A monitoring information integration method, system and electronic equipment for intelligent buildings

Abstract

The present invention relates to the field of fire monitoring technology, and in particular to a monitoring information integration method, system and electronic equipment for intelligent buildings. The method collects different data in the building through various sensors, and calculates the fire hazard coefficient of each floor at each moment according to the temperature anomaly index of each floor at each moment, the power consumption of the floor at that moment, the slope of the fitted straight line of the power consumption data sequence up to that moment, the correlation between the temperature of the floor and the changing trend of CO2 concentration, the correlation between the CO2 concentration of the floor and the changing trend of the flow of people, and the correlation between the power consumption of the floor and the changing trend of the flow of people. Finally, a targeted solution is provided according to the fire hazard situation of each floor. The present invention can solve the problem that it is difficult to monitor the aging of lines or equipment in modern buildings and it is difficult to monitor the occurrence of hidden fire points.

Description

Technical Field

[0001] The present invention relates to the technical field of fire monitoring, and in particular to a monitoring information integration method, system and electronic equipment for intelligent buildings. Background Art

[0002] Smart buildings have been developed for many years. At present, various facilities in many buildings have been used for more than ten years or even decades. Due to the particularity of the building, it is difficult to replace all the lines and equipment in the building at one time. However, the aging of lines and equipment may become a safety hazard of the building. For example, the wiring of smart buildings is often hidden inside the building. When the lines or equipment are aged for a long time, the insulation layer of the lines is damaged, resulting in the risk of short circuits, which in turn causes fires. Such problems are difficult to prevent in advance. In addition, many fires are caused by hidden fire points in the building. The flames generated by these fire points have not yet caused the smoke alarm to react, but they have come into contact with a flammable and explosive combustion point, and the resulting fire exceeds the processing capacity of the smoke alarm, thus evolving into an uncontrollable fire. If these situations occur using traditional monitoring and processing methods, serious consequences will occur due to untimely response. Summary of the invention

[0003] In order to solve the technical problems in the prior art that it is difficult to monitor the aging of lines or equipment in modern buildings, it is difficult to monitor the occurrence of hidden fire points, and these risks are not reflected in time and easily cause fires in buildings, the purpose of the present invention is to provide a monitoring information integration method, system and electronic equipment for intelligent buildings. The technical solutions adopted are as follows:

[0004] In a first aspect, a method for integrating monitoring information of an intelligent building is provided, the method comprising:

[0005] Step S1: using various sensors to collect different data in the building, and forming different data sequences for each floor of the building according to the collection time, floor and data type;

[0006] Step S2: Calculate the temperature anomaly index of each floor at each moment according to the correlation measurement between the change trend of the temperature of each floor in the building at each moment and the change trend of the outdoor temperature in different data sequences, and the change trend measurement between the temperature of each floor in the building at each moment and the temperature of the adjacent floor;

[0007] Step S3: According to the temperature anomaly index of each floor at each moment, the power consumption of the floor at that moment, the slope of the fitted straight line of the power consumption data sequence up to that moment, the temperature of the floor and CO 2 The correlation between the concentration trend and the CO 2The correlation between the concentration and the changing trend of the flow of people, the correlation between the power consumption of the floor and the changing trend of the flow of people, and the calculation of the fire hazard coefficient of each floor at each moment;

[0008] Step S4: Set the first threshold of the fire hazard coefficient as the fire hazard risk value. When the fire hazard coefficient of the floor exceeds the first threshold at this moment, mark the floor as an unsafe floor.

[0009] Furthermore, the various sensors include temperature sensors, non-dispersive infrared sensors, and power consumption data sensors.

[0010] Furthermore, in step S1, the collected data is used to form different data sequences for each floor of the building according to the collection time, floor and data type, specifically:

[0011] The average temperature value collected by all temperature sensors on the same floor at the same time is taken as the temperature data of the floor at that time, and combined with the collection time sequence to form a temperature data sequence;

[0012] The CO collected by all non-dispersive infrared sensors on the same floor at the same time 2 The average concentration is taken as the CO 2 The concentration data is combined with the acquisition time sequence to form CO 2 concentration data series;

[0013] The sum of the power consumption data collected by all the electric meters on the same floor at the same time is taken as the power consumption data of the floor at that time, and combined with the collection time sequence to form a power consumption data sequence;

[0014] The average of the pedestrian flow changes collected by all monitoring on the same floor at the same time is taken as the pedestrian flow change data of the floor at that time, and combined with the collection time sequence to form a pedestrian flow data sequence.

[0015] Furthermore, the correlation measure between the changing trend of the temperature of the floor in the building and the changing trend of the outdoor temperature in step S2 is specifically: the Pearson correlation coefficient between the temperature sequence of the floor in the building up to that moment and the outdoor temperature sequence of the corresponding floor.

[0016] Furthermore, the variation trend measurement between the temperature of this floor and the temperature of adjacent floors in the building in step S2 is specifically: according to the derivative of the temperature fitting curve function of the floor and the previous floor corresponding to the floor, and the next floor corresponding to the floor, the variation trend measurement between the temperature of this floor and the temperature of adjacent floors is calculated.

[0017] Furthermore, when the temperature change of a floor at a certain moment is greater, the correlation measure between the change trend of the temperature of this floor and the change trend of the outdoor temperature becomes smaller, the change trend measure between the temperature of this floor and the temperature of the adjacent floor in the building is negative, the temperature anomaly index of this floor at this moment is positive, and the greater the temperature anomaly index of this floor is.

[0018] Furthermore, in step S3, when the temperature anomaly index of a floor at a certain moment is greater, the power consumption of the floor at that moment is greater, and the slope of the fitted straight line of the power consumption data sequence up to that moment is greater, the temperature of the floor is related to CO 2 The correlation between the concentration trend and the CO 2 The smaller the sum of the correlation between the concentration and the changing trend of the flow of people, and the correlation between the power consumption of the floor and the changing trend of the flow of people, the greater the fire hazard coefficient of the floor at that moment.

[0019] Furthermore, the step S4 further includes:

[0020] When the fire hazard coefficient is less than the first threshold, it is marked as a safe floor and no inspection is required;

[0021] Set a second threshold for the fire hazard coefficient. When the fire hazard coefficient exceeds the first threshold and is less than the second threshold, use monitoring and arrange offline staff to check for hidden fire points on the floor;

[0022] When the fire hazard coefficient is greater than the second threshold, the floor is marked as a line aging floor, and professional power personnel are arranged to check the lines on the floor and perform maintenance based on the results.

[0023] In a second aspect, a monitoring information integration system for an intelligent building is provided, the system comprising:

[0024] The data acquisition module is used to acquire different data collected by various sensors in the building, and to form different data sequences for each floor in the building according to the collection time, floor and data type;

[0025] The temperature anomaly index acquisition module is used to measure the correlation between the temperature change trend of each floor in the building at each moment and the outdoor temperature change trend in different data sequences, and the change trend between the temperature of each floor in the building at each moment and the temperature of the adjacent floor, and obtain the temperature anomaly index of each floor at each moment;

[0026] The fire hazard coefficient acquisition module is used to calculate the fire hazard coefficient according to the temperature anomaly index of each floor at each moment, the power consumption of the floor at that moment, the slope of the fitted straight line of the power consumption data sequence up to that moment, the temperature of the floor and CO 2 The correlation between the concentration trend and the CO 2The correlation between the concentration and the changing trend of the flow of people, the correlation between the power consumption of the floor and the changing trend of the flow of people, and the fire hazard coefficient of each floor at each moment;

[0027] The fire risk assessment and processing module is used to assess each floor or fire risk and give processing suggestions. When the fire hazard coefficient of the floor exceeds the first threshold at this moment, the floor is marked as an unsafe floor.

[0028] According to a third aspect, an electronic device is provided, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the monitoring information integration method for the intelligent building.

[0029] The present invention has the following beneficial effects:

[0030] 1. It can effectively monitor fire hazards such as aging of lines or equipment in modern buildings, hidden fire points, etc. First, various sensors are used to collect different data in the building, and the collected data are organized into different data sequences for each floor in the building according to the collection time, floor and data type; then, according to the correlation measurement between the temperature change trend of each floor in the building at each moment and the outdoor temperature change trend in different data sequences, and the change trend measurement between the temperature of each floor in the building at each moment and the temperature of the adjacent floors, the temperature anomaly index of each floor at each moment is calculated; finally, according to the temperature anomaly index of each floor at each moment, the power consumption of the floor at that moment, the slope of the fitted straight line of the power consumption data sequence up to that moment, the temperature of the floor and CO 2 The correlation between the concentration trend and the CO 2 The correlation between the concentration and the changing trend of the flow of people, the correlation between the power consumption of the floor and the changing trend of the flow of people, and the fire hazard coefficient of each floor at each moment are calculated. This can effectively determine whether the building has fire hazards and determine the floor where the hazard is located.

[0031] Second, it can provide targeted solutions based on the fire hazards in the monitored buildings. By setting up a fire risk assessment and processing module, each floor or fire risk is evaluated and treatment suggestions are given, and the first threshold and the second threshold are set for the fire hazard coefficient. When the fire hazard coefficient is less than the first threshold, it is marked as a safe floor and no investigation is required; when the fire hazard coefficient exceeds the first threshold and is less than the second threshold, the floor is monitored and offline staff are arranged to investigate hidden fire points; when the fire hazard coefficient is greater than the second threshold, it is marked as an aging line floor, and professional power personnel are arranged to investigate the lines on the floor, and repairs are carried out according to the investigation results. In this way, it is helpful to arrange personnel to conduct investigations and repairs in time before a fire occurs, so as to avoid the occurrence of fire problems as much as possible and avoid greater losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A flow chart of a monitoring information integration method for an intelligent building provided by an embodiment of the present invention.

[0034] Figure 2 A block diagram of a monitoring information integration system for an intelligent building provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following is a detailed description of the monitoring information integration method, system and electronic equipment of an intelligent building proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. Unless otherwise defined, all technical and scientific terms used in this article have the same meaning as commonly understood by technicians in the technical field of the present invention.

[0036] The following is a detailed description of a monitoring information integration method, system and electronic equipment for an intelligent building provided by the present invention in conjunction with the accompanying drawings.

[0037] First, see Figure 1 , which shows a flow chart of a monitoring information integration method for an intelligent building provided by an embodiment of the present invention, the method comprising the following steps:

[0038] Step S1: Use various sensors to collect different data in the building, and form different data sequences for each floor in the building according to the collection time, floor and data type.

[0039] Specifically, the various sensors include temperature sensors, non-dispersive infrared sensors, and power consumption data sensors. It should be noted that the temperature and CO sensors integrated in smart meters can also be used. 2 Concentration and power consumption data sensors are used to collect different data information in the building.

[0040] First, temperature sensors and non-dispersive infrared sensors are installed indoors and outdoors on each floor of the building. There are 10 indoor temperature sensors and non-dispersive infrared sensors on each floor. They are installed at the main lines or power equipment, as well as the necessary paths on the floors, such as elevator entrances and staircases. The power energy consumption data sensor can collect power energy consumption data with the help of the electric meters installed on each floor. The temperature, CO 2 Concentration, power consumption data, all data are collected once every minute.

[0041] Then, the average temperature value collected by all temperature sensors on the same floor at the same time is used as the temperature data of the floor at that time, and a temperature data sequence is formed in combination with the collection time sequence; specifically, through the temperature sensors installed outdoors, the average outdoor ambient temperature value collected by all outdoor temperature sensors on the same floor at the same time is used as the outdoor temperature data of the floor at that time, and an outdoor temperature data sequence is formed in combination with the collection time sequence; through the temperature sensors installed indoors, the average indoor temperature value collected by all temperature sensors on the same floor at the same time is used as the indoor temperature data of the floor at that time, and an indoor temperature data sequence is formed in combination with the collection time sequence.

[0042] Finally, the CO collected by all non-dispersive infrared sensors on the same floor at the same time 2 The average concentration is taken as the CO 2 The concentration data is combined with the acquisition time sequence to form CO 2 concentration data sequence; the sum of the power consumption data collected by all the electric meters on the same floor at the same time is taken as the power consumption data of the floor at that time, and combined with the collection time sequence to form a power consumption data sequence; the average of the change in passenger flow collected by all the monitoring on the same floor at the same time is taken as the passenger flow change data of the floor at that time, and combined with the collection time sequence to form a passenger flow data sequence.

[0043] Step S2: Calculate the temperature anomaly index of each floor at each moment according to the correlation measurement between the change trend of the temperature of each floor in the building at each moment and the change trend of the outdoor temperature in different data sequences, and the change trend measurement between the temperature of each floor in the building at each moment and the temperature of the adjacent floors.

[0044] For a building, its thermal conductivity is related to the building materials, so the thermal conductivity of the entire building is basically fixed. As the outdoor temperature changes, the temperature inside the building will also change accordingly, and because the thermal conductivity of the building is basically fixed, the temperature change trend inside the building is basically consistent with the outdoor temperature change trend. Secondly, because the intensity and time of solar radiation exposed to different floors of the building are inconsistent, the higher the floor, the greater the intensity of solar radiation and the longer the radiation time, so the higher the floor, the higher the temperature.

[0045] In this embodiment, the temperature of the floor is used as the dependent variable and the number of floors is used as the independent variable, so that a series of points can be obtained. For example, the temperature of the first floor is t 1 , then the point formed is (1, t 1 ); the temperature of the second layer is t 2 , then the point formed is (1, t 2 ); and so on. A series of points can be obtained at each moment. The points obtained at the current moment and all the moments in the last 24 hours are used as inputs, and a polynomial fitting algorithm is used to obtain a fitting curve function between the floor and the temperature. The polynomial fitting algorithm is a well-known technology, and the specific process is not repeated here.

[0046] Furthermore, the change trend measure between the temperature of the floor in the building and the temperature of the adjacent floor in step S2 is specifically: according to the derivative of the temperature fitting curve function of the floor and the previous floor corresponding to the floor, and the next floor corresponding to the floor, the change trend measure between the temperature of the floor and the temperature of the adjacent floor is calculated. The correlation measure between the change trend of the temperature of the floor in the building and the change trend of the outdoor temperature in step S2 is specifically: the Pearson correlation coefficient between the temperature sequence of the floor in the building up to that moment and the outdoor temperature sequence of the corresponding floor.

[0047] When the temperature change of a floor at a certain moment is greater, the correlation measure between the change trend of the temperature of this floor and the change trend of the outdoor temperature becomes smaller, the change trend measure between the temperature of this floor and the temperature of the adjacent floor in the building is negative, the temperature anomaly index of this floor at this moment is positive, and the greater the temperature anomaly index of this floor is.

[0048] Specifically, the variation trend measurement between the temperature of each floor and the temperature of the adjacent floors at each moment in the building proposed in this embodiment can be expressed by the following formula:

[0049]

[0050] in, i is the floor number of this floor, n is the total number of floors, a i It is the derivative of the fitting curve function between the floor and temperature when the independent variable is the number of floors. a i-1 , a i+1 are the derivatives of the fitting curve function between the floor and the temperature when the independent variables are the number of the previous and next floors, sgn () is a symbolic function, A 2 It is a measure of the changing trend between the temperature of each floor and the temperature of the adjacent floors in a building at each moment.

[0051] In the above-mentioned formula for measuring the change trend between the temperature of each floor and the temperature of the adjacent floors at each moment in the building, by calculating the difference between the product of the derivative of the fitting curve function between the floor and the temperature at the previous floor number and the derivative of the fitting curve function between the floor and the temperature at the current floor number and the product of the derivative of the fitting curve function between the floor and the temperature at the next floor number and the derivative of the fitting curve function between the floor and the temperature at the current floor number, it can reflect the change of the derivative of the fitting curve function between the floor and the temperature at the previous floor, the current floor, and the next floor number, and can reflect whether the temperature of the current floor is abnormal. The role of the sign function is to avoid focusing only on the change trend of the floor temperature, thereby affecting the subsequent calculation of the temperature anomaly index. ( a i-1 - a i+1 )× a i Perform symbolic function calculation. When the independent variable is a positive number, the function value is 1, and the rest of the function values ​​are -1. That is, when ( a i-1 - a i+1 )× a i When is a positive number, A 2 The value is 1, otherwise, A 2 The value is -1.

[0052] Furthermore, the temperature anomaly index formula of each floor at each moment in a building of a monitoring information integration method for an intelligent building proposed in this embodiment is:

[0053]

[0054] Among them, e is a natural constant, A 1 It is a measure of the correlation between the temperature change trend of each floor in the building and the outdoor temperature change trend at each moment. Specifically, A 1 It can be the Pearson correlation coefficient between the temperature series of the layer in the building and the outdoor temperature series up to that moment, A 2 It is a measure of the change trend between the temperature of each floor and the temperature of the adjacent floors at each moment in the building. A It is the temperature anomaly index of each floor at each moment.

[0055] In the temperature anomaly index formula of each floor at each moment in the building of the above-mentioned intelligent building monitoring information integration method, the function of performing index calculation is that if the exponential function is not used, A 1 When it is 0, the temperature anomaly index will be always 0, making it impossible to consider A 2 The influence of temperature anomaly is one-sided and inaccurate. It reflects the correlation measurement of the temperature change trend of each floor in the building at each moment and the outdoor temperature change trend, and the change trend measurement between the temperature of each floor and the temperature of the adjacent floor at each moment, and comprehensively obtains the temperature anomaly index of each floor at each moment. It is understandable that when the temperature of a floor is abnormal at a certain moment, the temperature change of the floor will not be completely consistent with the outdoor temperature change, that is, the positive correlation between the temperature change trends of the two is poor, so A 1 The smaller the floor temperature, the greater the change in the floor temperature. A 1 The smaller it is; at the same time, the temperature of the floor is abnormal, which will make the temperature of this floor not follow the characteristic that the higher the floor, the higher the temperature. The temperature at this floor is not increasing, that is, A 2 is a negative value ( A 2 =-1); therefore, when the temperature of a floor is abnormal at a certain moment, the corresponding temperature anomaly index is positive, and the more severe the temperature anomaly is, the greater the temperature anomaly index is. On the contrary, when the floor temperature is normal, the temperature anomaly index is negative.

[0056] It should be noted that the temperature anomaly index of each floor at each moment in the above step S2 only reflects the abnormality of the temperature change trend within the floor, but cannot determine the cause of the temperature change, so it is impossible to directly judge whether there is a safety hazard. It is necessary to combine other sensor data for further judgment.

[0057] Step S3: According to the temperature anomaly index of each floor at each moment, the power consumption of the floor at that moment, the slope of the fitted straight line of the power consumption data sequence up to that moment, the temperature of the floor and CO 2 The correlation between the concentration trend and the CO 2 The correlation between the concentration and the changing trend of the flow of people, the correlation between the power consumption of the layer and the changing trend of the flow of people, and the fire hazard coefficient of each floor at each moment are calculated.

[0058] In the actual situation of changes in monitoring information data in buildings, when the number of people on a floor in a building increases or there are safety hazards related to fire, the CO 2 Changes in concentration and temperature, and an increase in the number of people will also lead to an increase in power consumption on the floor, but the increase in power consumption may also be related to the aging of the lines or equipment on the floor. Therefore, it is necessary to further judge the cause of the temperature change based on the correlation between the changing trends of various monitoring data to determine whether there is a fire hazard.

[0059] First, the power consumption sequence and seasonal cycle length τ of each layer are taken as input, and the STL time series decomposition algorithm is used to output the trend sequence of power consumption. In this embodiment, the seasonal cycle length τ is 1 day. Then, each element in the power consumption trend sequence is used as the dependent variable, and the index of the element is used as the independent variable to obtain a series of points. These points are used as input, and the straight line fitting technology is used to output the fitting straight line equation of the power consumption trend sequence. The STL time series decomposition algorithm and straight line fitting are well-known technologies, and the specific process will not be repeated.

[0060] In step S3, when the temperature anomaly index of a floor at a certain moment is greater, the power consumption of the floor at that moment is greater, and the slope of the fitted straight line of the power consumption data sequence up to that moment is greater, the temperature of the floor is related to CO 2 The correlation between the concentration trend and the CO 2 The smaller the sum of the correlation between the concentration and the changing trend of the flow of people, and the correlation between the power consumption of the floor and the changing trend of the flow of people, the greater the fire hazard coefficient of the floor at that moment.

[0061] It should be noted that, in this embodiment, the temperature of this layer is related to CO 2 The correlation between the concentration trend and the CO2 The correlation between the concentration and the changing trend of the flow of people, and the correlation between the power consumption of the layer and the changing trend of the flow of people can be measured by the Kendall rank correlation coefficient.

[0062] Furthermore, the fire hazard coefficient formula of each floor in a building at each moment in a monitoring information integration method of an intelligent building proposed in this embodiment is:

[0063]

[0064] in, B 1 Represents the fire hazard coefficient of each floor at each moment, A represents the temperature anomaly index, U Represents the power consumption of the layer at the current moment, K Represents the slope of the fitted straight line of the power consumption trend series up to that moment, B 1 is the temperature of the layer and CO 2 The correlation between the concentration trends, B 2 is the CO 2 The correlation between the concentration and the changing trend of human flow, B 3 It is the correlation between the power consumption of this layer and the changing trend of the flow of people.

[0065] In the fire hazard coefficient formula of each floor at each moment in the building of the above-mentioned intelligent building monitoring information integration method, temperature and CO 2 The correlation between the concentration trends, CO 2 The sum of the correlation between concentration and the changing trend of human flow, and the correlation between power consumption and the changing trend of human flow are negatively correlated with the fire hazard coefficient. The purpose of the exponential function is to avoid the denominator being 0; while the temperature anomaly index, the power consumption of the layer at the current moment, and the slope of the fitting straight line of the power consumption trend sequence up to that moment are all positively correlated with the fire hazard coefficient.

[0066] It is understandable that when a floor has a temperature anomaly and there is no aging problem with the power lines or equipment, if the temperature change is caused by an increase in the flow of people on the floor, the degree of temperature anomaly caused by the flow of people will not be particularly large, that is, A is a positive number, but small; secondly, the increase in human traffic will also increase CO 2 The concentration and power consumption increase, B 1 + B 2 + B 3If the power lines or equipment are not aging, the daily power consumption of the floor will not increase gradually over a long period of time, so the slope of the fitted straight line of the power consumption trend series is small. Since the denominator is an exponential function, it changes quickly, so the fire hazard coefficient is small. If the temperature change is not caused by an increase in the flow of people on the floor, it means that there may be a fire hazard on the floor (such as a fire point that does not cause a smoke alarm to respond), and the temperature abnormality will be relatively large, that is, A is a positive number, but relatively large; secondly, the ignition point will cause CO 2 The concentration increases, thus breaking down the CO 2 The correlation between the concentration and the changing trend of human flow, that is, B 2 Smaller, so B 1 + B 2 + B 3 Smaller; finally, because there is no aging problem in the power lines or equipment, the slope of the fitted straight line of the power energy consumption trend series is larger, so the fire hazard coefficient is relatively large. On the other hand, when a floor has a temperature anomaly and the power lines or equipment have aging problems, the aging problem will cause the resistance of the main lines or power equipment to increase, which will require more power consumption and increase heat generation, thereby increasing the temperature anomaly and power consumption in the floor, that is, A and U are both larger, and due to the irreversibility of the aging problem, the power consumption will gradually increase over a long period of time, so the slope of the fitted straight line of the power consumption trend series is larger, that is K Large; the temperature change caused by aging will destroy the relationship between temperature and CO 2 The correlation between the concentration, power consumption and the changing trend of human flow, that is, B 1 , B 3 Smaller, so B 1 + B 2 + B 3 It is relatively small, so the fire hazard coefficient is relatively the largest.

[0067] Step S4: Set the first threshold of the fire hazard coefficient as the fire hazard risk value. When the fire hazard coefficient of the floor exceeds the first threshold at this moment, mark the floor as an unsafe floor.

[0068] Specifically, when the fire hazard coefficient of the floor exceeds the first threshold at this moment, the floor is marked as an unsafe floor; in this embodiment, it is defined that an unsafe floor may have hidden fire points or aging lines, which require further investigation.

[0069] According to the calculation method of the floor fire hazard coefficient in step S3 of this embodiment, a large number of floor data are collected to calculate the fire hazard coefficient of each floor. Among them, the fire hazard coefficient of the floor without safety hazards is the smallest, the fire hazard coefficient of the floor with hidden fire points is relatively large, and the fire hazard coefficient of the floor with aging lines is the largest. In this embodiment, the fire hazard coefficients of all floors and the number of clusters are combined. θ As input, the k-means clustering algorithm is used to output the clustering results. In this embodiment, the number of clusters θ Take the empirical value 3. The k-means clustering algorithm is a well-known technology, and the specific process is not repeated here. In the three clusters, find the largest fire hazard coefficient in each cluster, compare the sizes of the three, record the cluster with the minimum value as the cluster with no safety hazard, and record the cluster with the maximum value as the cluster with line aging.

[0070] Therefore, in addition to setting the first threshold of the fire hazard coefficient as the fire hazard risk value, it is necessary to set the second threshold of the fire hazard coefficient, which is the line aging threshold.

[0071] Furthermore, when conducting safety monitoring of intelligent buildings, the fire hazard coefficient of each floor is calculated based on the sensor monitoring data of each floor. When the fire hazard coefficient is less than the first threshold, it is marked as a safe floor and no investigation is required. For example, the first threshold can be 0.6; when the fire hazard coefficient exceeds the first threshold and is less than the second threshold, monitoring is used and offline staff are arranged to investigate hidden fire points on the floor. For example, the second threshold can be 0.8; when the fire hazard coefficient is greater than the second threshold, it is marked as a line aging floor, and professional power personnel are arranged to investigate the lines on the floor, and repairs are performed according to the investigation results. It should be noted that when comparing the size relationship between the first threshold, the second threshold and the fire hazard coefficient to investigate the floor, the normalized fire hazard coefficient is used.

[0072] Through the integrated solution of intelligent building monitoring information in this embodiment, monitoring data can be used to determine whether there is a fire hazard and locate the floor where the hazard is located, so that personnel can be arranged in time to check and repair different safety hazards before a fire occurs, so as to avoid the occurrence of fire problems as much as possible and avoid greater losses.

[0073] Second, see Figure 2 , which shows a block diagram of a monitoring information integration system for an intelligent building provided by an embodiment of the present invention, the system comprising:

[0074] The data acquisition module 101 is used to acquire different data collected by various sensors in the building, and to form different data sequences for each floor in the building according to the collection time, floor and data type;

[0075] The temperature anomaly index acquisition module 102 is used to measure the correlation between the change trend of the temperature of each floor in the building at each moment and the change trend of the outdoor temperature in different data sequences, and the change trend between the temperature of each floor in the building at each moment and the temperature of the adjacent floor, and obtain the temperature anomaly index of each floor at each moment;

[0076] The fire hazard coefficient acquisition module 103 is used to obtain the fire hazard coefficient according to the temperature anomaly index of each floor at each moment, the power consumption of the floor at that moment, the slope of the fitted straight line of the power consumption data sequence up to that moment, the temperature of the floor and the CO 2 The correlation between the concentration trend and the CO 2 The correlation between the concentration and the changing trend of the flow of people, the correlation between the power consumption of the floor and the changing trend of the flow of people, and the fire hazard coefficient of each floor at each moment;

[0077] The fire risk assessment and processing module 104 is used to assess each floor or fire risk and give processing suggestions. When the fire hazard coefficient of the floor exceeds the first threshold at this moment, the floor is marked as an unsafe floor. When the fire hazard coefficient is less than the first threshold, it is marked as a safe floor and no investigation is required; when the fire hazard coefficient exceeds the first threshold and is less than the second threshold, the floor is monitored and offline staff are arranged to investigate hidden fire points; when the fire hazard coefficient is greater than the second threshold, it is marked as a line aging floor, and professional power personnel are arranged to investigate the lines on the floor, and repairs are carried out according to the investigation results.

[0078] In a third aspect, this embodiment provides an electronic device, including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the monitoring information integration method for intelligent buildings described in any one of the embodiments.

[0079] The present embodiment provides a monitoring information integration method, system and electronic equipment for intelligent buildings. Various sensors are used to collect different data in the building. The collected data are used to form different data sequences for each floor in the building according to the collection time, floor and data type. Then, according to the correlation measurement of the change trend of the temperature of each floor in the building at each moment and the change trend of the outdoor temperature in different data sequences, and the change trend measurement between the temperature of each floor in the building at each moment and the temperature of the adjacent floor, the temperature anomaly index of each floor is calculated at each moment. Finally, according to the temperature anomaly index of each floor at each moment, the power consumption of the floor at that moment, the slope of the fitted straight line of the power consumption data sequence up to that moment, the relationship between the temperature of the floor and the CO 2 The correlation between the concentration trend and the CO 2 By calculating the correlation between the concentration and the changing trend of the flow of people, the correlation between the power consumption of the layer and the changing trend of the flow of people, and the fire hazard coefficient of each floor at each moment, it is possible to effectively judge whether there is a fire hazard in the building and determine the floor where the hazard is located; by setting up the fire risk assessment and processing module 104, each floor or fire risk is assessed and processing suggestions are given, and a first threshold and a second threshold are set for the fire hazard coefficient. When the fire hazard coefficient is less than the first threshold, it is marked as a safe floor and no investigation is required; when the fire hazard coefficient exceeds the first threshold and is less than the second threshold, monitoring is used and offline staff are arranged to investigate hidden fire points on the floor; when the fire hazard coefficient is greater than the second threshold, it is marked as a line aging floor, and professional power personnel are arranged to investigate the lines on the floor, and maintenance is carried out according to the investigation results. In this way, it is helpful to arrange personnel for investigation and maintenance in time before a fire occurs, so as to avoid the occurrence of fire problems as much as possible and avoid greater losses.

[0080] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The embodiments described above 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, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions 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 monitoring information integration method for intelligent buildings, characterized in that: The method comprises: Step S1: using various sensors to collect different data in the building, and forming different data sequences for each floor of the building according to the collection time, floor and data type; Step S2: Calculate the temperature anomaly index of each floor at each moment according to the correlation measurement A1 between the change trend of the temperature of each floor in the building at each moment and the change trend of the outdoor temperature, and the change trend measurement A2 between the temperature of each floor in the building at each moment and the temperature of the adjacent floor: -e multiplied by -A1 and A2; A1 is the Pearson correlation coefficient between the temperature series of the floor in the building and the outdoor temperature series up to that moment, A2 is the difference between the product of the derivative of the temperature fitting curve function of the previous floor and the derivative of the temperature fitting curve function of the current floor and the product of the derivative of the temperature fitting curve function of the next floor and the derivative of the temperature fitting curve function of the current floor; Step S3: Calculate the fire hazard coefficient of each floor at each moment according to the temperature anomaly index A of each floor at each moment, the power consumption U of the floor at that moment, the slope K of the fitted straight line of the power consumption data sequence up to that moment, the correlation B1 between the temperature of the floor and the change trend of CO2 concentration, the correlation B2 between the CO2 concentration of the floor and the change trend of the flow of people, and the correlation B3 between the power consumption of the floor and the change trend of the flow of people, The quotient of the natural exponent of the product of A, U and K and the sum of B1, B2 and B3; Step S4: Set the first threshold value of the fire hazard coefficient as the fire hazard risk value. When the fire hazard coefficient of the floor exceeds the first threshold value of the fire hazard coefficient at this moment, mark the floor as an unsafe floor.

2. The monitoring information integration method of an intelligent building according to claim 1, characterized in that: The various sensors include temperature sensors, non-dispersive infrared sensors, and power consumption data sensors.

3. The monitoring information integration method of an intelligent building according to claim 2, characterized in that: In step S1, the collected data is used to form different data sequences for each floor of the building according to the collection time, floor and data type, specifically: The average temperature value collected by all temperature sensors on the same floor at the same time is taken as the temperature data of the floor at that time, and combined with the collection time sequence to form a temperature data sequence; The average CO2 concentration collected by all non-dispersive infrared sensors on the same floor at the same time is used as the CO2 concentration data of the floor at that time, and combined with the collection time sequence to form a CO2 concentration data sequence; The sum of the power consumption data collected by all the electric meters on the same floor at the same time is taken as the power consumption data of the floor at that time, and combined with the collection time sequence to form a power consumption data sequence; The average of the pedestrian flow changes collected by all monitoring on the same floor at the same time is taken as the pedestrian flow change data of the floor at that time, and combined with the collection time sequence to form a pedestrian flow data sequence.

4. The monitoring information integration method of an intelligent building according to claim 3 is characterized in that: The correlation measurement between the temperature change trend of each floor in the building at each moment and the outdoor temperature change trend in step S2 is specifically: the Pearson correlation coefficient between the temperature sequence of the floor in the building up to that moment and the outdoor temperature sequence of the corresponding floor.

5. The monitoring information integration method of an intelligent building according to claim 4, characterized in that: The variation trend measurement between the temperature of each floor and the temperature of the adjacent floors at each moment in the building in step S2 is specifically as follows: the variation trend measurement between the temperature of the floor and the temperature of the adjacent floors is calculated based on the derivative of the temperature fitting curve function of the floor and the previous floor corresponding to the floor and the next floor corresponding to the floor.

6. The monitoring information integration method of an intelligent building according to claim 1, characterized in that: When the temperature change of a floor at a certain moment is greater, the correlation measure between the change trend of the temperature of this floor and the change trend of the outdoor temperature becomes smaller, the change trend measure between the temperature of this floor and the temperature of the adjacent floor in the building is negative, the temperature anomaly index of this floor at this moment is positive, and the greater the temperature anomaly index of this floor is.

7. The monitoring information integration method of an intelligent building according to claim 1, characterized in that: The step S4 further comprises: When the fire hazard coefficient is less than the first threshold of the fire hazard coefficient, it is marked as a safe floor and no inspection is required; Set a second threshold for the fire hazard coefficient. When the fire hazard coefficient exceeds the first threshold and is less than the second threshold, use monitoring and arrange offline staff to check for hidden fire points on the floor. When the fire hazard coefficient is greater than the second threshold value of the fire hazard coefficient, it is marked as a line aging floor, and professional power personnel are arranged to check the lines on this floor and perform maintenance based on the inspection results.

8. A monitoring information integration system for intelligent buildings, implementing the method according to any one of claims 1 to 7, characterized in that: The system comprises: The data acquisition module is used to acquire different data collected by various sensors in the building, and to form different data sequences for each floor in the building according to the collection time, floor and data type; The temperature anomaly index acquisition module is used to measure the correlation between the temperature change trend of each floor in the building at each moment and the outdoor temperature change trend in different data sequences, and the change trend between the temperature of each floor in the building at each moment and the temperature of the adjacent floor, and obtain the temperature anomaly index of each floor at each moment; The fire hazard coefficient acquisition module is used to obtain the fire hazard coefficient of each floor at each moment according to the temperature anomaly index of each floor at each moment, the power energy consumption of the floor at that moment, the slope of the fitted straight line of the power energy consumption data sequence up to that moment, the correlation between the temperature of the floor and the change trend of CO2 concentration, the correlation between the CO2 concentration of the floor and the change trend of the flow of people, and the correlation between the power energy consumption of the floor and the change trend of the flow of people; The fire risk assessment and processing module is used to assess each floor or fire risk and give processing suggestions. When the fire hazard coefficient of the floor exceeds the first threshold of the fire hazard coefficient at this moment, the floor is marked as an unsafe floor.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the monitoring information integration method of the intelligent building as described in any one of claims 1 to 7.

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