CN117909667B - Internet of things acquisition method and acquisition device suitable for complex environment - Google Patents

Abstract

The invention relates to the technical field of Internet, in particular to an Internet of things acquisition method and an Internet of things acquisition device suitable for a complex environment. Upon detecting an abnormality in an environmental parameter, entering an emergency second environment; under the condition, the priority and the ventilation equipment power are adjusted according to the severity degree and the emergency degree of the abnormality, so that the key parameters are ensured to be acquired accurately in real time; determining an acquisition period of the environmental parameters through analyzing the change speed and the instantaneity of the parameters, further formulating an acquisition strategy, and preprocessing and analyzing the environmental parameters to optimize the result; based on these results, the system will dynamically adjust the time interval and priority of the acquisition strategy to accommodate the sustained changes in the tunnel. The invention can improve the efficiency and accuracy of environmental parameter acquisition by dynamically adjusting the acquisition strategy.

Description

Internet of things acquisition method and acquisition device suitable for complex environment

Technical Field

The invention relates to the technical field of the Internet of things, in particular to an Internet of things acquisition method and an acquisition device suitable for a complex environment.

Background

In the aspect of data acquisition of the Internet of things equipment, the principle mainly comprises three aspects of a sensor, communication and a cloud platform. The sensor is responsible for sensing and collecting environmental parameters, data are transmitted to the upper-layer equipment or the cloud platform in a communication mode, and the cloud platform is used for storing, processing and analyzing the data. The data acquisition method is various, including direct acquisition, indirect acquisition, batch acquisition, event trigger acquisition, compression acquisition and the like, and the selection of a proper method according to different requirements and application scenes is very important.

Patent document with publication number CN115014444A discloses a data acquisition method based on the Internet of things. The method comprises the following steps: installing various sensors and video acquisition devices according to the layout of rooms and personal habits, and connecting the sensors and the video acquisition devices with a processor and corresponding intelligent furniture in a wireless or wired mode; according to the overall shape layout of the house, at least three sensors are arranged outdoors, and the sensors and a processor are connected together in a wireless or wired mode; the processor controls the corresponding device according to the preset parameters, stores the use records and the parameters of each device, and transmits the use records and the stored parameters to the mobile terminal of the user.

Therefore, the data acquisition method based on the Internet of things has the following problems: the wireless connection may be interfered by signals, and the burden of the mobile device is increased when a large amount of data is transmitted to the mobile terminal of the user, so that the problems of low data collection efficiency and low accuracy occur.

Disclosure of Invention

Therefore, the invention provides an Internet of things acquisition method and an Internet of things acquisition device suitable for a complex environment, which are used for solving the problems of low data acquisition efficiency and low data acquisition accuracy in the prior art.

In order to achieve the above object, an aspect of the present invention provides an internet of things acquisition method adapted to a complex environment, including:

Acquiring environmental parameters of a tunnel in a first environment under preset output power of ventilation equipment;

Acquiring initial priorities of all the environmental parameters in the first environment, and determining acquisition sequences and/or transmission sequences of the environmental parameters according to the initial priorities;

When any environmental parameter in the first environment is abnormal and enters a second environment, the initial priority is adjusted according to the emergency degree of the second environment and the abnormal parameter of the second environment to obtain a target priority, and the preset output power is adjusted according to the real-time environmental parameter of the second environment to obtain a target output power;

Acquiring environmental parameters in the second environment according to the target priority and the target output power to obtain the change speed and instantaneity of the environmental parameters of the second environment;

Determining the acquisition period of each environmental parameter according to the change speed of the environmental parameter and the instantaneity;

formulating an acquisition strategy according to the target priority and the acquisition period;

Preprocessing and analyzing according to the acquisition strategy to obtain a processing result;

and dynamically adjusting the time interval and the priority of the acquisition strategy according to the processing result.

Further, acquiring the environmental parameters of the tunnel in the first environment of the ventilation device under the preset output power includes:

installing a state sensor on the ventilation equipment to detect the output power of the ventilation equipment as preset output power;

the environmental parameters include temperature, humidity, illumination and air quality;

deploying temperature sensors at different positions along the length of the tunnel to obtain the temperature distribution of the whole tunnel;

Installing humidity sensors at a plurality of locations of the tunnel to monitor humidity levels throughout the tunnel;

Installing a photosensitive sensor to monitor the illumination intensity in the tunnel;

Gas sensors are deployed to monitor the air quality within the tunnel.

Further, adjusting the initial priority according to the emergency degree of the second environment and the abnormal parameter of the second environment to obtain a target priority includes:

Analyzing the environmental parameters of the first environment, and identifying the environmental parameters deviating from the first environment so as to detect abnormal values of the abnormal parameters by adopting a standard deviation method;

establishing an emergency degree assessment model, and assessing the emergency degree according to the number and the severity degree of the abnormal parameters;

And adjusting the initial priority according to the emergency degree to obtain a target priority.

Further, adjusting the preset output power according to the real-time environmental parameter of the second environment to obtain the target output power includes:

continuously monitoring real-time environmental parameters of the second environment;

analyzing real-time environmental parameters of the second environment, and determining the power required by the current ventilation equipment;

and adjusting the preset output power of the ventilation equipment according to the power required by the current ventilation equipment to obtain the target output power.

Further, determining the acquisition period of each environmental parameter according to the change speed and the real-time property of the environmental parameter includes:

analyzing the collected environmental parameters, and determining the change speed of each environmental parameter;

The acquisition period comprises a first acquisition time length T1, a second acquisition time length T2 and a third acquisition time length T3, and T1 is less than T2 and less than T3;

Calculating a rate of change of the actual environmental parameter and an average rate of the historical environmental parameter, wherein

If the change rate of the actual environmental parameter is higher than the average rate of the historical environmental parameter, the environmental parameter is considered to change rapidly;

If the change rate of the actual environmental parameter is lower than the average rate of the historical environmental parameter, the environmental parameter is considered to be slowly changed;

For the environmental parameters with high change speed, adopting a first acquisition time length;

For the environmental parameters with slow change speed, adopting a second acquisition time length;

the third acquisition time length is adopted for the parameters with low real-time requirements and slow change of the environmental parameters;

for environmental parameters with high real-time requirements, the first acquisition time length should be adopted even if the change speed of the environmental parameters is not fast.

Further, for the environmental parameters, the real-time performance of the environmental parameters comprises an immediate response layer, a short-term dynamic adjustment layer and a medium-term and long-term trend monitoring layer according to the hierarchy; wherein,

And setting an acquisition time threshold T4 for the environmental parameters of the immediate response layer, wherein the real-time performance is high, setting an acquisition time threshold T5 for the environmental parameters of the short-term dynamic adjustment layer, setting a acquisition time threshold T6 for the environmental parameters of the medium-term and long-term trend monitoring layer, and setting the acquisition time threshold T6 with low real-time performance, wherein T4 is less than T5 is less than T6.

Further, formulating an acquisition strategy according to the target priority and the acquisition period includes:

for the environmental parameters with the highest priority, the acquisition time length is set to be second level, and high-frequency acquisition is adopted;

For the environment parameters with the next highest priority, the acquisition time length is set to be in the order of minutes, and intermittent acquisition is adopted;

for the environment parameters with medium priority, the acquisition time length is set to be an hour level, and timing acquisition is adopted;

For the lowest priority environmental parameters, the acquisition time length is set to be once a day, and low-frequency acquisition is adopted.

Further, preprocessing and analyzing according to the acquisition strategy to obtain a processing result comprises:

the method comprises the steps of preprocessing filtering, denoising and analyzing the acquired original environment parameters;

integrating the environmental parameters acquired by different devices and sensors to form a unified environmental parameter format;

And carrying out trend analysis on the integrated environmental parameters to obtain environmental parameter processing results.

Further, dynamically adjusting the time interval and the priority of the acquisition strategy according to the processing result includes:

Monitoring environmental parameters in real time, and evaluating the importance degree of the environmental parameters;

For the environmental parameters with high environmental parameter change speed and high real-time requirement, the time interval is shortened, and the acquisition frequency is increased;

for the environment parameters with low change speed and low real-time requirement, the time interval is prolonged, and the acquisition frequency is reduced;

For the environmental parameters with high real-time performance and importance, the priority is improved;

for environment parameters with high real-time performance and no importance, the priority is unchanged;

For environment parameters with low real-time performance and no importance, the priority is reduced;

And establishing a feedback control mechanism and monitoring the adjusted effect.

In another aspect, the present invention further provides an internet of things collection device adapted to a complex environment, including:

the environment parameter acquisition module is used for acquiring the environment parameters of the tunnel in a first environment under the preset output power of the ventilation equipment;

The priority initialization module is used for acquiring initial priorities of the environment parameters in the first environment so as to determine acquisition sequences and/or transmission sequences of the environment parameters according to the initial priorities;

The abnormality processing and priority adjusting module is used for adjusting the initial priority according to the emergency degree of the second environment and the abnormal parameters of the second environment when any environment parameter in the first environment is abnormal and enters the second environment to obtain a target priority, and adjusting preset output power according to the real-time environment parameters of the second environment to obtain a target output power;

The data acquisition module acquires the environmental parameters in the second environment according to the target priority and the target output power so as to obtain the change speed and instantaneity of the environmental parameters of the second environment;

the acquisition time length determining module is used for determining an acquisition period according to the change speed of the environmental parameter and the instantaneity;

The acquisition strategy making module makes an acquisition strategy according to the target priority and the acquisition period;

The data preprocessing and analyzing module is used for preprocessing and analyzing according to the acquisition strategy to obtain a processing result;

and the strategy dynamic adjustment module is used for dynamically adjusting the time interval and the priority of the acquisition strategy according to the processing result.

Compared with the prior art, the invention has the beneficial effects that the priority is dynamically adjusted according to the importance and the emergency degree of the parameters, so that the most effective allocation and use of the limited resources are ensured. According to the change speed of the environmental parameters and the real-time requirement, the acquisition time length is adjusted, so that the data can reflect the real-time change, and resources are not wasted due to too frequent acquisition. The system can automatically adjust the acquisition strategy by analyzing the processing result in real time, and adapt to the dynamic property of environmental change. The optimization of the preprocessing and analysis links can carry out targeted processing according to the characteristics of the acquired data, so that invalid data processing is reduced, and the processing efficiency is improved. For the parameters with high emergency degree and high change speed, even if the time interval is shorter, timely acquisition can be ensured, and key data omission is avoided. By continuously monitoring and adjusting the acquisition strategy, the system can maintain the optimal working state, and the reliability and stability of long-term operation are improved.

In particular, environmental conditions in the tunnel are comprehensively known through deployment of temperature, humidity, illumination and air quality sensors, and the environmental parameters in the tunnel are ensured to be in ideal states. The state sensor detects the change of the output power of the ventilation equipment in advance, so that the potential failure of the equipment is predicted, preventive maintenance is carried out, and the unexpected downtime is reduced. Through temperature, humidity and air quality in the real-time supervision tunnel, can adjust ventilation system's running mode, realize the optimal use of energy, reduce operation cost.

In particular, the anomaly parameters can be accurately identified by the standard deviation method, which helps to quickly locate the root cause of the problem. And analyzing the environmental parameters of the first environment, so that the current state of the environment can be comprehensively known, and the parameters deviating from the normal range can be timely found. And (3) establishing an emergency degree assessment model, combining the number and the severity of the abnormal parameters, and quantitatively assessing the emergency degree. Dynamic adjustment of priorities based on the degree of urgency ensures that channel resources can be efficiently allocated to the most urgent and critical issues.

In particular, by continuously monitoring real-time environmental parameters, the ventilation system is ensured to adjust power according to actual needs, thereby improving ventilation efficiency. The ventilation power is adjusted according to the actual environment demands, so that excessive ventilation and unnecessary energy waste are avoided, energy is saved, and carbon emission is reduced. The environmental parameters are monitored in real time, the ventilation power is adjusted, harmful gases are removed in time, the proper air quality is maintained, and the safety of tunnel users is guaranteed. By precisely adjusting the ventilation power, the operating costs are reduced, since high power operation need not be maintained for a long period of time.

In particular, for parameters that vary rapidly and have high real-time requirements, a fast response may prevent potential safety problems. For parameters with slow change speed, reducing the acquisition frequency can save energy and reduce equipment burden. The frequent collection of parameters with low real-time requirements is reduced, and the cost of data processing and transmission can be reduced. By setting the appropriate acquisition time length for different parameters, the overall performance and efficiency of the system can be improved.

In particular, the high priority of the immediate response layer ensures the immediate safety monitoring in the tunnel, and reduces the damage caused by accidents. The short-term dynamic adjustment layer has the beneficial effects that the short-term dynamic adjustment layer can flexibly adjust the acquisition frequency according to traffic flow, weather change and the like at the entrance of the tunnel so as to adapt to environmental change. The lower acquisition frequency of the medium-and-long-term trend monitoring layer helps to reduce long-term running costs while still being able to collect data critical to tunnel health.

In particular, for the highest priority environmental parameters, the adoption of second-level acquisition can ensure the latest nature of the environmental parameters. For the environment parameters with the next highest, medium and lowest priorities, different acquisition time intervals are adopted according to the importance and change speed, so that the resource use can be effectively optimized, unnecessary environment parameter transmission and processing are reduced, and meanwhile, the effectiveness of the environment parameters is ensured. By reasonably distributing the acquisition frequency, the system can better process the environmental parameters with different priorities, avoid delay caused by processing the environmental parameters with high priority, and improve the performance and response speed of the whole system.

Particularly, the original data is filtered and denoised in the preprocessing stage, so that the signal to noise ratio of the data can be remarkably improved, irrelevant interference information is removed, and key data characteristics are reserved. And integrating data from different devices and sensors, realizing unification of data formats, and facilitating cross-device and cross-platform data exchange and sharing. And trend analysis is carried out on the integrated data, so that the change rule of the data with time or other variables can be revealed, and a basis is provided for decision making.

In particular, by real-time monitoring and periodic evaluation, more accurate and efficient acquisition of environmental parameters can be ensured. For the environment parameters with high change speed and high real-time requirement, the environment change can be captured faster by shortening the time interval, and timely response to emergency is ensured. For the environment parameters with low change speed and low real-time requirement, the unnecessary environment parameter acquisition can be reduced by prolonging the time interval, and the system resources and the energy consumption are saved. The dynamic adjustment of the acquisition strategy is beneficial to the system to make more accurate decisions according to the actual environment demands and changes, so that the decision quality is improved. The priority and the time interval are adjusted in real time, so that the system can acquire key environment parameters at key moments and can respond to environment changes quickly.

Drawings

Fig. 1 is a schematic flow chart of an internet of things acquisition method adapted to a complex environment according to an embodiment of the present invention;

FIG. 2 is a flow chart of adjusting the initial priority according to the emergency degree of the second environment and the abnormal parameters of the second environment to obtain a target priority according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart of determining an acquisition period according to the change speed and the real-time performance of the environmental parameter in the embodiment of the invention;

fig. 4 is a schematic structural diagram of an internet of things acquisition device adapted to a complex environment according to an embodiment of the present invention.

Detailed Description

In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.

It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.

Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.

Referring to fig. 1, an internet of things acquisition method suitable for a complex environment provided by an embodiment of the present invention includes:

s100, acquiring environmental parameters of a tunnel in a first environment of ventilation equipment under preset output power;

S200, acquiring initial priorities of the environment parameters in the first environment, and determining acquisition sequences and/or transmission sequences of the environment parameters according to the initial priorities;

S300, when any environmental parameter in the first environment is abnormal and enters a second environment, adjusting the initial priority according to the emergency degree of the second environment and the abnormal parameter of the second environment to obtain a target priority, and adjusting preset output power according to the real-time environmental parameter of the second environment to obtain a target output power;

s400, acquiring environmental parameters in the second environment according to the target priority to obtain the change speed and instantaneity of the environmental parameters of the second environment;

s500, determining an acquisition period according to the change speed of the environmental parameter and the instantaneity;

s600, formulating an acquisition strategy according to the priority and the acquisition period;

s700, preprocessing and analyzing according to the acquisition strategy to obtain a processing result;

s800, dynamically adjusting the time interval and the priority of the acquisition strategy according to the processing result.

Specifically, the first environment refers to an environment of the tunnel in a normal operation state, that is, an environment condition when the ventilation device operates according to a preset output power. In this environment, the initial priority of the environmental parameters is set, and the acquisition order and transmission order are also determined. The second environment is sudden, such as an emergency situation of fire, gas leakage, equipment failure, etc., or a special operating state under a specific event (such as peak traffic hours).

Specifically, the system acquires initial environmental parameters (such as temperature, humidity, illumination, air quality and the like) of the tunnel through the sensor under the preset output power of the ventilation equipment. Based on these initial parameters, the system sets an initial priority, which may be preset by the system or dynamically generated by an algorithm. The system continuously monitors the environmental parameters, and once any parameter is detected to be out of the normal range, the system enters a second environment, which indicates that the environment is abnormal. The second environment is to indicate the operating conditions in the event of an abnormality, where more stringent monitoring and control of the environment is required. For abnormal situations, the system evaluates the urgency of the environment, which involves a comprehensive consideration of the number and severity of the abnormal parameters. And according to the evaluation result, the system adjusts the initial priority to reflect the new emergency degree and obtain the target priority. And simultaneously, according to the real-time environmental parameters of the second environment, adjusting the preset output power of the ventilation equipment to obtain the target output power. And according to the adjusted target priority and target output power, the system starts to acquire environmental parameters in the second environment, wherein the acquired environmental parameters comprise the change speed and instantaneity of the environmental parameters. Analyzing the collected environmental parameters and determining the change speed of each environmental parameter. And setting different preferential acquisition time lengths for different parameters according to the change speed and the real-time requirement. And according to the target priority and the priority acquisition time length, a specific acquisition strategy is formulated. The acquisition strategy comprises key parameters such as acquisition time interval, acquisition method and the like. And preprocessing and analyzing the acquired environmental parameters according to an acquisition strategy. The processing result is used for understanding and interpreting the environmental change and providing basis for subsequent decision. And dynamically adjusting the time interval, the priority and the acquisition method of the acquisition strategy according to the analysis result.

Specifically, the priority is dynamically adjusted according to the importance and urgency of the parameters, ensuring that limited resources are most efficiently allocated and used. According to the change speed and the real-time requirement of the environmental parameters, the acquisition time length is adjusted, so that the environmental parameters can reflect the real-time change, and resources are not wasted due to too frequent acquisition. The system can automatically adjust the acquisition strategy by analyzing the processing result in real time, and adapt to the dynamic property of environmental change. The optimization of the preprocessing and analysis links can carry out targeted processing according to the collected environmental parameter characteristics, so that invalid environmental parameter processing is reduced, and the processing efficiency is improved. For the parameters with high emergency degree and high change speed, even if the time interval is shorter, timely collection can be ensured, and the omission of key environmental parameters is avoided. By continuously monitoring and adjusting the acquisition strategy, the system can maintain the optimal working state, and the reliability and stability of long-term operation are improved.

Specifically, acquiring the environmental parameters of the tunnel in the first environment of the ventilation device at the preset output power includes:

installing a state sensor on the ventilation equipment to detect the output power of the ventilation equipment as preset output power;

the environmental parameters include temperature, humidity, illumination and air quality;

deploying temperature sensors at different positions along the length of the tunnel to obtain the temperature distribution of the whole tunnel;

Installing humidity sensors at a plurality of locations of the tunnel to monitor humidity levels throughout the tunnel;

Installing a photosensitive sensor to monitor the illumination intensity in the tunnel;

Gas sensors are deployed to monitor the air quality within the tunnel.

In particular, status sensors are mounted on the ventilation device, which sensors are able to detect the current output power of the ventilation device. Temperature sensors are installed at different positions of the entrance, the middle, the exit and the like of the tunnel. High temperature, moisture resistant sensors are used and ensure that they are able to withstand the environmental conditions within the tunnel. Humidity sensors are installed at various critical locations of the tunnel (e.g., tunnel doorways, tunnel middle sections, ventilation areas, drainage areas, etc.) to monitor humidity levels throughout the tunnel. Humidity sensors suitable for tunnels, such as dust-proof and water-proof sensors, are chosen. A light sensitive sensor is installed in the tunnel to monitor the intensity of illumination in the tunnel. Light-resistant, stable photosensitive sensors are used to accommodate different lighting conditions within the tunnel. A gas sensor is deployed within the tunnel to monitor the air quality within the tunnel. A sensor is selected that is capable of detecting a harmful gas (e.g., carbon monoxide, carbon dioxide, hydrogen sulfide, etc.). The temperature environmental parameters, the humidity environmental parameters, the illumination environmental parameters and the gas environmental parameters are transmitted to a central monitoring system through a wired or wireless network. The central monitoring system periodically receives environmental parameters from the sensors. The collected environmental parameters are analyzed to obtain real-time readings of temperature, humidity, illumination and air quality.

Specifically, by deploying temperature, humidity, illumination and air quality sensors, the environment conditions in the tunnel can be comprehensively known, and the environment parameters in the tunnel are ensured to be in ideal states. The state sensor can detect the change of the output power of the ventilation equipment in advance, so that the potential failure of the equipment is predicted, preventive maintenance is carried out, and the unexpected downtime is reduced. Through temperature, humidity and air quality in the real-time supervision tunnel, can adjust ventilation system's running mode, realize the optimal use of energy, reduce operation cost.

Specifically, as shown in fig. 2, adjusting the initial priority according to the emergency degree of the second environment and the abnormal parameter of the second environment to obtain the target priority includes:

s301, analyzing the environmental parameters of the first environment, and identifying the environmental parameters deviating from the first environment so as to detect abnormal values of the abnormal parameters by adopting a standard deviation method;

s302, establishing an emergency degree assessment model, and assessing the emergency degree according to the number and the severity degree of the abnormal parameters;

s303, adjusting the initial priority according to the emergency degree to obtain a target priority.

Specifically, the standard deviation method is used to analyze the environmental parameter distribution of each environmental parameter and determine the normal range of the parameter. The standard deviation of each parameter is calculated, and a threshold (e.g., a multiple of the standard deviation) is set, and parameter values exceeding the threshold are regarded as outliers. Analyzing the collected environmental parameters of the first environment, and comparing the environmental parameters with preset environmental parameter standards or historical environmental parameters. Parameters deviating from the normal range are identified, which parameters indicate that an environment is abnormal. And establishing an emergency degree assessment model by combining the number and the severity of the abnormal parameters. The model may evaluate the degree of urgency based on logical operations (e.g., AND, OR) OR using machine learning algorithms. For example, if a plurality of key parameters are abnormal at the same time or the degree of abnormality of one parameter is high, the evaluation result indicates that the degree of urgency is high. The detected abnormal parameters and their emergency level evaluation results are used to adjust the initial priority. The higher the degree of anomaly or the greater the number of anomaly parameters, the higher the priority should be after adjustment to indicate a faster or more urgent response is required. And determining the target priority of each environmental parameter according to the adjusted priority.

In particular, the anomaly parameters can be accurately identified by the standard deviation method, which helps to quickly locate the root cause of the problem. And analyzing the environmental parameters of the first environment, so that the current state of the environment can be comprehensively known, and the parameters deviating from the normal range can be timely found. By establishing an emergency degree assessment model, the number and the severity degree of the abnormal parameters can be combined, and the emergency degree can be quantitatively assessed. Dynamic adjustment of priorities based on the degree of urgency ensures that resources can be efficiently allocated to the most urgent and critical questions.

Specifically, adjusting the preset output power according to the real-time environmental parameter of the second environment to obtain the target output power includes:

continuously monitoring real-time environmental parameters of the second environment;

analyzing real-time environmental parameters of the second environment, and determining the power required by the current ventilation equipment;

and adjusting the preset output power of the ventilation equipment according to the power required by the current ventilation equipment to obtain the target output power.

In particular, environmental parameters such as temperature, humidity, light and air quality are continuously monitored using sensors deployed within the tunnel. Ensuring that the sensor is able to transmit environmental parameters in real time for timely adjustments. The collected real-time environmental parameters are analyzed to determine the amount of power required by the current ventilation device. For example, if the temperature and humidity exceed preset safety thresholds, the power of the ventilation device needs to be increased. And according to the analysis result, combining a preset safety standard and an environment parameter target value, and calculating the power required by the current ventilation equipment. The required power is determined taking into account the maximum capacity and energy consumption efficiency of the ventilation device. And adjusting the preset output power of the ventilation equipment according to the calculated required power. The ventilation device control system integrates an environmental monitoring sensor and an algorithm, and can automatically receive real-time environmental parameters. An algorithm in the control system calculates the required ventilation power based on real-time environmental parameters such as temperature, humidity, etc. The control system directly adjusts the motor speed or fan blade angle of the ventilation device to vary the output power of the ventilation device. After adjustment, the ventilation device will operate at the new preset output power to meet the requirements of the second environment.

Specifically, by continuously monitoring real-time environmental parameters, the ventilation system can be ensured to adjust power according to actual needs, thereby improving ventilation efficiency. By adjusting the ventilation power according to the actual environmental requirements, excessive ventilation and unnecessary energy waste can be avoided, thereby saving energy and reducing carbon emission. The environmental parameters are monitored in real time, the ventilation power is adjusted, harmful gases are removed in time, the proper air quality is maintained, and the safety of tunnel users is guaranteed. By precisely adjusting the ventilation power, the operating costs can be reduced, since there is no need to maintain high power operation for a long time.

Specifically, as shown in fig. 3, determining the acquisition period of each environmental parameter according to the change speed and the real-time property of the environmental parameter includes:

S501, analyzing the collected environmental parameters, and determining the change speed of each environmental parameter;

S502, the acquisition period comprises a first acquisition time length T1, a second acquisition time length T2 and a third acquisition time length T3, and T1 is less than T2 is less than T3;

S503 calculates a rate of change of the actual environmental parameter and an average rate of the historical environmental parameter, wherein,

If the change rate of the actual environmental parameter is higher than the average rate of the historical environmental parameter, the environmental parameter is considered to change rapidly;

If the change rate of the actual environmental parameter is lower than the average rate of the historical environmental parameter, the environmental parameter is considered to be slowly changed;

S504, for the environment parameters with high change speed, adopting a first acquisition time length;

S505, for the environment parameters with slow change speed, adopting a second acquisition time length;

S506, adopting a third acquisition time length for the parameters with low real-time requirements and slow change of the environmental parameters;

s507, for the environment parameter with high real-time requirement, the first acquisition time length is adopted even if the change speed of the environment parameter is not fast.

Specifically, historical environmental parameters of various environmental parameters within the tunnel are collected, including temperature, humidity, illumination, air quality, and the like. The time sequence analysis method is used for analyzing the change trend of each environmental parameter along with time, and the frequency spectrum analysis is performed to determine the fluctuation frequency and amplitude of each environmental parameter. For each environmental parameter, the rate of change of each parameter is determined by comparing the difference between two consecutive acquisitions. A sliding window algorithm is used to monitor the change in the parameter over the last period of time (e.g., 1 hour, 1 day, or1 week) and calculate the average rate of change. If the current rate of change is higher than the historical average, it may be considered a rapid change, and for parameters with a rapid rate of change, such as a sudden rise in temperature, a first acquisition time period T1 is employed to ensure a rapid response. For parameters with slow change speed, such as humidity fluctuation, the second acquisition time length T2 is adopted to reduce the acquisition frequency. For parameters with high real-time requirements, even if the change speed is not fast, the first acquisition time length T1 is adopted to ensure that the environmental parameters are updated in time. For parameters with low real-time requirements and slow change speed, the third acquisition time length T3 is adopted to reduce the cost of processing and transmitting the environmental parameters.

In particular, for parameters that vary rapidly and have high real-time requirements, a fast response may prevent potential safety problems. For parameters with slow change speed, reducing the acquisition frequency can save energy and reduce equipment burden. The frequent collection of parameters with low real-time requirements is reduced, and the cost of processing and transmitting the environmental parameters can be reduced. By setting the appropriate acquisition time length for different parameters, the overall performance and efficiency of the system can be improved.

Specifically, for the environmental parameters, the real-time performance of the environmental parameters comprises an immediate response layer, a short-term dynamic adjustment layer and a medium-term and long-term trend monitoring layer according to the hierarchy; wherein,

And setting an acquisition time threshold T4 for the environmental parameters of the immediate response layer, wherein the real-time performance is high, setting an acquisition time threshold T5 for the environmental parameters of the short-term dynamic adjustment layer, setting a acquisition time threshold T6 for the environmental parameters of the medium-term and long-term trend monitoring layer, and setting the acquisition time threshold T6 with low real-time performance, wherein T4 is less than T5 is less than T6.

Specifically, the acquisition time range threshold T4 is set to 10 seconds to 40 seconds for the environmental parameter at the immediate response layer for emergency situations occurring in the tunnel, such as fire, gas leakage, and the like. This means that the monitoring system needs to collect data over this time frame in order to immediately sound an alarm and take emergency action. And setting an acquisition time range threshold T5 for parameters such as air quality, temperature, humidity and the like in the tunnel by the short-term dynamic adjustment layer to be 2-5 minutes. The variation of these parameters has a large impact on comfort and safety in the tunnel, requiring timely adjustment. And setting an acquisition time range threshold T6 for the medium-long term trend monitoring layer to be 1 to 3 hours for the integrity of the tunnel structure, the monitoring of the underground water level and the like. Changes in these parameters require long-term observation and analysis to assess the long-term stability and health of the tunnel.

Specifically, the immediate response layer is provided with a sensor to monitor parameters such as smoke, temperature, vehicle collision and the like in the tunnel in real time. The environmental parameters are processed locally and instantaneously using edge computing techniques and an alarm is triggered. The short-term dynamic adjustment layer dynamically adjusts the monitoring frequency according to traffic flow, weather change and other information. In special cases, such as peak hours or bad weather, the monitoring times are increased to ensure the safety and smoothness in the tunnel. The medium-and-long-term trend monitoring layer collects and stores long-term tunnel environmental parameters such as structural integrity, ventilation efficiency, and the like. Environmental parameter analysis and prediction algorithms are used to analyze trends, discover potential problems ahead of time and plan maintenance.

Specifically, the high priority of the immediate response layer ensures the immediate safety monitoring in the tunnel, and reduces the damage caused by accidents. The short-term dynamic adjustment layer has the beneficial effects that the short-term dynamic adjustment layer can flexibly adjust the acquisition frequency according to traffic flow, weather change and the like at the entrance of the tunnel so as to adapt to environmental change. The lower acquisition frequency of the medium-and-long-term trend monitoring layer helps to reduce long-term running costs while still being able to collect environmental parameters critical to tunnel health.

Specifically, formulating an acquisition strategy according to the target priority and the acquisition period includes:

for the environmental parameters with the highest priority, the acquisition time length is set to be second level, and high-frequency acquisition is adopted;

For the environment parameters with the next highest priority, the acquisition time length is set to be in the order of minutes, and intermittent acquisition is adopted;

for the environment parameters with medium priority, the acquisition time length is set to be an hour level, and timing acquisition is adopted;

For the lowest priority environmental parameters, the acquisition time length is set to be once a day, and low-frequency acquisition is adopted.

Specifically, an event trigger based acquisition mechanism is used for the highest priority environmental parameters. For example, the environmental parameters are collected every few seconds to ensure real-time and accuracy of the environmental parameters. The environmental parameters for the next highest priority may be set to be collected once every certain number of minutes, such as 1, 5 or 10 minutes. Ambient parameter acquisition is performed at a fixed point in time (e.g., every hour or every two hours) for medium priority ambient parameter settings. The environmental parameter acquisition is performed at a fixed point in time every day for the lowest priority environmental parameter or triggered when the environmental parameter sender has a new environmental parameter.

In particular, for the highest priority environmental parameters, the adoption of second-level acquisition can ensure the latest nature of the environmental parameters. For the environment parameters with the next highest, medium and lowest priorities, different acquisition time intervals are adopted according to the importance and change speed, so that the resource use can be effectively optimized, unnecessary environment parameter transmission and processing are reduced, and meanwhile, the effectiveness of the environment parameters is ensured. By reasonably distributing the acquisition frequency, the system can better process the environmental parameters with different priorities, avoid delay caused by processing the environmental parameters with high priority, and improve the performance and response speed of the whole system.

Specifically, preprocessing and analyzing according to the acquisition strategy to obtain a processing result includes:

the method comprises the steps of preprocessing filtering, denoising and analyzing the acquired original environment parameters;

integrating the environmental parameters acquired by different devices and sensors to form a unified environmental parameter format;

And carrying out trend analysis on the integrated environmental parameters to obtain environmental parameter processing results.

Specifically, a filtering algorithm, such as a low-pass filter, a high-pass filter, a band-pass filter, etc., is applied to the acquired raw environmental parameters to remove high-frequency noise or to preserve signals within a specific frequency range. The filtering process should dynamically adjust the filtering parameters based on the characteristics of the environmental parameters and the environmental noise level. And a denoising algorithm such as wavelet denoising, homomorphic filtering, non-local mean denoising and the like is used, so that the noise influence is further reduced, and the quality of environmental parameters is improved. The denoising process should take into account maintaining the original characteristics of the environmental parameters, avoiding excessive smoothing. Analyzing the collected environmental parameters, and extracting effective information such as time stamp, environmental parameter type, environmental parameter unit, etc. The analysis processing also comprises environmental parameter checking and error processing, so that the integrity and the accuracy of the environmental parameters are ensured. Environmental parameters collected by different devices and sensors are converted into standard environmental parameter types, such as numerical values, character strings and the like. The environmental parameters are mapped to ensure that the environmental parameters of different environmental parameter sources can be compared under the same coordinate system or time scale. And the integration degree and consistency of the environmental parameters are ensured by applying the environmental parameter fusion technology, such as time synchronization, environmental parameter alignment and the like. For heterogeneous environment parameters, a proper fusion algorithm such as weighted average, bayesian estimation and the like is adopted to synthesize information of different environment parameters. And (3) carrying out time sequence analysis on the integrated environment parameters, and identifying the change trend of the environment parameters along with time. Statistical methods, such as moving averages, exponential smoothing, etc., are applied to predict the direction of change of future environmental parameters. Patterns and anomalies in environmental parameters are identified using machine learning algorithms, such as K-means clustering, decision tree classification, and the like. Deep learning models, such as CNN, LSTM, etc., are applied to extract deep features of environmental parameters to more accurately identify patterns. And the result of trend analysis is output in the form of report, chart or alarm, etc., which is convenient for the user to understand and decide.

Specifically, the original environment parameters are filtered and denoised in the preprocessing stage, so that the signal to noise ratio of the environment parameters can be remarkably improved, irrelevant interference information is removed, and key environment parameter characteristics are reserved. And integrating environment parameters from different devices and sensors, realizing the unification of environment parameter formats, and facilitating the exchange and sharing of environment parameters of cross-device and cross-platform. And trend analysis is carried out on the integrated environmental parameters, so that the change rule of the environmental parameters with time or other variables can be revealed, and basis is provided for decision making.

Specifically, dynamically adjusting the time interval and the priority of the acquisition strategy according to the processing result includes:

Monitoring environmental parameters in real time, and evaluating the importance degree of the environmental parameters;

For the environmental parameters with high environmental parameter change speed and high real-time requirement, the time interval is shortened, and the acquisition frequency is increased;

for the environment parameters with low change speed and low real-time requirement, the time interval is prolonged, and the acquisition frequency is reduced;

For the environmental parameters with high real-time performance and importance, the priority is improved;

for environment parameters with high real-time performance and no importance, the priority is unchanged;

For environment parameters with low real-time performance and no importance, the priority is reduced;

And establishing a feedback control mechanism, and continuously monitoring the adjusted effect.

Specifically, sensors and monitoring devices are deployed, and environmental parameter data, such as temperature, humidity, contaminant concentration, water quality, etc., are collected in real time. Immediate acquisition and transmission of data is achieved using a Data Acquisition System (DAS) or internet of things (IoT) platform. An evaluation period is set, such as quarterly or annually, and the environmental parameters are evaluated according to the influence scope, the risk degree, the social attention degree and other standards of the parameters. And monitoring the change speed of the environmental parameter, and triggering shortening of the time interval when the change speed exceeds a preset threshold value. For environment parameters with high real-time requirements (such as emergency alarm, traffic flow environment parameters and the like), the time interval is immediately shortened to the second level, so that the real-time performance and the accuracy of the environment parameters are ensured. For environmental parameters with low change speed (such as meteorological environmental parameters, environmental pollution environmental parameters and the like), the time interval is properly prolonged according to historical change trend and model prediction, and unnecessary environmental parameter collection is reduced, so that resources are saved. Increasing the priority ensures that these parameters enjoy a higher priority in data transmission and processing. For example, these parameters are marked as urgent data, ensuring that they are transmitted preferentially when the network is congested. For environmental parameters with high real-time performance but not necessarily important, the priority is unchanged, and the original acquisition and transmission priority is maintained. These parameters still need to be monitored, but do not necessarily need to respond immediately to environmental parameters that are low in real-time and not important, lowering priority, reducing the frequency of acquisition and transmission of these parameters. These parameters need to be monitored only in certain situations or are not highly real-time. By setting priority labels and rules, it is ensured that high-priority environmental parameters acquire priority during acquisition and processing. And a feedback loop is established, the acquisition strategy is adjusted according to the monitoring result, and the effectiveness and adaptability of the strategy are ensured. And reasonably distributing network bandwidth, storage and calculation resources according to the priority and real-time requirements of the environmental parameters. The high-priority environment parameters are ensured to be supported by enough resources, and meanwhile, the resource waste is avoided.

Specifically, through real-time monitoring and periodic evaluation, more accurate and efficient collection of environmental parameters can be ensured. For the environment parameters with high change speed and high real-time requirement, the environment change can be captured faster by shortening the time interval, and timely response to emergency is ensured. For the environment parameters with low change speed and low real-time requirement, the unnecessary environment parameter acquisition can be reduced by prolonging the time interval, and the system resources and the energy consumption are saved. The dynamic adjustment of the acquisition strategy is beneficial to the system to make more accurate decisions according to the actual environment demands and changes, so that the decision quality is improved. The priority and the time interval are adjusted in real time, so that the system can acquire key environment parameters at key moments and can respond to environment changes quickly.

Specifically, as shown in fig. 4, the internet of things acquisition device suitable for a complex environment provided by the embodiment of the invention includes:

The environment parameter acquisition module 10 acquires the environment parameters of the tunnel in a first environment of the ventilation equipment under preset output power;

a priority initializing module 20, configured to obtain an initial priority of each environmental parameter in the first environment, so as to determine an acquisition order and/or a transmission order of the environmental parameters according to the initial priority;

the abnormality processing and priority adjusting module 30 is configured to adjust the initial priority according to the emergency degree of the second environment and the abnormal parameter of the second environment when any environmental parameter in the first environment is abnormal and enters the second environment, so as to obtain a target priority, and adjust a preset output power according to the real-time environmental parameter of the second environment, so as to obtain a target output power;

the environmental parameter acquisition module 40 acquires environmental parameters in the second environment according to the target priority and the target output power to obtain the change speed and instantaneity of the environmental parameters of the second environment;

the acquisition time length determining module 50 determines an acquisition period according to the change speed of the environmental parameter and the real-time property;

an acquisition strategy formulation module 60 for formulating an acquisition strategy according to the target priority and the acquisition period;

the environmental parameter preprocessing and analyzing module 70 performs preprocessing and analysis according to the acquisition strategy to obtain a processing result;

the policy dynamic adjustment module 80 dynamically adjusts the time interval and the priority of the collection policy according to the processing result.

Specifically, the internet of things acquisition device suitable for the complex environment provided by the embodiment of the invention can execute the internet of things acquisition method suitable for the complex environment in the embodiment of the invention, can realize the same technical effects, and is not described herein.

Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.

The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. The Internet of things acquisition method suitable for the complex environment is characterized by comprising the following steps of:

Acquiring environmental parameters of a tunnel in a first environment under preset output power of ventilation equipment;

Acquiring initial priorities of all the environmental parameters in the first environment, and determining acquisition sequences and/or transmission sequences of the environmental parameters according to the initial priorities;

When any environmental parameter in the first environment is abnormal and enters a second environment, the initial priority is adjusted according to the emergency degree of the second environment and the abnormal parameter of the second environment to obtain a target priority, and the preset output power is adjusted according to the real-time environmental parameter of the second environment to obtain a target output power;

Acquiring environmental parameters in the second environment according to the target priority and the target output power to obtain the change speed and instantaneity of the environmental parameters of the second environment;

Determining the acquisition period of each environmental parameter according to the change speed of the environmental parameter and the instantaneity;

formulating an acquisition strategy according to the target priority and the acquisition period;

Preprocessing and analyzing according to the acquisition strategy to obtain a processing result;

dynamically adjusting the time interval and the priority of the acquisition strategy according to the processing result;

Adjusting the initial priority according to the emergency degree of the second environment and the abnormal parameters of the second environment to obtain a target priority comprises:

Analyzing the environmental parameters of the first environment, and identifying the environmental parameters deviating from the first environment so as to detect abnormal values of the abnormal parameters by adopting a standard deviation method;

establishing an emergency degree assessment model, and assessing the emergency degree according to the number and the severity degree of the abnormal parameters;

adjusting the initial priority according to the emergency degree to obtain a target priority;

adjusting the preset output power according to the real-time environment parameter of the second environment to obtain the target output power comprises:

continuously monitoring real-time environmental parameters of the second environment;

analyzing real-time environmental parameters of the second environment, and determining the power required by the current ventilation equipment;

according to the power required by the current ventilation equipment, adjusting the preset output power of the ventilation equipment to obtain target output power;

the determining the acquisition period of each environmental parameter according to the change speed and the real-time performance of the environmental parameter comprises the following steps:

analyzing the collected environmental parameters, and determining the change speed of each environmental parameter;

The acquisition period comprises a first acquisition time length T1, a second acquisition time length T2 and a third acquisition time length T3, and T1 is less than T2 and less than T3;

the rate of change of the actual environmental parameter and the average rate of the historical environmental parameter are calculated, wherein,

If the change rate of the actual environmental parameter is higher than the average rate of the historical environmental parameter, the environmental parameter is considered to change rapidly;

If the change rate of the actual environmental parameter is lower than the average rate of the historical environmental parameter, the environmental parameter is considered to be slowly changed;

For the environmental parameters with high change speed, adopting a first acquisition time length;

For the environmental parameters with slow change speed, adopting a second acquisition time length;

the third acquisition time length is adopted for the parameters with low real-time requirements and slow change of the environmental parameters;

for the environment parameters with high real-time requirements, even if the change speed of the environment parameters is not fast, the first acquisition time length is adopted;

For the environment parameters, the real-time performance of the environment parameters comprises an immediate response layer, a short-term dynamic adjustment layer and a medium-long term trend monitoring layer according to the hierarchy; wherein,

And setting an acquisition time threshold T4 for the environmental parameters of the immediate response layer, wherein the real-time performance is high, setting an acquisition time threshold T5 for the environmental parameters of the short-term dynamic adjustment layer, setting a acquisition time threshold T6 for the environmental parameters of the medium-term and long-term trend monitoring layer, and setting the acquisition time threshold T6 with low real-time performance, wherein T4 is less than T5 is less than T6.

2. The internet of things collection method suitable for a complex environment according to claim 1, wherein the process of obtaining the environmental parameters of the tunnel in the first environment of the ventilation device at the preset output power comprises:

installing a state sensor on the ventilation equipment to detect the output power of the ventilation equipment as preset output power;

the environmental parameters include temperature, humidity, illumination and air quality;

deploying temperature sensors at different positions along the length of the tunnel to obtain the temperature distribution of the whole tunnel;

Installing humidity sensors at a plurality of locations of the tunnel to monitor humidity levels throughout the tunnel;

Installing a photosensitive sensor to monitor the illumination intensity in the tunnel;

Gas sensors are deployed to monitor the air quality within the tunnel.

3. The method for acquiring the internet of things adapted to a complex environment according to claim 2, wherein the formulating an acquisition strategy according to the target priority and the acquisition period comprises:

for the environmental parameters with the highest priority, the acquisition time length is set to be second level, and high-frequency acquisition is adopted;

For the environment parameters with the next highest priority, the acquisition time length is set to be in the order of minutes, and intermittent acquisition is adopted;

for the environment parameters with medium priority, the acquisition time length is set to be an hour level, and timing acquisition is adopted;

For the lowest priority environmental parameters, the acquisition time length is set to be once a day, and low-frequency acquisition is adopted.

4. The method for acquiring the internet of things adapted to a complex environment according to claim 3, wherein the preprocessing and analyzing according to the acquisition strategy to obtain the processing result comprises:

the method comprises the steps of preprocessing filtering, denoising and analyzing the acquired original environment parameters;

integrating the environmental parameters acquired by different devices and sensors to form a unified environmental parameter format;

And carrying out trend analysis on the integrated environmental parameters to obtain environmental parameter processing results.

5. The method for acquiring the internet of things adapted to a complex environment according to claim 4, wherein dynamically adjusting the time interval and the priority of the acquisition strategy according to the processing result comprises:

Monitoring environmental parameters in real time, and evaluating the importance degree of the environmental parameters;

For the environmental parameters with high environmental parameter change speed and high real-time requirement, the time interval is shortened, and the acquisition frequency is increased;

for the environment parameters with low change speed and low real-time requirement, the time interval is prolonged, and the acquisition frequency is reduced;

For the environmental parameters with high real-time performance and importance, the priority is improved;

for environment parameters with high real-time performance and no importance, the priority is unchanged;

For environment parameters with low real-time performance and no importance, the priority is reduced;

And establishing a feedback control mechanism and monitoring the adjusted effect.

6. An internet of things acquisition device adapted to a complex environment based on the internet of things acquisition method adapted to a complex environment according to any one of claims 1 to 5, comprising:

the environment parameter acquisition module is used for acquiring the environment parameters of the tunnel in a first environment under the preset output power of the ventilation equipment;

The priority initialization module is used for acquiring initial priorities of the environment parameters in the first environment so as to determine acquisition sequences and/or transmission sequences of the environment parameters according to the initial priorities;

The abnormality processing and priority adjusting module is used for adjusting the initial priority according to the emergency degree of the second environment and the abnormal parameters of the second environment when any environment parameter in the first environment is abnormal and enters the second environment to obtain a target priority, and adjusting preset output power according to the real-time environment parameters of the second environment to obtain a target output power;

The data acquisition module acquires the environmental parameters in the second environment according to the target priority and the target output power so as to obtain the change speed and instantaneity of the environmental parameters of the second environment;

the acquisition time length determining module is used for determining an acquisition period according to the change speed of the environmental parameter and the instantaneity;

The acquisition strategy making module makes an acquisition strategy according to the target priority and the acquisition period;

The data preprocessing and analyzing module is used for preprocessing and analyzing according to the acquisition strategy to obtain a processing result;

and the strategy dynamic adjustment module is used for dynamically adjusting the time interval and the priority of the acquisition strategy according to the processing result.