Intelligent early warning method and related device for tunnel safety

Abstract

The present application provides an intelligent early warning method and related device for tunnel safety, the method comprising: obtaining n target temperature data sets of n detection areas within a preset time period through a temperature detection module; determining n first abnormal values ​​according to the n target temperature data sets; obtaining n target structure data sets of n detection areas within a preset time period through a data acquisition module; determining n second abnormal values ​​according to the n target structure data sets; determining n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values ​​through a control module; obtaining a third abnormal values ​​greater than a preset first threshold value among the n third abnormal values; a is a natural number less than or equal to n; determining a warning prompt information corresponding to a third abnormal value; and executing corresponding warning prompt operations according to a warning prompt information through a warning prompt module. The accuracy and timeliness of tunnel safety early warning can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel safety early warning, and in particular to an intelligent early warning method and related devices for tunnel safety. Background Art

[0002] At present, the safety issues of tunnel projects are becoming increasingly prominent, especially during the operation of tunnels, where the safety monitoring and early warning of tunnel structures are particularly important. As highway tunnels have poor ventilation, poor lighting, poor visibility, and difficult communication and rescue, once a traffic accident or tunnel collapse occurs in a tunnel, it will be difficult for rescue workers to carry out rescue work, and the accident that has occurred will easily lead to a secondary accident. Existing tunnel safety monitoring technologies mostly rely on manual inspections and traditional monitoring equipment, which have certain limitations, such as inaccurate monitoring data and untimely early warnings.

[0003] Therefore, how to improve the accuracy and timeliness of tunnel safety warnings needs to be solved urgently. Summary of the invention

[0004] The embodiments of the present application provide an intelligent early warning method and related devices for tunnel safety. By combining temperature data and structural data, each detection area of ​​the tunnel is detected, which is convenient for timely detection and response to abnormal conditions in the detection area, thereby improving the accuracy and timeliness of tunnel safety early warnings.

[0005] In a first aspect, an embodiment of the present application provides an intelligent early warning method for tunnel safety, which is applied to an intelligent early warning system, wherein the intelligent early warning system includes a data acquisition module, a temperature detection module, a control module, and an early warning prompt module, wherein the data acquisition module includes n displacement sensors and n strain sensors, and the temperature detection module includes n temperature detection sensors. The target tunnel includes n detection areas, each detection area corresponds to a displacement sensor, a strain sensor, and a temperature detection sensor, and n is an integer greater than 1. The method includes:

[0006] Acquire n target temperature data sets of the n detection areas within a preset time period through the temperature detection module; each target temperature data set corresponds to one detection area;

[0007] Determine n first abnormal values ​​according to the n target temperature data sets;

[0008] Acquire, by the data acquisition module, n target structure data sets of the n detection areas within the preset time period; each target structure data set corresponds to one detection area;

[0009] Determine n second outliers according to the n target structure data sets;

[0010] Determining n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values ​​by the control module; each third abnormal value corresponds to a detection area;

[0011] Obtain a third abnormal values ​​greater than a preset first threshold value from the n third abnormal values; a is a natural number less than or equal to n;

[0012] Determine a pieces of warning prompt information corresponding to the a third abnormal values;

[0013] The early warning prompt module performs corresponding early warning prompt operations according to the a early warning prompt information.

[0014] In a second aspect, an embodiment of the present application provides an intelligent early warning device for tunnel safety, which is applied to an intelligent early warning system. The intelligent early warning system includes a data acquisition module, a temperature detection module, a control module, and an early warning prompt module. The data acquisition module includes n displacement sensors and n strain sensors. The temperature detection module includes n temperature detection sensors. The target tunnel includes n detection areas, each detection area corresponds to a displacement sensor, a strain sensor, and a temperature detection sensor. n is an integer greater than 1. The device includes an acquisition unit, a determination unit, and an execution unit, wherein:

[0015] The acquisition unit is used to acquire n target temperature data sets of the n detection areas within a preset time period through the temperature detection module; each target temperature data set corresponds to one detection area;

[0016] The determining unit is used to determine n first abnormal values ​​according to the n target temperature data sets;

[0017] The acquisition unit is further used to acquire, through the data acquisition module, n target structure data sets of the n detection areas within the preset time period; each target structure data set corresponds to one detection area;

[0018] The determining unit is further used to determine n second outliers according to the n target structure data sets;

[0019] The determination unit is further configured to determine n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values ​​through the control module; each third abnormal value corresponds to a detection area;

[0020] The acquisition unit is further used to acquire a third abnormal values ​​greater than a preset first threshold value among the n third abnormal values; a is a natural number less than or equal to n;

[0021] The determining unit is further used to determine a warning prompt information corresponding to the a third abnormal values;

[0022] The execution unit is used to perform corresponding warning prompt operations according to the a warning prompt information through the warning prompt module.

[0023] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps of any method in the first aspect of the embodiment of the present application.

[0024] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in any method of the first aspect of the embodiment of the present application.

[0025] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0026] By implementing the embodiments of the present application, temperature data and structural data can be combined to detect various detection areas of the tunnel, so as to facilitate timely discovery and response to abnormal conditions in the detection areas, thereby improving the accuracy and timeliness of tunnel safety warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a system architecture diagram of an intelligent early warning system provided by an embodiment of the present application;

[0029] Figure 2 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application;

[0030] Figure 3 It is a flow chart of an intelligent early warning method for tunnel safety provided by an embodiment of the present application;

[0031] Figure 4 This is a block diagram of the functional units of an intelligent early warning device for tunnel safety provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] 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 are within the scope of protection of this application.

[0033] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0034] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship. The "plurality" appearing in the embodiments of the present application refers to two or more.

[0035] In the embodiments of the present application, "at least one item" or similar expressions refer to any combination of these items, including any combination of single items or plural items, and refer to one or more, and multiple refers to two or more. For example, at least one item of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0036] The "connection" that appears in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0037] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0038] The following is an explanation of the relevant terms involved in this application, as follows:

[0039] Temperature detection sensor: used to monitor temperature changes inside and outside the tunnel, which is crucial for the safe operation of the tunnel. Temperature changes may affect the stability of the tunnel structure, especially in a freeze-thaw environment. Temperature fluctuations may cause expansion or contraction of concrete and other materials, causing structural problems.

[0040] Displacement sensors: used to monitor the stability and safety of tunnel structures. The displacement sensors can detect small displacement changes of tunnel walls, vaults or other key structures, thus providing early warning of possible collapse, deformation or other structural problems.

[0041] Strain sensor: used to measure the strain change of tunnel structure materials due to stress. The strain sensor can provide structural data corresponding to possible damage, deformation or cracks in the tunnel structure, so it is crucial to evaluate the integrity and stability of the tunnel structure.

[0042] At present, the safety issues of tunnel projects are becoming increasingly prominent, especially during the operation of tunnels, where safety monitoring and early warning of tunnel structures are particularly important. Since highway tunnels have the characteristics of poor ventilation, poor lighting, poor visibility, and difficult communication and rescue, once a traffic accident or tunnel collapse occurs in the tunnel, it will be difficult for rescue workers to carry out rescue work, and the accident that has occurred will easily lead to a secondary accident. However, the existing tunnel safety monitoring technology mostly relies on manual inspections and traditional monitoring equipment, which has certain limitations, such as inaccurate monitoring data and untimely early warning. Therefore, how to improve the accuracy and timeliness of tunnel safety early warnings needs to be solved urgently.

[0043] To solve the above problems, an embodiment of the present application provides an intelligent early warning method and related devices for tunnel safety, which are applied to an intelligent early warning system. The intelligent early warning system includes a data acquisition module, a temperature detection module, a control module and an early warning prompt module. The data acquisition module includes n displacement sensors and n strain sensors. The temperature detection module includes n temperature detection sensors. The target tunnel includes n detection areas, each detection area corresponds to a displacement sensor, a strain sensor and a temperature detection sensor, and n is an integer greater than 1. First, the temperature detection module is used to obtain n target temperature data sets of the n detection areas within a preset time period; each target temperature data set corresponds to a detection area; n first abnormal values ​​are determined according to the n target temperature data sets; then, the data acquisition module is used to obtain n target structure data sets of the n detection areas within the preset time period; each target structure data set corresponds to a detection area; n second abnormal values ​​are determined according to the n target structure data sets; the control module is used to determine n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values; each third abnormal value corresponds to a detection area; a third abnormal value greater than a preset first threshold value is obtained from the n third abnormal values; a is a natural number less than or equal to n; a warning prompt information corresponding to the a third abnormal value is determined; finally, the warning prompt module is used to perform corresponding warning prompt operations according to the a warning prompt information. By combining temperature data and structure data, each detection area of ​​the tunnel is detected, so as to facilitate timely discovery and response to abnormal conditions in the detection area, thereby improving the accuracy and timeliness of tunnel safety warnings.

[0044] See also Figure 1 , Figure 1 is a system architecture diagram of an intelligent early warning system provided in an embodiment of the present application, such as Figure 1 As shown, the intelligent early warning system includes a data acquisition module, a temperature detection module, a control module, and an early warning prompt module. The data acquisition module includes n displacement sensors and n strain sensors. The temperature detection module includes n temperature detection sensors. The target tunnel includes n detection areas, each detection area corresponds to a displacement sensor, a strain sensor, and a temperature detection sensor, and n is an integer greater than 1. Among them, the target tunnel includes but is not limited to a highway tunnel, a railway tunnel, and a subway tunnel, which are not specifically limited here.

[0045] The data acquisition module is used to collect the structural data of the target tunnel. The data acquisition module includes n displacement sensors and n strain sensors, and each detection area of ​​the target tunnel corresponds to a displacement sensor and a strain sensor. The displacement sensor is responsible for real-time monitoring of the structural displacement changes of the tunnel, and the strain sensor load monitors the structural deformation degree of the target tunnel, so as to timely discover potential structural problems in different areas of the target tunnel.

[0046] Among them, the temperature detection module is used to detect the temperature change in the target tunnel. The temperature detection module includes n temperature detection sensors, which correspond to each detection area of ​​the target tunnel. The temperature detection sensor can identify potential overheating or other abnormal conditions, and send the abnormal data to the control module for analysis to trigger the early warning module to issue an alarm. For example, the ambient temperature of the target tunnel, or the heat emitted by the engine, tires and other components of the vehicle in the target tunnel can be obtained. If the control module analyzes and determines that the temperature is too high, dangerous events such as fire may occur, and then the early warning module will issue an alarm based on this information.

[0047] Among them, the control module is responsible for coordinating the data processing and system operation of each sensor. The control module determines whether to trigger an early warning by performing real-time analysis of the data, thereby ensuring the efficient operation of the intelligent early warning system.

[0048] Among them, the early warning prompt module can promptly notify relevant personnel or send out an alarm when an abnormal situation is detected. The early warning prompt module can take various forms of prompts, such as sound alarms, SMS notifications or system interface displays to ensure a quick response.

[0049] Among them, the target tunnel is divided into n detection areas, each detection area is equipped with a displacement sensor, a strain sensor and a temperature detection sensor to achieve comprehensive monitoring of the overall condition of the target tunnel and ensure the safety of each detection area.

[0050] Combine the following Figure 2 The electronic device in the embodiment of the present application is described. Figure 2 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 2 As shown, the electronic device includes one or more processors, a memory, a communication interface and one or more programs, and the processor is communicatively connected with the memory and the communication interface via an internal communication bus.

[0051] Among them, the processor is mainly used for:

[0052] Obtain n target temperature data sets of n detection areas within a preset time period through a temperature detection module; each target temperature data set corresponds to one detection area;

[0053] Determine n first abnormal values ​​according to n target temperature data sets;

[0054] Obtaining n target structure data sets of n detection areas within a preset time period through a data acquisition module; each target structure data set corresponds to one detection area;

[0055] Determine n second outliers according to n target structure data sets;

[0056] Determine n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values ​​by a control module; each third abnormal value corresponds to a detection area;

[0057] Obtain a third abnormal values ​​greater than a preset first threshold value from the n third abnormal values; a is a natural number less than or equal to n;

[0058] Determine a warning prompt information corresponding to a third abnormal values;

[0059] The early warning prompt module executes corresponding early warning prompt operations according to a early warning prompt information.

[0060] The one or more programs are stored in the above-mentioned memory and are configured to be executed by the above-mentioned processor, and the one or more programs include instructions for executing any step in the above-mentioned method embodiment.

[0061] Among them, the processor can be, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, units and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of DSP and microprocessors, and the like. The communication unit can be a communication interface, a transceiver, a transceiver circuit, and the like, and the storage unit can be a memory.

[0062] The memory may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM).

[0063] It is understandable that the electronic device may include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, physical buttons, Wi-Fi module, speaker, Bluetooth module, sensor, display module, etc., which are not limited here. It is understandable that the electronic device may be equipped with Figure 1 The system architecture described.

[0064] After understanding the software and hardware architecture of this application, Figure 3 An intelligent early warning method for tunnel safety in an embodiment of the present application is described. Figure 3 The present invention is a flowchart of an intelligent early warning method for tunnel safety provided by an embodiment of the present application, which is applied to an intelligent early warning system. The intelligent early warning system includes a data acquisition module, a temperature detection module, a control module and an early warning prompt module. The data acquisition module includes n displacement sensors and n strain sensors. The temperature detection module includes n temperature detection sensors. The target tunnel includes n detection areas, each detection area corresponds to a displacement sensor, a strain sensor and a temperature detection sensor, and n is an integer greater than 1. Specifically, the following steps are included:

[0065] Step S301: acquiring n target temperature data sets of the n detection areas within a preset time period through the temperature detection module.

[0066] Each target temperature data set corresponds to a detection area.

[0067] Specifically, the temperature detection module includes n temperature detection sensors, and each detection area is provided with a temperature detection sensor, and each temperature detection sensor obtains a target temperature data set corresponding to each detection area within a preset time period, thereby obtaining n target temperature data sets. Among them, the preset time period includes but is not limited to one minute, one hour, and one day, and the target temperature data set includes multiple temperature data, which is not specifically limited here.

[0068] Step S302: determining n first abnormal values ​​according to the n target temperature data sets.

[0069] The step of determining n first abnormal values ​​according to the n target temperature data sets specifically includes:

[0070] Obtain a reference temperature data set corresponding to a first detection area in the n target temperature data sets; the first detection area is any one of the n detection areas; obtain the highest temperature data in the reference temperature data set; obtain the average value of all temperature data in the reference temperature data set to obtain average temperature data; fit a straight line to all temperature data in the reference temperature data set to obtain a first fitted straight line; the horizontal axis of the first fitted straight line is time, and the vertical axis is temperature data; determine a first slope corresponding to the first fitted straight line; determine a first abnormal value corresponding to the first detection area based on the highest temperature data, the average temperature data, and the first slope.

[0071] Specifically, one detection area is arbitrarily selected from the n detection areas of the target tunnel and marked as the first detection area. Then, all temperature data of the first detection area within a preset time period are obtained through the temperature detection module to form a reference temperature data set. In the reference temperature data set, the maximum value in the temperature data is found and recorded as the maximum temperature data. All temperature data in the reference temperature data set are averaged to obtain average temperature data. Then, all temperature data in the reference temperature data set are linearly fitted to generate a first fitting straight line, the horizontal axis of the first fitting straight line is time, and the vertical axis is temperature data. The slope of the first fitting straight line is calculated and recorded as the first slope. The first abnormal value corresponding to the first detection area is determined based on the maximum temperature data, the average temperature data and the first slope. It should be noted that the operations of the first detection area are performed on the n detection areas of the target tunnel.

[0072] The step of determining the first abnormal value corresponding to the first detection area according to the maximum temperature data, the average temperature data, and the first slope specifically includes:

[0073] According to the mapping relationship between the preset temperature data and the abnormal value, the reference first abnormal value corresponding to the average temperature data is determined; if the maximum temperature data is less than or equal to the preset first temperature threshold, the first abnormal value corresponding to the first detection area is determined according to the reference first abnormal value; if the maximum temperature data is greater than the first temperature threshold, the difference between the maximum temperature data and the first temperature threshold is calculated to obtain the first temperature difference; the first adjustment factor corresponding to the first temperature difference is determined; if the first slope is greater than the preset reference slope threshold, the difference between the first slope and the reference slope threshold is calculated to obtain the first slope difference; the second adjustment factor corresponding to the first slope difference is determined; the reference first abnormal value is adjusted according to the first adjustment factor and the second adjustment factor to obtain the first abnormal value corresponding to the first detection area; if the first slope is less than or equal to the reference slope threshold, the step of determining the first abnormal value corresponding to the first detection area according to the reference first abnormal value is performed.

[0074] Specifically, according to the mapping relationship between the preset temperature data and the abnormal value, the reference first abnormal value corresponding to the average temperature data is determined. If the maximum temperature data is less than or equal to the preset first temperature threshold, and the first slope is less than or equal to the reference slope threshold, the first abnormal value corresponding to the first detection area can be directly determined according to the reference first abnormal value, that is, the first abnormal value of the first detection area is the reference first abnormal value. If the maximum temperature data is greater than the first temperature threshold, the first temperature difference between the maximum temperature data and the first temperature threshold is calculated. According to the mapping relationship between the preset first temperature difference and the first adjustment factor, the first adjustment factor corresponding to the first temperature difference is determined. Among them, the larger the first temperature difference, the larger the first adjustment factor. Then, the size between the first slope and the preset reference slope threshold is judged. If the first slope is greater than the reference slope threshold, the first slope difference between the first slope and the reference slope threshold is calculated. According to the mapping relationship between the preset first slope difference and the second adjustment factor, the second adjustment factor corresponding to the first slope difference is determined. Among them, the larger the first slope difference, the larger the second adjustment factor. Then, the reference first abnormal value is adjusted according to the first adjustment factor and the second adjustment factor to obtain the first abnormal value corresponding to the first detection area. For example, the first abnormal value = the reference first abnormal value × (1 + the first adjustment factor) × (1 + the second adjustment factor), which is not specifically limited here. It should be noted that the range of the abnormal value can be 1-10, which is not specifically limited here.

[0075] In a possible embodiment, if the maximum temperature data is less than or equal to the first temperature threshold, and the first slope is greater than the reference slope threshold, the first slope difference between the first slope and the reference slope threshold is calculated, the second adjustment factor corresponding to the first slope difference is determined, and the reference first abnormal value is adjusted according to the second adjustment factor to obtain the first abnormal value corresponding to the first detection area. For example, the first abnormal value = the reference first abnormal value × (1 + the second adjustment factor), which is not specifically limited here.

[0076] In a possible embodiment, if the maximum temperature data is greater than the first temperature threshold, and the first slope is less than or equal to the reference slope threshold, the first temperature difference between the maximum temperature data and the first temperature threshold is calculated, the first adjustment factor corresponding to the first temperature difference is determined, and the reference first abnormal value is adjusted according to the first adjustment factor to obtain the first abnormal value corresponding to the first detection area. For example, the first abnormal value = the reference first abnormal value × (1 + the first adjustment factor), which is not specifically limited here.

[0077] It can be seen that by real-time detection of temperature data in the target tunnel, potential fires or other abnormal situations can be discovered in time, facilitating rapid response and handling of abnormal situations, thereby improving the safety and reliability of the tunnel.

[0078] Step S303: acquiring, by the data acquisition module, n target structure data sets of the n detection areas within the preset time period.

[0079] Among them, each target structure data set corresponds to a detection area.

[0080] Specifically, the data acquisition module includes n displacement sensors and n strain sensors, and each detection area is provided with a displacement sensor and a strain sensor, and the target structure data set corresponding to each detection area within a preset time period is obtained through the displacement sensor and strain sensor corresponding to each detection area, thereby obtaining n target structure data sets. Among them, the target structure data set includes multiple displacement data and multiple strain data.

[0081] Step S304: determining n second outliers according to the n target structure data sets.

[0082] The step of determining n second outliers according to the n target structure data sets specifically comprises:

[0083] A reference structure data set corresponding to a second detection area in the n target structure data sets is obtained; the second detection area is any one of the n detection areas; the reference structure data set includes: reference displacement data and reference strain data; initial displacement data and initial strain data corresponding to the second detection area are obtained; the initial displacement data and the initial strain data both belong to tunnel structure data of the second detection area of ​​the target tunnel at the last maintenance moment; a target displacement change between the largest reference displacement data in the reference structure data set and the initial displacement data is determined; a reference second abnormal value corresponding to the target displacement change is determined according to a preset mapping relationship between the displacement change and the abnormal value; the larger the target displacement change is, the larger the reference second abnormal value is; a straight line is fitted to all reference strain data in the reference structure data set to obtain a second fitting straight line; the horizontal axis of the second fitting straight line is time and the vertical axis is strain data; a second slope of the second fitting straight line is determined; a third adjustment factor corresponding to the second slope is determined; the reference second abnormal value is adjusted according to the third adjustment factor to obtain a second abnormal value corresponding to the second detection area.

[0084] Specifically, any one detection area is selected from the n detection areas of the target tunnel as the second detection area. A reference structure data set corresponding to the second detection area is obtained, and the reference structure data set includes at least one of the following types of data: reference displacement data and reference strain data. Then, according to the historical structure data set of the target tunnel, the initial displacement data and initial strain data corresponding to the second detection area at the last maintenance moment are determined. The target displacement change between the largest reference displacement data and the initial displacement data in the reference structure data set is obtained, and the reference second abnormal value corresponding to the target displacement change is determined according to the mapping relationship between the preset displacement change and the abnormal value. Among them, the larger the target displacement change, the larger the reference second abnormal value. Then, linear fitting is performed on all reference strain data in the reference structure data set to obtain a second fitting straight line, the horizontal axis of the second fitting straight line is time, and the vertical axis is strain data. The slope of the second fitting straight line is obtained and recorded as the second slope. According to the mapping relationship between the preset second slope and the third adjustment factor, the third adjustment factor corresponding to the second slope is determined. The reference second abnormal value is adjusted according to the third adjustment factor to obtain the second abnormal value corresponding to the second detection area. It should be noted that the calculation formula of the second abnormal value corresponding to the second detection area includes: second abnormal value=reference second abnormal value×(1+third adjustment factor).

[0085] It can be seen that by detecting the displacement data and strain data of the target tunnel, abnormal changes in the tunnel structure can be discovered in a timely and accurate manner, potential safety problems can be prevented, and the corresponding abnormal values ​​can be determined, so that the overall situation of the target tunnel can be determined based on the abnormal values ​​and early warning can be issued in a timely manner, thereby improving the accuracy and timeliness of tunnel safety early warnings.

[0086] Step S305: determining n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values ​​by the control module.

[0087] Each third abnormal value corresponds to a detection area.

[0088] The intelligent early warning system further includes a camera module, the camera module includes n cameras, and each detection area corresponds to a camera; the control module determines n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values, and the specific steps include:

[0089] Determine n reference third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values; obtain the number of vehicles corresponding to each of the n detection areas of the target tunnel through the camera module to obtain n vehicle numbers; obtain a preset vehicle number range corresponding to the target tunnel; determine b vehicle numbers that are not within the preset vehicle number range among the n vehicle numbers; b is a natural number less than or equal to n; determine b influencing factors corresponding to the b vehicle numbers; adjust the b reference third abnormal values ​​corresponding to the b vehicle numbers among the n reference third abnormal values ​​according to the b influencing factors to obtain b third abnormal values; determine the reference third abnormal values ​​other than the b reference third abnormal values ​​among the n reference third abnormal values ​​as third abnormal values ​​to obtain nb third abnormal values.

[0090] Specifically, according to n first abnormal values ​​and n second abnormal values, n reference third abnormal values ​​are calculated. Then, the number of vehicles in each detection area of ​​the n detection areas of the target tunnel is obtained by the camera module to obtain the number of n vehicles. Then, the preset vehicle number range corresponding to the target tunnel is obtained, wherein the preset vehicle number range is the normal vehicle number range corresponding to each detection area. Then, the number of b vehicles that are not within the preset vehicle number range among the n vehicle numbers is determined, and b is a natural number less than or equal to n. The number of b impact factors corresponding to the number of b vehicles is determined, and the number of b reference third abnormal values ​​corresponding to the number of b vehicles in the n reference third abnormal values ​​is adjusted according to the b impact factors to obtain b third abnormal values. For example, if the impact factor is 0.2, and the reference third abnormal value corresponding to the impact factor is 3, then the third abnormal value corresponding to the reference third abnormal value is 3.6, wherein the calculation formula corresponding to the third abnormal value is as follows: 3×(1+0.2)=3.6. Finally, the reference third abnormal value other than the b reference third abnormal values ​​among the n reference third abnormal values ​​is determined to be the third abnormal value, and nb third abnormal values ​​are obtained. According to the b third outlier values ​​and the nb third outlier values, n third outlier values ​​are determined.

[0091] It can be seen that the outliers are adjusted according to the influence of the number of vehicles to ensure the accuracy and reliability of the outliers, thereby improving the accuracy of tunnel safety warnings and facilitating timely safety warnings.

[0092] The step of determining n reference third outliers according to the n first outliers and the n second outliers comprises:

[0093] Obtain the usage time of the target tunnel; determine a first weight and a second weight according to the usage time; the longer the usage time, the smaller the first weight and the larger the second weight; obtain the product of the first weight and each first outlier value among the n first outliers to obtain n first products; obtain the product of the second weight and each second outlier value among the n second outliers to obtain n second products; determine the n reference third outliers according to the n first products and the n second products.

[0094] Specifically, the length of time the target tunnel has been put into use, that is, the length of use, is obtained, and the first weight and the second weight are determined based on the length of use. The longer the length of use of the target tunnel, the more likely its tunnel structure is to be abnormal. Therefore, the longer the length of use, the smaller the first weight and the larger the second weight. It should be noted that the sum of the first weight and the second weight is 1. For example, the first weight is 0.6 and the second weight is 0.4. Then, the first weight is multiplied by each of the n first abnormal values ​​to obtain n first products. The second weight is multiplied by each of the n second abnormal values ​​to obtain n second products. Finally, based on the n first products and the n second products, n reference third abnormal values ​​are calculated. The sum of any first product among the n first products and its corresponding second product is the reference third abnormal value corresponding to the first product.

[0095] It can be seen that allocating weights according to the usage time of the target tunnel can help improve the flexibility and adaptability of outlier detection and reduce subsequent false positives and missed positives due to inaccurate outlier calculations.

[0096] Step S306: Obtain a third abnormal values ​​that are greater than a preset first threshold value among the n third abnormal values.

[0097] Wherein, a is a natural number less than or equal to n.

[0098] Specifically, a first threshold is preset according to the design specification, historical data and expert experience of the tunnel. Then, n third abnormal values ​​are traversed, and all third abnormal values ​​greater than the first threshold are recorded to obtain a third abnormal values.

[0099] Step S307, determining a warning prompt information corresponding to the a third abnormal values.

[0100] The specific steps of determining the a warning prompt information corresponding to the a third abnormal values ​​include:

[0101] Obtain a target abnormal value; the target abnormal value is any one of the a third abnormal values; determine the target detection area corresponding to the target abnormal value in the n detection areas; if the target abnormal value is less than a preset second threshold, determine that the target danger level of the target detection area is low; the second threshold is greater than the first threshold; if the target abnormal value is greater than or equal to the second threshold, and less than or equal to a preset third threshold, determine that the target danger level corresponding to the target detection area is medium; the third threshold is greater than the second threshold; if the target abnormal value is greater than the third threshold, determine that the target danger level of the target detection area is high; determine the warning prompt information corresponding to the target abnormal value in the a warning prompt information according to the target danger level.

[0102] Specifically, one is arbitrarily selected from a third abnormal values ​​as the target abnormal value. If the target abnormal value is less than the preset second threshold value, the target danger level of the target detection area is determined to be low. Among them, the second threshold value is greater than the first threshold value. If the target abnormal value is greater than or equal to the second threshold value, and less than or equal to the preset third threshold value, the target danger level of the target detection area is determined to be medium. Among them, the third threshold value is greater than the second threshold value and the first threshold value. If the target abnormal value is greater than the third threshold value, the target danger level of the target detection area is determined to be high. Finally, according to the determined target danger level, a corresponding early warning prompt information is generated for the target abnormal value. For example, if the target danger level corresponding to the target abnormal value is medium, the corresponding early warning prompt information can be "The target danger level of the target detection area is medium, please stay away from the target detection area as soon as possible", which is not specifically limited here.

[0103] In a possible embodiment, the intelligent early warning system also includes a gas detection module, which includes n gas detection sensors, and each detection area corresponds to a gas detection sensor. Among them, the gas detection sensor includes but is not limited to an infrared gas sensor and an electrochemical gas sensor. When the target hazard level of the target detection area is intermediate or advanced, the composition and concentration of a specific gas in the air can be detected by the gas detection sensor corresponding to the target detection area, wherein the specific gas includes but is not limited to carbon monoxide, carbon dioxide, and methane. Then, according to the concentration change of the specific gas in the target detection area, the possibility of a fire is determined. For example, when it is detected that the concentration of carbon monoxide or carbon dioxide in the target detection area increases significantly within a preset time period and exceeds a pre-set safety threshold, it can be determined that a fire may occur. Then, corresponding early warning prompt information can be generated according to the concentration change of the specific gas, wherein the greater the concentration change, the greater the possibility of a fire. For example, when the target danger level of the target detection area is medium, if the concentration change of a specific gas is less than a preset change threshold, the early warning prompt message may be "The target danger level of the target detection area is medium, the fire risk is low, please move away from the target detection area as soon as possible"; if the concentration change of the specific gas is greater than or equal to the preset change threshold, the early warning prompt message may be "The target danger level of the target detection area is medium, the fire risk is high, please move away from the target detection area as soon as possible"; no specific limitation is made here.

[0104] It can be seen that by analyzing and judging the abnormal values, determining the danger level corresponding to the abnormal values, and generating corresponding early warning information according to the danger level, accurate early warning can be given before the danger occurs, which can buy precious time for taking preventive measures and reduce the possibility of accidents.

[0105] Step S308: executing corresponding warning prompt operations according to the a warning prompt information through the warning prompt module.

[0106] Specifically, if the danger level corresponding to the early warning prompt information is low, the first early warning prompt operation is performed through the early warning prompt module, and the first early warning prompt operation includes but is not limited to sending a low-level early warning notification, flashing the warning light, and recording the early warning information, which is convenient for subsequent regular inspections, and is not specifically limited here. If the danger level corresponding to the early warning prompt information is intermediate, the second early warning prompt operation is performed through the early warning prompt module, and the second early warning prompt operation includes but is not limited to sending an intermediate early warning notification, the warning light is always on, and the voice alarm, and the early warning information is recorded, which is convenient for organizing professionals to conduct on-site inspections and assess the severity of the situation. If the danger level corresponding to the early warning prompt information is high, the third early warning prompt operation is performed through the early warning prompt module, and the third early warning prompt operation includes but is not limited to sending a high-level early warning notification, the warning light is always on, the voice alarm, and the tunnel closure prompt, and the early warning information is recorded, notifying all relevant personnel to respond quickly, and according to the predetermined emergency plan, starting rapid response measures, such as temporarily closing the tunnel, coordinating rescue and other measures. It should be noted that the early warning prompt information also includes the location information of the detection area corresponding to each abnormal value, which is convenient for quickly locating the specific location where the abnormality occurs, thereby improving the efficiency and effect of the emergency response.

[0107] It can be seen that through the above method, the potential dangers of the target tunnel can be identified in time, and corresponding early warning prompts can be issued to reduce the probability of accidents and improve the accuracy and timeliness of tunnel safety early warnings.

[0108] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0109] The embodiment of the present application can divide the electronic device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0110] In the case of dividing each functional unit into corresponding functional units, Figure 4 1 is a functional unit composition block diagram of an intelligent early warning device for tunnel safety provided by an embodiment of the present application, which is applied to an intelligent early warning system. The intelligent early warning system includes a data acquisition module, a temperature detection module, a control module and an early warning prompt module. The data acquisition module includes n displacement sensors and n strain sensors. The temperature detection module includes n temperature detection sensors. The target tunnel includes n detection areas, each detection area corresponds to a displacement sensor, a strain sensor and a temperature detection sensor, and n is an integer greater than 1. The intelligent early warning device 400 for tunnel safety includes an acquisition unit 410, a determination unit 420 and an execution unit 430, wherein:

[0111] The acquisition unit 410 is used to acquire n target temperature data sets of the n detection areas within a preset time period through the temperature detection module; each target temperature data set corresponds to one detection area;

[0112] The determining unit 420 is configured to determine n first abnormal values ​​according to the n target temperature data sets;

[0113] The acquisition unit 410 is further configured to acquire, through the data acquisition module, n target structure data sets of the n detection areas within the preset time period; each target structure data set corresponds to one detection area;

[0114] The determining unit 420 is further configured to determine n second outliers according to the n target structure data sets;

[0115] The determination unit 420 is further configured to determine n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values ​​through the control module; each third abnormal value corresponds to a detection area;

[0116] The acquisition unit 410 is further configured to acquire a third abnormal values ​​greater than a preset first threshold value among the n third abnormal values; a is a natural number less than or equal to n;

[0117] The determining unit 420 is further configured to determine a pieces of warning prompt information corresponding to the a third abnormal values;

[0118] The execution unit 430 is used to execute corresponding warning prompt operations according to the a warning prompt information through the warning prompt module.

[0119] Optionally, in determining the n first abnormal values ​​according to the n target temperature data sets, the determining unit 420 is specifically configured to:

[0120] Acquire a reference temperature data set corresponding to a first detection area among the n target temperature data sets; the first detection area is any one detection area among the n detection areas;

[0121] Acquire the highest temperature data in the reference temperature data set;

[0122] Obtaining the average value of all temperature data in the reference temperature data set to obtain average temperature data;

[0123] Fitting a straight line to all temperature data in the reference temperature data set to obtain a first fitting straight line; the horizontal axis of the first fitting straight line is time, and the vertical axis is temperature data;

[0124] Determining a first slope corresponding to the first fitting straight line;

[0125] A first abnormal value corresponding to the first detection area is determined according to the maximum temperature data, the average temperature data, and the first slope.

[0126] Optionally, in determining the first abnormal value corresponding to the first detection area according to the maximum temperature data, the average temperature data, and the first slope, the determining unit 420 is further specifically configured to:

[0127] Determine a reference first abnormal value corresponding to the average temperature data according to a preset mapping relationship between the temperature data and the abnormal value;

[0128] If the highest temperature data is less than or equal to a preset first temperature threshold, determining a first abnormal value corresponding to the first detection area according to the reference first abnormal value;

[0129] If the maximum temperature data is greater than the first temperature threshold, calculating the difference between the maximum temperature data and the first temperature threshold to obtain a first temperature difference;

[0130] Determining a first adjustment factor corresponding to the first temperature difference;

[0131] If the first slope is greater than a preset reference slope threshold, calculating a difference between the first slope and the reference slope threshold to obtain a first slope difference;

[0132] Determining a second adjustment factor corresponding to the first slope difference;

[0133] Adjusting the reference first abnormal value according to the first adjustment factor and the second adjustment factor to obtain a first abnormal value corresponding to the first detection area;

[0134] If the first slope is less than or equal to the reference slope threshold, the step of determining the first abnormal value corresponding to the first detection area according to the reference first abnormal value is performed.

[0135] Optionally, in determining the n second outliers according to the n target structure data sets, the determining unit 420 is further specifically configured to:

[0136] Acquire a reference structure data set corresponding to a second detection area in the n target structure data sets; the second detection area is any one of the n detection areas; the reference structure data set includes: reference displacement data and reference strain data;

[0137] Acquire initial displacement data and initial strain data corresponding to the second detection area; the initial displacement data and the initial strain data both belong to tunnel structure data of the second detection area of ​​the target tunnel at the last maintenance moment;

[0138] Determine a target displacement change between the largest reference displacement data in the reference structure data set and the initial displacement data;

[0139] Determine a reference second abnormal value corresponding to the target displacement change according to a preset mapping relationship between the displacement change and the abnormal value; the larger the target displacement change is, the larger the reference second abnormal value is;

[0140] Fitting a straight line to all reference strain data in the reference structure data set to obtain a second fitting straight line; the horizontal axis of the second fitting straight line is time, and the vertical axis is strain data;

[0141] determining a second slope of the second fitted straight line;

[0142] determining a third adjustment factor corresponding to the second slope;

[0143] The reference second abnormal value is adjusted according to the third adjustment factor to obtain a second abnormal value corresponding to the second detection area.

[0144] Optionally, the intelligent early warning system further includes a camera module, the camera module includes n cameras, and each detection area corresponds to a camera; in determining the n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values ​​by the control module, the determination unit 420 is further specifically used to:

[0145] Determine n reference third outliers according to the n first outliers and the n second outliers;

[0146] Acquire the number of vehicles corresponding to each of the n detection areas of the target tunnel by the camera module to obtain the number of n vehicles;

[0147] Obtaining a preset vehicle quantity range corresponding to the target tunnel;

[0148] Determine the number of b vehicles that are not within the preset vehicle number range among the n vehicles; b is a natural number less than or equal to n;

[0149] Determine b influencing factors corresponding to the b number of vehicles;

[0150] Adjusting b reference third abnormal values ​​corresponding to the b number of vehicles in the n reference third abnormal values ​​according to the b influencing factors to obtain b third abnormal values;

[0151] The reference third outlier values ​​other than the b reference third outlier values ​​among the n reference third outlier values ​​are determined as third outliers, so as to obtain nb third outliers.

[0152] Optionally, in the aspect of determining the n reference third outliers according to the n first outliers and the n second outliers, the determining unit 420 is further specifically configured to:

[0153] Obtaining the usage time of the target tunnel;

[0154] Determine a first weight and a second weight according to the usage time; the longer the usage time, the smaller the first weight and the larger the second weight;

[0155] Obtaining a product of the first weight and each of the n first outlier values ​​to obtain n first products;

[0156] Obtaining a product of the second weight and each of the n second outlier values ​​to obtain n second products;

[0157] The n reference third outliers are determined according to the n first products and the n second products.

[0158] Optionally, in determining the a warning prompt information corresponding to the a third abnormal values, the determining unit 420 is further specifically configured to:

[0159] Obtain a target outlier value; the target outlier value is any one of the a third outliers;

[0160] Determine a target detection area corresponding to the target abnormal value in the n detection areas;

[0161] If the target abnormal value is less than a preset second threshold, it is determined that the target danger level of the target detection area is low; and the second threshold is greater than the first threshold;

[0162] If the target abnormal value is greater than or equal to the second threshold value and less than or equal to a preset third threshold value, it is determined that the target danger level corresponding to the target detection area is medium; and the third threshold value is greater than the second threshold value;

[0163] If the target abnormal value is greater than the third threshold, determining that the target danger level of the target detection area is high;

[0164] The warning prompt information corresponding to the target abnormal value in the a warning prompt information is determined according to the target danger level.

[0165] It can be seen that by combining temperature data and structural data to detect various detection areas of the tunnel, it is convenient to promptly discover and respond to abnormal conditions in the detection area, thereby improving the accuracy and timeliness of tunnel safety warnings.

[0166] It should be noted that the specific implementation of each operation can adopt the corresponding description of the method embodiment shown above. The intelligent early warning device 400 for tunnel safety can be used to execute the above method embodiment of the present application, which will not be repeated here.

[0167] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes an electronic device.

[0168] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0169] It should be noted that, for the above-mentioned various embodiments, for the sake of simple description, they are all expressed as a series of action combinations. Those skilled in the art should be aware that the present application is not limited by the described order of actions, because some steps in the embodiments of the present application can be performed in other orders or simultaneously. In addition, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily required by the embodiments of the present application.

[0170] In the above embodiments, the embodiments of the present application have different focuses on the description of each embodiment. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0171] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

[0172] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by executing software instructions by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, read-only compact disks (CD-ROMs) or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist in a terminal device or a management device as discrete components.

[0173] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0174] The modules / units included in the devices and products described in the above embodiments may be software modules / units or hardware modules / units, or may be partially software modules / units and partially hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as chip, circuit module, etc.) or different components of the chip module, or at least some of the modules / units may be implemented in the form of software programs. It is implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in hardware such as circuits.

[0175] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific implementation method of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. An intelligent early warning method for tunnel safety, characterized in that: Applied to an intelligent early warning system, the intelligent early warning system includes a data acquisition module, a temperature detection module, a control module and an early warning prompt module, the data acquisition module includes n displacement sensors and n strain sensors, the temperature detection module includes n temperature detection sensors, the target tunnel includes n detection areas, each detection area corresponds to a displacement sensor, a strain sensor and a temperature detection sensor, n is an integer greater than 1, and the method includes: Acquire n target temperature data sets of the n detection areas within a preset time period through the temperature detection module; each target temperature data set corresponds to one detection area; Determine n first abnormal values ​​according to the n target temperature data sets; Acquire, by the data acquisition module, n target structure data sets of the n detection areas within the preset time period; each target structure data set corresponds to one detection area; Determine n second outliers according to the n target structure data sets; Determining n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values ​​by the control module; each third abnormal value corresponds to a detection area; Obtain a third abnormal values ​​greater than a preset first threshold value from the n third abnormal values; a is a natural number less than or equal to n; Determine a pieces of warning prompt information corresponding to the a third abnormal values; Executing corresponding warning prompt operations according to the a warning prompt information by the warning prompt module; The step of determining n first abnormal values ​​according to the n target temperature data sets includes: Acquire a reference temperature data set corresponding to a first detection area among the n target temperature data sets; the first detection area is any one detection area among the n detection areas; Acquire the highest temperature data in the reference temperature data set; Obtaining the average value of all temperature data in the reference temperature data set to obtain average temperature data; Fitting a straight line to all temperature data in the reference temperature data set to obtain a first fitting straight line; the horizontal axis of the first fitting straight line is time, and the vertical axis is temperature data; Determining a first slope corresponding to the first fitting straight line; A first abnormal value corresponding to the first detection area is determined according to the maximum temperature data, the average temperature data, and the first slope.

2. The method according to claim 1, characterized in that The determining the first abnormal value corresponding to the first detection area according to the maximum temperature data, the average temperature data, and the first slope includes: Determine a reference first abnormal value corresponding to the average temperature data according to a preset mapping relationship between the temperature data and the abnormal value; If the highest temperature data is less than or equal to a preset first temperature threshold, determining a first abnormal value corresponding to the first detection area according to the reference first abnormal value; If the maximum temperature data is greater than the first temperature threshold, calculating the difference between the maximum temperature data and the first temperature threshold to obtain a first temperature difference; Determining a first adjustment factor corresponding to the first temperature difference; If the first slope is greater than a preset reference slope threshold, calculating a difference between the first slope and the reference slope threshold to obtain a first slope difference; Determining a second adjustment factor corresponding to the first slope difference; Adjusting the reference first abnormal value according to the first adjustment factor and the second adjustment factor to obtain a first abnormal value corresponding to the first detection area; If the first slope is less than or equal to the reference slope threshold, the step of determining the first abnormal value corresponding to the first detection area according to the reference first abnormal value is performed.

3. The method according to claim 1 or 2, characterized in that The determining of n second outliers according to the n target structure data sets comprises: Acquire a reference structure data set corresponding to a second detection area in the n target structure data sets; the second detection area is any one of the n detection areas; the reference structure data set includes: reference displacement data and reference strain data; Acquire initial displacement data and initial strain data corresponding to the second detection area; the initial displacement data and the initial strain data both belong to tunnel structure data of the second detection area of ​​the target tunnel at the last maintenance moment; Determine a target displacement change between the largest reference displacement data in the reference structure data set and the initial displacement data; Determine a reference second abnormal value corresponding to the target displacement change according to a preset mapping relationship between the displacement change and the abnormal value; the larger the target displacement change is, the larger the reference second abnormal value is; Fitting a straight line to all reference strain data in the reference structure data set to obtain a second fitting straight line; the horizontal axis of the second fitting straight line is time, and the vertical axis is strain data; determining a second slope of the second fitted straight line; determining a third adjustment factor corresponding to the second slope; The reference second abnormal value is adjusted according to the third adjustment factor to obtain a second abnormal value corresponding to the second detection area.

4. The method according to claim 3, characterized in that The intelligent early warning system further includes a camera module, the camera module includes n cameras, and each detection area corresponds to a camera; the control module determines n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values, including: Determine n reference third outliers according to the n first outliers and the n second outliers; Acquire the number of vehicles corresponding to each of the n detection areas of the target tunnel by the camera module to obtain the number of n vehicles; Obtaining a preset vehicle quantity range corresponding to the target tunnel; Determine the number of b vehicles that are not within the preset vehicle number range among the n vehicles; b is a natural number less than or equal to n; Determine b influencing factors corresponding to the b number of vehicles; Adjusting b reference third abnormal values ​​corresponding to the b number of vehicles in the n reference third abnormal values ​​according to the b influencing factors to obtain b third abnormal values; The reference third outlier values ​​other than the b reference third outlier values ​​among the n reference third outlier values ​​are determined as third outliers, so as to obtain nb third outliers.

5. The method according to claim 4, characterized in that The determining n reference third outlier values ​​according to the n first outlier values ​​and the n second outlier values ​​comprises: Obtaining the usage time of the target tunnel; Determine a first weight and a second weight according to the usage time; the longer the usage time, the smaller the first weight and the larger the second weight; Obtaining a product of the first weight and each of the n first outlier values ​​to obtain n first products; Obtaining a product of the second weight and each of the n second outlier values ​​to obtain n second products; The n reference third outliers are determined according to the n first products and the n second products.

6. The method according to claim 1 or 2, characterized in that: The determining a pieces of warning prompt information corresponding to the a third abnormal values ​​comprises: Obtain a target outlier value; the target outlier value is any one of the a third outliers; Determine a target detection area corresponding to the target abnormal value in the n detection areas; If the target abnormal value is less than a preset second threshold, it is determined that the target danger level of the target detection area is low; and the second threshold is greater than the first threshold; If the target abnormal value is greater than or equal to the second threshold value and less than or equal to a preset third threshold value, it is determined that the target danger level corresponding to the target detection area is medium; and the third threshold value is greater than the second threshold value; If the target abnormal value is greater than the third threshold, determining that the target danger level of the target detection area is high; The warning prompt information corresponding to the target abnormal value in the a warning prompt information is determined according to the target danger level.

7. An intelligent early warning device for tunnel safety, characterized in that: Applied to an intelligent early warning system, the intelligent early warning system includes a data acquisition module, a temperature detection module, a control module and an early warning prompt module, the data acquisition module includes n displacement sensors and n strain sensors, the temperature detection module includes n temperature detection sensors, the target tunnel includes n detection areas, each detection area corresponds to a displacement sensor, a strain sensor and a temperature detection sensor, n is an integer greater than 1, the device includes an acquisition unit, a determination unit and an execution unit, wherein: The acquisition unit is used to acquire n target temperature data sets of the n detection areas within a preset time period through the temperature detection module; each target temperature data set corresponds to one detection area; The determining unit is used to determine n first abnormal values ​​according to the n target temperature data sets; The acquisition unit is further used to acquire, through the data acquisition module, n target structure data sets of the n detection areas within the preset time period; each target structure data set corresponds to one detection area; The determining unit is further used to determine n second outliers according to the n target structure data sets; The determination unit is further configured to determine n third abnormal values ​​according to the n first abnormal values ​​and the n second abnormal values ​​through the control module; each third abnormal value corresponds to a detection area; The acquisition unit is further used to acquire a third abnormal values ​​greater than a preset first threshold value among the n third abnormal values; a is a natural number less than or equal to n; The determining unit is further used to determine a warning prompt information corresponding to the a third abnormal values; The execution unit is used to perform corresponding warning prompt operations according to the a warning prompt information through the warning prompt module.

8. An electronic device, characterized in that: include: A processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs comprising instructions for executing the steps in the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 6.

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