Real-time ambient air detection method and system based on wireless transmission

Abstract

The invention relates to the technical field of environment monitoring, in particular to a real-time environment air detection method and system based on wireless transmission, and the method comprises the steps of receiving a detection instruction, setting sampling parameters, calculating a gas concentration fluctuation value, carrying out depth sampling, carrying out feature extraction, carrying out abnormal mode recognition, carrying out pollution diffusion modeling, carrying out comprehensive evaluation and the like. By combining the gas sensor array and the wireless communication module, real-time monitoring and data transmission of the gas concentration of the target area are realized, and environmental risks are quantified by using multi-dimensional data analysis and a pollution diffusion model. The real-time performance and the intelligent level of environment air detection can be improved, efficient data transmission and accurate result evaluation are ensured, and reliable technical support is provided for environment monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and in particular relates to a method and system for monitoring environmental noise based on machine vision. Background Art

[0002] With the continuous advancement of environmental monitoring technology, real-time and accurate detection of pollutant concentrations in ambient air has become a crucial tool for safeguarding public health and protecting the environment. In modern environmental monitoring, real-time data transmission, system intelligence, and ease of operation are key performance indicators for measuring detection devices. However, existing ambient air monitoring devices still have significant shortcomings in these areas, making them difficult to fully meet practical application requirements.

[0003] A search revealed patent publication number CN118480437B (publication date September 24, 2024) involving a device for collecting and detecting microbial particles using a sampling tray and culture dish. This technical solution effectively improves the escape and uneven distribution of microbial particles through optimization of the physical structure, thereby enhancing the accuracy and efficiency of detection results. However, the device does not incorporate improvements to data transmission methods and lacks wireless transmission capabilities, requiring manual export or wired transmission of detection data, making real-time data sharing and remote monitoring difficult. Furthermore, its complex structure increases maintenance costs and operational complexity, limiting its widespread application in large-scale environmental monitoring.

[0004] Another existing technology, patent publication number CN111398528B (publication date August 23, 2022), proposes a portable air detection system that achieves high detection accuracy and portability through the collaborative work of a micro air pump, a gas containment chamber, and a pressure detector. However, this system also does not use wireless transmission technology, and the output of detection data relies on the device's own storage or wired connection, which cannot meet the needs of real-time data sharing and remote monitoring. At the same time, although its portability has been improved, frequent manual intervention is still required in long-term operation or multi-point monitoring scenarios, making it difficult to adapt to the urgent need for automation and intelligence in modern environmental monitoring.

[0005] The above problems show that existing ambient air detection devices have obvious technical bottlenecks in terms of wireless transmission function, real-time data sharing capability and intelligent operation. These problems not only affect the detection efficiency and data availability, but also limit its applicability in complex environmental monitoring scenarios. Therefore, the development of a real-time ambient air detection method and system based on wireless transmission has important practical significance. The present invention aims to achieve real-time data transmission and remote monitoring by introducing wireless communication technology, while optimizing the detection process, improving the automation level and applicability of the system, thereby meeting the urgent demand for efficient and intelligent detection systems in modern environmental monitoring. Summary of the Invention

[0006] The present invention provides a real-time environmental air detection method and system based on wireless transmission, the main purpose of which is to improve the real-time and intelligent level in environmental air detection, and ensure the efficiency of data transmission and the accuracy of monitoring results. In order to achieve the above purpose, the present invention provides a real-time environmental air detection method based on wireless transmission, comprising: receiving an environmental air detection instruction, using the detection instruction to start a pre-built detection unit and set the sampling frequency and sampling duration, wherein the detection unit comprises: a gas sensor array and a wireless communication module, the gas sensor array is used to collect gas concentration data in the target area, and the wireless communication module is used to realize real-time data transmission; based on the sampling frequency and sampling duration, perform gas concentration sampling, and calculate the gas concentration fluctuation value in real time; determine whether the gas concentration fluctuation value is within a preset safety range; if the gas concentration fluctuation value is within the safety range, return to the above-mentioned gas concentration sampling based on the sampling frequency and sampling duration. similar steps; if the gas concentration fluctuation value is not within a safe range, deep sampling is performed on the target area based on the gas sensor array to obtain depth sampling data, and multi-dimensional feature extraction is performed on the depth sampling data to obtain a feature set; abnormal pattern recognition is performed based on the feature set to obtain an abnormal risk level; the target area is positioned in real time by using the wireless communication module to obtain spatial distribution information of the target area, and a pollution diffusion model is generated in combination with the spatial distribution information; a historical concentration baseline value of the target area is obtained, and a pollution diffusion index is calculated based on the historical concentration baseline value and the pollution diffusion model; a comprehensive assessment is performed based on the abnormal risk level and the pollution diffusion index to obtain an environmental risk value, and complete real-time environmental air detection.

[0007] Optionally, the gas concentration sampling is performed based on the sampling frequency and sampling duration, and the gas concentration fluctuation value is calculated in real time, including: obtaining the initial concentration value and the current concentration value of the target area, and calculating the gas concentration change rate based on the initial concentration value, the current concentration value, the sampling frequency and the sampling duration; obtaining the gas diffusion coefficient and the ambient temperature of the target area, and calculating the gas concentration fluctuation value based on the gas diffusion coefficient, the ambient temperature and the gas concentration change rate.

[0008] Optionally, the multi-dimensional feature extraction of the depth sampling data to obtain a feature set includes: obtaining the time series characteristics and spatial distribution characteristics of the depth sampling data, segmenting the depth sampling data based on the time series characteristics and spatial distribution characteristics to obtain a segmented data set; obtaining key feature points of the segmented data set, and performing feature aggregation on the segmented data set based on the key feature points to obtain a feature set.

[0009] Optionally, performing abnormal pattern recognition based on the feature set to obtain the abnormal risk level includes: obtaining feature weights and feature distribution density of the feature set, classifying the feature set based on the feature weights and feature distribution density to obtain abnormal categories; obtaining risk weights and occurrence probabilities of the abnormal categories, and calculating the abnormal risk level based on the risk weights and occurrence probabilities.

[0010] Optionally, the wireless communication module is used to perform real-time positioning of the target area to obtain spatial distribution information of the target area, including: obtaining the geographic coordinates and signal strength of the target area, and constructing a spatial distribution map based on the geographic coordinates and signal strength; obtaining the node connection relationship of the spatial distribution map, and optimizing the spatial distribution information based on the node connection relationship.

[0011] Optionally, the calculating of the pollution diffusion index based on the historical concentration reference value and the pollution diffusion model includes: obtaining the propagation rate and attenuation coefficient of the pollution diffusion model, and calculating the pollution diffusion index based on the propagation rate, attenuation coefficient and the historical concentration reference value.

[0012] Optionally, performing a comprehensive assessment based on the abnormal risk level and the pollution diffusion index to obtain the environmental risk value includes: obtaining a risk coefficient of the abnormal risk level and a diffusion coefficient of the pollution diffusion index, and calculating the environmental risk value based on the risk coefficient and the diffusion coefficient.

[0013] To achieve the above-mentioned purpose, the present invention also provides a real-time ambient air detection system based on wireless transmission, comprising: a gas concentration fluctuation calculation module for receiving ambient air detection instructions, using the detection instructions to start a pre-built detection unit and set the sampling frequency and sampling duration, wherein the detection unit comprises: a gas sensor array and a wireless communication module, the gas sensor array is used to collect gas concentration data in the target area, and the wireless communication module is used to realize real-time data transmission; based on the sampling frequency and sampling duration, gas concentration sampling is performed, and the gas concentration fluctuation value is calculated in real time; an abnormal pattern recognition module is used to determine whether the gas concentration fluctuation value is within a preset safety range; if the gas concentration fluctuation value is within the safety range, the above-mentioned gas concentration sampling based on the sampling frequency and sampling duration is returned to. similar steps; if the gas concentration fluctuation value is not within a safe range, deep sampling is performed on the target area based on the gas sensor array to obtain depth sampling data, and multi-dimensional feature extraction is performed on the depth sampling data to obtain a feature set; abnormal pattern recognition is performed based on the feature set to obtain an abnormal risk level; a pollution diffusion model generation module is used to perform real-time positioning of the target area using the wireless communication module, obtain spatial distribution information of the target area, and generate a pollution diffusion model based on the spatial distribution information; an environmental risk value calculation module is used to obtain a historical concentration baseline value of the target area, and calculate a pollution diffusion index based on the historical concentration baseline value and the pollution diffusion model; a comprehensive assessment is performed based on the abnormal risk level and the pollution diffusion index to obtain an environmental risk value, and complete real-time environmental air detection.

[0014] In order to solve the above problems, the present invention also provides an electronic device, which includes: a memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the above-mentioned real-time ambient air detection method based on wireless transmission.

[0015] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned real-time ambient air detection method based on wireless transmission.

[0016] In order to solve the problems described in the background technology, the present invention realizes real-time monitoring and risk assessment of the ambient air status by combining the reception of ambient air detection instructions, constructing a comprehensive detection process covering multiple steps such as real-time sampling of gas concentration, deep data extraction, abnormal pattern recognition and pollution diffusion modeling, etc., which not only ensures the accurate monitoring of gas concentration changes in the target area, but also realizes real-time sharing of data through wireless communication technology, significantly improving the efficiency and intelligence level of environmental monitoring. In addition, through systematic pollution diffusion analysis and risk assessment, potential environmental risks can be discovered in time, and effective early warning can be provided to decision makers, meeting the needs of modern environmental monitoring for efficient and intelligent detection systems; first, the ambient air detection instruction is received, and the pre-constructed The detection unit built in this system can quickly enable the detection unit by receiving the detection instruction, and realize dynamic monitoring of the air status in the target area. This instant response capability ensures the efficiency of the detection process, helps to discover potential problems in time, and thus improves environmental safety; secondly, the sampling frequency and sampling time are set, and the gas concentration fluctuation value is calculated in real time based on the sampling frequency and sampling time. By accurately setting the sampling parameters, it is ensured that the collection of gas concentration data is highly representative, which not only improves the detection accuracy, but also reduces the generation of redundant data. The real-time calculation of the gas concentration fluctuation value can timely identify the concentration changes caused by external factors. This process helps to monitor the working status of the ambient air and avoid misjudgment due to abnormal fluctuations, thereby Improve the continuity and stability of monitoring; then, determine whether the gas concentration fluctuation value is within the preset safety range. By judging whether the fluctuation value is within the safety range in real time, potential environmental risks can be effectively prevented. When the fluctuation value exceeds the range, further operations are performed on the target area; then, deep sampling is performed on the target area based on the gas sensor array. Through deep sampling, more comprehensive gas concentration data in the target area can be obtained, which provides an important basis for judging the health status of the ambient air and can effectively predict possible pollution risks; further, multi-dimensional feature extraction is performed on the deep sampling data to obtain a feature set. Feature extraction can reveal potential problems in the target area and help technicians diagnose the status of the ambient air more accurately; then Through abnormal pattern recognition, the abnormal risk level of the target area is quantified, and its working status and health level are further analyzed; then, the wireless communication module is used for real-time positioning to generate a pollution diffusion model. The pollution diffusion model provides visual data of pollutant diffusion in the target area, enhancing the monitoring ability of its status. By analyzing the pollution diffusion model, the diffusion trend of pollutants can be better judged; then, the pollution diffusion index is calculated based on the historical concentration baseline value and the pollution diffusion model to quantify the pollution diffusion situation in the target area; finally, a comprehensive assessment is performed based on the abnormal risk level and the pollution diffusion index to obtain the environmental risk value. By comprehensively analyzing the abnormal risk and pollution diffusion, the risk level of the ambient air can be accurately assessed and the reliability of monitoring can be improved.Therefore, the present invention can improve the real-time and intelligent level of ambient air detection, and ensure the high efficiency of data transmission and the accuracy of monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of a real-time ambient air detection method based on wireless transmission provided in one embodiment of the present invention; Figure 2 A functional module diagram of a real-time ambient air detection system based on wireless transmission provided by one embodiment of the present invention; Figure 3 A schematic structural diagram of an electronic device for implementing the real-time ambient air detection method based on wireless transmission provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention provides a real-time ambient air detection method and system based on wireless transmission. The specific embodiments of the present invention are described in detail by combining the specific drawings in the accompanying drawings and the individual Arabic numerals of the components in the drawings (if any). Figure 1 To the attached Figure 3 Provide detailed explanation.

[0019] In one embodiment of the present invention, first refer to Figure 1 , which shows a flow chart of a real-time ambient air detection method based on wireless transmission. The method starts with receiving an ambient air detection instruction, using the detection instruction to start a pre-built detection unit and set the sampling frequency and sampling duration. The detection unit includes a gas sensor array and a wireless communication module, wherein the gas sensor array is used to collect gas concentration data in the target area, while the wireless communication module is responsible for realizing real-time data transmission. After setting the sampling parameters, the system starts to perform gas concentration sampling and calculates the gas concentration fluctuation value in real time. This process ensures accurate monitoring of gas concentration changes in the target area, while improving the continuity and stability of monitoring.

[0020] In order to calculate the gas concentration fluctuation value more accurately, the present invention introduces a specific algorithm formula. Assuming that the initial concentration value is , the current concentration value is , the sampling frequency is , the sampling time is T, then the gas concentration change rate The formula Furthermore, the gas diffusion coefficient D and the ambient temperature of the target area are combined. , the gas concentration fluctuation value F can be obtained by the formula These formulas not only improve detection accuracy but can also effectively identify concentration changes caused by external factors, thereby avoiding misjudgments due to abnormal fluctuations.

[0021] Next, the system determines whether the gas concentration fluctuation value is within the preset safety range. If the fluctuation value is within the safety range, the system will continue to execute the gas concentration sampling steps based on the sampling frequency and sampling duration; if the fluctuation value exceeds the safety range, deep sampling of the target area is required. The purpose of deep sampling is to obtain more comprehensive gas concentration data in the target area, which provides an important basis for judging the health status of the ambient air and can effectively predict possible pollution risks. The time series characteristics and spatial distribution characteristics of the deep sampling data will be extracted, and the data will be segmented to obtain a segmented data set. Key feature points will be extracted from it and feature aggregation will be performed on the segmented data set to finally generate a feature set.

[0022] Based on the feature set, the system will further perform abnormal pattern recognition to quantify the abnormal risk level of the target area. This process involves multiple steps. First, obtain the feature weights of the feature set. and feature distribution density , the feature set is divided into different abnormal categories through classification algorithms. The classification algorithm can use machine learning models such as support vector machines or decision trees. Then, according to the risk weight of the abnormal category, and the probability of occurrence , through the formula Calculates the risk level of abnormalities. This process not only reveals potential problems in the target area, but also helps technicians diagnose the status of the ambient air more accurately.

[0023] In order to enhance the monitoring capability of pollutant diffusion trend in target area, the system uses wireless communication module to perform real-time positioning of target area and obtain spatial distribution information of target area. Figure 2 As shown in the figure, the system constructs a spatial distribution map by obtaining the geographic coordinates and signal strength of the target area, and optimizes the spatial distribution information by combining the node connection relationship. On this basis, the system generates a pollution diffusion model, which provides visual data on the diffusion of pollutants in the target area, helping to better judge the diffusion trend of pollutants. The core of the pollution diffusion model lies in the propagation rate v and the attenuation coefficient , combined with historical concentration benchmark values , through the formula Calculate the pollution diffusion index (DI). This index quantifies the pollution diffusion in the target area and provides an important basis for subsequent risk assessment.

[0024] Finally, the system conducts a comprehensive assessment based on the abnormal risk level and pollution diffusion index to obtain the environmental risk value. The comprehensive assessment process involves the calculation of multiple parameters. First, obtain the risk coefficient of the abnormal risk level and the diffusion coefficient of the pollution diffusion index , through the formula Calculate the environmental risk value (ER). This process enables an accurate assessment of the ambient air risk level and significantly improves monitoring reliability.

[0025] In terms of system implementation, the present invention also provides a real-time ambient air detection system based on wireless transmission, whose functional modules are as follows: Figure 2 As shown. The system includes a gas concentration fluctuation calculation module, an abnormal pattern recognition module, a pollution diffusion model generation module and an environmental risk value calculation module. The gas concentration fluctuation calculation module is responsible for receiving ambient air detection instructions and starting the detection unit, setting the sampling frequency and sampling duration, performing gas concentration sampling and calculating the gas concentration fluctuation value in real time. The abnormal pattern recognition module is used to determine whether the gas concentration fluctuation value is within the preset safety range, and perform deep sampling and feature extraction when necessary. The pollution diffusion model generation module uses the wireless communication module to perform real-time positioning of the target area, and generates a pollution diffusion model based on the spatial distribution information. The environmental risk value calculation module calculates the pollution diffusion index based on the historical concentration baseline value and the pollution diffusion model, and comprehensively evaluates the abnormal risk level and the pollution diffusion index to finally obtain the environmental risk value.

[0026] In addition, the present invention also provides an electronic device, the structure of which is as follows Figure 3 As shown, the electronic device includes a memory and a processor. The memory is used to store at least one instruction, while the processor is responsible for executing the instruction stored in the memory to implement the above-mentioned real-time ambient air detection method based on wireless transmission. The hardware configuration of the electronic device ensures efficient operation of the system, while the storage of relevant instructions in a computer-readable storage medium further enhances the system's scalability.

[0027] In practical application scenarios, the present invention can be used in areas such as urban air quality monitoring, industrial park pollution emission monitoring, and emergency response to sudden incidents. For example, in urban air quality monitoring, the system can be deployed at multiple monitoring sites, and real-time data sharing can be achieved through wireless communication modules, significantly improving monitoring efficiency and intelligence. In industrial park pollution emission monitoring, the system can promptly identify potential environmental risks and provide effective early warnings to decision makers. In emergency response to sudden incidents, the system can quickly generate pollution diffusion models, providing a scientific basis for relevant departments to formulate response measures.

[0028] In summary, the present invention realizes real-time monitoring and risk assessment of the ambient air status by combining the reception of ambient air detection instructions, and constructing a comprehensive detection process covering multiple steps such as real-time sampling of gas concentration, deep data extraction, abnormal pattern recognition and pollution diffusion modeling. It not only ensures accurate monitoring of changes in gas concentration in the target area, but also realizes real-time sharing of data through wireless communication technology, significantly improving the efficiency and intelligence level of environmental monitoring. Through systematic pollution diffusion analysis and risk assessment, potential environmental risks can be discovered in a timely manner, providing effective early warnings for decision makers, and meeting the needs of modern environmental monitoring for efficient and intelligent detection systems.

Claims

1. A real-time ambient air detection method based on wireless transmission, characterized in that: The method comprises: Receive an ambient air detection instruction, use the detection instruction to start a pre-built detection unit and set the sampling frequency and sampling duration, wherein the detection unit includes a gas sensor array and a wireless communication module, the gas sensor array is used to collect gas concentration data in the target area, and the wireless communication module is used to realize real-time data transmission; Performing gas concentration sampling based on the sampling frequency and sampling duration, and calculating the gas concentration fluctuation value in real time; Determining whether the gas concentration fluctuation value is within a preset safety range; If the gas concentration fluctuation value is within the safe range, return to the above step of performing gas concentration sampling based on the sampling frequency and sampling duration; If the gas concentration fluctuation value is not within a safe range, performing depth sampling on the target area based on the gas sensor array to obtain depth sampling data, and performing multi-dimensional feature extraction on the depth sampling data to obtain a feature set; performing abnormal pattern recognition based on the feature set to obtain an abnormal risk level; Utilizing the wireless communication module to perform real-time positioning of the target area, obtaining spatial distribution information of the target area, and generating a pollution diffusion model based on the spatial distribution information; Obtaining historical concentration baseline values ​​for the target area, and calculating a pollution diffusion index based on the historical concentration baseline values ​​and a pollution diffusion model; Based on a comprehensive assessment of the abnormal risk level and pollution diffusion index, the environmental risk value is obtained and real-time environmental air detection is completed.

2. The real-time ambient air detection method based on wireless transmission according to claim 1, characterized in that: The performing of gas concentration sampling based on the sampling frequency and sampling duration, and calculating the gas concentration fluctuation value in real time, includes: Obtain the initial concentration value and current concentration value of the target area, and calculate the gas concentration change rate based on the initial concentration value, current concentration value, sampling frequency and sampling time; The gas diffusion coefficient and ambient temperature of the target area are obtained, and the gas concentration fluctuation value is calculated based on the gas diffusion coefficient, ambient temperature and gas concentration change rate.

3. The real-time ambient air detection method based on wireless transmission according to claim 1, characterized in that: The multi-dimensional feature extraction of the depth sampling data is performed to obtain a feature set, including: Obtain the time series characteristics and spatial distribution characteristics of the depth sampling data, and segment the depth sampling data based on the time series characteristics and spatial distribution characteristics to obtain a segmented data set; The key feature points of the segmented data set are obtained, and the feature aggregation of the segmented data set is performed based on the key feature points to obtain a feature set.

4. The real-time ambient air detection method based on wireless transmission according to claim 1, characterized in that: The performing abnormal pattern recognition based on the feature set to obtain an abnormal risk level includes: Obtain feature weights and feature distribution density of feature sets, classify feature sets based on feature weights and feature distribution density, and obtain abnormal categories; Obtain the risk weight and occurrence probability of the abnormal category, and calculate the abnormal risk level based on the risk weight and occurrence probability.

5. The real-time ambient air detection method based on wireless transmission according to claim 1, characterized in that: The method of using the wireless communication module to perform real-time positioning of the target area to obtain spatial distribution information of the target area includes: Obtain the geographic coordinates and signal strength of the target area, and construct a spatial distribution map based on the geographic coordinates and signal strength; Obtain the node connection relationship of the spatial distribution graph and optimize the spatial distribution information based on the node connection relationship.

6. The real-time ambient air detection method based on wireless transmission according to claim 1, characterized in that: The calculation of the pollution diffusion index based on the historical concentration baseline value and the pollution diffusion model includes: Obtain the propagation rate and attenuation coefficient of the pollution diffusion model, and calculate the pollution diffusion index based on the propagation rate, attenuation coefficient and historical concentration benchmark value.

7. The real-time ambient air detection method based on wireless transmission according to claim 1, characterized in that: The environmental risk value is obtained by conducting a comprehensive assessment based on the abnormal risk level and the pollution diffusion index, including: Obtain the risk coefficient of the abnormal risk level and the diffusion coefficient of the pollution diffusion index, and calculate the environmental risk value based on the risk coefficient and the diffusion coefficient.

8. A real-time ambient air detection system based on wireless transmission, characterized in that: The system comprises: a gas concentration fluctuation calculation module, configured to receive ambient air detection instructions, use the detection instructions to activate a pre-built detection unit, and set a sampling frequency and sampling duration, wherein the detection unit includes a gas sensor array and a wireless communication module, the gas sensor array is configured to collect gas concentration data within a target area, and the wireless communication module is configured to achieve real-time data transmission; perform gas concentration sampling based on the sampling frequency and sampling duration, and calculate gas concentration fluctuation values ​​in real time; an abnormal pattern recognition module, configured to determine whether the gas concentration fluctuation value is within a preset safety range; if the gas concentration fluctuation value is within the safety range, returning to the above-mentioned step of performing gas concentration sampling based on the sampling frequency and sampling duration; if the gas concentration fluctuation value is not within the safety range, performing depth sampling on the target area based on the gas sensor array to obtain depth sampling data, performing multi-dimensional feature extraction on the depth sampling data to obtain a feature set; performing abnormal pattern recognition based on the feature set to obtain an abnormal risk level; a pollution diffusion model generation module, configured to utilize the wireless communication module to perform real-time positioning of a target area, obtain spatial distribution information of the target area, and generate a pollution diffusion model based on the spatial distribution information; The environmental risk value calculation module is used to obtain the historical concentration baseline value of the target area and calculate the pollution diffusion index based on the historical concentration baseline value and the pollution diffusion model; perform a comprehensive assessment based on the abnormal risk level and the pollution diffusion index to obtain the environmental risk value and complete real-time environmental air detection.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory is used to store at least one instruction, and the processor is used to execute the instruction stored in the memory to implement the real-time ambient air detection method based on wireless transmission according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the real-time ambient air detection method based on wireless transmission according to any one of claims 1 to 7.

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