Water leakage detection method and device for building roof with distributed optical fiber system

Abstract

The invention provides a water leakage detection method and device for a building roof with a distributed optical fiber system, and the method comprises the steps: processing a temperature matrix through employing a fuzzy theory and a neural network model technology based on the influence of shadow and accumulated water on the temperature, and obtaining a temperature compensation matrix; the temperature compensation matrix is adopted to compensate each temperature value in the temperature matrix to obtain the final temperature matrix, factors influencing the temperature are considered, so that the subsequent judgment is more in line with the actual working condition, the accuracy of water leakage detection of the building roof is improved through point-by-point investigation, and compared with an existing scheme, a manual inspection mode is not needed, and the detection efficiency is improved. Therefore, the detection efficiency is improved, and the problem of low detection efficiency caused by the fact that water leakage detection of the roof of the building provided with the distributed optical fiber system still uses a manual inspection mode in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the technical field of water leakage detection on the roof of a building, and in particular, to a water leakage detection method, device, computer-readable storage medium and system for the roof of a building equipped with a distributed optical fiber system. Background Art

[0002] In a high temperature and high humidity environment, the waterproof coating on the roof of the substation building is prone to aging and failure, which may cause water to fall on the substation equipment, causing unplanned equipment failure and shutdown. At present, the system uses manual inspections to check for hidden dangers of water seepage on the roof of substation buildings. Operation and maintenance personnel conduct periodic daily inspections of the roofs of each substation and special inspections in strong convective weather. Manual inspections are difficult to detect hidden dangers of water seepage on the roof in time before leakage, and will also increase human resource costs and the risk of people falling from heights.

[0003] That is, the existing solution for detecting water leakage on the roof of a building equipped with a distributed optical fiber system still uses a manual inspection method, which results in low detection efficiency. Summary of the invention

[0004] The main purpose of the present application is to provide a method, device, computer-readable storage medium and system for detecting water leakage on the roof of a building equipped with a distributed optical fiber system, so as to at least solve the problem that the existing solution for detecting water leakage on the roof of a building equipped with a distributed optical fiber system still uses manual inspection, resulting in low detection efficiency.

[0005] In order to achieve the above object, according to one aspect of the present application, a method for detecting water leakage on the roof of a building equipped with a distributed optical fiber system is provided, the method comprising:

[0006] Acquisition step: acquiring a temperature matrix of a distributed optical fiber system, wherein the temperature matrix includes temperatures collected by an optical fiber sensor on each optical fiber in the distributed optical fiber system;

[0007] The first processing step: based on the influence of shadow and water accumulation on temperature, the temperature matrix is ​​processed by using fuzzy theory and neural network model technology to obtain a temperature compensation matrix, and each temperature value in the temperature matrix is ​​compensated by using the temperature compensation matrix to obtain a final temperature matrix;

[0008] The second processing step: comparing each temperature value in the final temperature matrix with a temperature threshold value to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value;

[0009] The third processing step: repeat the acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value.

[0010] Optionally, obtaining a temperature matrix of the distributed optical fiber system includes:

[0011] according to Determining each temperature value in the temperature matrix;

[0012] Wherein, T is each temperature value in the temperature matrix, T0 is the temperature of the constant temperature box where the detection optical fiber in the distributed optical fiber system is placed, h is Planck's constant, k B is the Boltzmann constant, c is the speed of light, Δv is, and F(T) is the intermediate constant.

[0013] Optionally, before acquiring the temperature matrix of the distributed optical fiber system, the method further includes:

[0014] according to The intermediate constant is determined.

[0015] Optionally, based on the influence of shadow and water accumulation on temperature, fuzzy theory and neural network model technology are used to process the temperature matrix to obtain a temperature compensation matrix, including:

[0016] The fuzzy theory is used to define the influence of shadow and water accumulation on temperature, respectively, to obtain a first influence degree and a second influence degree, wherein the first influence degree represents the influence of light intensity on temperature, and the second influence degree represents the influence of precipitation on temperature;

[0017] Based on the first influence degree, a first neural network model is used to process the regional position of each optical fiber and the corresponding light intensity to obtain a first temperature compensation matrix;

[0018] Based on the second influence degree, a second neural network model is used to process the regional position of each optical fiber and the corresponding precipitation to obtain a second temperature compensation matrix;

[0019] Determining the temperature compensation matrix includes the first temperature compensation matrix and the second temperature compensation matrix.

[0020] Optionally, using the temperature compensation matrix to compensate each temperature value in the temperature matrix to obtain a final temperature matrix includes:

[0021] Using the first temperature compensation matrix to compensate each temperature value in the temperature matrix to obtain an intermediate temperature matrix;

[0022] The second temperature compensation matrix is ​​used to compensate each temperature value in the intermediate temperature matrix to obtain the final temperature matrix.

[0023] Optionally, comparing each temperature value in the final temperature matrix with a temperature threshold value respectively to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value includes:

[0024] When the target temperature value in the final temperature matrix is ​​greater than or equal to the temperature threshold, determining the target temperature value to be the abnormal temperature value;

[0025] In a case where the target temperature value in the final temperature matrix is ​​less than the temperature threshold, the target temperature value is determined to be a normal temperature value.

[0026] Optionally, in the process of repeatedly performing the obtaining step, the first processing step and the second processing step at least a preset number of times, the method further includes:

[0027] When it is determined in two adjacent detections that an abnormal point spreads in all directions, an alarm message is generated to indicate that the waterproof function of the location where the abnormal point is located has failed and needs to be repaired. The abnormal point represents the location point where the target temperature value in the final temperature matrix is ​​determined to be an abnormal temperature value.

[0028] According to another aspect of the present application, there is provided a water leakage detection device for a roof of a building equipped with a distributed optical fiber system, the device comprising:

[0029] An acquisition unit, used for the acquisition step: acquiring a temperature matrix of a distributed optical fiber system, wherein the temperature matrix includes a temperature collected by an optical fiber sensor on each optical fiber in the distributed optical fiber system;

[0030] A first processing unit is used for a first processing step: based on the influence of shadow and water accumulation on temperature, the temperature matrix is ​​processed by using fuzzy theory and neural network model technology to obtain a temperature compensation matrix, and each temperature value in the temperature matrix is ​​compensated by using the temperature compensation matrix to obtain a final temperature matrix;

[0031] A second processing unit, used for a second processing step: respectively comparing each temperature value in the final temperature matrix with a temperature threshold value to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value;

[0032] The third processing unit is used for the third processing step: repeatedly executing the acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value.

[0033] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0034] According to another aspect of the present application, a water leakage detection system for a roof of a building equipped with a distributed optical fiber system is provided, the system comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the methods described.

[0035] By applying the technical solution of the present application, based on the influence of shadow and water accumulation on temperature, fuzzy theory and neural network model technology are used to process the temperature matrix to obtain a temperature compensation matrix, and the temperature compensation matrix is ​​used to compensate each temperature value in the temperature matrix to obtain a final temperature matrix, thereby taking into account the factors affecting the temperature, so that the subsequent judgment is more in line with the actual working conditions, and each temperature value in the final temperature matrix is ​​compared with the temperature threshold, and it is determined whether each temperature value in the final temperature matrix is ​​an abnormal temperature value, and the acquisition step, the first processing step and the second processing step are repeatedly performed at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value, that is, the accuracy of water leakage detection on the roof of the building is improved by point-by-point investigation, and compared with the existing solution, there is no need to use the manual inspection method, thereby improving the detection efficiency, thereby solving the problem that the existing solution still uses the manual inspection method for water leakage detection on the roof of the building equipped with a distributed optical fiber system, resulting in low detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1A schematic flow chart of a method for detecting water leakage on a roof of a building equipped with a distributed optical fiber system according to an embodiment of the present application is shown;

[0038] Figure 2 A schematic diagram of an optical fiber device provided according to an embodiment of the present application is shown;

[0039] Figure 3 A schematic diagram of a substation roof structure provided according to an embodiment of the present application is shown;

[0040] Figure 4 A structural block diagram of a water leakage detection device for a roof of a building equipped with a distributed optical fiber system provided according to an embodiment of the present application is shown.

[0041] The above drawings include the following reference numerals:

[0042] 100. Armored optical fiber; 200. Optical fiber junction box; 300. Demodulation analyzer. DETAILED DESCRIPTION

[0043] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0044] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0045] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0046] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0047] Optical Time Domain Reflectometry (OTDR) is a technique used to detect and analyze losses, faults, and connection problems in fiber-optic communication lines. By sending a pulsed light signal and measuring the intensity and time delay of the signal as it reflects and scatters in the optical fiber, OTDR can determine the location and nature of attenuation, breaks, bends, connection points, and other problems in a fiber-optic communication line. This technique plays an important role in the construction, maintenance, and troubleshooting of fiber-optic communication networks.

[0048] ODF tray is a fiber optic distributed splice box used in fiber optic communication systems. It is used to connect fiber optic cables and provide protection and management functions. ODF tray can connect multiple fiber optic cables together and realize the distribution and management of optical fibers through different wiring methods. It is one of the indispensable equipment in fiber optic communication systems. ODF tray is usually installed in the machine room or communication equipment room to connect different fiber optic equipment and terminal equipment to ensure the normal operation of the fiber optic communication system.

[0049] As introduced in the background technology, in a high temperature and high humidity environment, the waterproof coating on the roof of the substation building is prone to aging and failure, which may cause water to fall on the substation equipment, causing unplanned equipment failure and shutdown. At present, the system uses manual patrols to check for hidden dangers of water seepage on the roof of the substation building. Operation and maintenance personnel conduct periodic daily patrols and special patrols in strong convective weather on the roof of each substation. Manual patrols are difficult to detect hidden dangers of roof seepage in time before leakage, and will also increase human resource costs and the risk of people falling from heights. In order to solve the problem that the existing solution for water leakage detection on the roof of a building equipped with a distributed optical fiber system is still using a manual patrol method, resulting in low detection efficiency, the embodiments of the present application provide a method, device, computer-readable storage medium and system for water leakage detection on the roof of a building equipped with a distributed optical fiber system.

[0050] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0051] In this embodiment, a method for detecting water leakage on the roof of a building equipped with a distributed optical fiber system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0052] Figure 1 FIG. 1 is a flow chart of a method for detecting water leakage on the roof of a building equipped with a distributed optical fiber system according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0053] Step S101, an acquisition step: acquiring a temperature matrix of a distributed optical fiber system, wherein the temperature matrix includes the temperature collected by the optical fiber sensor on each optical fiber in the distributed optical fiber system;

[0054] Specifically, the fuzzy adaptive theory is first used to adjust the measured temperature values ​​of each measuring point to form a roof temperature field state model. On this basis, a global building roof area temperature field model is established to identify temperature abnormality areas. Finally, by interactively comparing the real-time data of the temperature field in the abnormal roof area at different time scales, the temperature abnormality points where leakage may occur are located. Overcome the complex electromagnetic environment in the substation and the uncertain natural environment interference such as light and shadow, accurately analyze the location of water leakage, and ensure the safe and stable operation of the power grid.

[0055] When water flows through the cracks in the roof, heat exchange is inevitable when there is a temperature difference between the two media. Since concrete has a low thermal conductivity and a relatively uniform temperature field distribution, the temperature field of the optical fiber sensor laid with concrete changes little. It will only change with the change of the roof temperature in a day. When there is a trace amount of seepage, the flow rate is slow and stable. After the water fills the cracks to saturation, the concrete cracks have sufficient time and sufficient contact space to change the temperature field. The transfer of heat by conduction in concrete will be significantly surpassed by the transfer of convective heat caused by fluid movement and the heat carried away by liquid evaporation, thereby causing changes in the temperature field. By establishing a global roof temperature state model, the characteristic information of the micro-environment field of early water leakage on the roof of a building can be simulated.

[0056] Compared with traditional instruments, fiber optic sensing technology has the characteristics of high sensitivity, strong adaptability to the environment, easy integration and high degree of automation. Fiber optic temperature measurement is widely used in fiber optic sensing technology. The distributed fiber optic Raman temperature measurement system is based on the optical time domain reflectometry (OTDR) technology for temperature measurement. The system is stable and reliable, with a temperature measurement accuracy of 1°C and a temperature resolution of 0.1°C, which meets the needs of sensitive data collection of micro-temperature changes in micro-rainfall scenarios. The distributed fiber optic Raman temperature measurement system uses anti-Stokes and Stokes light intensity ratios as temperature-sensitive signals, taking advantage of its unique advantages such as being free from electromagnetic interference, corrosion resistance, and simple and convenient system laying. The distributed fiber optic Raman temperature measurement system can be used to monitor the hidden dangers of water leakage on the roof of substation buildings. The distributed fiber optic Raman temperature measurement system measures temperature based on the optical time domain reflectometry (OTDR) technology.

[0057] In one embodiment of the present application, obtaining a temperature matrix of a distributed optical fiber system includes:

[0058] according to Determine each temperature value in the temperature matrix;

[0059] Wherein, T is each temperature value in the temperature matrix, T0 is the temperature of the constant temperature box where the detection optical fiber in the distributed optical fiber system is placed, h is Planck's constant, k B is the Boltzmann constant, c is the speed of light, Δv is, and F(T) is the intermediate constant.

[0060] Specifically, a laser beam enters the sensing fiber through an optical circulator. The pulse light will continue to be backscattered during the forward transmission process, which is caused by the inhomogeneity of the optical fiber. The generated backscattered light will return along the original path of the optical fiber and enter the photodetector through the optical circulator. The distance L from a certain point where the light beam is scattered in the optical fiber to the incident end face is:

[0061]

[0062] L is the distance from a point where the light beam is scattered in the optical fiber to the incident end face;

[0063] t / 2 is the time it takes for the pulse light to reach L;

[0064] c is the speed of light, and its unit is m / s;

[0065] n is the refractive index, which is an intrinsic parameter of the optical fiber.

[0066] To know the position of the reflected light at a certain point, we only need to record the time t when the incident light returns to the incident end.

[0067] When the light transmitted in the optical fiber meets the optical fiber medium, an inelastic or elastic collision will occur. If the collision is not elastic, the frequency of the light will change. According to the amount of change, it can be divided into Raman scattering and Brillouin scattering. The frequency shift of Brillouin scattering light is small, so it is closer to the center frequency in the spectrum, while Raman scattering is slightly farther from the center frequency of the incident light. If an elastic collision occurs, Rayleigh scattering will occur, and the photon will only deviate from the original propagation direction, and its energy will not change.

[0068] Raman-Stokes line λ S The strength is based on Determined;

[0069] Raman anti-Stokes line λ AS The strength is based on Determined;

[0070] Where I0 is the incident light intensity, I S Raman-Stokes reflected light intensity, I AS Raman anti-Stokes reflection intensity, h is Planck constant, k is B is the Boltzmann constant, ω m Raman photon frequency, T is the temperature in the optical fiber.

[0071] The basic formula for the spontaneous Raman temperature sensor principle can then be derived:

[0072]

[0073] In order to set the temperature standard for the system, a section of the optical fiber is cut to become the calibration fiber, and this section of the optical fiber is placed in a constant temperature box at temperature T0, so that the expression of Raman intensity and temperature can be calculated:

[0074]

[0075] In one embodiment of the present application, before obtaining the temperature matrix of the distributed optical fiber system, the method further includes:

[0076] according to Determine the intermediate constants mentioned above.

[0077] By using a distributed fiber optic temperature measurement system, armored optical fibers are laid out horizontally inside and outside the roof of substation buildings to obtain a global time series model of the building roof temperature field and simulate the characteristic information of the microenvironment field of early water leakage on the building roof.

[0078] Step S102, a first processing step: based on the influence of shadow and water accumulation on temperature, the temperature matrix is ​​processed by using fuzzy theory and neural network model technology to obtain a temperature compensation matrix, and each temperature value in the temperature matrix is ​​compensated by using the temperature compensation matrix to obtain a final temperature matrix;

[0079] In step S102, based on the influence of shadow and water accumulation on temperature, fuzzy theory and neural network model technology are used to process the temperature matrix to obtain a temperature compensation matrix, including:

[0080] The above fuzzy theory is used to define the influence of shadow and water accumulation on temperature respectively, and the first influence degree and the second influence degree are obtained. The above first influence degree represents the influence of light intensity on temperature, and the above second influence degree represents the influence of precipitation on temperature.

[0081] Based on the first influence degree, a first neural network model is used to process the regional position of each of the optical fibers and the corresponding light intensity to obtain a first temperature compensation matrix;

[0082] Based on the second influence degree, a second neural network model is used to process the regional position of each of the optical fibers and the corresponding precipitation to obtain a second temperature compensation matrix;

[0083] Determining the temperature compensation matrix includes the first temperature compensation matrix and the second temperature compensation matrix.

[0084] In one embodiment of the present application, the temperature compensation matrix is ​​used to compensate each temperature value in the temperature matrix to obtain a final temperature matrix, including:

[0085] Using the first temperature compensation matrix to compensate each temperature value in the temperature matrix to obtain an intermediate temperature matrix;

[0086] The second temperature compensation matrix is ​​used to compensate each temperature value in the intermediate temperature matrix to obtain the final temperature matrix.

[0087] Specifically, the distributed optical fiber temperature measurement system can obtain the temperature field model of the roof of the building. The model is an [M, N]-order matrix, where m is the number of optical fibers arranged on the roof, n is the number of optical fiber sensors on each optical fiber, and each optical fiber sensor can obtain a temperature measurement point. Then the temperature matrix of each measurement point of the distributed optical fiber system is:

[0088]

[0089] Considering that both sunlight and water accumulation will affect the temperature of each measuring point, this algorithm introduces fuzzy adaptive theory to analyze the impact of different locations on the temperature of the measuring point according to the sunlight and precipitation at different times.

[0090] Effect of shadow on temperature:

[0091] Input 1 is the region position, and the fuzzy domain is [L1, H1], where L1 represents points not affected by shadows and H1 represents points affected by shadows.

[0092] Input 2 is the light intensity, and the fuzzy domain is [L2, LM2, M2, MH2, H2]. According to the average sunshine duration of each local month, L2 represents the month with little impact of light, LM2 represents the month with little impact of light, M2 represents the month with moderate impact of light, MH2 represents the month with great impact of light, H2 represents the month with great impact of light, MH2 represents the degree of impact of light is greater than the degree of impact of light represented by H2, and LM2 represents the degree of impact of light is greater than the degree of impact of light represented by L2.

[0093] The output is the temperature adjustment value θ 1，ij , the domain space is [0, 0.1], and the fuzzy domain is [ZO1, PM1, PH1], where ZO1 represents that the shadow has little impact, PM1 represents that the shadow has a medium impact, and PH1 represents that the shadow has a great impact. The fuzzy rules should be as shown in Table 1.

[0094] Table 1

[0095]

[0096]

[0097] The impact of water accumulation on temperature:

[0098] Input 1 is the regional location, and the fuzzy domain is [L3, M3, H3], where L3 represents low-lying points, M3 represents medium-lying points, and H3 represents high-lying points.

[0099] Input 2 is precipitation, and the fuzzy domain is [L4, LM4, M4, MH4, H4]. According to the average precipitation of each month in the local area, L4 represents the month with very little precipitation, LM4 represents the month with small precipitation, M4 represents the month with medium precipitation, MH4 represents the month with large precipitation, and H4 represents the month with large precipitation. The precipitation represented by H4 is greater than that represented by MH4, and the precipitation represented by LM4 is greater than that represented by L4.

[0100] The output is the temperature adjustment value θ 2,ij , the domain space is [0, 0.1], and the fuzzy domain is [ZO2, PM2, PH2], where ZO2 represents being slightly affected by waterlogging, PM2 represents being moderately affected by waterlogging, and PH2 represents being greatly affected by waterlogging. The fuzzy rules should be as shown in Table 2.

[0101] Table 2

[0102]

[0103]

[0104] Then the compensated temperature of each measuring point should be: t 11 =t′ 1，1 +θ 1,ij +θ 2,ij

[0105] After temperature compensation, the temperature field model of the building roof is:

[0106]

[0107] Based on the roof temperature field model of the building, this algorithm converts four adjacent points (t i ,j,t i ,j +1 ,t i+1 ,j,t i+1 ,j +1 ) creates a virtual work area and names it T i ,j, for example t 11 ,t 12 ,t 21,t 22 The area enclosed is T 11 Then the [M, N]-order matrix temperature field model can generate a total of M×N building roof areas.

[0108] Step S103, a second processing step: comparing each temperature value in the final temperature matrix with a temperature threshold value to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value;

[0109] In one embodiment of the present application, comparing each temperature value in the final temperature matrix with a temperature threshold value to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value includes:

[0110] When the target temperature value in the final temperature matrix is ​​greater than or equal to the temperature threshold, determining the target temperature value to be the abnormal temperature value;

[0111] When the target temperature value in the final temperature matrix is ​​less than the temperature threshold, the target temperature value is determined to be a normal temperature value.

[0112] Specifically, the temperature threshold is set by the user according to different installation areas and climatic conditions. The recommended reference setting is 0.2℃-0.4℃.

[0113] Abnormal area determination: Each area T i,j The temperature of the four adjacent points t that make up the area i,j ,t i,j+1 ,t i+1,j ,t i+1,j+1 The average temperature is:

[0114] If the temperature of the area is T i,j With t i,j ,t i,j+1 ,t i+1,j ,t i+1,j+1 If the absolute value of the temperature difference is greater than the threshold ζ, the area is defined as an abnormal area, and the point causing the regional anomaly is the regional anomaly point.

[0115]

[0116] Step S104, the third processing step: repeat the above-mentioned acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the above-mentioned preset number of times all indicate that the target temperature value in the above-mentioned final temperature matrix is ​​the above-mentioned abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the above-mentioned target temperature value.

[0117] In the above steps, based on the influence of shadow and water accumulation on temperature, fuzzy theory and neural network model technology are used to process the temperature matrix to obtain a temperature compensation matrix, and the temperature compensation matrix is ​​used to compensate each temperature value in the temperature matrix to obtain a final temperature matrix, thereby taking into account the factors affecting the temperature, so that the subsequent judgment is more in line with the actual working conditions, and the temperature values ​​in the final temperature matrix are compared with the temperature threshold respectively to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value, and the acquisition step, the first processing step and the second processing step are repeatedly performed at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value, that is, the accuracy of water leakage detection on the roof of the building is improved by point-by-point investigation, and compared with the existing scheme, there is no need to use the manual inspection method, thereby improving the detection efficiency, thereby solving the problem that the existing scheme still uses the manual inspection method for water leakage detection on the roof of the building equipped with a distributed optical fiber system, resulting in low detection efficiency.

[0118] Leakage area identification:

[0119] When an abnormal temperature area is found in the continuous monitoring mode, the device immediately enters the abnormal event monitoring mode and measures the temperature every i hours. By comparing and judging the abnormal temperature field areas in the sky area at different time scales in real time, the abnormal temperature points where leakage may occur can be located.

[0120] If the initial abnormal points are independent of each other, it means that leakage may have just occurred. After three temperature measurements, if the point is an abnormal point, and the abnormal points and abnormal areas have increased, it may be that the waterproofing around the point has failed and is in an accelerated leakage state. An alarm should be issued immediately and the abnormal event monitoring mode should be exited; if the point is an abnormal point, but the abnormal points and abnormal areas have not increased, it may be that the local waterproofing effect of the point has decreased or the data of the measurement point is abnormal. The device marks and shields the point, and exits the abnormal event monitoring mode. If the measurement points around the point are abnormal in subsequent continuous monitoring, an alarm should be issued immediately.

[0121] like Figure 2As shown, the optical fiber device consists of an armored optical fiber 100, an optical fiber junction box 200, and a demodulator analyzer 300. The armored optical fiber and the optical sensor are used to measure the temperature field of the roof of the building, the optical fiber junction box is used for optical fiber switching, and the demodulator analyzer provides laser power for the armored optical fiber and monitors the temperature data in real time. The armored optical fiber converts the roof temperature signal into an optical signal; the demodulator analyzer includes a laser light source, a processing chip, a data calculation and a power module, which are used to send and receive lasers, analyze, process and collect the spectrum data of the armored optical fiber. The demodulator analyzer is fused with the optical fiber junction box through the armored optical cable, and the armored optical fiber is connected to the ODF disk in the optical fiber junction box.

[0122] Armored optical fiber is used to detect and collect temperature field data on the sky. The installation method of armored optical fiber is as follows: Figure 3 As shown. The roof structure of the substation includes a concrete protective layer, a waterproof layer, a concrete leveling layer, a multi-layer temperature measuring optical fiber, and a concrete roof. The concrete protective layer is used to protect the steel bars in the building concrete structure. The waterproof layer is a material layer designed to prevent indoor water, other water or groundwater from entering the roof, penetrating into the walls, basements and underground structures, and penetrating into the floors and walls. Multi-layer armored optical fibers are laid in the concrete leveling layer, and the number of layers laid is not less than 2. Multi-layer temperature measuring optical fibers are used to predict leakage of the substation roof in a three-dimensional manner and are placed above the concrete roof.

[0123] The implementation steps are as follows:

[0124] Step 1: Fiber optic layout: Lay out the fiber optic cables along the predetermined path on the roof. The path is in a straight line or grid form, ensuring that the fiber optic layout covers known or potential problem areas and critical areas on the roof.

[0125] Step 2: Connect the equipment: Connect the laid optical cable to the demodulator analyzer. The demodulator analyzer can send optical pulses and receive the signals scattered and reflected by the sensors in the optical fiber.

[0126] Step 3: Initial calibration: After the demodulator analyzer is powered on, perform initial calibration so that the device can correctly obtain the temperature data of the building roof.

[0127] Step 4: Data collection and monitoring:

[0128] Once the system is installed and the initial calibration is complete, the demodulator analyzer will begin collecting temperature data at regular intervals;

[0129] Continuous monitoring: The demodulation analyzer will automatically record once every 12 hours on the entire fiber optic cabling.

[0130] Specific event monitoring: In severe convective weather, the system enters abnormal event monitoring mode and automatically collects temperature data every 4 hours.

[0131] Abnormal event monitoring: After the device finds an abnormal area, it automatically collects temperature data every 2 hours. Through real-time comparison and analysis of abnormal areas of the temperature field in the sky area at different time scales, the abnormal temperature points where leakage may occur can be located.

[0132] Step 5: Data Analysis:

[0133] Establishment of regional model: Analyze the collected roof temperature field data, use fuzzy adaptive theory to perform temperature compensation on each measurement point, and establish a global building roof regional temperature field model based on the formation of the roof temperature field state model.

[0134] Abnormal area determination: The temperature Ti,j of each area is the temperature of the four adjacent points ti,j, ti,j+1, t i+1,j ,t i+1,j+1 If the absolute value of any temperature difference is greater than the threshold ζ, the area is defined as an abnormal area, and the point causing the regional anomaly is the regional anomaly point.

[0135] Leakage area identification: By interactively comparing the real-time time series data of the temperature field in the sky area at different time scales and spatial scales, the temperature anomaly points where leakage may occur can be located.

[0136] Step 6: On-site inspection and repair:

[0137] After the suspected leakage area of ​​the device is identified, an on-site inspection is conducted to confirm;

[0138] Check the actual situation: Go to the designated roof location and check if there is any obvious damage or water marks.

[0139] Emergency repair: After confirming a leakage incident at an abnormal point, repair it immediately to prevent water damage from affecting the building structure or internal facilities.

[0140] Step 7: Subsequent monitoring:

[0141] After the repair, continue to monitor the area using the distributed temperature measurement fiber optic system to ensure the repair was effective and prevent future leaks:

[0142] Verify Repair: Continue to monitor the temperature of the repaired area to verify that it returns to within normal range.

[0143] In one embodiment of the present application, during the process of repeatedly performing the acquisition step, the first processing step and the second processing step at least a preset number of times, the method further includes:

[0144] When it is determined in two adjacent tests that an abnormal point spreads in all directions, an alarm message is generated to indicate that the waterproof function of the location where the abnormal point is located has failed and needs to be repaired. The abnormal point represents the location point where the target temperature value in the final temperature matrix is ​​determined to be an abnormal temperature value.

[0145] If the initial abnormal points are interconnected and there are multiple abnormal areas, it means that the leakage may have been developing for some time. After the second temperature measurement, if the abnormal points spread to the surrounding areas, and the abnormal points and abnormal areas increase, it is possible that the waterproofing surrounded by the abnormal points has failed and is in an accelerated leakage state. An alarm should be issued immediately and the abnormal event monitoring mode should be exited. If the abnormal points do not increase after three temperature measurements (i.e. the preset number of times), it is possible that the local waterproofing effect of the area surrounded by the abnormal points has decreased. The device marks these areas and exits the abnormal event monitoring mode. If abnormalities occur in the areas around these areas during subsequent continuous monitoring, an alarm should be issued immediately.

[0146] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0147] The embodiment of the present application also provides a kind of water leakage detection device for the roof of a building equipped with a distributed optical fiber system. It should be noted that the water leakage detection device for the roof of a building equipped with a distributed optical fiber system in the embodiment of the present application can be used to execute the water leakage detection method for the roof of a building equipped with a distributed optical fiber system provided by the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0148] The following is an introduction to a water leakage detection device for a roof of a building equipped with a distributed optical fiber system provided in an embodiment of the present application.

[0149] Figure 4 FIG. 1 is a structural block diagram of a water leakage detection device for a roof of a building equipped with a distributed optical fiber system according to an embodiment of the present application. Figure 4 As shown, the device comprises:

[0150] An acquisition unit 41 is used for the acquisition step: acquiring a temperature matrix of the distributed optical fiber system, wherein the temperature matrix includes the temperature collected by the optical fiber sensor on each optical fiber in the distributed optical fiber system;

[0151] The first processing unit 42 is used for the first processing step: based on the influence of shadow and water accumulation on temperature, the temperature matrix is ​​processed by using fuzzy theory and neural network model technology to obtain a temperature compensation matrix, and each temperature value in the temperature matrix is ​​compensated by using the temperature compensation matrix to obtain a final temperature matrix;

[0152] The second processing unit 43 is used for the second processing step: respectively comparing each temperature value in the final temperature matrix with the temperature threshold value to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value;

[0153] The third processing unit 44 is used for the third processing step: repeatedly executing the above-mentioned acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the above-mentioned preset number of times all indicate that the target temperature value in the above-mentioned final temperature matrix is ​​the above-mentioned abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the above-mentioned target temperature value.

[0154] In the above device, based on the influence of shadow and water accumulation on temperature, fuzzy theory and neural network model technology are used to process the temperature matrix to obtain a temperature compensation matrix, and the temperature compensation matrix is ​​used to compensate each temperature value in the temperature matrix to obtain a final temperature matrix, thereby taking into account the factors affecting the temperature, so that the subsequent judgment is more in line with the actual working conditions, and the temperature values ​​in the final temperature matrix are compared with the temperature threshold respectively to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value, and the acquisition step, the first processing step and the second processing step are repeatedly performed at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value, that is, the accuracy of water leakage detection on the roof of the building is improved by point-by-point investigation, and compared with the existing scheme, there is no need to use the manual inspection method, thereby improving the detection efficiency, thereby solving the problem that the existing scheme still uses the manual inspection method for water leakage detection on the roof of the building equipped with a distributed optical fiber system, resulting in low detection efficiency.

[0155] In one embodiment of the present application, the acquiring unit includes a first determining unit,

[0156] The first determining unit is used to Determine each temperature value in the temperature matrix;

[0157] Wherein, T is each temperature value in the temperature matrix, T0 is the temperature of the constant temperature box where the detection optical fiber in the distributed optical fiber system is placed, h is Planck's constant, k B is the Boltzmann constant, c is the speed of light, Δv is, and F(T) is the intermediate constant.

[0158] In one embodiment of the present application, the apparatus further includes a second determining unit, which, before acquiring the temperature matrix of the distributed optical fiber system,

[0159] The second determining unit is used to Determine the intermediate constants mentioned above.

[0160] In one embodiment of the present application, the first processing unit includes a first processing module, a second processing module, a third processing module and a first determining module.

[0161] The first processing module is used to define the influence of shadow and water accumulation on temperature respectively by using the fuzzy theory, and obtain a first influence degree and a second influence degree, wherein the first influence degree represents the influence of light intensity on temperature, and the second influence degree represents the influence of precipitation on temperature;

[0162] The second processing module is used to process the regional position and the corresponding light intensity of each of the optical fibers based on the first influence degree using a first neural network model to obtain a first temperature compensation matrix;

[0163] The third processing module is used to process the regional position and the corresponding precipitation of each of the optical fibers based on the second influence degree using a second neural network model to obtain a second temperature compensation matrix;

[0164] The first determination module is used to determine that the temperature compensation matrix includes the first temperature compensation matrix and the second temperature compensation matrix.

[0165] In one embodiment of the present application, the first processing unit includes a fourth processing module and a fifth processing module.

[0166] The fourth processing module is used to compensate each temperature value in the temperature matrix using the first temperature compensation matrix to obtain an intermediate temperature matrix;

[0167] The fifth processing module is used to use the second temperature compensation matrix to compensate each temperature value in the intermediate temperature matrix to obtain the final temperature matrix.

[0168] In one embodiment of the present application, the second processing unit includes a second determining module and a third determining module.

[0169] The second determination module is used for determining that the target temperature value is the abnormal temperature value when the target temperature value in the final temperature matrix is ​​greater than or equal to the temperature threshold;

[0170] The third determining module is used for determining that the target temperature value is a normal temperature value when the target temperature value in the final temperature matrix is ​​less than the temperature threshold.

[0171] In one embodiment of the present application, the third processing unit includes a generating module, in the process of repeatedly performing the above-mentioned obtaining step, the first processing step and the second processing step at least a preset number of times;

[0172] The generation module is used to generate an alarm message when it is determined in two adjacent detections that an abnormal point spreads to the surrounding areas, to prompt that the waterproof function of the location where the abnormal point is located has failed and needs to be repaired. The abnormal point represents the location point where the target temperature value in the final temperature matrix is ​​determined to be an abnormal temperature value.

[0173] The above-mentioned water leakage detection device for the roof of a building equipped with a distributed optical fiber system includes a processor and a memory. The above-mentioned acquisition unit, the first processing unit, the second processing unit and the third processing unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0174] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to solve the problem that the existing solution still uses manual inspection for water leakage detection on the roof of a building equipped with a distributed optical fiber system, resulting in low detection efficiency.

[0175] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0176] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the water leakage detection method for the roof of a building equipped with a distributed optical fiber system.

[0177] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program, when running, executes the water leakage detection method for the roof of a building equipped with a distributed optical fiber system.

[0178] The embodiment of the present invention provides a device, the device includes a processor, a memory and a program stored in the memory and can be run on the processor, and the processor implements at least the following steps when executing the program: an acquisition step: acquiring a temperature matrix of a distributed optical fiber system, the temperature matrix including the temperature collected by the optical fiber sensor on each optical fiber in the distributed optical fiber system; a first processing step: based on the influence of shadow and water accumulation on temperature, the temperature matrix is ​​processed by fuzzy theory and neural network model technology to obtain a temperature compensation matrix, and the temperature compensation matrix is ​​used to compensate each temperature value in the temperature matrix to obtain a final temperature matrix; a second processing step: respectively comparing each temperature value in the final temperature matrix with a temperature threshold, and determining whether each temperature value in the final temperature matrix is ​​an abnormal temperature value; a third processing step: repeating the acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value. The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0179] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with at least the following method steps: an acquisition step: acquiring a temperature matrix of a distributed optical fiber system, wherein the temperature matrix includes the temperature collected by the optical fiber sensor on each optical fiber in the distributed optical fiber system; a first processing step: based on the effects of shadows and accumulated water on temperature, the temperature matrix is ​​processed using fuzzy theory and neural network model technology to obtain a temperature compensation matrix, and the temperature compensation matrix is ​​used to compensate for each temperature value in the temperature matrix to obtain a final temperature matrix; a second processing step: respectively comparing each temperature value in the final temperature matrix with a temperature threshold to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value; a third processing step: repeatedly executing the acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value.

[0180] The present application also provides a water leakage detection system for the roof of a building equipped with a distributed optical fiber system, the system comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the above methods.

[0181] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0182] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0183] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0184] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0186] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0187] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0188] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0189] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0190] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0191] 1) The method for detecting water leakage on the roof of a building equipped with a distributed optical fiber system of the present application processes the temperature matrix based on the influence of shadow and water accumulation on temperature, adopts fuzzy theory and neural network model technology, obtains a temperature compensation matrix, and uses the temperature compensation matrix to compensate each temperature value in the temperature matrix to obtain a final temperature matrix, thereby taking into account the factors affecting the temperature, so that the subsequent judgment is more in line with the actual working conditions, and compares each temperature value and the temperature threshold in the final temperature matrix to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value, and repeats the acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value, that is, the accuracy of water leakage detection on the roof of the building is improved by point-by-point investigation, and compared with the existing scheme, there is no need to use the manual inspection method, thereby improving the detection efficiency, thereby solving the problem that the existing scheme still uses the manual inspection method for water leakage detection on the roof of a building equipped with a distributed optical fiber system, resulting in low detection efficiency.

[0192] 2) The water leakage detection device for the roof of a building equipped with a distributed optical fiber system of the present application processes the temperature matrix based on the influence of shadow and water accumulation on temperature, adopts fuzzy theory and neural network model technology, obtains a temperature compensation matrix, and uses the temperature compensation matrix to compensate each temperature value in the temperature matrix to obtain a final temperature matrix, thereby taking into account the factors affecting the temperature, so that the subsequent judgment is more in line with the actual working conditions, and compares each temperature value and the temperature threshold in the final temperature matrix to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value, and repeats the acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value, that is, the accuracy of water leakage detection on the roof of the building is improved by point-by-point investigation, and compared with the existing scheme, there is no need to use the manual inspection method, thereby improving the detection efficiency, thereby solving the problem that the existing scheme still uses the manual inspection method for water leakage detection on the roof of a building equipped with a distributed optical fiber system, resulting in low detection efficiency.

[0193] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting water leakage on the roof of a building equipped with a distributed optical fiber system, characterized in that: include: Acquisition step: acquiring a temperature matrix of a distributed optical fiber system, wherein the temperature matrix includes temperatures collected by an optical fiber sensor on each optical fiber in the distributed optical fiber system; The first processing step: based on the influence of shadow and water accumulation on temperature, the temperature matrix is ​​processed by using fuzzy theory and neural network model technology to obtain a temperature compensation matrix, and each temperature value in the temperature matrix is ​​compensated by using the temperature compensation matrix to obtain a final temperature matrix; The second processing step: comparing each temperature value in the final temperature matrix with a temperature threshold value to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value; The third processing step: repeat the acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value.

2. The method according to claim 1, characterized in that Get the temperature matrix of the distributed fiber optic system, including: according to Determining each temperature value in the temperature matrix; Wherein, T is each temperature value in the temperature matrix, T0 is the temperature of the constant temperature box where the detection optical fiber in the distributed optical fiber system is placed, h is Planck's constant, k B is the Boltzmann constant, c is the speed of light, Δv is, and F(T) is the intermediate constant.

3. The method according to claim 2, characterized in that Before obtaining the temperature matrix of the distributed optical fiber system, the method further includes: according to The intermediate constant is determined.

4. The method according to claim 1, characterized in that: Based on the influence of shadow and water accumulation on temperature, the temperature matrix is ​​processed by using fuzzy theory and neural network model technology to obtain a temperature compensation matrix, including: The fuzzy theory is used to define the influence of shadow and water accumulation on temperature, respectively, to obtain a first influence degree and a second influence degree, wherein the first influence degree represents the influence of light intensity on temperature, and the second influence degree represents the influence of precipitation on temperature; Based on the first influence degree, a first neural network model is used to process the regional position of each optical fiber and the corresponding light intensity to obtain a first temperature compensation matrix; Based on the second influence degree, a second neural network model is used to process the regional position of each optical fiber and the corresponding precipitation to obtain a second temperature compensation matrix; Determining the temperature compensation matrix includes the first temperature compensation matrix and the second temperature compensation matrix.

5. The method according to claim 4, characterized in that The temperature compensation matrix is ​​used to compensate each temperature value in the temperature matrix to obtain a final temperature matrix, including: Using the first temperature compensation matrix to compensate each temperature value in the temperature matrix to obtain an intermediate temperature matrix; The second temperature compensation matrix is ​​used to compensate each temperature value in the intermediate temperature matrix to obtain the final temperature matrix.

6. The method according to claim 1, characterized in that Comparing each temperature value in the final temperature matrix with a temperature threshold value respectively, and determining whether each temperature value in the final temperature matrix is ​​an abnormal temperature value, comprises: When the target temperature value in the final temperature matrix is ​​greater than or equal to the temperature threshold, determining the target temperature value to be the abnormal temperature value; In a case where the target temperature value in the final temperature matrix is ​​less than the temperature threshold, the target temperature value is determined to be a normal temperature value.

7. The method according to any one of claims 1 to 6, characterized in that In the process of repeatedly performing the obtaining step, the first processing step and the second processing step at least a preset number of times, the method further includes: When it is determined in two adjacent detections that an abnormal point spreads in all directions, an alarm message is generated to indicate that the waterproof function of the location where the abnormal point is located has failed and needs to be repaired. The abnormal point represents the location point where the target temperature value in the final temperature matrix is ​​determined to be an abnormal temperature value.

8. A water leakage detection device for a roof of a building equipped with a distributed optical fiber system, characterized in that: include: An acquisition unit, used for the acquisition step: acquiring a temperature matrix of a distributed optical fiber system, wherein the temperature matrix includes a temperature collected by an optical fiber sensor on each optical fiber in the distributed optical fiber system; A first processing unit is used for a first processing step: based on the influence of shadow and water accumulation on temperature, the temperature matrix is ​​processed by using fuzzy theory and neural network model technology to obtain a temperature compensation matrix, and each temperature value in the temperature matrix is ​​compensated by using the temperature compensation matrix to obtain a final temperature matrix; A second processing unit, used for a second processing step: respectively comparing each temperature value in the final temperature matrix with a temperature threshold value to determine whether each temperature value in the final temperature matrix is ​​an abnormal temperature value; The third processing unit is used for the third processing step: repeatedly executing the acquisition step, the first processing step and the second processing step at least a preset number of times, and when the judgment results of the preset number of times all indicate that the target temperature value in the final temperature matrix is ​​the abnormal temperature value, it is determined that there is a water leakage phenomenon at the position corresponding to the target temperature value.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A water leakage detection system for a building roof equipped with a distributed optical fiber system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

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