzed monitoring point and each adjacent monitoring point.

[0060] Among them, for each to-be-analyzed monitoring point, take each monitoring point within the preset neighborhood range of the to-be-analyzed monitoring point as the adjacent monitoring point of the to-be-analyzed monitoring point. The preset neighborhood range can be a circular range with the to-be-analyzed monitoring point as the center and a preset radius. The implementer can set it according to the specific implementation scenario, and there is at least one adjacent monitoring point within the preset neighborhood range. The spatial distance is an index that measures the actual distance distribution between two positions. In the space of the current scenario, each monitoring point corresponds to a position coordinate, and the Euclidean distance of the position coordinates between every two monitoring points is calculated to obtain the spatial distance corresponding to the two monitoring points.

[0061] As a specific example, take the th to-be-analyzed monitoring point and the corresponding th adjacent monitoring point as an example to illustrate. Then the similarity degree of the state between the th to-be-analyzed monitoring point and the corresponding th adjacent monitoring point can be expressed as:

[0062] Among them, represents at the current moment the The similarity degree of the state between the monitoring point to be analyzed and the corresponding adjacent monitoring points, represents the environmental anomaly index of the th monitoring point to be analyzed at the current moment, represents the environmental anomaly index of the th adjacent monitoring point of the th monitoring point to be analyzed at the current moment, represents the spatial distance between the monitoring point to be analyzed and the corresponding adjacent monitoring points, is a preset hyperparameter, and its value range is (0, 0.1). The purpose of setting the hyperparameter is to avoid the situation where the denominator becomes 0 when the calculation result of the absolute value of the difference is 0, which affects the data calculation result. In this embodiment, the value can be 0.01. is a normalization function.

[0063] is the difference feature coefficient. When the spatial distance distribution between the monitoring point to be analyzed and the adjacent monitoring points is farther, it indicates that the possibility of being affected by the environmental anomaly of the fire in a short time is smaller. The larger the value of, the greater the difference in the environmental anomaly performance degree between the monitoring point to be analyzed and the adjacent monitoring points. Furthermore, the similarity of the environmental distribution state between the two at the current moment is relatively small. The larger the value of the difference feature coefficient, the smaller the value of the state similarity degree at this time.

[0064] At the same time, considering that the state similarity degree is used to merge the monitoring point to be analyzed with the adjacent monitoring points to measure the similarity state of the environmental anomalies at the locations of the monitoring points within a certain range. The larger the value of the environmental anomaly index of the monitoring point to be analyzed, the greater the possibility of a fire at the corresponding location. Therefore, in the process of measuring the similarity state, the environmental anomaly index of the monitoring point to be analyzed is added as a feature to screen out adjacent monitoring points with small environmental anomaly differences from the monitoring point to be analyzed and close distances. At the same time, the environmental anomaly degrees of both are relatively large, indicating that the possibility of environmental anomalies in the area where these similar monitoring points are located is relatively large, and it can be used as a suspected fire occurrence area for further feature judgment.

[0065] Step S303: Divide the adjacent monitoring points corresponding to the monitoring point to be analyzed with a state similarity degree greater than the preset similarity threshold into the abnormal monitoring group where the monitoring point to be analyzed is located.

[0066] Within the neighborhood range of the monitoring point to be analyzed, the similarity degree of the status corresponding to each adjacent monitoring point of the monitoring point to be analyzed is judged respectively. When the similarity degree of the status between the monitoring point to be analyzed and the adjacent monitoring point is greater than the preset similarity threshold, it indicates that the similarity degree of the environmental abnormal conditions between the monitoring point to be analyzed and the adjacent monitoring point is relatively large, and the distance is relatively close. There is a high possibility of environmental abnormal diffusion caused by a fire. At this time, the adjacent monitoring points whose similarity degree of the status is greater than the requirement of the similarity threshold are divided into the same abnormal monitoring group as the monitoring point to be analyzed.

[0067] Among them, in this embodiment, the value of the similarity threshold is 0.8, which is used to measure the similarity degree between the monitoring point to be analyzed and the adjacent monitoring point. The greater the similarity degree, that is, when it is greater than the similarity threshold, the more it should belong to the same abnormal monitoring group.

[0068] Specifically, when the similarity degree of the status corresponding to all adjacent monitoring points within the neighborhood range of the monitoring point to be analyzed is not greater than the similarity threshold, and at the same time the environmental abnormal index of the monitoring point to be analyzed is greater than the preset severe threshold, the monitoring point to be analyzed is taken as an abnormal monitoring group, that is, there is only one monitoring point in the abnormal monitoring group at this time.

[0069] Among them, considering that the value of the environmental abnormal index is a normalized value, the value range of the severe threshold is (0, 1), and the value in this embodiment is 0.7, which is used to measure the severity of the abnormal status of the monitoring point to be analyzed. When the environmental abnormal index of the monitoring point to be analyzed meets the requirement of the severe threshold, it indicates that the environmental abnormal status of the monitoring point to be analyzed is relatively severe, and it is more likely to belong to the monitoring point where the fire occurs.

[0070] So far, each monitoring point to be analyzed is judged respectively to obtain multiple abnormal monitoring groups. The abnormal monitoring group represents the monitoring points with a high possibility of environmental abnormal conditions caused by a fire. It can be understood that in the process of judging each monitoring point to be analyzed in turn according to the order of the environmental abnormal index from large to small at the current moment, the monitoring points to be analyzed that have already formed abnormal monitoring points do not need to be judged anymore.

[0071] Step S400, according to the difference between the environmental abnormal indexes of each monitoring point in each abnormal monitoring group at the current moment, analyze the diffusion trend at the current moment, and combine the difference situation of the diffusion trends at the current moment and the adjacent historical moments and the environmental abnormal indexes of the monitoring points in each abnormal monitoring group at the current moment to judge whether a fire occurs at the current moment.

[0072] Considering that there is a certain diffusion phenomenon when a fire occurs. For example, when a fire breaks out, the temperature at the fire source point rises rapidly and spreads outwards. Due to uneven oxygen distribution or the influence of combustible distribution, the fire source may shift, that is, the diffusion direction of the abnormal environment changes. At the same time, due to factors such as heat convection of smoke particles, the light extinction rate at each monitoring point fluctuates significantly, and its diffusion direction also changes to a certain extent. When there is a local abnormality caused by normal equipment operation, there is a possibility that the insulating material is slightly carbonized due to heat accumulation, resulting in the generation of a small amount of smoke or volatile organic compounds. The temperature and light extinction rate at the corresponding position slowly and stably spread along the heat dissipation air flow direction of the abnormal equipment.

[0073] Based on this feature, for the abnormal monitoring groups that have been screened out and may have a fire, combined with the diffusion phenomenon of the environment when a real fire occurs, further determine the specific situation of the fire in the substation scenario at the current moment. As Figure 4 shown, the specific method for judging whether a fire occurs at the current moment can be realized by steps S401 to S404.

[0074] Step S401, obtain the first concerned monitoring group and the second concerned monitoring group, and determine the spatial distribution direction between the first concerned monitoring group and the second concerned monitoring group as the diffusion direction at the current moment.

[0075] Among them, the abnormal monitoring group corresponding to the maximum value of the mean of the environmental abnormality indicators of all monitoring points in each abnormal monitoring group at the current moment is recorded as the first concerned monitoring group, and the abnormal monitoring group corresponding to the minimum value of the mean of the environmental abnormality indicators of all monitoring points in each abnormal monitoring group at the current moment is recorded as the second concerned monitoring group.

[0076] The first concerned monitoring group represents the abnormal monitoring group with the greatest degree of environmental abnormality among all abnormal monitoring groups. That is, if a fire occurs, the position of the first concerned monitoring group is most likely to be the location of the fire source. The second concerned monitoring group represents the abnormal monitoring group with the smallest degree of environmental abnormality among all abnormal monitoring groups.

[0077] Since when a fire occurs, it is often accompanied by heat radiation and concentrated transmission of smoke, the greater the degree of reduction in the fire abnormality at the monitoring points in the direction of fire spread. Based on this, the direction from the first concerned monitoring group to the second concerned monitoring group may be the fire diffusion direction of the current environmental condition. Specifically, the direction from the regional center point of all monitoring points included in the first concerned monitoring group to the regional center point of all monitoring points included in the second concerned monitoring group is used as the diffusion direction at the current moment, which represents the environmental abnormality diffusion trend when a fire may occur in the substation scenario at the current moment.

[0078] It should be noted that when there is only one monitoring point in any monitoring group, there is no need to obtain the center point, and the location of the monitoring point is directly used to participate in the feature analysis process of the diffusion direction. It should be understood that the regional center point of the abnormal monitoring group can be the geometric center point of all monitoring points in the abnormal monitoring group.

[0079] Step S402: Determine the abnormal change index of each monitoring point at the current moment based on the ratio of the environmental anomaly index between the current moment and the adjacent historical moment of each monitoring point.

[0080] For each monitoring point in any abnormal monitoring group, the ratio between the environmental anomaly index of the monitoring point at the current moment and the environmental anomaly index of the adjacent previous historical moment is used as the abnormal change index of the monitoring point at the current moment. The abnormal change index reflects the change in the environmental anomaly degree of the monitoring point at the current moment compared with the historical moment. When the value of the abnormal change index is greater than 1, and the larger it is, it indicates that there is an increasing trend in the environmental anomaly degree at the current moment compared with the historical moment. At this time, it may be due to the occurrence of a fire that causes the environmental anomaly degree to intensify, and further indicates that the possibility of a fire at the current moment is greater.

[0081] Step S403: Based on the quantity distribution of all abnormal monitoring groups, combined with the difference in the diffusion direction between the current moment and the adjacent historical moment and the abnormal change index, obtain the possibility of a suspected fire at the current moment.

[0082] If the current moment is in a fire monitoring abnormal period, the more uneven the distribution of the fire anomaly degree in each region within this period, the greater the degree of growth with time series, and the more unstable the fire anomaly diffusion direction, that is, the larger the deviation angle of the diffusion direction with time series growth, the greater the possibility of a fire in the current moment scenario.

[0083] Based on this feature, the possibility of a fire at the current moment is measured from three aspects. First, analyze the proportion of monitoring points with a large environmental anomaly degree in the current moment scenario. The larger the proportion, the greater the possibility of a fire. Second, analyze the angular deviation between the diffusion angle at the current moment and the diffusion angle at the historical moment. The larger the deviation, the more unstable the diffusion direction, and the greater the possibility of a fire. Third, analyze the change difference between the abnormal change index of each abnormal monitoring group at the current moment and the historical moment. The greater the growth amplitude, the greater the possibility of a fire.

[0084] Specifically, determine the first characteristic coefficient based on the proportion of the number of abnormal monitoring groups; determine the second characteristic coefficient based on the mean value of the abnormal change indexes of all monitoring points within all abnormal monitoring groups at the current moment; determine the third characteristic coefficient based on the cumulative result of the included angles between the diffusion directions between every two adjacent moments within the growth time period where the current moment is located; obtain the likelihood of a suspected fire at the current moment according to the first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient. Both the first characteristic coefficient and the second characteristic coefficient are positively correlated with the likelihood of a suspected fire, and the third characteristic coefficient is negatively correlated with the likelihood of a suspected fire.

[0085] More specifically, as a specific example, the calculation formula for the likelihood of a suspected fire at the current moment can be expressed as: , where represents the likelihood of a suspected fire at the current moment, t represents the current moment, represents the number of abnormal monitoring groups at the current moment, represents the number of all monitoring points, represents the mean value of the environmental abnormal indexes of all monitoring points within all abnormal monitoring groups at the current moment, represents the diffusion direction at the m-th moment within the growth time period where the current moment is located, represents the diffusion direction at the previous moment adjacent to the m-th moment within the growth time period where the current moment is located, represents the number of moments within the growth time period where the current moment is located, is the cosine function, is a preset hyperparameter, and its value range is (0, 0.1). The purpose of setting the hyperparameter is to avoid the situation where the denominator becomes 0 when the cosine function value is 0, which affects the data calculation result. In this embodiment, its value can be 0.01, is the normalization function.

[0086] is the third characteristic coefficient, It represents the cosine value of the angle between the diffusion direction at the m-th moment in the current growth time period and the diffusion direction at the historical moment of that time. The larger the angle, the smaller the cosine value. When the angle is larger, it indicates that there is a relatively large deviation in the diffusion angle at the m-th moment compared to the historical moment. At this time, the possibility of a fire existing is greater. Therefore, the smaller the cosine value, the greater the possibility of a suspected fire. Further accumulating the changes in the diffusion angles of all moments in the current growth time period where the current moment is located can reflect the stability degree of the fire diffusion trend in a period of time before the current moment. The larger the cumulative result of the angle deviation, the smaller the cumulative result of the cosine value, the smaller the stability degree, and the greater the corresponding possibility of a suspected fire. It should be noted that the angle is generally between 0° and 180°. Therefore, in this embodiment, a cosine function that is monotonic within this range is selected to quantify the angle.

[0087] Furthermore, is the first characteristic coefficient, is the second characteristic coefficient. The larger the proportion of the number of abnormal monitoring groups at the current moment, the more area points with a greater degree of environmental abnormality. At the same time, combined with the overall abnormality degree of the environmental abnormality index being greater, it further indicates that the possibility of a fire existing at the current moment is greater, and the corresponding value of the possibility of a suspected fire is larger. The possibility of a suspected fire at the current moment represents the magnitude of the possibility of a fire occurring in the current monitoring scenario at the current moment.

[0088] Step S404, determine whether a fire occurs at the current moment according to the possibility of a suspected fire at the current moment.

[0089] If the value of the possibility of a suspected fire at the current moment is larger and meets the threshold requirement, it indicates that the result of analyzing the characteristics of the environmental data of all monitoring points at the current moment shows that the possible degree of environmental abnormality at the current moment is greater. If the value of the possibility of a suspected fire at the current moment is smaller and does not meet the threshold requirement, it indicates that the result of analyzing the characteristics of the environmental data of all monitoring points at the current moment shows that the possible degree of environmental abnormality at the current moment is smaller. It can be understood that the threshold requirement means that the current possibility of a suspected fire is greater than the abnormality threshold.

[0090] Specifically, if the possibility of a suspected fire at the current moment is greater than the preset abnormality threshold, then a fire occurs at the current moment. Furthermore, it is necessary to further specifically analyze which abnormal monitoring groups need to activate the fire extinguishing device at present. If the possibility of a suspected fire at the current moment is less than or equal to the preset abnormality threshold, then no fire occurs at the current moment. At this time, there is no need to activate the fire extinguishing device, and the current manifested environmental abnormality may be normal environmental fluctuations. In this embodiment, the value of the abnormality threshold is 0.7, and the implementer can set it according to the specific implementation scenario.

[0091] In some embodiments, a reminder function can be set for the current moment when no fire has occurred to remind relevant staff to conduct a simple confirmation of environmental anomalies, so as to take targeted measures to prevent the current environmental fluctuations from further evolving into a more serious situation.

[0092] Step S500, if so, determine the control scheme of the fire extinguishing device corresponding to each abnormal monitoring group at the current moment according to the growth trend, diffusion trend distribution characteristics and location characteristics of the environmental anomaly indicators of each monitoring point in each abnormal monitoring group at the current moment.

[0093] To accurately control the fire extinguishing device and avoid waste of resources, the fire extinguishing devices in the substation are grouped for control, that is, the opening of the fire extinguishing devices is adaptively determined according to the specific fire development situation in different abnormal monitoring groups. If, after the spraying of the fire extinguishing equipment in the fire source area of a certain monitoring point, the included angle between the abnormal diffusion direction of its area and the abnormal diffusion direction of the fire at the corresponding moment is smaller, and the distance from its area to the fire source area is closer, then this area is more in line with the fire spreading trend, and if the degree of abnormal fire monitoring in this area is still large, then the severity of the fire in this area is greater, and it is more necessary to turn on the corresponding aerosol fire extinguishing device.

[0094] Based on this feature, when it is determined that there is a fire at the current moment, it is necessary to more specifically judge which positions need to turn on the fire extinguishing device according to the severity of environmental anomalies in different abnormal monitoring groups. That is, the first step is to obtain the abnormal severity index of each abnormal monitoring group according to the abnormal change index of all monitoring points in each abnormal monitoring group at the current moment, the difference between the diffusion situation in the abnormal monitoring group and the diffusion direction at the current moment, and the location distribution of each abnormal monitoring group.

[0095] More specifically, for any abnormal monitoring group, calculate the mean value of the abnormal change indexes of all monitoring points in the abnormal monitoring group at the current moment to obtain the first coefficient; obtain the spatial distribution direction from the maximum value to the minimum value of the environmental anomaly indicators of all monitoring points in the abnormal monitoring group at the current moment as the abnormal diffusion direction of the abnormal monitoring group at the current moment; calculate the cosine function value of the included angle between the abnormal diffusion direction of the abnormal monitoring group at the current moment and the diffusion direction at the current moment to obtain the second coefficient; take the spatial distance between the center point position of the abnormal monitoring group and the center point position of the first concerned monitoring group as the third coefficient; calculate the product of the second coefficient and the third coefficient, and take the result of normalizing the ratio of the first coefficient to the product as the abnormal severity index of the abnormal monitoring group.

[0096] As a specific example, the calculation formula of the abnormal severity index of the abnormal monitoring group can be expressed as:

[0097] Wherein, represents the anomaly severity index of the x-th anomaly monitoring group at the current moment, t represents the current moment, represents the mean value of the environmental anomaly indicators of all monitoring points within the x-th anomaly monitoring group at the current moment, that is, the first coefficient; represents the spatial distance between the center point of the x-th anomaly monitoring group and the center point of the first concerned monitoring group at the current moment, that is, the third coefficient; represents the cosine function value of the included angle between the anomaly diffusion direction of the x-th anomaly monitoring group and the diffusion direction at the current moment, that is, the second coefficient; is a normalization function.

[0098] Among them, , represents the diffusion direction at the current moment, represents the anomaly diffusion direction of the x-th anomaly monitoring group at the current moment, is a cosine function. Based on the method of obtaining a similar diffusion direction, for all monitoring points within the x-th anomaly monitoring group, the direction from the monitoring point corresponding to the maximum value of the environmental anomaly indicator to the monitoring point corresponding to the minimum value of the environmental anomaly indicator is the corresponding anomaly diffusion direction, which reflects the diffusion trend of the environmental anomaly degree within a similar group. Specifically, if there is only one monitoring point within an anomaly monitoring group, it means that there is no diffusion trend for this monitoring point. At this time, it indicates that the severity level is relatively low, and the value of the corresponding second coefficient is the largest, and its value can be set to 1.

[0099] It can be understood that the center point position of the anomaly monitoring group represents the geometric center position within the plane formed by all monitoring points within the anomaly monitoring group. Furthermore, by calculating the Euclidean distance of the position coordinates between the two center point positions, the linear distance distribution between the two in the plane can be reflected, measuring the distance between the current x-th anomaly monitoring group and the position of the suspected fire source. The farther the distance, the milder the environmental anomaly degree. The anomaly severity index also characterizes the severity of the environmental anomaly of the monitoring points within the corresponding anomaly monitoring group.

[0100] In the second step, when the anomaly severity index of the anomaly monitoring group is greater than the preset activation threshold, activate the fire extinguishing device within the anomaly monitoring group. Among them, the value of the severity threshold is 0.5, and the implementer can determine it according to the specific implementation scenario. It can be understood that activating the fire extinguishing device should follow the principle of proximity. In some embodiments, a warning device can also be set to simultaneously remind relevant staff to quickly go to the scene to check the relevant situation, and further response measures need to be taken when it is more serious.

[0101] In some embodiments, a hot aerosol fire extinguishing device integrating environmental perception functions includes a hot aerosol fire extinguishing equipment, which is a conventional fire extinguishing device. A hot aerosol fire extinguishing device integrating environmental perception functions further includes a data acquisition device and a controller. The data acquisition device includes a temperature sensor and a line-type beam smoke fire detector, and is used to collect temperature data and light extinction rate data of each monitoring point in real time. The controller can be a data processing chip such as a CPU or an MCU, or a data processing device such as a computer host, and is used to implement the control process of data processing.

[0102] The data acquisition device is signal-connected to the controller. The two can be connected by wire through a data transmission line, or can be wirelessly connected through wireless communication methods such as Bluetooth and WiFi. In addition, the data acquisition device and the controller can also be integrated to form a device integrating data acquisition and data processing functions.

[0103] It should be understood that the data acquisition device and the controller are used to implement the steps of the control method introduced in the foregoing embodiments. Since they have been described in detail, no further introduction will be made here.

[0104] The above-described 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and 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, and should all be included in the protection scope of the present application.

Claims

1. A hot aerosol fire extinguishing device integrating environmental sensing function, characterized in that: The system comprises a processor and a memory, wherein the processor is used to process instructions stored in the memory to implement the following control process: Obtain different types of environmental data at each monitoring point in the monitored environment at each time, wherein the time includes the current time and the historical time; According to the similarity of the changing trends of the same type of environmental data at each monitoring point at each moment, combined with the growth rate of environmental data, the environmental anomaly index of each monitoring point at each moment is obtained; According to the environmental anomaly index of each monitoring point at the current moment and the distance distribution between different monitoring points, all monitoring points are divided to obtain different abnormal monitoring groups; According to the difference between the environmental anomaly indicators of each monitoring point in each abnormal monitoring group at the current moment, the diffusion trend at the current moment is analyzed, and the difference between the diffusion trend at the current moment and the adjacent historical moments and the environmental anomaly indicators of the monitoring points in each abnormal monitoring group at the current moment are combined to determine whether a fire has occurred at the current moment; If so, the fire extinguishing device control scheme corresponding to each abnormal monitoring group at the current moment is determined according to the growth trend, diffusion trend distribution characteristics and location characteristics of the environmental abnormality index of each monitoring point in each abnormal monitoring group at the current moment.

2. A hot aerosol fire extinguishing device integrating environmental sensing function according to claim 1, characterized in that: The environmental abnormality index of each monitoring point at each moment is obtained based on the similarity of the change trends of the same type of environmental data at each monitoring point at each moment, combined with the growth rate of the environmental data, and specifically includes: Determine the difference of each type of environment at each monitoring point at each moment based on the difference of the same type of environment data between each monitoring point at each moment and the adjacent moment; the environment data includes temperature data and dimming rate data; The continuous moments when the environmental difference of the temperature data of each monitoring point at each moment belongs to the growth trend constitute the growth time period; Based on the cumulative results of the difference between the environmental difference value of the temperature data and the environmental difference value of the dimming rate data at the same time in the growth period at each moment of each monitoring point, determine the data increase difference index of each monitoring point at each moment; The temperature growth index of each monitoring point at each moment is obtained according to the temperature data of each monitoring point at each moment and the temperature data at the same moment in the historical data; Based on the ratio between the temperature growth index and the data increase difference index of each monitoring point at each moment, the environmental anomaly index of each monitoring point at each moment is determined.

3. A hot aerosol fire extinguishing device integrating environmental sensing function according to claim 2, characterized in that: The step of obtaining the temperature growth index of each monitoring point at each moment according to the temperature data of each monitoring point at each moment and the temperature data at the same moment in the historical data specifically includes: The ratio between the temperature data of each monitoring point at each moment and the mean of the temperature data at the same moment in the historical data is taken as the temperature growth index of each monitoring point at each moment.

4. The hot aerosol fire extinguishing device integrating environmental sensing function according to claim 1 is characterized in that: According to the environmental anomaly index of each monitoring point at the current moment and the distance distribution between different monitoring points, all monitoring points are divided to obtain different abnormal monitoring groups, specifically including: According to the order of the environmental abnormality index of each monitoring point at the current moment from large to small, the monitoring points are selected in turn as the monitoring points to be analyzed; For each monitoring point to be analyzed, the degree of similarity between the state of each monitoring point to be analyzed and the adjacent monitoring points is obtained based on the difference in environmental abnormality indicators between the monitoring point to be analyzed and the adjacent monitoring points at the current moment, combined with the position distribution between the monitoring point to be analyzed and other monitoring points; Adjacent monitoring points corresponding to the monitoring point to be analyzed whose state similarity is greater than a preset similarity threshold are divided into the abnormal monitoring group where the monitoring point to be analyzed is located.

5. A hot aerosol fire extinguishing device integrating environmental sensing function according to claim 4, characterized in that: The method of obtaining the degree of similarity between the state of each monitoring point to be analyzed and the adjacent monitoring points according to the difference in the environmental anomaly index between the monitoring point to be analyzed and the adjacent monitoring points at the current moment and combining the position distribution between the monitoring point to be analyzed and other monitoring points specifically includes: Determine the difference characteristic coefficient based on the spatial distance between the monitoring point to be analyzed and each adjacent monitoring point, and the absolute value of the difference in environmental anomaly index between the monitoring point to be analyzed and each adjacent monitoring point at the current moment; The normalized value of the ratio of the environmental anomaly index of the monitoring point to be analyzed at the current moment to the difference characteristic coefficient is determined as the state similarity between the monitoring point to be analyzed and each adjacent monitoring point.

6. The hot aerosol fire extinguishing device integrating environmental sensing function according to claim 2 is characterized in that: The method of analyzing the diffusion trend at the current moment according to the difference between the environmental anomaly indicators of each monitoring point in each abnormal monitoring group at the current moment, combining the difference between the diffusion trends at the current moment and the adjacent historical moments and the environmental anomaly indicators of the monitoring points in each abnormal monitoring group at the current moment, and judging whether a fire occurs at the current moment specifically includes: The abnormal monitoring group corresponding to the maximum value of the mean value of the environmental abnormality index of all monitoring points in each abnormal monitoring group at the current moment is recorded as the first monitoring group of interest, and the abnormal monitoring group corresponding to the minimum value of the mean value of the environmental abnormality index of all monitoring points in each abnormal monitoring group at the current moment is recorded as the second monitoring group of interest; Determine the spatial distribution direction between the first monitoring group of interest and the second monitoring group of interest as the diffusion direction at the current moment; Based on the ratio of the environmental abnormality index between the current moment and the adjacent historical moment of each monitoring point, determine the abnormal change index of each monitoring point at the current moment; According to the quantity distribution of all abnormal monitoring groups, combined with the difference in diffusion direction between the current moment and the adjacent historical moments and the abnormal change index, the possibility of suspected fire at the current moment is obtained; Based on the possibility of a suspected fire at the current moment, determine whether a fire has occurred at the current moment.

7. A hot aerosol fire extinguishing device integrating environmental sensing function according to claim 6, characterized in that: The possibility of a suspected fire at the current moment is obtained based on the quantity distribution of all abnormal monitoring groups, combined with the difference in diffusion direction between the current moment and the adjacent historical moment and the abnormal change index, specifically including: The first characteristic coefficient is determined based on the proportion of the number of abnormal monitoring groups; the second characteristic coefficient is determined based on the average of the abnormal change indicators of all monitoring points in all abnormal monitoring groups at the current moment; the third characteristic coefficient is determined based on the cumulative result of the angle between the diffusion direction of each adjacent time in the growth time period where the current moment is located; The possibility of a suspected fire at the current moment is obtained according to the first characteristic coefficient, the second characteristic coefficient and the third characteristic coefficient. The first characteristic coefficient and the second characteristic coefficient are both positively correlated with the possibility of the suspected fire, and the third characteristic coefficient is negatively correlated with the possibility of the suspected fire.

8. The hot aerosol fire extinguishing device integrating environmental sensing function according to claim 6 is characterized in that: The step of judging whether a fire has occurred at the current moment based on the possibility of a suspected fire at the current moment specifically includes: If the probability of a suspected fire at the current moment is greater than the preset abnormal threshold, a fire occurs at the current moment; If the possibility of a suspected fire at the current moment is less than or equal to the preset abnormal threshold, then no fire has occurred at the current moment.

9. The hot aerosol fire extinguishing device integrating environmental sensing function according to claim 6 is characterized in that: The method of determining the fire extinguishing device control scheme corresponding to each abnormal monitoring group at the current moment according to the growth trend, diffusion trend distribution characteristics and location characteristics of the environmental abnormality index of each monitoring point in each abnormal monitoring group at the current moment specifically includes: According to the abnormal change index of all monitoring points in each abnormal monitoring group at the current moment, the difference between the diffusion situation in the abnormal monitoring group and the diffusion direction at the current moment, and the location distribution of each abnormal monitoring group, the abnormal severity index of each abnormal monitoring group is obtained; When the abnormal severity index of the abnormal monitoring group is greater than the preset activation threshold, the fire extinguishing device in the abnormal monitoring group is activated.

10. The hot aerosol fire extinguishing device integrating environmental sensing function according to claim 9, characterized in that: The abnormal severity index of each abnormal monitoring group is obtained according to the abnormal change index of all monitoring points in each abnormal monitoring group at the current moment, the difference between the diffusion situation in the abnormal monitoring group and the diffusion direction at the current moment, and the position distribution of each abnormal monitoring group, specifically including: For any abnormal monitoring group, the average value of the abnormal change index of all monitoring points in the abnormal monitoring group at the current moment is calculated to obtain the first coefficient; Obtain the spatial distribution direction from the maximum value to the minimum value of the environmental anomaly index of all monitoring points in the anomaly monitoring group at the current moment as the anomaly diffusion direction of the anomaly monitoring group at the current moment; calculate the cosine function value of the angle between the anomaly diffusion direction of the anomaly monitoring group at the current moment and the diffusion direction at the current moment to obtain the second coefficient; The spatial distance between the center point position of the abnormal monitoring group and the center point position of the first focus monitoring group is taken as the third coefficient; the product of the second coefficient and the third coefficient is calculated, and the ratio of the first coefficient to the product is normalized and the result is taken as the abnormal severity index of the abnormal monitoring group.

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