A road tunnel environment monitoring method, device and medium

Abstract

The present application discloses a method, device and medium for monitoring the environment of a highway tunnel, which belongs to the field of data processing technology and is used to solve the technical problem of lacking a more effective technical solution for monitoring the environment of a highway tunnel. The method comprises: obtaining a meteorological data sequence of the area where the highway tunnel is located within a preset time in the future; retrieving a coupling relationship prediction model corresponding to the type of highway tunnel; inputting the meteorological data sequence into the coupling relationship prediction model to generate a first environmental parameter value distribution of the highway tunnel; determining the actual environmental parameter value of the highway tunnel at the current time; generating a second environmental parameter value distribution of the highway tunnel according to the first environmental parameter value distribution and the actual environmental parameter value; determining the reference environmental parameter value distribution of the highway tunnel every day according to the season in which the highway tunnel is located; and controlling the environmental equipment of the highway tunnel according to the reference environmental parameter value and the second environmental parameter value distribution. The comfort and safety of the tunnel environment are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and medium for monitoring the environment of a highway tunnel. Background Art

[0002] Highway tunnels are an important part of modern transportation. In order to ensure the safe operation of tunnels, it is necessary to monitor and control the environmental parameters inside the tunnels.

[0003] At present, sensing equipment is installed in the internal environment of highway tunnels, and parameter requirement values ​​of the internal environment of highway tunnels are pre-set. When the sensing equipment has detected that the parameter values ​​do not meet the parameter requirement values, the environmental parameters in the tunnel are maintained by adjusting the environmental equipment so that the parameter values ​​meet the parameter requirement values ​​again. The adjustment time is not fixed. If the environmental equipment cannot adjust the parameters in a timely and accurate manner, it may cause the environmental parameter values ​​in the tunnel to deviate from the pre-set requirement values, thereby affecting the comfort and safe operation inside the tunnel.

[0004] In addition, for a single environmental parameter, the parameter requirement value is set in a one-size-fits-all manner, while the daily meteorological conditions are actually changing all the time, so the daily internal suitable environment is diverse. In fact, meeting the parameter requirement value is not the safest and most comfortable environment. It can be seen that the comfort and safety inside the tunnel are reduced.

[0005] In addition, the location and number of sensor devices for the internal environment of highway tunnels are limited. Monitoring only through sensor devices cannot accurately and effectively reflect the actual parameter values ​​of the internal environment of the tunnel, so the accuracy of environmental monitoring is also low.

[0006] Therefore, there is a lack of a more effective technical solution for highway tunnel environmental monitoring. Summary of the invention

[0007] The embodiments of the present application provide a highway tunnel environment monitoring method, device and medium, which are used to solve the problem of lack of a more effective highway tunnel environment monitoring technical solution.

[0008] The present application embodiment adopts the following technical solutions:

[0009] On the one hand, an embodiment of the present application provides a method for monitoring the environment of a highway tunnel, the method comprising: obtaining a meteorological data sequence of an area where a highway tunnel is located within a preset time period in the future; the meteorological data sequence comprises meteorological data corresponding to a plurality of first time points arranged in chronological order, the first first time point being the current time point, and the meteorological data comprising temperature, humidity, sunshine, precipitation and visibility; retrieving a coupling relationship prediction model corresponding to the highway tunnel type in a coupling relationship model library; inputting the meteorological data sequence into the coupling relationship prediction model to generate a first environmental parameter value distribution of the highway tunnel within the preset time period in the future; environmental parameters comprising temperature, road humidity and light degree; the first environmental parameter value distribution includes first environmental parameter values ​​corresponding to multiple second time points respectively, the first second time point is the current time point; the interval between adjacent first time points is an integer multiple of the interval between adjacent second time points; determine the actual environmental parameter value of the highway tunnel at the current time; generate the second environmental parameter value distribution of the highway tunnel within the future preset time period according to the first environmental parameter value distribution and the actual environmental parameter value; determine the reference environmental parameter value distribution of the highway tunnel every day according to the season in which the highway tunnel is located; control the environmental equipment of the highway tunnel according to the reference environmental parameter value and the second environmental parameter value distribution.

[0010] In one example, before the meteorological data sequence is input into the coupling relationship prediction model to generate the first environmental parameter value distribution of the highway tunnel within the future preset time period, the method also includes: obtaining a sample meteorological data sequence of the sample highway tunnel within a preset time period, and a sample environmental parameter value distribution of the sample highway tunnel within the preset time period; within the preset time period, the equipment parameters corresponding to the environmental equipment of the sample highway tunnel have not changed; the sample highway tunnel type is the same as the highway tunnel type; using the sample meteorological data sequence as an input sample and the sample environmental parameter value distribution as a sample label, and performing supervised training on the initial neural network model to obtain the coupling relationship prediction model.

[0011] In one example, generating the second environmental parameter value distribution of the highway tunnel within the future preset time period based on the first environmental parameter value distribution and the actual environmental parameter value specifically includes: calculating the parameter change differences between adjacent first environmental parameter values ​​in order from morning to night to obtain an environmental parameter difference sequence of the first environmental parameter value distribution; the environmental parameter difference sequence includes environmental parameter differences in different time intervals; by traversing the environmental parameter difference sequence, continuously summing the actual environmental parameter values ​​multiple times to obtain initial second environmental parameter values ​​corresponding to the multiple second time points; generating the second environmental parameter value distribution of the highway tunnel within the future preset time period based on the initial second environmental parameter values ​​corresponding to the multiple second time points.

[0012] In one example, generating the distribution of second environmental parameter values ​​of the highway tunnel within the future preset time period based on the initial second environmental parameter values ​​corresponding to the multiple second time points specifically includes: retrieving standard environmental equipment configuration information of the highway tunnel type in an equipment configuration database; comparing the actual environmental equipment configuration information of the highway tunnel with the standard environmental equipment configuration information to obtain a relative adjustment score of the environmental parameters of the highway tunnel; the relative adjustment score of the environmental parameters is used to indicate the actual degree of change of the environmental parameter values ​​when the climate conditions change; if the relative adjustment score of the environmental parameters is higher than the preset score threshold, determining the initial second environmental parameter values ​​corresponding to the multiple second time points as the distribution of second environmental parameter values ​​of the highway tunnel within the future preset time period.

[0013] In one example, the method also includes: if the relative adjustment score of the environmental parameter is lower than or equal to a preset score threshold, obtaining the historical meteorological data sequence and the historical actual environmental parameter value distribution of the highway tunnel within a preset historical time length; determining the positive correlation score of the historical meteorological data sequence and the historical actual environmental parameter value distribution; when the positive correlation score is greater than the preset correlation threshold, updating the initial second environmental parameter value of the corresponding second time point so that the difference between each initial second environmental parameter value and the corresponding meteorological data value is less than the preset difference threshold; and determining the updated initial second environmental parameter value of the corresponding second time point and the unupdated initial second environmental parameter values ​​of other second time points as the second environmental parameter value distribution of the highway tunnel within the future preset time length.

[0014] In one example, determining the positive correlation score between the historical meteorological data sequence and the historical actual environmental parameter value distribution specifically includes: determining a pre-constructed correlation prediction model; inputting the historical meteorological data sequence and the historical actual environmental parameter value distribution into the correlation prediction model to obtain the positive correlation score.

[0015] In one example, the actual environmental equipment configuration information of the highway tunnel is compared with the standard environmental equipment configuration information to obtain the relative adjustment score of the environmental parameters of the highway tunnel. The relative adjustment score of the environmental parameters of the highway tunnel is obtained, specifically including: determining that the environmental equipment configuration information includes the number of lighting devices, the lighting intensity of the lighting devices, the number of ventilation devices, and the ventilation intensity of the ventilation devices; the lighting devices are related to the light intensity, and the ventilation devices are related to the temperature and humidity; according to the equipment type, the actual environmental equipment configuration information is compared with the standard environmental equipment configuration information respectively to obtain the lighting relative adjustment score for the lighting devices and the ventilation relative adjustment score for the ventilation devices; weighted summing the lighting relative adjustment score and the ventilation relative adjustment score is performed to obtain the relative adjustment score of the environmental parameters of the highway tunnel.

[0016] In one example, controlling the environmental equipment of the highway tunnel according to the reference environmental parameter value and the second environmental parameter value distribution specifically includes: generating a parameter difference distribution between the reference environmental parameter value and the second environmental parameter value distribution; matching the parameter differences in the parameter difference distribution according to a mapping relationship between environmental equipment parameters and changes in environmental parameters to obtain an equipment parameter value distribution; and controlling the environmental equipment according to the equipment parameter value distribution.

[0017] On the other hand, an embodiment of the present application provides a highway tunnel environment monitoring device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a highway tunnel environment monitoring method described in any one of the above items.

[0018] On the other hand, an embodiment of the present application provides a non-volatile computer storage medium for monitoring the environment of a highway tunnel, storing computer executable instructions, wherein the computer executable instructions can execute any of the above-mentioned methods for monitoring the environment of a highway tunnel.

[0019] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0020] By obtaining the meteorological data sequence in the future and combining it with the coupling relationship prediction model, the first environmental parameter value distribution of the corresponding internal environment of the highway tunnel type as the meteorological conditions change in the future can be predicted, and the changing relationship between the internal environment and the meteorological conditions can be obtained. The second environmental parameter value distribution of the internal environment of the highway tunnel itself as the meteorological conditions change in the future can be obtained by continuing to combine the actual environmental parameter value. The most suitable reference environmental parameter value distribution of the highway tunnel every day can be understood by continuing to combine the season in which the highway tunnel is located. Therefore, using the second environmental parameter value distribution as a variable, it is possible to obtain in advance how to control and adjust the environmental equipment so that the actual environmental parameter value inside the highway tunnel in the future can be close to the reference environmental parameter value. Finally, the environmental equipment control of the tunnel can be optimized in advance, and the environmental equipment is no longer controlled by the data of the sensor equipment as a reference variable, which can improve the accuracy of environmental monitoring of the highway tunnel and ultimately enhance the safety and comfort of the highway tunnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solution of the present application, some embodiments of the present application will be described in detail below in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A schematic diagram of a process for monitoring a highway tunnel environment provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram of the structure of a highway tunnel environment monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. 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 the field without creative work are within the scope of protection of the present application.

[0025] Some embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0026] Figure 1 A schematic diagram of a process flow of a highway tunnel environment monitoring method provided in an embodiment of the present application. The method can be applied to different business fields, and some input parameters or intermediate results in the process allow manual intervention and adjustment to help improve accuracy.

[0027] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For the convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.

[0028] It should be noted that the server may be a single device or a system consisting of multiple devices, that is, a distributed server, and this application does not make any specific limitation on this.

[0029] Figure 1 The process in includes the following steps:

[0030] S101: Obtain a meteorological data sequence of the area where the highway tunnel is located within a preset time period in the future; the meteorological data sequence includes meteorological data corresponding to multiple first time points arranged in chronological order, the first first time point is the current time point, and the meteorological data includes temperature, humidity, sunshine, precipitation and visibility.

[0031] In some embodiments of the present application, a highway tunnel is more susceptible to the influence of external meteorological conditions because a highway tunnel is usually an open system with openings at both ends and is easily affected by external airflow.

[0032] Therefore, by obtaining weather data for the area in which you are located through a weather station or weather forecast service, the future preset time period can be set according to actual needs. For example, the future preset time period refers to the next 7 days.

[0033] It should be noted that a meteorological data sequence refers to a series of meteorological data arranged in chronological order, which usually include the changes in meteorological elements such as temperature, humidity, wind speed, sunshine, precipitation and visibility in the future. That is, the meteorological data sequence includes meteorological data corresponding to multiple first time points arranged in chronological order, and the first first time point is the current time point, that is, the meteorological data sequence starts with the meteorological data at the current time point.

[0034] For example, the interval between adjacent first time points is 1 hour. The meteorological data at 10:00 noon on day B of month A includes temperature 1, humidity 2, sunshine 3, precipitation 4 and visibility 5. The meteorological data at 11:00 noon on day B of month A includes temperature 6, humidity 7, sunshine 8, precipitation 9 and visibility 12.

[0035] It can be understood that through the meteorological data series, it is possible to understand the meteorological conditions of the area in a specific time period in the future.

[0036] S102: Retrieve a coupling relationship prediction model corresponding to the highway tunnel type in a coupling relationship model library.

[0037] In some embodiments of the present application, since there is a mutual influence and interaction between meteorological conditions and the internal environment of the tunnel, the internal environment of the tunnel will be affected by the meteorological conditions.

[0038] In addition, one environmental parameter may be affected by multiple meteorological data of meteorological conditions at the same time, and the difference is that the degree of influence of each meteorological data is different.

[0039] For example, the external temperature is a factor that directly affects the temperature inside the tunnel. If the external temperature is high, the temperature inside the tunnel will also rise accordingly, and vice versa. However, in the case of sunshine, solar radiation will directly act on the tunnel surface and enter the tunnel, which will also increase the temperature inside the tunnel.

[0040] For example, the change in visibility between day and night has a particularly significant impact on the light intensity inside the tunnel.

[0041] For example, changes in wind speed will affect the speed and direction of air flow, and thus affect the ventilation conditions inside the tunnel. If the outside wind speed is high, it may increase the air flow speed inside the tunnel, change the air distribution, and even affect parameters such as air pressure and temperature in the tunnel.

[0042] For example, external precipitation is a factor that directly affects the humidity of the road inside the tunnel. However, when the external humidity is high, the humidity of the air inside the tunnel will also increase accordingly, causing the road surface inside the tunnel to become more slippery.

[0043] In summary, meteorological factors such as temperature, humidity, sunshine, precipitation and visibility will affect the temperature, road humidity and light intensity inside the tunnel, and then affect the ventilation, temperature and lighting control equipment inside the tunnel.

[0044] Furthermore, different types of highway tunnels may be affected by meteorological conditions to different degrees due to their different structures, designs, and geographical locations. For example, highway tunnel types are divided from the urban dimension, including urban area types and non-urban area types. On this basis, they are further divided from the length dimension, including long tunnel types and short tunnel types. For example, highway tunnel types are non-urban area and long tunnel types.

[0045] Among them, from the urban dimension, highway tunnels located in mountains or highlands may be more susceptible to meteorological conditions because they may be exposed to stronger wind speeds, rain, snow or other severe weather conditions. In this case, strong winds may increase ventilation needs, and rain and snow may cause the inside of the tunnel to be cold and the road to be slippery, affecting driving safety. On the other hand, highway tunnels in urban areas are more likely to be artificially designed and controlled, and they may be affected by the urban environment, such as surrounding buildings, traffic flow, etc., rather than directly by meteorological conditions. However, changes in meteorological conditions may still affect the ventilation, internal coldness and driving safety of tunnels in urban areas.

[0046] In addition, for the same type of highway tunnel, in terms of length, long tunnels are usually longer than short tunnels, so there may be more complex ventilation systems and airflow conditions to ensure air circulation and temperature and humidity control, which may cause the meteorological conditions in long tunnels to change more slowly. In contrast, short tunnels may not require such a complex ventilation system and have strong circulation with the outside environment. Therefore, the internal environment of the tunnel may be more susceptible to external meteorological conditions.

[0047] Based on this, different coupling relationship prediction models are constructed for different types of highway tunnels. By considering the coupling relationship between the meteorological data series and the internal environment of the tunnel, the impact of meteorological conditions on the internal environment of the tunnel can be obtained more accurately.

[0048] In some embodiments of the present application, taking the highway tunnel type in S101 as an example, the process of constructing a coupling relationship prediction model for the highway tunnel type is as follows:

[0049] Firstly, a sample meteorological data sequence of a sample highway tunnel within a preset time period and a sample environmental parameter value distribution of the sample highway tunnel within a preset time period are obtained.

[0050] The sample environmental parameter value distribution refers to the numerical distribution of the environmental parameters inside the tunnel at different time points, which can be obtained by setting sensors inside the tunnel for regular sampling and analysis. For example, since the time interval of meteorological data broadcast is usually 1 hour, sampling is performed every 1 hour. That is, the sample environmental parameter value distribution includes the environmental parameter values ​​at different time points.

[0051] It should be noted that the interval between adjacent time points of regular sampling needs to be an integer multiple of the interval between adjacent time points of sample meteorological data.

[0052] In addition, the average value of the environmental parameter values ​​at different locations inside the tunnel can be taken as the tunnel internal environmental parameter value. For example, the temperature at three locations at the tunnel entrance, middle and exit, and then the average value of the three temperatures is calculated as the temperature of the tunnel internal environment.

[0053] It should be noted that, within the preset time period, the equipment parameters corresponding to the environmental equipment of the sample highway tunnel have not changed. That is, the distribution of sample environmental parameter values ​​can reflect the situation that the sample environmental parameter values ​​change with the sample meteorological data sequence.

[0054] Then, the sample meteorological data sequence is used as the input sample, and the sample environmental parameter value distribution is used as the sample label. The initial neural network model is trained in a supervised manner to obtain the coupling relationship prediction model.

[0055] The architecture of the initial neural network model can be a variety of different structures, including fully connected neural networks, convolutional neural networks, recurrent neural networks, etc. The specific architecture to be selected depends on the characteristics of the data and the requirements of the task. For example, for the prediction task of meteorological data series and environmental parameter value distribution, you can consider using recurrent neural networks (RNN) or long short-term memory networks (LSTM) for modeling, because these structures can effectively process time series data and capture long-term dependencies in the data.

[0056] Specifically, the specific architecture can be as follows:

[0057] Input layer: accepts meteorological data sequences as input, for example, sequences consisting of data points such as temperature, humidity, sunshine, precipitation, and visibility.

[0058] RNN layer: includes multiple RNN units, which are used to process time series data and capture the temporal dependencies in the data. You can choose LSTM or GRU as the RNN unit to handle long-term dependencies.

[0059] Output layer: used to output the distribution of sample environment parameter values. A fully connected layer or other output layer structure suitable for the task can be used.

[0060] In this architecture, the input layer takes the meteorological data sequence as input samples, the RNN layer processes the sequence data and learns the time dependency, and the output layer predicts the distribution of environmental parameter values. Through supervised training, we can adjust the model parameters so that the model can learn and predict the distribution of environmental parameter values ​​from the meteorological data sequence.

[0061] S103: Input the meteorological data sequence into the coupling relationship prediction model to generate the first environmental parameter value distribution of the highway tunnel within the future preset time period; the environmental parameters include temperature, road humidity and light intensity; the first environmental parameter value distribution includes first environmental parameter values ​​corresponding to multiple second time points, the first second time point is the current time point; the interval between adjacent first time points is an integer multiple of the interval between adjacent second time points.

[0062] For example, the interval between adjacent first time points is the same as the interval between adjacent second time points, that is, the multiple is 1.

[0063] S104: Determine actual environmental parameter values ​​of the highway tunnel at the current time.

[0064] Among them, the actual environmental parameter values ​​can be obtained by setting sensors inside the tunnel.

[0065] S105: Generate a second environmental parameter value distribution of the highway tunnel within the future preset time period according to the first environmental parameter value distribution and the actual environmental parameter value.

[0066] In some embodiments of the present application, it can be considered that the coupling relationship prediction model can predict the environmental parameter value that conforms to the environmental equipment configuration information in the training sample. However, for the same type of highway tunnel, the environmental equipment configuration and configuration parameters of the two highway tunnels may be different, and the actual environmental parameter values ​​at the current time may also be different, but the coupling relationship between the meteorological conditions and the environmental parameter values ​​is roughly the same, that is, the environmental parameter values ​​are the same as the changes in meteorological conditions. Therefore, the coupling relationship is used as a benchmark to predict the second environmental parameter value.

[0067] Based on this, the parameter change differences between adjacent first environmental parameter values ​​are calculated in sequence from morning to night, and an environmental parameter difference sequence of the first environmental parameter value distribution is obtained. The environmental parameter difference sequence includes environmental parameter differences in different time intervals. For example, the second time point is 1 o'clock, 2 o'clock, 3 o'clock, and 4 o'clock. Then the time intervals are 1 o'clock to 2 o'clock, 2 o'clock to 3 o'clock, and 3 o'clock to 4 o'clock.

[0068] By traversing the environmental parameter difference sequence and continuously summing the actual environmental parameter values ​​multiple times, initial second environmental parameter values ​​corresponding to multiple second time points are obtained.

[0069] A second environmental parameter value distribution of the highway tunnel within a preset time period in the future is generated according to the initial second environmental parameter values ​​respectively corresponding to the plurality of second time points.

[0070] For example, taking temperature as an example, the first environmental parameter values ​​at the second time points a, b, c, and d are 8, 9, 10, and 7, and the environmental parameter difference sequence is: 1, 1, and -3. The actual environmental parameter value at the current time point is 12, that is, the current time point refers to time point a, and the initial second environmental parameter values ​​at the second time points a, b, c, and d are 12, 13, 14, and 11.

[0071] In some embodiments of the present application, considering that when the environmental equipment is in a stronger state, the adjustment ability is usually relatively high and the impact of climate is relatively small, at this time, the coupling relationship is relatively weak. On the contrary, when the environmental equipment is in a weaker state, the adjustment speed may be slow, or the adjustment may not meet the requirements, and it may be more affected by climate conditions. At this time, the adjustment ability of the environmental equipment continues to be verified in combination with the historical environmental parameter values.

[0072] Therefore, in the equipment configuration database, the standard environment equipment configuration information of the highway tunnel type is retrieved.

[0073] Then, the actual environment equipment configuration information is compared with the standard environment equipment configuration information to obtain the relative adjustment score of the environmental parameters of the highway tunnel. The relative adjustment score of the environmental parameters is used to indicate the actual degree of change of the environmental parameter value when the climate conditions change.

[0074] The process of obtaining the relative adjustment score of the environmental parameters of the highway tunnel is as follows:

[0075] First, determine the environmental equipment configuration information including the number of lighting equipment, the lighting intensity of the lighting equipment, the number of ventilation equipment, and the ventilation intensity of the ventilation equipment. Among them, the lighting equipment is related to the light intensity, and the ventilation equipment is related to the temperature and humidity.

[0076] According to the equipment type, the actual environment equipment configuration information is compared with the standard environment equipment configuration information to obtain the lighting relative adjustment score for the lighting equipment and the ventilation relative adjustment score for the ventilation equipment.

[0077] The relative adjustment score of the lighting and ventilation is weighted and summed to obtain the relative adjustment score of the environmental parameters of the highway tunnel.

[0078] On the one hand, if the relative adjustment score of the environmental parameter is higher than the preset score threshold, the initial second environmental parameter values ​​corresponding to multiple second time points are determined as the second environmental parameter value distribution of the highway tunnel within the future preset time period.

[0079] On the other hand, if the relative adjustment score of the environmental parameter is lower than or equal to the preset score threshold, the historical meteorological data sequence and the historical actual environmental parameter value distribution of the highway tunnel within the preset historical time period are obtained.

[0080] It should be noted that when the two are positively correlated, it can be verified that the climate conditions have a greater impact, that is, at this time, the gap between the meteorological data value and the initial second environmental parameter value at the same time point will not be too large.

[0081] Therefore, the positive correlation score between the historical meteorological data sequence and the historical actual environmental parameter value distribution is determined. When the positive correlation score is greater than a preset correlation threshold, the initial second environmental parameter values ​​at different future time points are adjusted so that the difference between each initial second environmental parameter value and the corresponding meteorological data value is less than the preset difference threshold.

[0082] Finally, the updated initial second environmental parameter value at the corresponding second time point and the unupdated initial second environmental parameter values ​​at other second time points are determined as the second environmental parameter value distribution of the highway tunnel within a future preset time period.

[0083] It should be noted that when the positive correlation score is less than or equal to the preset correlation threshold, the initial second environmental parameter values ​​corresponding to the multiple second time points are determined as the second environmental parameter value distribution of the highway tunnel within the future preset time period.

[0084] The process of determining the positive correlation score between the historical meteorological data series and the distribution of historical actual environmental parameter values ​​is as follows:

[0085] Pre-select an appropriate machine learning neural network model to establish a correlation prediction model between meteorological data and actual environmental parameter values. For example, consider using a recurrent neural network or a long short-term memory network for modeling, referring to the construction process of the coupling relationship prediction model.

[0086] Among them, the sample historical meteorological data sequence and the sample historical actual environmental parameter value distribution, as well as the positive correlation score between the two are obtained, the sample historical meteorological data sequence and the sample historical actual environmental parameter value distribution are used as input samples, and the positive correlation score is used as the sample label. The original network model is supervised trained to obtain the correlation prediction model.

[0087] Based on this, the historical meteorological data series and the distribution of historical actual environmental parameter values ​​are input into the correlation prediction model, and the output is a positive correlation score.

[0088] S106: Determine reference environmental parameter values ​​of the highway tunnel every day according to the season in which the highway tunnel is located.

[0089] It should be noted that with the change of seasons, the appropriate parameters of the tunnel's internal environment may need to be adjusted accordingly to ensure the comfort and safety of users. Different suitable environments need to be set in different seasons to adapt to the impact of seasonal changes on the tunnel's internal environment.

[0090] S107: Controlling environmental equipment of the highway tunnel according to the reference environmental parameter value and the second environmental parameter value distribution.

[0091] In some embodiments of the present application, the second environmental parameter value distribution can be used to predict how the internal environment of the tunnel will change with future external meteorological conditions. Therefore, the range of environmental equipment that needs to be adjusted can be known in advance, so that parameters of the environmental equipment can be adjusted in advance.

[0092] Based on this, a parameter difference distribution between the reference environmental parameter value and the second environmental parameter value distribution is generated.

[0093] Then, according to the mapping relationship between the environmental equipment parameters and the factors causing the environmental parameter changes, the parameter differences in the parameter difference distribution are matched to obtain the equipment parameter value distribution.

[0094] Finally, the environmental equipment is controlled according to the distribution of equipment parameter values.

[0095] It should be noted that although the embodiments of the present application are based on Figure 1 Steps S101 to S107 are described in sequence, but this does not mean that steps S101 to S107 must be performed in a strict order. Figure 1 The order shown in the figure is to introduce and explain step S101 to step S107 in sequence, in order to facilitate those skilled in the art to understand the technical solution of the embodiment of the present application. In other words, in the embodiment of the present application, the order between step S101 to step S107 can be appropriately adjusted according to actual needs.

[0096] pass Figure 1 By using the method, the meteorological data sequence in the future period is obtained, and combined with the coupling relationship prediction model, the first environmental parameter value distribution of the corresponding internal environment of the highway tunnel type can be predicted as the meteorological conditions change in the future, and the changing relationship between the internal environment and the meteorological conditions can be obtained. The actual environmental parameter value is further combined to obtain the second environmental parameter value distribution of the internal environment of the highway tunnel itself as the meteorological conditions change in the future. The season in which the highway tunnel is located is further combined to understand the most suitable reference environmental parameter value distribution of the highway tunnel every day, so that the second environmental parameter value distribution can be used as a variable to obtain in advance how to control and adjust the environmental equipment so that the actual environmental parameter value of the highway tunnel in the future period is close to the reference environmental parameter value. Finally, the environmental equipment control of the tunnel can be optimized in advance, and the environmental equipment is no longer controlled by the data of the sensor equipment as the reference variable, which can improve the accuracy of environmental monitoring of the highway tunnel and ultimately enhance the safety and comfort of the highway tunnel.

[0097] Based on the same idea, some embodiments of the present application also provide devices and non-volatile computer storage media corresponding to the above methods.

[0098] Figure 2A schematic diagram of a highway tunnel environment monitoring device provided in an embodiment of the present application includes:

[0099] at least one processor; and,

[0100] a memory communicatively connected to the at least one processor; wherein,

[0101] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform any of the above methods.

[0102] Some embodiments of the present application provide a non-volatile computer storage medium for monitoring a highway tunnel environment, storing computer executable instructions, wherein the computer executable instructions can execute any of the methods described above.

[0103] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0104] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0105] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0107] 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.

[0108] 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.

[0109] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0110] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0111] 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 tape 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.

[0112] 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.

[0113] The above is only an 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 changes and variations. Any modification, equivalent replacement, improvement, etc. made within the technical principle of the present application should all fall within the protection scope of the present application.

Claims

1. A method for monitoring the environment of a highway tunnel, characterized in that: The method comprises: Obtaining a meteorological data sequence of the area where the highway tunnel is located within a preset time period in the future; the meteorological data sequence includes meteorological data corresponding to a plurality of first time points arranged in chronological order, the first first time point being the current time point, and the meteorological data including temperature, humidity, sunshine, precipitation and visibility; In the coupling relationship model library, the coupling relationship prediction model corresponding to the highway tunnel type is retrieved; Input the meteorological data sequence into the coupling relationship prediction model to generate a first environmental parameter value distribution of the highway tunnel within the future preset time period; the environmental parameters include temperature, road humidity and light intensity; the first environmental parameter value distribution includes first environmental parameter values ​​corresponding to a plurality of second time points, the first second time point being the current time point; and the interval between adjacent first time points is an integer multiple of the interval between adjacent second time points; Determining actual environmental parameter values ​​of the highway tunnel at the current time; Generating a second environmental parameter value distribution of the highway tunnel within the future preset time period according to the first environmental parameter value distribution and the actual environmental parameter value; Determining the reference environmental parameter value distribution of the highway tunnel on a daily basis according to the season in which the highway tunnel is located; Controlling environmental equipment of the highway tunnel according to the reference environmental parameter value and the second environmental parameter value distribution; Before inputting the meteorological data sequence into the coupling relationship prediction model to generate the first environmental parameter value distribution of the highway tunnel within the future preset time period, the method further includes: Obtaining a sample meteorological data sequence of a sample highway tunnel within a preset time period, and a sample environmental parameter value distribution of the sample highway tunnel within the preset time period; within the preset time period, the equipment parameters corresponding to the environmental equipment of the sample highway tunnel have not changed; the type of the sample highway tunnel is the same as the highway tunnel type; Using the sample meteorological data sequence as an input sample and the sample environmental parameter value distribution as a sample label, the initial neural network model is supervisedly trained to obtain the coupling relationship prediction model; The step of generating, according to the first environmental parameter value distribution and the actual environmental parameter value, a second environmental parameter value distribution of the highway tunnel within the future preset time period specifically includes: Calculate the parameter change differences between adjacent first environmental parameter values ​​in order from early to late, and obtain an environmental parameter difference sequence of the first environmental parameter value distribution; the environmental parameter difference sequence includes environmental parameter differences in different time intervals; By traversing the environmental parameter difference sequence, the actual environmental parameter values ​​are continuously summed multiple times to obtain initial second environmental parameter values ​​corresponding to the multiple second time points respectively; Generate a distribution of second environmental parameter values ​​of the highway tunnel within the future preset time period according to the initial second environmental parameter values ​​respectively corresponding to the multiple second time points; The step of generating the second environmental parameter value distribution of the highway tunnel within the future preset time period according to the initial second environmental parameter values ​​respectively corresponding to the multiple second time points specifically includes: Retrieving standard environment equipment configuration information of the highway tunnel type from an equipment configuration database; Comparing the actual environment equipment configuration information of the highway tunnel with the standard environment equipment configuration information to obtain a relative adjustment score of the environment parameter of the highway tunnel; the relative adjustment score of the environment parameter is used to indicate the actual degree of change of the environment parameter value when the climate conditions change; If the relative adjustment score of the environmental parameter is higher than the preset score threshold, the initial second environmental parameter values ​​corresponding to the plurality of second time points are determined as the second environmental parameter value distribution of the highway tunnel within the future preset time period; The method further comprises: If the relative adjustment score of the environmental parameter is lower than or equal to the preset score threshold, then obtaining the historical meteorological data sequence and the historical actual environmental parameter value distribution of the highway tunnel within a preset historical time period; Determining a positive correlation score between the historical meteorological data sequence and the historical actual environmental parameter value distribution; When the positive correlation score is greater than a preset correlation threshold, the initial second environmental parameter value at the corresponding second time point is updated so that the difference between each initial second environmental parameter value and the corresponding meteorological data value is less than a preset difference threshold; The updated initial second environmental parameter value at the corresponding second time point and the unupdated initial second environmental parameter values ​​at other second time points are determined as the second environmental parameter value distribution of the highway tunnel within the future preset time period.

2. The method according to claim 1, characterized in that Determining the positive correlation score between the historical meteorological data sequence and the historical actual environmental parameter value distribution specifically includes: Identify pre-built correlation prediction models; The historical meteorological data sequence and the historical actual environmental parameter value distribution are input into the correlation prediction model to obtain the positive correlation score.

3. The method according to claim 1, characterized in that Comparing the actual environment equipment configuration information of the highway tunnel with the standard environment equipment configuration information to obtain the relative adjustment score of the environmental parameters of the highway tunnel, specifically includes: Determine the environmental equipment configuration information including the number of lighting equipment, the lighting intensity of the lighting equipment, the number of ventilation equipment, and the ventilation intensity of the ventilation equipment; lighting equipment is related to light intensity, and ventilation equipment is related to temperature and humidity; According to the equipment type, the actual environment equipment configuration information is compared with the standard environment equipment configuration information to obtain a lighting relative adjustment score for the lighting equipment and a ventilation relative adjustment score for the ventilation equipment; The lighting relative adjustment score and the ventilation relative adjustment score are weightedly summed to obtain the environmental parameter relative adjustment score of the highway tunnel.

4. The method according to claim 1, characterized in that: The controlling of the environmental equipment of the highway tunnel according to the reference environmental parameter value and the second environmental parameter value distribution specifically includes: generating a parameter difference distribution between the reference environmental parameter value and the second environmental parameter value distribution; According to the mapping relationship between the environmental equipment parameters and the changes that cause the environmental parameters, the parameter differences in the parameter difference distribution are matched to obtain the equipment parameter value distribution; The environmental equipment is controlled according to the equipment parameter value distribution.

5. A road tunnel environment monitoring device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the highway tunnel environment monitoring method described in any one of claims 1 to 4.

6. A non-volatile computer storage medium for monitoring the environment of a highway tunnel, storing computer executable instructions, characterized in that: The computer executable instructions can execute a highway tunnel environment monitoring method as described in any one of claims 1 to 4 above.

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