Analysis and / or prediction method based on a sensor network and remote monitoring sensor device

Abstract

The invention relates to a sensor network-based analysis and / or prediction method for protecting against natural disasters, comprising at least the method steps: receiving and collecting electronic sensor data from distributed sensor modules of an outdoor sensor network in an outdoor analysis and / or prediction unit, wherein the sensor data comprises at least outdoor corrosion measurement data, impact sensor data and / or rope force sensor data, the sensor data comprises at least tropospheric measurement data, and at least one tropospheric measurement data set, in particular assigned geographically to each outdoor corrosion measurement data set, storing the received sensor data of the outdoor sensor network in a memory unit of the outdoor analysis and / or prediction unit; analyzing the received sensor data of the outdoor sensor network by the outdoor analysis and / or prediction unit to determine a natural disaster risk in the respective application area of a sensor module of the outdoor sensor network, at least one further information about the application area, different from the outdoor corrosion measurement data and the tropospheric measurement data, being directly integrated into the analysis to determine the natural disaster risk, and providing the natural disaster risk determined by the outdoor analysis and / or prediction unit to in particular an authorized user group.

Description

Technical Field

[0001] This invention relates to a sensor network-based analysis and / or prediction method, a remote monitoring sensor device, an outdoor sensor network, and a structure. Background Technology

[0002] Sensor networks have been proposed for monitoring corrosion progression in structures such as pipelines (see US 7,526,944B2) or reinforced concrete (see US 8,886,468B1). However, the fundamental problem of corrosion has not yet been satisfactorily addressed in the field of natural disaster prevention. Summary of the Invention

[0003] The object of the present invention is, in particular, to provide a universal method and / or apparatus with advantageous characteristics in preventing and / or protecting against natural disasters. According to the invention, this object is achieved through sensor network-based analysis and / or prediction methods, remote monitoring sensor devices, outdoor sensor networks, and structures, while advantageous embodiments and further improvements of the invention can be obtained from other advantageous modifications.

[0004] This invention proposes a sensor network-based analysis and / or prediction method for protecting against natural disasters, comprising at least the following method steps: - In the external analysis and / or prediction unit, electronic sensor data is received and collected from sensor modules in a distributed arrangement of an outdoor sensor network. This sensor data includes at least outdoor corrosion measurement data and / or rope force sensor data, which includes at least tropospheric measurement data, and at least one tropospheric measurement dataset, particularly geographically assigned to each outdoor corrosion measurement dataset. - The received sensor data from the outdoor sensor network is stored in the memory cells of an external analysis and / or prediction unit, particularly in memory cells arranged centrally or distributedly, for example, in electronic (volatile or non-volatile) data storage media of the external analysis and / or prediction unit, particularly in magnetic storage media of the external analysis and / or prediction unit, particularly in magnetic storage media arranged centrally or distributedly. - The external analysis and / or prediction unit analyzes the received, and in particular the stored, sensor data of the outdoor sensor network to determine the natural disaster risk in the corresponding application area of ​​the sensor modules of the outdoor sensor network, wherein at least one further piece of information regarding the application area, different from outdoor corrosion measurement data and tropospheric measurement data, is directly integrated into the analysis to determine the natural disaster risk, and - Provide the user group, particularly the authorized user group, with the natural disaster risks determined by external analysis and / or forecasting units.

[0005] This allows for the advantageous acquisition, processing, and / or conversion into comprehensive and / or actionable instructions regarding natural disaster risks. Advantageously, outdoor sensor networks, which in particular also collect data from different sensor types, can be used to achieve particularly accurate and reliable mapping and / or monitoring of the application area or multiple application areas. In sensor network-based analysis and / or prediction methods, the data collected by distributed sensor modules is analyzed, preferably in an automated and / or computer-based manner. In sensor network-based analysis and / or prediction methods, predictions of future developments in the application area, particularly based on the analysis of data collected by distributed sensor modules, are created.

[0006] "Natural disaster" is particularly understood as a geophysical natural disaster. Natural disasters specifically refer to natural phenomena, preferably geological, physical, and / or geophysical phenomena, that may have negative impacts on humans, animals, or structures. Natural disasters can in particular be rockfalls, debris flows, avalanches, erosion, and can also be natural processes affecting the stability of structures, such as corrosion, especially atmospheric corrosion, atmospheric corrosion of at least part of a structure, or simply the possibility of (atmospheric) corrosion in a particular area. "Natural disaster risk" here can, for example, be an estimated risk of one of the aforementioned phenomena occurring, particularly before the establishment of preventative measures or in the presence of existing natural disaster preventative measures, an estimated risk based on the state of natural disaster preventative measures and / or structures, or a risk prediction, such as a prediction of the lifespan of natural disaster preventative measures and / or structures. Preferably, "natural disaster risk" refers to the risk of corrosion, rockfalls, and / or the risk of debris flows.

[0007] "Outdoor sensor network" should be understood in particular as (exclusively) a sensor network comprising sensor modules located externally, preferably outside of structures or other enclosures (such as pipes), and preferably exposed to the open atmosphere (outdoor atmosphere). Specifically, "outdoor sensor network" refers to an open-air sensor network and / or a sensor network exposed to the outside atmosphere and measuring the outside atmosphere or effects caused by the outside atmosphere. "External analysis and / or prediction unit" specifically refers to a data processing unit or data processing network, such as a computer or computer network (e.g., cloud) having at least one processor, at least one memory unit (RAM, ROM, etc.), and at least one operating program that can be called by the processor from the memory unit. In particular, the external analysis and / or prediction unit is implemented at least partially separately from the sensor modules of the outdoor sensor network. Specifically, the external analysis and / or prediction unit is located at a distance from the sensor modules of the outdoor sensor network, preferably at a distance of at least one kilometer. Specifically, the external analysis and / or prediction unit is configured to receive, collect, analyze, and / or provide sensor data from different sensor modules of the outdoor sensor network, preferably sensor data from sensor modules of the outdoor sensor network allocated to different application areas. The external analysis and / or prediction unit is implemented, for example, as a central computing center or a distributed computing network (keyword "cloud computing") that receives, collects, analyzes, and / or provides data from outdoor sensor networks distributed worldwide or from multiple outdoor sensor networks located worldwide. The external analysis and / or prediction unit is particularly configured to wirelessly receive electronic sensor data from the outdoor sensor network measured by the sensor modules. The external analysis and / or prediction unit is specifically configured to store the received sensor data in a (centrally or distributedly arranged) memory unit. The external analysis and / or prediction unit is specifically configured to receive raw sensor data and / or sensor data that has already been pre-analyzed in the sensor module. "Configuration" specifically refers to being specifically programmed, designed, and / or equipped. "An object is configured for a specific function" should be understood in particular as the object implementing and / or performing said specific function in at least one usage state and / or operating state.

[0008] "Outdoor corrosion measurement data" should be understood in particular as measurement data that allows for the inference of corrosion caused by the external atmosphere. "Outdoor corrosion measurement data" should be understood in particular as open-air corrosion measurement data. "Outdoor corrosion measurement data" is determined, for example, by measuring the corrosion current generated by the corrosion of the measuring device caused by the external atmosphere. Specifically, outdoor corrosion measurement data is measured by an outdoor corrosion sensor of a sensor module, particularly an open-air corrosion sensor. "Rope force sensor data" should be understood in particular as measurement data that allows for the derivation of ropes, particularly wire ropes, applied to, and in particular pulling, structures, especially intercepting and / or stabilizing structures. Rope force sensor data can be specifically configured to detect impacts in structures, impact intensity in structures, the filling grade of structures (keyword "debris flow barrier"), or similar things. "Impact sensor data" should be understood in particular as measurement data that allows for the inference of at least one impact of an impactor in a structure, particularly an intercepting structure. It is conceivable that impact sensor data can be determined based on the same measurement signal as rope force sensor data. "Tropospheric measurement data" should be specifically understood as measurement data that allows for the derivation of at least one parameter of the troposphere, particularly the troposphere surrounding the sensor module. "Tropospheric measurement data" can be, for example, temperature data, air humidity data, precipitation data, solar radiation data, wind speed data, wind direction data, air pressure data, dew point data, etc., and / or air pollution data, trace gas measurement data (e.g., sulfur or similar substances in volcanic areas), ozone measurement data, aerosol concentration measurement data, aerosol composition measurement data, hydroxide measurement data, pH measurement data, salt concentration measurement data, etc. Preferably, the tropospheric measurement dataset, particularly the tropospheric measurement dataset of the sensor module, includes at least two, preferably at least three, and particularly preferably more than three different types of tropospheric measurement data. "Distributed arrangement" specifically refers to an arrangement distributed over one application area and / or distributed over multiple application areas.

[0009] By assigning the tropospheric measurement dataset to the outdoor corrosion measurement dataset, it is particularly understood that the outdoor corrosion measurement dataset and the tropospheric measurement dataset are logically connected to each other. Preferably, the tropospheric measurement dataset is geographically assigned to the outdoor corrosion measurement dataset, particularly geographically such that the two measurement datasets are recorded very close to each other, for example, at a maximum distance of 10 cm, preferably at a maximum distance of 25 cm, advantageously at a distance of no more than 1 m, preferably at a distance of no more than 10 m, and particularly preferably at a distance of no more than 100 m. The term "application area" should be understood in particular as the location of a structure, a group of structures, natural disaster prevention measures, and / or the location of a structure, such as a slope. "Further information about the application area" can be any information different from the outdoor corrosion measurement data and the tropospheric measurement data, such as, among other things, the characteristics of the further measurement dataset or the structure and / or natural disaster prevention measures, for example, the thickness of the corrosion protective coating of a portion of the structure, the type of structure and / or natural disaster prevention measures, or the local topography. The identified natural disaster risks are provided to the user group, specifically electronically, for example, as electronic notifications or as retrieval via a portal (e.g., an internet portal). Preferably, authorized personnel of the user group have access to the portal where the identified natural disaster risks are displayed in a pre-processed manner, such as graphical pre-processing. Alternatively, it is conceivable that no special authorization is required, and at least a portion of the data provided is freely available. It is particularly conceivable to output warnings and / or alerts to the authorized user group based on the identified natural disaster risks. For this purpose, an external analysis and / or prediction unit performs, for example, an assessment of the identified natural disaster risks, particularly an intelligent and / or automated assessment, and autonomously warns and / or alerts the user group upon identification of a critical state. This could be, for example, an analysis and / or prediction unit that determines, based on analyzed sensor data, that an impact exists in the structure or that the corrosion state of the structure exceeds a predetermined tolerance threshold.

[0010] Furthermore, at least one application area comprises structures including metal components exposed to atmospheric corrosion, particularly externally located metal wires and / or wire ropes, preferably externally coated with anti-corrosion coatings (Zn, ZnAl, plastics, etc.) steel wires and / or stainless steel wires, and the remaining lifespan of the structures is provided to the user group based on sensor data determined by natural disasters. Therefore, a high level of safety can be advantageously achieved. Advantageously, the planning for the maintenance, renovation, and new construction of structures can be optimized. Advantageously, maintenance and / or renovation plans for structures can be organized. Furthermore, it is advantageous to optimize comprehensive maintenance plans, including the maintenance of multiple structures located in different locations. For example, the routes and / or operation times of maintenance vehicles and / or personnel can be optimized through appropriate maintenance sequences for different structures. Moreover, this method advantageously enables time-optimized ordering of maintenance materials and / or consumable materials, such as replacement parts. Therefore, storage costs and / or storage space requirements can be advantageously reduced. The structure is specifically implemented as an interception device, particularly an interception structure such as a rockfall barrier, debris flow barrier, avalanche barrier, rockfall curtain, attenuator, etc., as a stabilization device, particularly a stabilization structure such as slope protection, avalanche protection, etc., or as another structure including rope and / or wire structures, such as a suspension bridge, such as a pedestrian suspension bridge, a roof structure, such as a stadium roof structure, a glass facade, a mast anchor, a wind turbine anchor, etc.

[0011] The remaining life is specifically implemented as a remaining life parameter. The remaining life parameter is specifically implemented as (approximate) time information calculated based on sensor data, preferably at least based on outdoor corrosion measurement data and tropospheric measurement data. For example, the remaining life parameter gives the remaining life of a structure as the number of remaining years, months, and / or days. Alternatively, the remaining life parameter gives the remaining life of the structure as a target date (accurate to the year, month, or day). Alternatively, the remaining life parameter can also be implemented as a percentage, indicating, for example, the percentage of the remaining residual thickness of the anti-corrosion layer, the percentage of the thickness of the anti-corrosion layer that has been removed, the remaining percentage of the pre-calculated total life, etc. In particular, to calculate the remaining life in this case, at least the thickness of the anti-corrosion layer on the monitored metal components of the structure, especially the initial thickness, is taken into account with further information about the application area. Alternatively, the remaining life parameter can also be implemented as a percentage indicating, for example, the percentage of fill of a barrier (e.g., a debris flow barrier) (the percentage that has been filled or the percentage that is still usable). Specifically, to calculate the remaining lifetime in this case, at least one measured infill level parameter of the barrier, such as the rope force acting on the anchoring ropes of the barrier, is considered as further information about the application area. To calculate the remaining lifetime, it may also be considered, in both cases, to integrate at least one climate prediction, such as a climate prediction based on past measured weather data, and / or a climate prediction based on future expected weather data, particularly taking into account local and / or global climate change, into further information about the application area. Preferably, the determined remaining lifetime is provided worldwide to a group of users via, for example, an electronic display unit preferably with internet access.

[0012] If the provided natural disaster risk includes the removal rate of corrosion-resistant coatings, particularly galvanized metal components, and especially the standardized removal rate of corrosion-resistant coatings, particularly zinc protective coatings, determined based on sensor data, then the remaining life of structural components coated with corrosion-resistant coatings can advantageously be deduced in a particularly simple manner. In particular, the removal rate of other materials can be derived from the removal rate of one material, such as zinc, thus advantageously enabling high flexibility in use. "Standardized corrosion-resistant coating removal rate" should especially be understood as the corrosion-resistant coating removal rate, which can be interpreted as different types of corrosion-resistant coatings. The types of corrosion-resistant coatings can specifically be zinc coatings, ZnAl coatings, ZnAlMn coatings, PET sheaths, PVC sheaths, etc. In particular, the corrosion-resistant coating removal rate can also be converted into the corrosion rate of stainless steel.

[0013] Furthermore, it is proposed that the provided natural disasters include changes in rope force in ropes anchoring debris flow barriers, avalanche protection, rockfall barriers, and / or another structure, which is particularly capable of slow filling, and that these rope force changes are determined based on sensor data. This advantageously allows for the determination of the fill level of debris flow barriers, avalanche protection, rockfall barriers, and / or another structure, which defines the remaining lifespan of the debris flow barriers, avalanche protection, rockfall barriers, and / or another structure.

[0014] In addition, a corrosion rating for the geographical environment of the application area is proposed, taking into account the determined corrosion removal rate. This advantageously allows for a particularly accurate and / or reliable corrosion rating of the geographical environment of the application area. In particular, compared to widely used corrosion ratings based solely on geographical and / or climatic framework conditions, it advantageously allows for corrosion ratings based on actual corrosion measurements. Specifically, corrosion grading is performed according to category C1 bis CX of standard DIN EN ISO 12944-1:2019-01. Specifically, the environment of the application area is assigned a corrosion rating based on the determined value of the corrosion removal rate.

[0015] Furthermore, it is proposed that, in at least one application area, particularly in at least one previously unprotected location, sensor modules of an outdoor sensor network be installed prior to natural disaster protection measures, and then the necessity of implementing natural disaster protection measures be assessed based on the determined natural disaster risk. This advantageously allows for the assessment of the necessity of natural disaster safety measures in a particular location. Therefore, the overall utilization of resources for natural hazard prevention can be advantageously optimized. Advantageously, security can be enhanced, particularly when natural disaster protection measures can be effectively deployed. Advantageously, an expert tool can be created that significantly facilitates decisions to favor or oppose natural disaster safety measures. In particular, preferably over a considerably long time interval (e.g., at least one year or at least two years), the probability of occurrence of phenomena constituting natural disasters (e.g., rockfalls, debris flows, landslides, erosion, etc.) is calculated based on sensor data (e.g., tropospheric sensor data) collected by the sensor modules. In particular, the calculated probabilities are provided to a user group, including decision-makers, for weighing their support for or opposition to natural disaster safety measures. Specifically, the assessment of the necessity of implementing natural disaster preparedness measures includes specifying the probability of occurrence of phenomena constituting a natural disaster within a time interval, such as the average lifespan of the natural disaster preparedness measures. Specifically, natural disaster preparedness measures may include one or more of the aforementioned interception and / or stabilization structures. Furthermore, it is conceivable that the assessment of the necessity of implementing natural disaster preparedness measures includes risk classification (e.g., at least including categories such as "high risk", "medium risk", and "low risk").

[0016] Furthermore, it is proposed that, in at least one application area, sensor modules of an outdoor sensor network be installed prior to the planned construction measures, and then the planned construction measures be modulated according to the determined natural disaster risk. This advantageously allows for a high level of safety. Advantageously, suitable modulation of the structures can be achieved, particularly the modulation of one of the aforementioned interception and / or stabilization structures. "Modulation of the planned construction measures" specifically refers to, preferably, the design regarding the strength, robustness, etc., of the structure to be constructed. For example, the trapping capacity of a rockfall barrier can be adapted to the magnitude and / or frequency of the anticipated rockfall events. For example, the anchoring of slope protection can be adapted to the anticipated erosion intensity.

[0017] If construction measures include the installation of wire mesh and / or wire ropes, where the type and / or thickness of the corrosion-resistant layer of the wire mesh and / or wire ropes is selected based on the determined natural disaster risk, optimal corrosion protection is advantageously achieved, thus ensuring optimal and / or preferably long service life of the facility. "Type of corrosion-resistant layer" specifically refers to the material and / or composition of the corrosion-resistant layer (e.g., as described above). In particular, in highly corrosive environments, such as in humid climates and / or near the ocean, different corrosion-resistant layers with higher resistance (e.g., Zn / Al) or higher resistance (e.g., stainless steel wire) will be superior to less resistant layers (e.g., Zn). In particular, in highly corrosive environments, such as in humid climates and / or near the ocean, thicker corrosion-resistant layers (e.g., greater than 150 g / m²) are preferable. 2 Wires with a thinner anti-corrosion layer (e.g., less than 150 g / m) will be preferred. 2 Specifically, in less corrosive environments, such as arid desert regions, relatively inexpensive metal wires with a relatively thin anti-corrosion coating will be sufficient. Specifically, modulation calculated based on sensor data is provided to a user group, which specifically includes construction planners, to support the planning and / or design of construction measures. Specifically, to support the planning and / or design of construction measures, recommendations based on sensor data and relating to the type and / or thickness of the anti-corrosion coating are provided to a user group, which specifically includes construction planners.

[0018] Furthermore, a high level of security is advantageously achieved if the wire thickness and / or material of the wire mesh and / or wire rope are selected based on the determined natural disaster risk, and / or if the size of the wire mesh, particularly the total extension dimension and / or the mesh opening size, is selected based on the determined natural disaster risk. This advantageously allows the protection to be precisely adapted to the expected intensity and / or severity of the natural disaster. Advantageously, high cost efficiency and / or long service life can be achieved. Contemporaneous selectable (minimum) wire thicknesses are, for example, 2 mm, 3 mm, 4 mm, 5 mm, 6 mm, or 7 mm. Contemporaneous optional materials are especially steel, high-strength steel (which specifically means having 800 N / mm²). 2 (or steel with a higher nominal tensile strength) or stainless steel. Specifically, the tensile strength of the wire mesh and / or wire rope, particularly the tensile strength of the steel wire mesh and / or wire rope, can be determined based on the identified natural disaster risk. The conceivable minimum nominal tensile strength of the steel wire can be selected as, for example, 400 N / mm², 800 N / mm², 1000 N / mm², 1770 N / mm², 2200 N / mm², or 3000 N / mm². The size of the wire mesh is specifically defined based on the determined size of the hazardous area as determined by the sensor module. For example, sensor data determines which slope range, at which specific location, terrain needs to be stabilized by interception and / or stabilization structures. In particular, recommendations based on sensor data regarding wire thickness, wire material, wire mesh size, precise location of the wire mesh, and / or mesh size are provided to a user group, particularly including construction planners, to support the planning and / or design of construction measures.

[0019] Furthermore, it is proposed that further information regarding the application area includes at least the geographic coordinates of the corresponding sensor modules installed in the application area, and these geographic coordinates are used when determining the risk of natural disasters, which is implemented as a corrosion map of at least the application area and / or its vicinity and surrounding area, showing corrosion data such as corrosion grade, corrosion intensity value, anti-corrosion layer removal rate, etc. This advantageously allows for detailed coverage of the corrosivity of the application area and / or its vicinity, particularly detailed coverage of at least localized corrosivity. Advantageously, this also allows small-scale local differences to become visible through corrosion (such differences are, for example, due to location within or without rain shadows, due to location within or without high solar radiation intensity, or due to location within the splash range of waterfalls or waves, etc.). In particular, when installing the sensor modules, geographic coordinates assigned to the sensor modules are determined and / or copied onto the sensor modules. Alternatively, it is conceivable that the sensor modules have their own GPS functionality. The corrosion map is specifically implemented as a graphical representation including a combination of geographic data and corrosion data linked to geographic coordinates. Specifically, corrosion maps calculated based on sensor data are provided to the user group, for example, to support the planning and / or design of construction measures. In particular, the corrosion map, preferably provided to the user group, represents a map overlay, such as a political map, topographic map, and / or geological map, with spatially resolved, particularly measured, interpolated, and / or simulated corrosion data and / or corrosion levels.

[0020] If corrosion maps are incorporated into a Building Information Modeling (BIM) system, particularly for application areas implemented as natural disaster prevention facilities, preferably structures, and especially for interception and / or stabilization structures, then particularly effective, simple, and transparent management of the application area, and especially the structures, is advantageously achieved. Advantageously, property management, particularly facility management, can be improved. Advantageously, key locations of wear and tear due to corrosion can be clearly perceived in a user-friendly manner. The BIM system specifically includes a virtual, geometrically visualized model of the application area, and particularly the structures, on which the corrosion maps are preferably overlaid. This allows direct perception of which locations and / or portions of the application area, and particularly which locations and / or portions of the structures, may be exposed to increased corrosion. Advantageously, corrosion maps integrated into the BIM model can be implemented throughout the entire lifespan of the structure. In particular, the BIM model with the corrosion maps overlaid is provided to user groups, for example, to support structure management.

[0021] Furthermore, a high level of safety can be advantageously achieved if the application area of ​​the natural disaster prevention facility is optimized, particularly locally, based on the determined corrosion map. Advantageously, efficient and / or precise adaptation of the natural disaster prevention facility can be achieved. The term "natural disaster prevention facility" should be understood in particular as an interception and / or stabilization structure. "Local optimization" of the natural disaster prevention facility specifically refers to the local adaptation of the implementation of the facility, such as by locally expanding the facility (e.g., by modifying the thickness of the wire mesh, the thickness of the anti-corrosion coating, etc.), locally strengthening the facility, or something similar. For example, it is conceivable that if increased corrosion is determined in a sub-region of the coated area, a different corrosion protection is selected for the wire mesh arranged in that sub-region, and / or a second wire mesh is installed near or above the first wire mesh in that sub-region. It is conceivable that optimization is carried out before the initial installation of the natural disaster prevention facility, or only subsequently on the already installed facility.

[0022] Furthermore, it is proposed that, outside the area surrounding the application area, the corrosion map is filled with simulated corrosion data, wherein in areas of the corrosion map without sensor modules of the outdoor sensor network, the corrosion data is determined at least based on sensor data from sensor modules in other application areas, particularly in adjacent application areas and / or geographically and / or climatically similar application areas. Therefore, advantageously, corrosion maps covering particularly large areas can be obtained based on actual corrosion measurement data. Advantageously, this enables reliable support for structure planning for geographically large areas. Furthermore, advantageously, structure planning supported by actual measurement data can be achieved even in areas where no measurement data (e.g., corrosion measurement data) is available. Specifically, to determine corrosion data in areas of the outdoor sensor network without sensor modules, interpolation is performed between corrosion data in two adjacent application areas where sensor modules with corrosion sensors are present. Specifically, areas of the outdoor sensor network without sensor modules are compared with application areas with sensor modules containing corrosion sensors, wherein preferably, when determining geographical and / or climatic similarity, it is assumed that the corrosion data should be at least substantially the same in geographically and / or climatically similar application areas. Specifically, in this case, geographically and / or climatically similar application areas are then assigned the same corrosion data as application areas where corrosion data are actually measured. "Simulated corrosion data" should be understood in particular as corrosion data calculated not based on direct field measurements but, for example, empirical values, comparisons with known corrosion measurement data, and / or interpolation via known corrosion measurement data. In particular, corrosion maps supplemented in this way are provided to user groups, for example, to support additional new construction measures or to determine additional new construction measures.

[0023] Furthermore, it is proposed that additional information regarding the application area includes at least the intensity of wildlife activity and / or human activity (e.g., pedestrian activity) in the vicinity of the application area. Studies have shown that this approach can advantageously achieve substantial improvements in determining natural disaster risks. Therefore, in particular, natural disaster predictions, such as rockfall predictions, can be specified. High, especially seasonal, wildlife activity, or high, especially seasonal, human activity, such as pedestrian activity, can lead to increased material dislocation in the application area, which can significantly increase the probability of events detectable by the sensor module, particularly under certain atmospheric conditions, such as rockfall events. Therefore, natural disaster risk may, at least seasonally, be significantly influenced by the corresponding activity intensity, particularly increasing seasonally. In particular, an outdoor sensor network is configured to acquire wildlife activity and / or human activity. Preferably, the outdoor sensor network includes at least one camera, particularly at least one wildlife camera, configured to record and / or count wildlife and / or humans, such as pedestrians. Alternatively, wildlife activity and / or pedestrian activity can also be collected from external data on the application area and / or its surrounding environment, such as via external wildlife cameras, wildlife counts performed by caretakers, counts of parking tickets sold at pedestrian parking spaces, counts of mountain train tickets sold, etc. The term "nearby environment" specifically refers to the environment within a few kilometers of the application area, such as a maximum of 10 kilometers, a maximum of 5 kilometers, or a maximum of 2 kilometers, preferably the environment around the outermost edge of the application area, or preferably the environment within a few hundred meters of the application area, such as a maximum of 800 meters, a maximum of 500 meters, or a maximum of 300 meters, preferably the environment around the outermost edge of the application area.

[0024] As an alternative or supplement, it is proposed that further information regarding the application area include at least air quality data in the environment near the application area. Studies have shown that, in this way, substantial improvements can be advantageously achieved in determining the risk of natural disasters. Therefore, in particular, predictions of the remaining lifespan of metal components of corroded structures can be specified, especially when certain air pollutants may have a corrosion-enhancing effect. Air quality data may specifically include data on trace gases or suspended particulates that may be contained in the air. For example, aerosol droplets may have low pH values ​​or high concentrations of salt, which may deposit on metal components, thus affecting corrosion. For example, in certain areas (e.g., near volcanoes or in cities with high air pollution), the air may contain corrosion-enhancing gases and / or aerosols (e.g., sulfur compounds, such as sulfur dioxide). Preferably, the outdoor sensor network includes at least one air quality sensor. Alternatively, air quality may also be obtained from external data regarding the application area and / or its surrounding environment, such as through external air pollutant measurements or through air pollutant simulations.

[0025] Furthermore, it is proposed that the identified natural hazards include natural hazard predictions, which are preferably created through data mining based on previously obtained sensor data and, in particular, based on previously obtained further information about the application area. This advantageously allows for a high level of security. For example, in this way, the optimization and / or optimized modulation of structures exposed to natural hazard risks can be achieved. Thus, structures can be advantageously designed in such a way that they are resistant to anticipated events and / or provide sufficient protection against them. For example, the predictability of future events can be inferred using the correlation of different sensor data measured at the time of an event. For example, rainfall limits, wind speed limits, or wildlife activity limits can be determined above which rockfalls, especially within a certain range, may occur. For example, if parameters for natural hazard risk prediction are exceeded or fallen below, warnings can be given to the structure's supplier or manager, which may, for example, lead to emergency response or maintenance teams entering alarm standby mode. For example, natural hazard risk predictions can be made available to firefighters' control rooms, which can place certain units at an increased alert level whenever an increased probability of an event is predicted through natural hazard risk prediction. For example, natural disaster risk predictions could be used by railway providers, enabling them to block trains from passing through specific areas or redirect them if an increased probability of an event is predicted through these predictions. Similarly, natural disaster risk predictions could be provided to agencies managing pedestrian routes, which would then prohibit pedestrian access to specific areas if an increased probability of an event is predicted.

[0026] Furthermore, it is proposed that at least one sensor module of an outdoor sensor network is assigned to an application area implemented as an interception and / or stabilization structure for rocks, boulders, avalanches, debris flows, landslides, etc., and specifically as an interception and / or stabilization structure. The at least one sensor module of the outdoor sensor network includes an impact sensor for detecting impacts in the interception device. Analysis, particularly pattern recognition, is performed using impact data from the impact sensor and / or rope force sensor data from the sensor module. The rope force sensor data specifically provides measurements of the fill level of the debris flow barrier, along with a measurement sequence of tropospheric measurement data from the sensor module, and specifically along with further information about the application area. A natural disaster prediction is determined based on this analysis, implemented as an impact prediction. Combining past measurement data advantageously allows for improvements in natural disaster risk prediction. Pattern recognition is specifically implemented as automated pattern recognition, preferably performed by an algorithm of the analysis and / or prediction unit based on machine learning and / or neural network principles. Specifically, the natural disaster risk prediction determined in this way is provided to a user group. Specifically, pattern recognition also includes identifying faulty sensors and / or sensor modules in an outdoor sensor network. For example, potentially damaged, miscalibrated, or improperly installed sensors and / or sensor modules can be identified through outlier data. Pattern recognition is preferably also based on the principles of crowdsourced sensors.

[0027] Furthermore, it is proposed to establish maintenance plans for application areas, such as maintenance plans for natural disaster prevention facilities, based on identified natural disaster risks. This advantageously allows for high efficiency in the organization of personnel, materials, and machinery, particularly maintenance efficiency. Specifically, the maintenance plan is provided to the user group. Specifically, the maintenance plan is further based on the identified natural disaster risk predictions. Specifically, the maintenance plan is flexible enough to adapt to changes in sensor measurement data. Specifically, the maintenance plan is flexible enough to adapt to detected events, such as impact events and / or infill events. For example, it is conceivable that after the detection of one or more new impact or infill events (e.g., debris flows), an application area might be temporarily moved forward in the maintenance plan.

[0028] High maintenance efficiency is advantageously achieved when the organization of maintenance personnel, maintenance tools, and / or consumable materials is based on the determined natural disaster risks of multiple application areas, particularly those distributed within a single area. For example, the inspection routes of maintenance teams inspecting multiple application areas in a single maintenance run can be optimized in terms of total travel time and / or total travel distance. "Organization of maintenance personnel" specifically refers to the allocation of application areas to personnel performing maintenance. Preferably, maintenance orders are allocated here, particularly via analysis and / or prediction units, so that the workload can be distributed as evenly as possible across all available personnel in a single area, and / or the total travel distance of available personnel in that area can be minimized. "Organization of maintenance tools" specifically refers to the allocation of maintenance tools to personnel performing maintenance. Preferably, maintenance orders are allocated here, particularly via analysis and / or prediction units, so that maintenance tools are allocated to available personnel in general, making preferably short downtime for both maintenance tools and personnel achievable. "Organization of consumable materials" specifically refers to the distribution of consumable materials to maintenance personnel. Preferably, consumable materials are distributed to available personnel, particularly via analysis and / or prediction units, thus preferably requiring minimal storage.

[0029] Furthermore, it is proposed that, upon detection of an impact, particularly in a rockfall barrier, by an impact sensor, and / or upon detection of a filling event, such as a debris flow, particularly in a debris flow barrier, by a rope force sensor, a maintenance command, particularly implemented as a warning, and / or immediate repair, particularly implemented as an alarm, be triggered based on the intensity and / or type of the impact and / or filling event. This advantageously enables an appropriate and / or effective response to the occurrence of an event. Advantageously, safety can be further improved. In particular, an alarm is triggered if the impact intensity and / or filling event allows for the deduction of serious damage to the interceptor (e.g., exceeding or falling below predetermined limit values), which will preferably lead to the dispatch of a (emergency) repair team and / or the prohibition of unauthorized personnel from entering the application area as soon as possible. In particular, a warning is triggered if the impact intensity and / or filling event allows for the inference of less serious damage to the interceptor (e.g., by measurements within tolerance range), which will preferably lead to an advance inspection of the application area, and, if applicable, the corresponding interceptor is emptied.

[0030] Furthermore, the deployment of drones, particularly maintenance drones and / or reconnaissance drones, is triggered by events such as impacts in rockfall barriers and / or filling of debris flow barriers, and / or by the determined value of natural disaster risk, particularly the intensity of impacts and / or filling events. As a result, advantageously, high levels of maintenance efficiency and / or low organizational workload can be achieved. "Drone" should be understood in particular as an autonomously operated or remotely controlled unmanned aircraft. "Reconnaissance drone," particularly a purely sensor-based drone, especially a camera drone, should be understood as being configured to perform, in particular, optical assessments and / or inspections of the application area, particularly structures. "Maintenance drone" should be understood specifically as a drone configured to perform at least one maintenance activity in addition to the tasks of a reconnaissance drone. Maintenance activities may include, for example, reading data from sensor modules, charging the energy storage devices of sensor modules, replacing components of sensor modules (e.g., batteries), installing sensor modules, etc. It is particularly conceivable that drones use sensor modules at least partially autonomously as orientation points and / or for orientation assistance, said sensor modules being positioned in the application area for (GPS-independent) navigation, particularly under the principle of "virtual tracks." The “trigger” for drone deployment should be specifically understood as the direct autonomous initiation of the drone from its parking location. Alternatively, the “trigger” for drone deployment should be understood as notification by a person responsible for the deployment of the drone, who, upon notification, preferably transports the drone to the application area, where he initiates the drone autonomously or remotely.

[0031] Furthermore, a remote monitoring sensor device is proposed, comprising a sensor module for an outdoor sensor network, specifically configured to record and provide sensor data for sensor network-based analysis and / or prediction methods, having at least one outdoor corrosion sensor, at least one environmental sensor for determining tropospheric measurement data, and at least one communication unit, particularly wireless, for transmitting sensor data to an external analysis and / or prediction unit, wherein the sensor module includes a sensor module housing that is at least substantially hermetically sealed. This advantageously allows for obtaining comprehensive and / or significant information on natural disaster risks, particularly in the application area or in multiple application areas. Advantageously, reliable and / or robust outdoor sensor monitoring is possible. Advantageously, long-term outdoor monitoring is possible, preferably permanently operational even in locations with particularly severe weather conditions. The outdoor corrosion sensor is particularly implemented as an open-air corrosion sensor, preferably configured to detect the corrosivity of the external atmosphere. The outdoor corrosion sensor is particularly configured to detect corrosion by measuring corrosion current, preferably corrosion progression. The outdoor corrosion sensor includes a corrosion monitoring element. Outdoor corrosion sensors are specifically configured to measure the corrosion current generated by the corrosion process in a corrosion monitoring element, preferably for measuring the progression of the corrosion current value, wherein the corrosion monitoring element is supported such that it is exposed to the external atmosphere. Advantageously, by detecting a current proportional to the corrosion of the corrosion monitoring element, particularly the rate of removal of the anti-corrosion layer, especially the rate of removal of the anti-corrosion layer of the coating, via the outdoor corrosion sensor, the current particularly allows for the inference of the time progression of the anti-corrosion layer removal rate, the instantaneous anti-corrosion layer removal rate and / or the actual residual material strength of the anti-corrosion layer, particularly the corrosion monitoring element, and therefore also the actual residual material strength of the metal parts located in the application area.

[0032] The corrosion monitoring element is implemented as a modified ACM (Atmospheric Corrosion Monitor) sensor. Specifically, the ACM sensor is configured to determine the corrosivity of the environment, and / or the corrosion rate of the metal and / or alloy, particularly the erosion rate, preferably based on a galvanic current flowing between the metal and / or alloy. Specifically, the ACM sensor comprises at least two electrodes, which are electrically insulated from each other, particularly in a dry state. These electrodes are particularly at least partially made of different materials, preferably different metals of different noble metal properties. It is conceivable that at least one electrode has at least one coating, thereby, particularly, the surface materials of at least two electrodes are different. Preferably, the surface materials are made of metals of different noble metal properties. Advantageously, at least one electrode is implemented substantially identically to at least one section of the wire of the wire mesh. This advantageously achieves preferred good removability of the material removal measured on the corrosion monitoring element compared to the material removal of the wire mesh. Advantageously, at least one additional electrode of the ACM sensor is at least partially made of a more inert material than the electrode implemented substantially identically to a portion of the wire. More expensive materials may include steel, silver, gold, cobalt, nickel, copper, platinum, palladium, other elements above zinc in the potential series, and / or alloys above zinc in the potential series. Specifically, electrodes, particularly those with different surface materials, are arranged so as not to contact each other. Specifically, electrodes, particularly those with different surface materials, do not have direct mutual contact. Preferably, electrodes, particularly those with different surface materials, are electrically contacted in a wet state via water droplets forming an electrolyte. Specifically, in the case of electrode electrical contact, a galvanic current is present. The galvanic current particularly causes material removal and / or corrosion of less expensive electrodes. The current is advantageously proportional to the material removal. The presence and / or characteristics of the electrolyte, particularly its corrosive properties, depend particularly on the environmental conditions to which the corrosion monitoring element is exposed at a given point in time, thus advantageously allowing for the inference of the corrosivity of the environmental conditions at that point in time.

[0033] The environmental sensor includes at least one thermometer, at least one hygrometer, at least one solar intensity meter, at least one anemometer, at least one barometer, and / or at least one additional measuring device, such as a measuring device for detecting trace gases, salt concentrations, or aerosol concentrations. Specifically, the communication unit is configured to automatically, preferably periodically, transmit sensor data to an external analysis and / or prediction unit. Preferably, the communication unit has mobile communication capabilities. Specifically, the communication unit communicates via mobile communication protocols, such as EDGE, GPRS, HSCSD, and / or preferably via the GSM mobile communication protocol. However, additionally or alternatively, other wireless interfaces for communicating with the analysis and / or prediction unit are also contemplated. It is also contemplated that the sensor module, particularly the communication unit, includes additional wireless interfaces for communicating with electronic units in the nearby environment, such as with other sensor modules in an outdoor sensor network, drones, and / or with external sensors, such as external cameras, particularly external Bluetooth cameras. For example, this additional wireless interface may include a Bluetooth wireless interface, an NFC wireless interface, an RFID wireless interface, a LoRa wireless interface, or a similar short-range wireless interface. Preferably, in addition to sensor data, the communication unit also transmits other data about the sensor module, such as location, time, battery level, and functional status.

[0034] "A sensor module housing that is substantially airtight" should be particularly understood as a sensor module housing that is at least watertight, particularly at least sealed relative to a water column of at least 5 m, preferably at least 25 m, preferably at least 100 m, and especially preferably at least 250 m. Preferably, a sensor module housing that is at least substantially airtight is also at least substantially atmospherically and / or gaseously sealed. "At least substantially atmospherically and / or gaseously sealed" specifically means that the wet vapor transmission rate (MVTR) between the internal space of the sensor module housing and the surrounding environment of the sensor module housing is less than 100 cm³ / m² / 24h, preferably less than 25 cm³ / m² / 24h, preferably less than 10 cm³ / m² / 24h, and especially preferably less than 1 cm³ / m² / 24h. Alternatively or additionally, "at least substantially atmospherically and / or gaseously airtight" means that the oxygen permeability (OTR) between the internal space of the sensor module housing and its surrounding environment is less than 1000 cm³ / m² / 24h, preferably less than 250 cm³ / m² / 24h, more preferably less than 100 cm³ / m² / 24h, and particularly preferably less than 50 cm³ / m² / 24h. The airtight sensor module housing is specifically configured to prevent foreign objects from entering the interior of the sensor module housing, thus advantageously achieving a long lifespan. Advantageously, the sensor module housing resists damage caused by plants (e.g., resistance to root entry, etc.). Advantageously, the sensor module housing resists damage from animal populations (e.g., resistance to insect entry). (Resistance to bites, etc.). Specifically, the sensor module housing internally houses at least a communication unit, at least one energy storage unit of the sensor module, and / or at least one electronic control and / or regulation unit and / or computing unit, which interact with the outdoor corrosion sensor, the environmental sensor, the communication unit, and the energy storage unit of the sensor module. Specifically, the hermetically sealed sensor module housing includes at least one, preferably hermetically sealed and / or grouted, channel for at least one sensor probe, particularly the sensor probe of the outdoor corrosion sensor and / or environmental sensor. "Remote monitoring sensor device" should be understood in particular as a remote monitoring device for corrosion and / or impact on structures, particularly for interception and / or stabilization structures in the field of natural disasters. In particular, the remote monitoring sensor device is configured to enable remote monitoring of structures, particularly interception and / or stabilization structures, based on data from multiple sensor modules. The sensor modules are particularly configured to be installed in the application area, which means being fixed to the terrain or preferably fixed to the interception and / or stabilization structure, particularly fixed to the ropes of the interception and / or stabilization structure, preferably fixed to anchor ropes. Preferably, the sensor module is fixedly clamped to the anchoring rope of the interception and / or stabilization structure.

[0035] In addition, it is proposed that the sensor module housing is implemented without cable inlets, such as plugs, sockets, or cable conduits, without cable outlets, such as plugs, sockets, or cable conduits, without pressure switches, especially without mechanical switches, such as toggle switches and / or push-button switches, and without external antennas, such as plastic-sheathed rod antennas (“rubber tubes”) or dipole antennas. This advantageously allows for a long lifespan of the sensor module. Therefore, the sensor module is advantageously particularly resistant to bites and / or other damage caused by wild animals such as female deer, male deer, mink, wild boar, mice, rats, etc., which is especially important in outdoor applications of the sensor module. In particular, the outer side of the sensor module, especially the outer side of the sensor module housing, is at least substantially free of plastic coverings and / or other external plastic parts. “Substantially free” here specifically means that less than 25%, preferably less than 15%, advantageously less than 10%, preferably less than 5%, and particularly preferably less than 2% of the outer surface of the sensor module is made of plastic. In particular, the outer surface of the sensor module, especially the outer surface of the sensor module housing, is made of metal in at least most, preferably more than 75%, preferably more than 90%, and particularly preferably more than 95%.

[0036] It is also proposed that the sensor module, particularly the communication unit, includes a wireless camera interface for coupling with an external camera. This advantageously allows for the acquisition of comprehensive and / or important information regarding the risk of natural disasters. Advantageously, further information about the application area can be obtained, for example, this information can be taken into account in the pre-analysis of sensor data within the sensor module. Therefore, it is also advantageous to maintain at least substantially hermetically tight sealing of the sensor module housing. The wireless camera interface is particularly implemented as a Bluetooth interface, preferably as a Bluetooth Low Energy (BLE) interface. However, alternatively or additionally, other wireless interfaces, such as Near Field Communication (NFC) interfaces and / or ZigBee interfaces, are also conceivable.

[0037] Furthermore, it is proposed that the remote monitoring sensor device includes an external enable and / or disable element configured to enable and / or disable the sensor module based on the relative position of the external enable and / or disable element relative to the sensor module housing. This advantageously enables reliable and / or robust outdoor sensor monitoring. Advantageously, the sensor module, particularly enabling and / or disabling, can be controlled independently of external switching elements. The enable and / or disable element is preferably implemented as an enable and / or disable magnet. However, alternative implementations are also conceivable, such as as an adhesive element, as a clamping element, etc. The sensor module specifically includes a detection unit configured to detect the presence of the enable and / or disable element in the enable and / or disable position. The detection unit is implemented, for example, as a magnetic field sensor. However, alternatively, a mechanical switch is also conceivable, whose switching element is implemented as the enable and / or disable magnet attracting or repelling the enable and / or disable element, such that the switching process inside the sensor module can be controlled from outside the sensor module housing. Specifically, the sensor module is disabled whenever the external enabling and / or disabling elements are in the disabled position. Conversely, the sensor module is enabled whenever the external enabling and / or disabling elements are in the enabled position. For example, the sensor module is disabled whenever the enabling and / or disabling elements are attached to the sensor module housing, particularly in the disabled area of ​​the sensor module housing that forms the disabled position. Similarly, the sensor module is enabled whenever the enabling and / or disabling elements are located at a certain distance from the sensor module housing. The reverse switching is also conceivable.

[0038] If the communication unit is configured to directly transmit sensor data, preferably using the communication protocol of the GSM mobile communication standard, particularly without detours through one or more sensor data collection points to an external analysis and / or prediction unit, particularly implemented as a cloud, configured to receive sensor data from multiple sensor modules distributed across different application areas, particularly worldwide, a high level of data security is advantageously achieved. Advantageously, unauthorized access to sensor data can become quite difficult, especially since every individual communication of each sensor module must be acquired. Advantageously, this can be done without incurring additional costs and / or maintenance work at the collection points. Advantageously, high fault tolerance of the sensor network can be achieved, especially in the worst-case scenario where individual sensor modules may fail but not the entire collection point. Advantageously, the installation and / or setup of outdoor sensor networks can be simplified. The communication unit is particularly configured to encode the transmitted sensor data, preferably by means of an asymmetric cryptographic system. Preferably, the private and / or public keys assigned to the sensor modules in the asymmetric cryptographic system are already integrated into the sensor modules at the factory setting. This allows for a particularly high level of data security. Furthermore, to ensure high operational security, it is conceivable that sensor data is stored in a preferably encoded blockchain or a preferably encoded distributed ledger. Specifically, the external analysis and / or prediction unit includes a central communication unit configured to receive sensor data from multiple sensor modules of an outdoor sensor network distributed across different application areas, preferably from all sensor modules of the outdoor sensor network.

[0039] Furthermore, if the communication unit is configured to transmit sensor data to another, preferably adjacent, sensor module in the outdoor sensor network, in cases where external analysis and / or prediction units are inaccessible, particularly in cases of reduced and / or non-existent connectivity, especially in cases of GSM connectivity, then particularly wide coverage of the area can be advantageously achieved. Advantageously, sensor modules located in poorly connected or non-existent locations can be integrated into the outdoor sensor network. For this purpose, it is conceivable that communication between sensor modules also occurs via the communication unit, however, using alternative communication standards and / or alternative wireless interfaces, preferably wireless interfaces with relatively reduced range, such as LoRa. Specifically, sensor data is forwarded in the sensor module chain until it reaches a sensor module with sufficient connectivity for direct transmission to the external analysis and / or prediction unit.

[0040] Furthermore, it is proposed that the sensor module includes at least one acceleration sensor. In this way, reliable detection of impact events is advantageously achieved. As a result, it is possible to correlate the detected impact event with additional sensor data, such as tropospheric measurement data and / or further information about the application area (e.g., additional external measurement data). Specifically, the acceleration sensor implements an impact sensor. The acceleration sensor is configured to detect the acceleration occurring during the impact of an impactor in an intercepting and / or stabilizing structure monitored by at least one sensor module. Preferably, the acceleration sensor is configured to measure accelerations of at least 100g, preferably at least 150g, and preferably at least 200g, where 1g corresponds to 9.81 m / s². 2 The value of the acceleration sensor. Specifically, the acceleration sensor is configured to detect acceleration in all three spatial directions. Specifically, the acceleration sensor is configured to detect the direction of acceleration. The acceleration sensor is particularly composed of acceleration sensor types known to those skilled in the art, such as piezoelectric acceleration sensors, MEMS acceleration sensors, etc. Preferably, the function of the acceleration sensor is independent of the rope and / or the rope extending outside the housing unit of the monitoring device. Specifically, the acceleration sensor is entirely arranged inside the sensor module housing.

[0041] Furthermore, the sensor module includes at least one orientation sensor. In this way, high reliability of sensor data, particularly outdoor corrosion measurement data, is advantageously achieved. Specifically, the orientation sensor is configured to determine the orientation of the sensor module relative to the direction of operation of gravity. Specifically, the orientation sensor is configured to determine the orientation of the outdoor corrosion sensor relative to the direction of operation of gravity. Specifically, based on the orientation change after an event, such as after an impact from an impactor, additional information about the event, such as the impact intensity or impact direction, can be obtained. Specifically, using orientation measurement ensures the quality and / or reliability of the outdoor corrosion sensor data, particularly because it can identify erroneous orientations of the outdoor corrosion sensor, such as an inverted position. If the outdoor corrosion sensor is completely or partially inverted, there may be no or insufficient precipitated moisture reaching the outdoor corrosion sensor, causing the corrosion monitoring element of the outdoor corrosion sensor, particularly compared to metal parts fully exposed to total precipitated moisture, to experience less corrosion. This means it will generate a smaller corrosion current and therefore measure an excessively low corrosion value. Specifically, the orientation sensor is specifically implemented as an orientation or position sensor known to those skilled in the art. It is particularly noteworthy that the orientation sensor can also function as an acceleration sensor, and vice versa.

[0042] It is also proposed that the sensor module includes at least a rope force sensor. This advantageously enables effective and / or reliable monitoring of structures including ropes, particularly anchor ropes. Advantageously, impact events in intercepting structures such as rockfall barriers and / or infill events in intercepting structures such as debris flow barriers can be reliably detected. Furthermore, the rope force sensor allows for the advantageous measurement of the intensity of events, particularly impact events and / or infill events. Preferably, the rope force sensor is configured to measure rope forces up to 50 kN, advantageously up to 100 kN, particularly advantageously up to 150 kN, preferably up to 200 kN, and particularly preferably up to 294 kN. Preferably, the rope force sensor functions independently of the cable and / or the rope extending outside the housing unit of the monitoring device. In particular, the rope force sensor is entirely housed within the sensor module housing.

[0043] Furthermore, it is proposed that, for measuring rope force, the rope force sensor includes at least one strain gauge strip, which is preferably arranged separately from the rope, and the rope force is monitored by the rope force sensor. This advantageously achieves a particularly simple and / or uncomplicated measurement of rope force. Specifically, the strain gauge strip is configured to determine the deformation of the rope contact element of the sensor module caused by the occurring rope force. Specifically, the strain gauge strip is arranged inside the sensor module housing. Specifically, the strain gauge strip has temperature response compensation. Specifically, the strain gauge strip is implemented as a self-compensating strain gauge strip. Specifically, the strain gauge strip never directly contacts the rope to be monitored. Specifically, the strain gauge strip is arranged on the side of the rope contact element facing the interior of the sensor module housing. Specifically, the strain gauge strip is arranged on the side of the rope contact element opposite to the rope to be monitored.

[0044] It is also proposed that the rope force sensor is at least partially integrated with the connection unit of the sensor module, wherein the connection unit is configured to directly fix the sensor module to the structure, preferably to the rope of the structure, and preferably to the anchoring rope of the structure. This enables particularly advantageous and / or compact rope force measurement. Specifically, the connection unit is configured to deflect the rope, particularly the anchoring rope, via a rope contact element, such that the force acting on the rope, i.e., particularly the rope force, causes the rope contact element to deform in a measurable manner. Advantageously, the connection unit is universally designed for different ropes, particularly for ropes of different thicknesses. In particular, by means of the connection unit, the sensor module can be mounted on ropes having a thickness between 16 mm and 24 mm. Without departing from the invention, the connection unit can be easily adapted to thicker or thinner ropes. Advantageously, every structure including ropes capable of generating rope force, particularly anchoring ropes, can be modified with the sensor module. In particular, by means of the connection unit, the sensor module can be mounted on all structures having ropes, particularly anchoring ropes. The two units that are “partially integrated” should be understood in particular to include one, in particular at least two, advantageously at least three common elements that are components of the two units, particularly functionally related components.

[0045] Furthermore, it is proposed that the outdoor corrosion sensor is based on the measurement of corrosion current (referred to as "corrosion current") generated by corrosion, wherein the corrosion sensor includes at least one charge storage device, such as a capacitor, which is charged by the corrosion current until a charging limit is reached, and then the charge storage device, in particular the capacitor, discharges, and wherein the sensor module includes a galvanometer configured to measure the discharge current of the charge storage device, in particular the capacitor, to determine the outdoor corrosion measurement data. In this way, particularly accurate and / or reliable corrosion measurements can be advantageously achieved, especially because low corrosion currents, for example, in the μA range, can also be reliably measured. As a result, measurements of particularly low corrosion currents, particularly in the μA range, are possible, which typically occur in the outdoor corrosion sensors used, particularly in ACM-type sensors, without requiring excessive technical effort (e.g., without a zero-ohm galvanometer). Advantageously, this makes it possible to implement outdoor corrosion sensors particularly cost-effectively. The corrosion current is, in particular, a galvanic current.

[0046] Furthermore, it is proposed that the sensor module includes at least one battery configured to power at least one component of the sensor module, wherein the corrosion current of the outdoor corrosion sensor is used as the charging current for the battery. In this way, an exceptionally long battery life is advantageously achieved for the sensor module. Advantageously, the sensor module can operate in a self-holding manner for exceptionally long periods.

[0047] Furthermore, it is proposed that the sensor module includes a pre-analysis unit configured to perform at least one proximity sensor pre-analysis of measurement data (particularly raw measurement data) from at least one sensor of the sensor module and / or at least one external sensor (e.g., an external camera) coupled to the sensor module. This advantageously allows for particularly efficient use of electrical energy, where only a limited amount is available. Advantageously, the amount of data transmitted via the communication unit can be reduced and / or optimized. In this way, the lifespan of the sensor module, particularly the lifespan of the sensor module's battery and / or rechargeable battery, can be optimized, especially since data transmission constitutes a large portion of the energy consumption of the sensor module. Specifically, the pre-analysis unit is configured to perform proximity sensor analysis of the raw measurement data. Specifically, the pre-analysis unit is configured to average, summarize, and / or process the raw data. Specifically, the raw data remains stored in the sensor module and can be requested or read directly in the field for use in new analyses or for quality checks performed by the analysis and / or prediction unit. Specifically, the pre-analysis unit is configured to autonomously adapt the transmission intervals and / or transmission times of data transmission to the analysis and / or prediction unit based on the pre-analysis of the raw data. For example, in phases where pre-analyzed tropospheric measurements are typically expected to be low in activity (e.g., regarding corrosion and / or rockfalls), such as during calm and dry weather conditions, the transmission interval can be increased. Specifically, the pre-analysis of the measurement data is configured to minimize the amount of data transmitted. Specifically, the pre-analysis of the measurement data is configured to reduce the total power consumption of the sensor module. Specifically, the energy consumed due to the pre-analysis is less than the energy saved by eliminating the transmission of all raw data. The pre-analysis unit is particularly implemented as a computing unit allocated to the sensor module. The term "computing unit" should be understood in particular as a unit having information input, information processing, and information output. Advantageously, the computing unit includes at least one processor, memory, input and output components, additional electrical components, operating procedures, regulation routines, control routines, and / or calculation routines. Preferably, the components of the computing unit are arranged on a common circuit board and / or advantageously arranged in a common housing.

[0048] If the pre-analysis unit is also configured to autonomously select which portion of the sensor's measurement dataset is transmitted by the communication unit, and / or if the pre-analysis unit is configured to autonomously select whether the sensor's measurement dataset is transmitted by the communication unit, advantageous optimization of energy consumption can be achieved. For example, it is conceivable that the pre-analysis unit compares images recorded by an external camera with previously recorded images, and only transmits a new image by the communication unit if the new image includes a fundamental change compared to the previously recorded image. For example, it is conceivable that the communication unit transmits the sensor dataset of the sensor module only if another dataset from another sensor meets a specific criterion, such as indicating a specific event (e.g., only transmitting the data from the accelerometer and / or the accelerometer dataset if the data allows inference that an impact event or similar event has occurred).

[0049] If the pre-analysis unit is configured to define the transmission interval of the communication unit using measurement data from at least one sensor of the sensor module and / or at least one external sensor coupled to the sensor module, advantageous optimization of energy consumption can be achieved. Specifically, the pre-analysis unit shortens the transmission interval during periods of increased activity (e.g., increased corrosion, increased rockfall activity, increased wind speed, increased precipitation, etc.). Conversely, the pre-analysis unit increases the transmission interval during periods of low activity (e.g., low or no corrosion, low or no rockfall activity, low wind speed, no precipitation, etc.).

[0050] Furthermore, advantageous optimization of energy consumption can be achieved if the pre-analysis unit is configured to define the standby phase and / or measurement interval adjustment of at least one sensor and / or at least one additional sensor based on measurement data from at least one sensor of the sensor module and / or at least one external sensor coupled to the sensor module, which is specifically different from the sensor. For example, it is conceivable that an external camera records images only when the measurement data from the additional sensor indicates an event, such as an impact or similar event. In particular, in this case, image recording by the camera is triggered by measurement data from the sensor module, which is determined by another sensor and analyzed by the pre-analysis unit in a manner close to that of the sensor. For example, it is conceivable that the sensor of the sensor module is activated only when another dataset from the other sensor meets a specific criterion, such as indicating a specific event (e.g., the orientation sensor is activated only when the data from the accelerometer allows inference that an impact event, etc., has occurred). For example, it is conceivable that if the sensor's measurement data does not change over a long period of time, the sensor of the sensor module enters a standby operating state (e.g., if there is no precipitation and no significant wind force is measured, the orientation sensor enters a standby operating state).

[0051] Furthermore, it is proposed that the sensor module includes a computing unit with a specially developed operating system, which is not based on an existing operating system and is specifically configured to control and / or regulate the sensor, communication unit, pre-analysis unit, etc. In this way, exceptionally high data and / or misuse security is advantageously achieved. Advantageously, exceptionally high security against hacking attacks such as Trojan horses can be achieved, especially since any malicious software must be specifically built for the sensor module's specific operating system. The computing unit is specifically configured to control and / or regulate the sensor, communication unit, pre-analysis unit, etc. The computing unit specifically implements the pre-analysis unit at least in part.

[0052] Furthermore, it is proposed that the sensor module includes an energy harvesting unit configured to obtain current, particularly a charging current, from a temperature difference, especially the temperature difference within the sensor module housing, for charging the sensor module's battery. In this way, exceptionally high energy efficiency is advantageously achieved. Advantageously, this allows for an exceptionally long lifespan for the sensor module. Advantageously, the self-holding capacity of the sensor module is increased in this manner. Specifically, the energy harvesting unit includes at least one thermoelectric generator. Specifically, the thermoelectric generator is based on utilizing the Seebeck effect to generate the charging current. Specifically, the energy harvesting unit includes at least one Seebeck element. Specifically, the energy harvesting unit is configured to generate current and / or voltage by utilizing the temperature difference between the upper side of the sensor module housing (directly exposed to solar radiation) and the lower side of the sensor module housing (located in the shadow of the sensor module).

[0053] Furthermore, it is proposed that the remote monitoring sensor device includes at least one additional sensor module, which is implemented separately from the sensor module and assigned to the same application area as the sensor module. In this way, particularly comprehensive and precise monitoring of the application area is advantageously achieved. Consequently, it is advantageous to consider the possibility of different conditions existing within the same application area, for example, conditions that may lead to different corrosion intensities (windward side of the slope relative to the leeward / rain shadow side) or may lead to locally different rockfall frequencies (e.g., gradient / geology of the overlying terrain). In particular, the remote monitoring sensor device includes at least two, preferably at least three, preferably at least four, and particularly preferably more than five sensor modules, which are installed in different locations within the application area in each case. Preferably, the sensor modules of the remote monitoring sensor device, particularly the sensor module and the additional sensor modules, are at least substantially identical to each other. In particular, all sensor modules of the remote monitoring sensor device are wirelessly connected to the same analysis and / or prediction unit. In particular, the outdoor sensor network includes multiple remote monitoring sensor devices, each of which includes multiple sensor modules.

[0054] Furthermore, it is proposed that at least one additional sensor module is implemented without a (local) communication connection to the sensor module. This advantageously allows for a high level of data security. Preferably, each of the sensor modules in the remote monitoring sensor device communicates only directly with the analysis and / or prediction unit located outside the application area.

[0055] Furthermore, it is proposed that the sensor module includes a setup module configured to wirelessly communicate with an external setup device (e.g., a smartphone) of the installer, for example via an NFC interface of the communication unit, for configuration of the sensor module, particularly for initial configuration and / or reconfiguration of the sensor module. This advantageously achieves a particularly simple installation process. In particular, errors in sensor data that could lead to erroneous data during sensor module installation can be advantageously avoided. Specifically, the sensor module, preferably the communication unit, includes an interface for near-field data transmission, such as a Bluetooth interface, a BLE interface, or preferably an NFC interface, which is specifically configured for communication between the setup module and the external setup device.

[0056] Furthermore, it is proposed that the sensor module includes setting elements, such as QR codes, barcodes, NFC interfaces, or similar devices, which can be read, scanned, or activated by an external setting device to initiate the configuration of the sensor module, particularly the initial configuration and / or reconfiguration of the sensor module. This advantageously achieves a high degree of user-friendliness and advantageously reduces the risk of incorrect installation. Therefore, high reliability and / or high data quality of the sensor data can be ensured. Specifically, after reading, scanning, and / or activating the setting elements, the installer is guided through at least a semi-automatic setup process, during which the external setting device preferably communicates with the sensor module via a wireless interface, such as an NFC interface, and during this process, configuration data is preferably sent from the external setting device to the sensor module, or vice versa. Specifically, the setup process is guided by application software (app) installed on the external setting device. Specifically, the installer is guided through the setup process by the app. In particular, preferably after successful configuration, especially initial configuration and / or reconfiguration, at least a portion of the data exchanged between the external setup device and the sensor module and / or at least a portion of the configuration data of the sensor module will be automatically and wirelessly transmitted to the analysis and / or prediction unit.

[0057] Specifically, the setup process includes obtaining the company performing the setup, particularly the company name, and / or the installer performing the setup, particularly the personnel number and / or the installer's name. Specifically, the setup process includes obtaining the application area, such as project name, project number, structure name, etc. Specifically, the setup process includes (fully automatically) obtaining the sensor module's identifier, such as serial number or registration number. Specifically, the setup process includes obtaining the sensor module's geographic coordinates, such as GPS coordinates, particularly the sensor module's installation location. Preferably, the geographic coordinates are obtained through the geographic tracking function of the external setup device, particularly the GPS function. However, it is also generally conceivable that the sensor module includes a GPS sensor. To receive the most accurate geographic coordinates possible, during the setup process, when obtaining the geographic coordinates, the installer may be requested to bring the external setup device to a predetermined position relative to the sensor module, such as contact with a specific surface of the sensor module. Specifically, the setup process includes obtaining the time zone, date, and / or time. Preferably, the setup time zone, device date, and / or device time of the external setup device are used here. Specifically, the assembly process includes acquiring the precise installation position of the sensor module on the structure, particularly the precise fixation position of the sensor module on the interception and / or stabilization structure. Specifically, the setup process includes acquiring precise specifications of the structure, particularly the type specification, and specifically the interception and / or stabilization structure to which the sensor module is fixed. Specifically, the setup process includes acquiring images, particularly photographs, of the sensor module's installation status, particularly its assembly status. Preferably, the images are formed using an external setup device. Alternatively, the images may also be generated by a camera on the sensor module or by an external camera wirelessly connected to the sensor module. Specifically, the setup process includes acquiring the diameter of the ropes on the structure, particularly the diameter of the ropes on the interception and / or stabilization structure to which the sensor module is fixed via a connecting unit.

[0058] Furthermore, an outdoor sensor network with multiple remote monitoring sensor devices is proposed. These remote monitoring sensor devices comprise different application areas and each includes a sensor module. The sensor modules are assigned to different application areas and, in each case, communicate wirelessly, particularly directly, with a shared external analysis and / or prediction unit, preferably wirelessly directly connected in each case. Therefore, it is advantageous to obtain, process, and / or implement comprehensive and / or critical information regarding natural disaster risks, particularly in the application area or across multiple application areas, into activities and / or instructions for activities.

[0059] Furthermore, a structure, particularly a natural disaster prevention facility, preferably an interception and / or stabilization structure, such as a rockfall barrier, avalanche protection, rockfall curtain, slope protection, debris flow barrier, and / or attenuator, is proposed. This structure includes at least one rope, particularly an anchored wire rope, and at least one sensor module of a remote monitoring sensor device, the sensor module being fixed to the rope. This advantageously allows for obtaining comprehensive and / or significant information regarding the natural disaster risk of the structure and / or the environment surrounding the structure. The structure specifically includes at least one additional rope. In particular, another sensor module of the remote monitoring sensor device is fixed to this additional rope. It is also conceivable to allocate more than two sensor modules of the remote monitoring sensor device to the structure, and in particular, to fix more than two sensor modules of the remote monitoring sensor device to the structure.

[0060] The analytical and / or predictive methods and / or remote monitoring sensor devices according to the present invention are not limited to the applications and implementations described above. In particular, to achieve the functions described herein, the actuator device according to the present invention, the magnetic actuator according to the present invention, the engine mount according to the present invention, and the method according to the present invention may include a number of individual elements, components, method steps, and units that differ from the number given herein. Attached Figure Description

[0061] Further advantages will become apparent from the following description of the accompanying drawings. Exemplary embodiments of the invention are illustrated in the drawings. The drawings, description, and claims contain combinations of multiple features. Those skilled in the art may also purposefully consider these features individually and discover other advantageous combinations.

[0062] The attached diagram shows: Figure 1 : A schematic diagram of an outdoor sensor network with remote monitoring sensor devices. Figure 2 A schematic diagram of the application area of ​​the remote monitoring sensor device for an outdoor sensor network, which is implemented as a structure. Figure 3 : A schematic side view of the sensor module of the remote monitoring sensor device, showing the ropes securing the sensor module to the structure. Figure 4 Another schematic perspective view of the sensor module of the remote monitoring sensor device. Figure 5 A schematic flowchart illustrating the sensor module setup process. Figure 6 A schematic flowchart illustrating analytical and / or predictive methods for protection against natural disasters based on outdoor sensor networks, and Figure 7: A schematic flowchart of a method for performing proximity sensor analysis on sensor data by a sensor module near the sensor. Detailed Implementation

[0063] Figure 1 A schematic diagram of an outdoor sensor network 12 is shown. The outdoor sensor network 12 is configured at least to record sensor data for the sensor network-based analysis and / or prediction methods described below. The outdoor sensor network 12 includes multiple remote monitoring sensor devices 36 (see...). Figure 2 The outdoor sensor network 12 extends over multiple different application areas 20. The application areas 20 can be distributed worldwide. A remote monitoring sensor device 36 is assigned to each of the different application areas 20. Each remote monitoring sensor device 36 includes one or more sensor modules 10, which are therefore also fixedly assigned to the corresponding application area 20. Figure 1 An external analysis and / or prediction unit 14 is also shown, which may also be assigned to the outdoor sensor network 12. The external analysis and / or prediction unit 14 is implemented as a cloud. However, alternatively, the external analysis and / or prediction unit 14 may also be implemented as a single central server or a combination of servers. Remote monitoring sensor devices 36, preferably sensor modules 10 of each remote monitoring sensor device 36, communicate wirelessly with the external analysis and / or prediction unit 14. Remote monitoring sensor devices 36, preferably sensor modules 10 of each remote monitoring sensor device 36, communicate directly with the external analysis and / or prediction unit 14. Remote monitoring sensor devices 36, preferably sensor modules 10 of each remote monitoring sensor device 36, communicate with the external analysis and / or prediction unit 14 via a direct GSM mobile communication data connection. The same external analysis and / or prediction unit 14 communicates with all sensor modules 10 of all remote monitoring sensor devices 36 in the outdoor sensor network 12. The external analysis and / or prediction unit 14 is configured to receive sensor data from multiple sensor modules 10, 10', 10'' distributed across different application areas 20, 20', 20''. The external analysis and / or prediction unit 14 includes a communication device (not shown) for communicating with the outdoor sensor network 12.

[0064] An external analysis and / or prediction unit 14 is implemented as a shared external analysis and / or prediction unit for all sensor modules 10 of the outdoor sensor network 12. The external analysis and / or prediction unit 14 collects determined sensor data from all sensor modules 10 of the outdoor sensor network 12. The external analysis and / or prediction unit 14 includes a memory unit 16 having at least one data storage medium. The external analysis and / or prediction unit 14 is configured to store the collected sensor data of the sensor modules 10 of the outdoor sensor network 12 in the memory unit 16. The external analysis and / or prediction unit 14 is also configured to receive, collect, and / or store additional data from a database 90 outside the sensor network 12. This additional data from the database 90 outside the sensor network 12 specifically includes additional information about the application area 20.

[0065] The external analysis and / or prediction unit 14 includes a processor unit 88 having at least one processor. The external analysis and / or prediction unit 14 includes an operating procedure configured to process collected and / or stored data, and which can be invoked and executed by the processor unit 88. The external analysis and / or prediction unit 14 is configured to use the operating procedure to analyze and / or process the collected and / or stored data. The external analysis and / or prediction unit 14 is configured to use the operating procedure to establish relationships between the collected and / or stored data. The external analysis and / or prediction unit 14 is configured to perform pattern recognition based on the collected and / or stored data.

[0066] The external analysis and / or prediction unit 14 is configured to provide user group 18 with data processed by operating procedures and / or unprocessed data received from sensor module 10. User group 18 can access external analysis and / or prediction unit 14, for example, via display device 92, particularly the user interface (“dashboard”) of external analysis and / or prediction unit 14, which may be embodied in a PC or smartphone. Alternatively, it is also conceivable that external analysis and / or prediction unit 14 transmits data to user group 18, particularly to display device 92 of user group 18 (e.g., in the form of notifications). Furthermore, it is conceivable that user group 18 includes drone 34.

[0067] Some application areas 20 are structures 24. At least one application area 20 here is a structure 24 comprising metal components exposed to atmospheric corrosion. The metal components of structure 24 are, for example, ropes 56 (see...). Figure 2 ), especially with anchoring wire rope 228. Some of the practical areas 20 implemented by structure 24 are natural disaster prevention facilities 32. Figure 1 An exemplary application area 20 is implemented as a rockfall barrier 76. The rockfall barrier 76 includes a rope 56 to which the sensor module 10 is fixed. Figure 1 Another exemplary application area 20 is implemented as avalanche protection 78. Avalanche protection 78 includes a rope 56 to which the sensor module 10 is attached. Figure 1 Another exemplary application area 20 is implemented as a rockfall curtain 80. The rockfall curtain 80 includes a rope 56 to which the sensor module 10 is fixed. Figure 1 Another exemplary application area 20 is implemented as slope protection 82. Slope protection 82 includes a rope 56 to which the sensor module 10 is fixed. Figure 1 Another exemplary application area is implemented as a debris flow barrier 84 and / or debris flow blockage. The debris flow barrier 84 and / or debris flow blockage includes a rope 56 to which the sensor module 10 is fixed. Figure 1 Another exemplary application area 20 is implemented as an attenuator 86. The attenuator 86 includes a rope 56 to which the sensor module 10 is attached. Some of the application areas 20 implemented as structures 24 differ from the natural disaster prevention facility 32. Figure 1 An exemplary application area 20 is implemented as a suspension bridge 96. The suspension bridge 56 includes a rope 56 to which the sensor module 10 is fixed. Figure 1 Another exemplary application area 20 is implemented as a stadium roof anchor 98. The stadium roof anchor 98 includes a rope 56 to which the sensor module 10 is fixed. Figure 1 Another exemplary application area 20 is implemented as a wind turbine anchor 100, particularly a wind turbine mast anchor. The wind turbine anchor 100, particularly the wind turbine mast anchor, includes a rope 56 to which the sensor module 10 is secured. Figure 1 Another exemplary application area 20 is implemented as facade anchoring 102. Façade anchoring 102 includes a rope 56 to which the sensor module 10 is secured. Some other application areas 20 are locations without structures, such as slope 94.

[0068] Figure 2 A schematic diagram of an application area 20 is shown as an example, which is implemented as a structure 24. Figure 2 The structure 24 shown is implemented as a natural disaster prevention facility 32. Figure 2 The structure 24 shown is implemented as an interception and / or stabilization device 222, and in particular as an interception and / or stabilization structure. Figure 2 The structure 24 shown is implemented as a rockfall barrier 76. This rockfall barrier 76 includes a wire mesh 226, which, as an example, is specifically a ring-shaped mesh, and... Figure 2The diagram shows only a cross-section. In this case, the loops of the ring net are formed by the mesh openings of the wire mesh 226. Therefore, the diameter of the loops of the ring net constitutes the mesh size of the wire mesh 226. The application area 20, particularly the rockfall barrier 76, includes a remote monitoring sensor device 36. The remote monitoring sensor device 36 assigned to the application area 20, particularly the rockfall barrier 76, exemplarily includes three sensor modules 10, 10', and 10''. The sensor modules 10, 10', and 10'' are respectively fixed to different ropes 56, 56', and 56'' of the rockfall barrier 76. The sensor modules 10, 10', and 10'' are respectively arranged in different areas of the rockfall barrier 76. From the front view of the rockfall barrier 76, one of the sensor modules 10, 10', and 10'' is located in the upper left region of the rockfall barrier 76. Viewed from the front of the rockfall barrier 76, another sensor module 10' of sensor modules 10, 10', 10'' is located in the upper right region of the rockfall barrier 76. Viewed from the front of the rockfall barrier 76, yet another sensor module 10'' of sensor modules 10, 10', 10'' is located in the lower left region of the rockfall barrier 76. Alternative arrangements of sensor modules 10, 10', 10'' and / or arrangements of another sensor module 10, 10', 10'' on the rockfall barrier 76 are conceivable. Ropes 56, 56', 56'' are, in each case, anchoring wire ropes 228 for the rockfall barrier 76. In the event of an impact from an impactor (not shown) within the rockfall barrier 76, rope forces are applied to ropes 56, 56', 56''. Sensor modules 10, 10', and 10'' are respectively arranged on the upper side of the correspondingly assigned ropes 56, 56', and 56'', especially relative to the direction of gravity 126.

[0069] Figure 3A schematic side view of a sensor module 10 of a remote monitoring sensor device 36 is shown, which is fixed to a rope 56. The sensor module 10 includes a connection unit 224. The connection unit 224 is configured to directly fix the sensor module 10 to the rope 56 of the structure 24. The connection unit 224 includes a rope receiving element 104. The rope receiving element 104 is implemented as a U-hook. The connection unit 224 includes a tensioning element 106. The connection unit 224 includes another tensioning element 108. The tensioning elements 106 and 108 are implemented as nuts. The rope receiving element 104 has threads at each end for tightening the tensioning elements 106 and 108 thereon. To secure the sensor module 10 to the rope 56, a rope receiving element 104 is placed on the rope 56, guided through a tunnel-like recess 110 within the sensor module 10, and secured to the side of the sensor module 10 opposite to the rope 56 by screwing tensioning elements 106 and 108 onto the rope receiving element 104. The tensioning elements 106 and 108 are tightly screwed onto the rope receiving element 104, such that one side of the rope 56 is pressed against the outside of the sensor module 10 by the rope receiving element 104. The connecting unit 224 is configured to secure the sensor module 10 to the rope 56 in a non-slip manner relative to the longitudinal axis of the rope 56. The connecting unit 224 is also configured to secure the sensor module 10 to the rope 56 in a rotatably fixed manner relative to the longitudinal axis of the rope 56.

[0070] Figure 4 A schematic perspective view of the sensor module 10 (without connection unit 224) of the remote monitoring sensor device 36 is shown, particularly a schematic perspective view of the upper side 120 of the sensor module 10. The sensor module 10 shown is configured for use in an outdoor sensor network 12.

[0071] Sensor module 10 includes an outdoor corrosion sensor 38. The outdoor corrosion sensor 38 is configured to measure outdoor corrosion measurement data. The outdoor corrosion sensor 38 is configured to measure corrosion intensity. The outdoor corrosion sensor 38 is configured to measure the rate of removal of the anti-corrosion coating. The outdoor corrosion sensor 38 is implemented as an ACM sensor. The outdoor corrosion sensor 38 includes electrodes 112, 114. In the example shown, the outdoor corrosion sensor 38 includes exactly five electrodes 112, 114. The electrodes 112, 114 are arranged parallel to each other. In each case, two electrodes 114 are arranged above and below the central electrode 112, respectively, in a shared plane. The central electrode 112 forms the anode. The other electrodes 114 form the cathode. The electrodes 112, 114 have at least substantially the same external shape as each other. The surface of the cathode electrode 114 comprises a metal of higher nobility than the surface of the central electrode 112 forming the anode. In the illustrated example, the surface of electrode 114 forming the cathode is made of steel, while the surface of electrode 112 forming the anode is made of zinc, particularly a zinc coating of steel wire. Between electrodes 112 and 114, the outdoor corrosion sensor 38, implemented as an ACM sensor, has an air gap in each case. The air gap serves as an insulator. A maximum distance of 0.4 mm, preferably a maximum of 0.3 mm, and preferably not exceeding 0.2 mm exists between the cathode electrode 114 and the anode electrode 112. A maximum distance of 0.4 mm, preferably a maximum of 0.3 mm, and preferably not greater than 0.2 mm also exists between the cathode electrodes 114. An outdoor corrosion sensor 38 has two end caps 116 and 118 at its lateral ends, which are implemented as insulators. End caps 116 and 118 serve as holders for electrodes 112 and 114. The electrical contacts of electrodes 112 and 114 are guided within end caps 116 and 118. The passageway from the electrical contacts of end caps 116, 118 and / or electrodes 112, 114 to the interior of the sensor module housing 44 of the sensor module 10 is at least substantially hermetically sealed. Due to the air gap, there is no current flowing from the anode to the cathode in a dry state. When the outdoor corrosion sensor 38 becomes wet, for example due to condensation or precipitation, current can flow due to conductive particles, such as ions, dissolved in the water and specifically from one of electrodes 112, 114. This current is driven by the different redox potentials of the different materials of the anode electrode 112 and the cathode electrode 114. With the current flowing, material is removed from the anode. The current is proportional to the material removed. The current depends on the type and amount of chemicals dissolved in the water. For example, an increase in the amount of salt, such as sulfate or sodium chloride, will result in an increased current. The outdoor corrosion sensor 38 is arranged on the upper side 120 of the sensor module 10. To measure the corrosion current generated by corrosion, the outdoor corrosion sensor 38 includes at least one charge storage device 58. The charge storage device 58 is implemented as a capacitor.The charge storage device 58 is charged by the flow of a corrosion current until it reaches its charging limit. When the charge storage device 58 has been charged to its charging limit by the corrosion current, it discharges with current pulses. The sensor module 10, particularly the outdoor corrosion sensor 38, includes a galvanometer 60. The galvanometer 60 is configured to measure the current pulses generated by the discharge current of the charge storage device 58. The sensor module 10 is configured to determine outdoor corrosion measurement data from the current pulses generated by the discharge current of the charge storage device 58.

[0072] Sensor module 10 includes an acceleration sensor 50. The acceleration sensor 50 is disposed inside sensor module housing 44. The acceleration sensor 50 is configured to detect disturbances in sensor module 10. Sensor module 10 includes an orientation sensor 52. The orientation sensor 52 is configured to detect the relative orientation of sensor module 10, particularly the relative orientation of the upper side 120 of sensor module 10 relative to the direction of gravity 126. Sensor module 10 includes a rope force sensor 30. The rope force sensor 30 is configured to detect forces acting on a rope 56 to which sensor module 10 is attached. The rope force sensor 30 includes strain gauge strips 54. Sensor module 10 includes a rope contact element 128. The rope contact element 128 is disposed on the outer side of sensor module 10, particularly on the lower side 130. The strain gauge strip 54 is configured to indirectly measure rope force by the degree and / or amplitude of deformation of the rope contact element 128 of sensor module 10 caused by rope 56. The strain gauge strip 54 is arranged separately from the rope 56 whose rope force is to be monitored by the rope force sensor 30. The strain gauge strip 54 is arranged on the side of the rope contact element 128 opposite to the rope 56. The strain gauge strip 54 is arranged inside the sensor module housing 44, particularly inside the sensor module housing 44. The rope force sensor 30 is at least partially integrated with the connection unit 224 of the sensor module 10. The surfaces of the sensor module 10, particularly the rope contact surfaces, in the (near) region of the rope contact element 128 and in the (near) region of the connection unit 224 are located in different planes that are preferably parallel to each other. The rope contact surfaces of the rope contact element 128 and the connection unit 224 are arranged along the longitudinal direction 132 of the sensor module 10 and / or the rope 56. The rope 56 is spaced apart from each other via the rope contact element 128 and the rope contact surfaces of the rope 56 and / or the sensor module 10. The rope 56 is adjacent to the sensor module 10 in the region of the rope contact element 128 and the region of the connection unit 224, thereby deflecting the rope 56 from a straight path. The connecting unit 224 and / or the rope contact element 128 are configured to deflect the rope 56 in segments. Since the connecting unit 224 significantly contributes to the deflection of the rope 56, the connecting unit 224, particularly the rope receiving element 104, constitutes a major part of the rope force sensor 30. The rope 56, preferably deflected via the rope contact element 128 and the connecting unit 224, is re-deflected and released from deflection by the rope force acting on, particularly pulling, the rope 56. The rope contact element 128 bends due to the rope force acting on, particularly pulling, the rope 56. In the illustrated embodiment, the rope contact element 128 is implemented as a metal rod, particularly an aluminum rod. The strain gauge strips 54 arranged on, particularly on, the inner side of the rope contact element 128, are stretched or compressed (unevenly) by the bending of the rope contact element 128.Based on the expansion of strain gauge strip 54, rope force sensor 30 determines the rope force that causes rope contact element 128 to bend.

[0073] The sensor module 10 includes an energy storage unit 124. The energy storage unit 124 can be implemented as a battery, particularly a battery with a minimum lifespan of 10 years under normal conditions. However, in... Figure 4 In the example shown, energy storage unit 124 is implemented as battery 62. Energy storage unit 124 is configured to provide power for at least one component of sensor module 10, such as at least one sensor of sensor module 10 and / or at least one computing unit 66 of sensor module 10. In the example shown, the corrosion current of outdoor corrosion sensor 38 is used as the charging current for battery 62. Sensor module 10 includes energy harvesting unit 68. Energy harvesting unit 68 is configured to obtain current from the temperature difference within sensor module 10, preferably within sensor module housing 44. Energy harvesting unit 68 includes a thermoelectric generator for generating electricity.

[0074] Sensor module 10 includes an environmental sensor unit 122. Environmental sensor unit 122 includes at least one environmental sensor 40, preferably multiple environmental sensors 40, such as a thermometer, hygrometer, solar intensity meter, anemometer, barometer, and / or at least additional measuring devices, such as measuring devices for detecting trace gases, salt concentrations, or aerosol concentrations. Environmental sensor unit 122, particularly environmental sensor 40, is configured to measure tropospheric measurement data. Sensor module housing 44 is hermetically sealed. Sensor module housing 44 is configured to hermetically separate the interior of sensor module housing 44 from the surrounding environment. At least one environmental sensor 40 includes a measuring probe (not shown) protruding from sensor module housing 44 of sensor module 10. The measuring probe is hermetically grouted to maintain hermetically sealed nature. Sensor module housing 44 is implemented without cable inlets. Sensor module housing 44 is implemented without cable outlets. Sensor module housing 44 is implemented without pressure switches. Sensor module housing 44 is implemented without mechanical switches. The sensor module housing 44 is implemented without an external antenna.

[0075] Sensor module 10 includes a communication unit 42. Communication unit 42 is configured to wirelessly and / or directly transmit sensor data, particularly sensor data from environmental sensor unit 122 and / or outdoor corrosion sensor 38, to an external analysis and / or prediction unit 14. Communication unit 42 is configured to transmit sensor data from the respective sensor module 10 to the shared external analysis and / or prediction unit 14 without detouring via one or more collection points for the sensor data. Communication unit 42 includes a GSM receiving and transmitting module. Communication unit 42 is equipped with a SIM card, which allows for data transfer of approximately 1GB (approximately 10 years) of continuous operation for all sensors of sensor module 10. Communication unit 42 is implemented without an external antenna. Communication unit 42 includes an integrated antenna.

[0076] Sensor module 10, particularly communication unit 42, includes a wireless camera interface. The wireless camera interface is configured to couple with an external camera 46. The external camera 46 can be configured as, for example, a wildlife camera and / or a surveillance camera monitoring sensor module 10 and / or building 24. The external camera 46 is specifically implemented as a Bluetooth camera. Remote monitoring sensor device 36 and / or outdoor sensor network 12 include the external camera 46. It is also conceivable that communication unit 42 may have another communication interface with a reduced transmission range, configured to transmit sensor data to another, preferably adjacent, sensor module 10' of the outdoor sensor network 12, which may be assigned to the same application area 20 or to another, particularly adjacent, application area 20, when the external analysis and / or prediction unit 14 is inaccessible.

[0077] The sensor module 10 includes a computing unit 66. The computing unit 66 is configured to inspect, control, and / or adjust the internal functions of the sensor module 10, such as the internal functions of the sensors of the sensor module 10, the internal functions of external sensors of the communication unit 42, the internal functions of the external camera 46, and encoding of sensor data transmitted by the communication unit 42. The computing unit 66 includes its own specially developed operating system, which is not based on existing operating systems.

[0078] Sensor module 10 includes a pre-analysis unit 64. The pre-analysis unit 64 is implemented as an integral part of computing unit 66. The pre-analysis unit 64 is configured to perform proximity sensor pre-analysis on measurement data, particularly raw data, from at least one sensor of sensor module 10. The pre-analysis unit 64 is also configured to perform proximity sensor pre-analysis on measurement data, particularly raw data, from at least one external sensor coupled to sensor module 10, such as external camera 46. The pre-analysis unit 64 is configured to automatically select which portion of the sensor's measurement dataset is transmitted by communication unit 42. The pre-analysis unit 64 is configured to automatically determine whether the sensor's measurement dataset is transmitted by communication unit 42. The pre-analysis unit 64 is configured to define the transmission interval of communication unit 42 using measurement data from at least one sensor of sensor module 10 and / or at least one external sensor coupled to sensor module 10 (e.g., external camera 46). The pre-analysis unit 64 is configured to use measurement data from at least one sensor of the sensor module 10 and / or at least one external sensor (e.g., external camera 46) coupled to the sensor module 10 to define the adjustment of the standby phase and / or measurement interval of at least one sensor and / or at least one additional sensor.

[0079] The remote monitoring sensor device 36 includes an external enable and / or disable element 48. The external enable and / or disable element 48 is configured to enable and / or disable the sensor module 10 based on its relative positioning with respect to the sensor module housing 44. The external enable and / or disable element 48 is implemented as an external enable and / or disable magnet, which is magnetically attracted to at least a portion of the sensor module housing 44 and / or has a magnetic attraction effect on at least a portion of the sensor module housing 44. The sensor module housing 44 includes an enable and / or disable surface 136. The enable and / or disable surface 136 comprises a magnetic material, preferably a ferromagnetic material. The sensor module 10 is in a disabled state as long as the external enable and / or disable element 48 is arranged on the sensor module housing 44 in the area of ​​the enable and / or disable surface 136. The sensor module 10 is in an enabled state as long as the external enable and / or disable element 48 is arranged outside the area of ​​the enable and / or disable surface 136 on the sensor module housing 44, and / or as long as the external enable and / or disable element 48 is completely spaced from the sensor module housing 44. The reverse is also conceivable. Depending on whether the external enable and / or disable element 48, particularly the enable and / or disable magnet, is arranged in the area of ​​the enable and / or disable surface 136, the sensor module 10 is in an enabled or disabled state (or vice versa).

[0080] Sensor module 10 includes a setting module 70. To enable the configuration of sensor module 10, setting module 70 is configured for wireless communication with an external setting device 72 (e.g., a smartphone) of the installer. Sensor module 10, particularly setting module 70, includes a setting element 74, which can be read from or actuated by external setting device 72 to initiate the configuration of sensor module 10. Figure 4 In the example shown, the setting element 74 is implemented as a QR code. In the illustrated case, the QR code is applied to the sensor module housing 44. The setting module 70 is configured, in particular, to cooperate with the external setting device 72, for the implementation of at least a semi-automatic setting process 134 (see reference). Figure 5 ).

[0081] Figure 5 A schematic flowchart of at least a semi-automatic setup process 134 is shown. In at least one setup step 138, the sensor module 10 is brought into the application area 20 and mounted on / in the application area 20. In at least one additional setup step 140, the sensor module 10 is enabled. To enable, the enable and / or disable element 48 is removed from the area of ​​the enable and / or disable surface 136, for example, in the assembly step 140. In at least one additional setup step 142, the setup element 74 is read out. In setup step 142, a QR code applied to the sensor module housing 44 is scanned, for example, by an external setup device 72. It is conceivable that basic data of the sensor module 10 is already contained in the setup element 74, which is then automatically integrated into the setup process 134 performed by means of the setup module 70. In at least one additional setup step 144, the app on the external setup device 72 is automatically opened. In setup step 144, the basic data of sensor module 10 obtained from setup element 74 (e.g., serial number, sensor module type, etc.) is automatically incorporated into the app. In at least one additional setup step 146, a particularly step-by-step setup process guided by the app is initiated. In the application-guided setup process 134, the aforementioned characteristics of sensor module 10, installer, application area 20, etc., are registered. In at least one additional setup step 148, the setup process ends, and the recorded data is transmitted (directly and wirelessly) to external analysis and / or prediction unit 14 via communication unit 42 and / or via external setup device 72.

[0082] Figure 6 A schematic flowchart is shown for a sensor network-based analysis and / or prediction method for protection against natural disasters.

[0083] In at least one method step 182, sensor modules 10, 10', 10' of the outdoor sensor network 12 are installed in the application area 20 before natural disaster safety measures or before planned construction measures. Here, the sensor modules 10, 10', 10'' of the outdoor sensor network 12 may be installed around the application area 20, for example on slope 94, where no natural disaster safety measures have been implemented to determine the local necessity of natural disaster safety measures. Alternatively or additionally, in at least one further method step 184, the sensor modules 10, 10', 10'' of the outdoor sensor network 12 are installed in the application area 20 of the structure 24 that has been implemented as already constructed. Here, the sensor modules 10, 10', 10'' of the outdoor sensor network 12 are fixed to the structure 24, particularly to the ropes 56 of the structure 24.

[0084] In at least one additional method step 150, electronic sensor data from the distributed sensor modules 10, 10', 10'' of the outdoor sensor network 12 are received by an external analysis and / or prediction unit 14. Here, the external analysis and / or prediction unit 14 collects the received sensor data. The received and collected sensor data includes at least outdoor corrosion measurement data, impact sensor data, rope force sensor data, and tropospheric measurement data. The tropospheric measurement data is assigned in each case to a corrosion measurement data set, an impact sensor data set, and a rope force sensor data set. In at least one additional method step 152, the received and collected sensor data of the outdoor sensor network 12 is stored in a shared storage unit 16 of the external analysis and / or prediction unit 14. In at least one additional method step 154, the received, collected, and stored sensor data of the outdoor sensor network 12 is analyzed by the external analysis and / or prediction unit 14 to determine the natural disaster risk in the respective application areas 20, 20', 20'' of the sensor modules 10, 10', 10'' of the outdoor sensor network 12. In the analysis of the sensor data performed in method step 154, at least one further piece of information about the corresponding application area 20 is directly integrated, which is different from the outdoor corrosion measurement data, impact sensor data, rope force sensor data, and tropospheric measurement data.

[0085] In at least one additional method step 156, the risk of natural disasters is determined based on the analysis of sensor data from the external sensor network 12 and further information regarding the application areas 20, 20', 20''. Further information regarding application area 20 directly integrated into the analysis performed in method step 154 ​​may be the intensity of wildlife activity and / or human activity (e.g., pedestrian activity) in the environment near application area 20. Increased wildlife activity and / or increased human activity will lead to an increase in the risk of natural disasters determined in method step 156. Further information regarding application area 20 directly integrated into the analysis performed in method step 154 ​​may be air quality data from the environment near application area 20. Increased concentrations of certain air pollutants will lead to an increase in the risk of natural disasters determined in method step 156.

[0086] In at least one additional method step 158, the natural disaster risk determined by the external analysis and / or prediction unit 14 is provided to the authorized user group 18. The natural disaster risk determined in method step 156 and provided in method step 158 includes the remaining lifespan of the structure 24, which has been determined based on sensor data. The natural disaster risk determined in method step 156 and provided in method step 158 includes the corrosion removal rate of the anti-corrosion coating on metal components with anti-corrosion coatings, such as wire rope 228, which is determined based on sensor data. In at least one additional method step 160, the determined corrosion removal rate is used to define the geographical environment of application areas 20, 20', 20'', particularly the corrosion level of the environment of each sensor module 10 installed in the respective application area 20. For this purpose, an average corrosion removal rate is obtained from real outdoor corrosion measurement data determined over a long period of time (e.g., at least one month, at least one year, or at least two years), which is matched with the standardized corrosion removal rate assigned to the appropriate corrosion class (e.g., according to standard ISO 12944-1:2019-01) for the corresponding corrosion class.

[0087] In at least one additional method step 162, a natural disaster risk, including a natural disaster prediction, is determined. In method step 162, a natural disaster risk prediction is obtained based on previously determined progress of sensor data. Alternatively or additionally, in method step 162, a natural disaster risk prediction is obtained based on previously determined further information regarding the application area 20. In method step 162, pattern recognition is performed based on sensor data and / or further information, wherein the progress of sensor data from individual sensors and / or the correlation of the progress of sensor data from different sensors are determined, which allows for the deduction of an increase or decrease in natural disaster risk, such as the risk of rockfall. In method step 162, among other things, using impact data from the impact sensor 28 of sensor module 10 and / or rope force sensor data from rope force sensor 30, together with a measurement sequence of tropospheric measurement data from sensor module 10 and / or together with further information regarding the application area 20, pattern recognition is performed, based on which a natural disaster risk prediction is obtained, which is implemented as an impact prediction. In method step 162, data mining of sensor data collected and stored by the external analysis and / or prediction unit 14 is preferably performed in conjunction with further information about the application area 20 collected and stored by the analysis and / or prediction unit 14.

[0088] In at least one additional method step 186, based on the natural disaster specifically identified in method step 156 and / or based on the natural disaster prediction specifically obtained in method step 162, in the case of the sensor module 10 installed in method step 182, an assessment of the necessity of implementing natural disaster safety measures in the application area 20 is performed until no natural disaster safety measures are subsequently implemented.

[0089] In at least one additional method step 188, the planned construction measures are modulated based on the determined natural disaster risk, including the installation of wire mesh 226 and / or wire rope 228. In at least one method sub-step 190 of method step 188, the type of anti-corrosion coating for the wire mesh 226 and / or wire rope 228 is selected based on the determined natural disaster risk, particularly based on the determined anti-corrosion coating removal rate. In at least one method sub-step 192 of method step 188, the thickness of the anti-corrosion coating for the wire mesh 226 and / or wire rope 228 is selected based on the determined natural disaster risk, particularly based on the determined anti-corrosion coating removal rate. In at least one method sub-step 194 of method step 188, the wire thickness of the wire mesh 226 and / or wire rope 228 is selected based on the determined natural disaster risk, particularly based on the determined natural disaster risk prediction (e.g., the expected frequency and / or intensity of the event). In at least one method sub-step 196 of method step 188, the materials for the wire mesh 226 and / or wire rope 228 are selected based on the determined natural disaster risk, particularly based on the determined natural disaster risk prediction (e.g., the expected frequency and / or intensity of the event). In at least one method sub-step 198 of method step 188, the size of the wire mesh 226 is selected based on the determined natural disaster risk, particularly based on the determined natural disaster risk prediction (e.g., the expected location of the event). In at least one method sub-step 200 of method step 188, the mesh size of the wire mesh 226 is selected based on the determined natural disaster risk, particularly based on the determined natural disaster risk prediction (e.g., the event type).

[0090] In at least one additional method step 22, a maintenance plan for application area 20, preferably for structure 24, is created based on the natural disasters identified in method step 156. The maintenance plan is obtained based on the determined remaining lifespan of structure 24, particularly the remaining lifespan of certain components of structure 24. In method step 22, maintenance sequences for multiple separately located structures 24 are defined. The maintenance sequences are defined according to a priority order of different structures 24, determined based on the corrosion state and / or remaining lifespan of the structures 24. Furthermore, maintenance times are specified for structures 24 in method step 22. The maintenance times are specified based on the measured corrosion state and / or the measured remaining lifespan of the structures 24. If these values ​​change significantly over time, the maintenance times are flexibly adapted to the measured corrosion state and / or the measured remaining lifespan of the structures 24.

[0091] In at least one method sub-step 164 of method step 22, maintenance personnel are organized based on the identified natural disaster risks of the multiple application areas 20. The designated maintenance time is allocated to maintenance personnel in such a way that a preferred uniform workload can be achieved for each personnel. The designated maintenance time is allocated to maintenance personnel at different maintenance stations to achieve a preferred short travel time to the structure 24 to be maintained. The designated maintenance time is allocated to maintenance personnel so that the maintenance process to be performed can be precisely adapted to the individual skills of the maintenance personnel. In at least one further method sub-step 166 of method step 22, maintenance tools are organized based on the identified natural disaster risks of the multiple application areas 20. Available maintenance tools are allocated to the designated maintenance time in such a way that a preferred uniform utilization of the maintenance tools can be achieved. Available maintenance tools are allocated to different maintenance stations in such a way that a preferred short downtime for the maintenance tools can be achieved, for example, due to travel to the structure 24 to be maintained. When allocating available maintenance tools to available maintenance personnel, the individual skills of the maintenance personnel in operating the corresponding maintenance tools are taken into account. In at least one additional method sub-step 168 of method step 22, the organization of consumable materials is carried out based on the identified natural disaster risks of the multiple application areas 20. The ordering and / or delivery of consumable materials is adapted to the specified maintenance time, thus requiring preferably small-scale storage. Here, the allocation of consumable materials to maintenance personnel is matched with the upcoming maintenance time, so that the total amount of consumable materials consumed during the maintenance schedule is kept as small as possible.

[0092] In at least one method step 170, an impact signal indicating an impact of an impactor is detected by at least one impact sensor 28 of a sensor module 10 assigned to an application area 20, which is implemented as a rockfall barrier 76. Alternatively, in at least one method step 172, a rope force signal indicating a filling event, particularly a debris flow, is detected by at least one rope force sensor 30 of a sensor module 10 assigned to an application area 20, which is implemented as a debris flow barrier 84. In at least one additional method step 174, after an impact is detected in method step 170 and / or after a filling event is detected in method step 172, depending on the intensity and / or type of the impact and / or the filling event, preferably automatically, particularly via the analysis and / or prediction unit 14, whether to trigger a maintenance command or to immediately repair the object. In at least one additional method step 176, a maintenance command is triggered. If the intensity of the impact and / or infill event measured by sensor module 10 allows the inference that rockfall barrier 76 and / or debris flow barrier 84 are not severely damaged by the impact and / or infill event, and / or are only damaged to the extent that they still provide sufficient protection against potential further events, a maintenance command is triggered. If the type of impact and / or infill event measured by sensor module 10, i.e., the progression of sensor data received during the event, allows the inference that rockfall barrier 76 and / or debris flow barrier 84 are not severely damaged by the impact and / or infill event, and / or are only damaged to the extent that they still provide sufficient protection against potential further events, a maintenance command is triggered. In at least one additional method step 178, an immediate repair command is triggered. If the intensity of the impact and / or infill event measured by sensor module 10 allows the inference that rockfall barrier 76 and / or debris flow barrier 84 are severely damaged by the impact and / or infill event, and / or are damaged to the extent that their protective effect against potential further events is no longer sufficient, an immediate repair command is triggered. If the type of impact and / or filling event measured by sensor module 10, i.e., the progress of sensor data received during the event, allows inference that the rockfall barrier 76 and / or debris flow barrier 84 are severely damaged by the impact and / or filling event, and / or they are damaged to the extent that their protective effect against potential further events is no longer sufficient, then an immediate wake command is triggered.

[0093] In at least one additional method step 180, the deployment of the drone 34 is triggered by the result and / or value of a determined natural disaster risk. This method step may be specifically applied to support the decision-making process of method step 174, but may also be implemented at any other time. The drone 34 is implemented as a maintenance drone or a reconnaissance drone. Based on the impact intensity and / or type in the rockfall barrier 76 and / or the filling event of the debris flow barrier 84, in method step 180a, it is preferably automatically, particularly via the analysis and / or prediction unit 14, to determine whether to initiate the deployment of the drone 34.

[0094] In at least one additional method step 202, a corrosion map is created based on the natural disaster risk determined in method step 156, indicating corrosion data and including at least the application area 20. For this purpose, in method step 202, the geographic coordinates of the various sensor modules 10 installed in the application area 20 are integrated into further information about the application area 20. The corrosion map shows the distribution of corrosion data, particularly corrosion intensity, over the geographic extent of the application area 20 and / or over the extent of the structure 24 where the application area 20 is implemented. In at least one additional method step 26, the corrosion map is incorporated into the BIM (Building Information Modeling) system of the application area 20, which is implemented, for example, as a natural disaster prevention facility 32. In at least one additional method step 204, particularly local optimization of the application area 20, which is implemented, for example, as a natural disaster prevention facility 32, is performed based on the obtained corrosion map. In method step 204, a portion of, for example, is reinforced, such as a rockfall barrier 76 or slope protection 82. Here, for example, parts or the like of the rockfall barrier 76, slope protection 82, are equipped with wire mesh 226 and / or wire rope 228 having increased tensile strength, increased anti-corrosion coating thickness, increased wire thickness, etc. In at least one additional method step 206, the corrosion map is filled with simulated corrosion data in areas outside the application area 20, specifically in areas of the outdoor sensor network 12 without sensor modules 10, 10', 10''. In method step 206, corrosion data for areas outside the application area 20 are obtained based on sensor data from sensor modules 10, 10', 10'' in adjacent application areas 20', specifically in areas without sensor modules 10, 10', 10'' of the outdoor sensor network 12. Alternatively, in method step 206, corrosion data of the corrosion map is obtained for an area outside the application area 20, specifically an area without sensor modules 10, 10', 10'' of the outdoor sensor network 12, based on sensor data from sensor modules 10, 10', 10'' in the geographically and / or climatically similar application area 20'.

[0095] Figure 7A schematic flowchart of a proximity sensor (pre)analysis method for sensor data performed by sensor module 10 is shown. In at least one method step 208, raw measurement data is recorded via a sensor of sensor module 10. Additionally, in method step 208, raw measurement data is recorded via an external sensor coupled to sensor module 10, such as an external camera 46. In at least one further method step 210, the raw measurement data is analyzed in terms of data processing by a pre-analysis unit 64 within the sensor module. In at least one method sub-step 212 of method step 210, the pre-analysis unit 64 autonomously selects which portion of the raw measurement data is transmitted to the analysis and / or prediction unit 14 based on the analysis of the raw measurement data. In method sub-step 212, for each raw measurement data point and / or each raw measurement dataset, it is determined whether to transmit that raw measurement data point and / or raw measurement dataset to the analysis and / or prediction unit 14. In at least one further method sub-step 214 of method step 210, based on the analysis unit 42 of the raw measurement dataset, a communication interval with analysis unit 14 is determined, and a connection with analysis unit 42 is established. In at least one additional method sub-step 216 of method step 210, a measurement interval for one or more sensors of sensor module 10 is defined based on the analysis of the raw measurement data. In at least one additional method sub-step 218 of method step 210, the duration of a standby phase for one or more sensors of sensor module 10 is defined based on the analysis of the raw measurement data. In at least one additional method sub-step 220 of method step 210, whether a corresponding image is transmitted to analysis and / or prediction unit 14 is defined based on internal sensor module analysis of the image from external camera 46. In method sub-step 220, a newly captured image is compared with a reference image, such as a previously captured image with the same image detail. Here, if the new image deviates substantially from the reference image, the new image is transmitted to analysis and / or prediction unit 14. If the new image is substantially identical to the reference image, the new image is not sent to analysis and / or prediction unit 14.

[0096] List of reference numerals 10 Sensor Modules 12 Outdoor Sensor Networks 14. Analysis and / or prediction unit 16 memory units 18 User Group 20 Application Area 22 Methods and Steps 24 Structures 26 Methods and Steps 28 Impact Sensors 30 Rope Force Sensor 32 Natural Disaster Prevention Facilities 34 drones 36 Remote monitoring sensor devices 38 Outdoor Corrosion Sensor 40 Environmental Sensors 42 Communication Units 44 Sensor module housing 46 External Cameras 48 Enable and / or disable components 50 Accelerometers 52 Orientation Sensor 54 strain gauges 56 Ropes 58 Charge Storage 60 Ammeter 62 storage batteries 64 Pre-analysis units 66 computing units 68 energy harvesting units 70 Setting Module 72 External setting device 74 Setting Components 76. Rockfall Barrier 78 Avalanche Protection 80 Falling Stone Curtain 82 Slope Protection 84 Debris Flow Barrier 86 Attenuator 88 processor units 90. External databases of sensor networks 92 Display devices 94 Slope 96 Suspension Bridge 98 Stadium roof anchoring 100 Wind turbine anchoring 102 Facade Anchoring 104 Rope Receiving Element 106 tensioning elements 108 Other tensioning elements 110 recess 112 electrode 114 electrodes 116 End Cap 118 End Cap 120 upper side 122 Environmental Sensor Unit 124 energy storage units 126. Direction of Gravity 128 Rope Contact Element 130 Lower side 132 Longitudinal wind direction 134 Setup Process 136 Enable and / or disable surfaces 138 Setup Steps 140 Setup Steps 142 Setup Steps 144 Setup Steps 146 Setup Steps 148 Setup Steps 150 Methods and Steps 152 Methods and Steps 154 Methods and Steps 156 Methods and Steps 158 Methods and Steps 160 Methods and Steps 162 Methods and Steps 164 Method Sub-steps 166 Method Sub-steps 168 Method Sub-steps 170 Methods and Steps 172 Methods and Steps 174 Methods and Steps 176 Methods and Steps 178 Methods and Steps 180 Methods and Steps 182 Methods and Steps 184 Methods and Steps 186 Methods and Steps 188 Methods and Steps 190 Method Steps 192 Method Sub-steps 194 Method Sub-steps 196 Method Sub-steps 198 Methods and Steps 200 Methods and Steps 202 Method and Steps 204 Method and Steps 206 Methods and Steps 208 Methods and Steps 210 Method and Steps 212 Method Sub-steps 214 Method Sub-steps 216 Method Sub-steps 218 Method Sub-steps 220 Method Sub-steps 222 Interception and / or stabilization devices 224 Connection Units 226 metal wire mesh 228 Metal wire rope.

Claims

1. A sensor network-based analysis and / or prediction method for protecting against natural disasters, comprising at least the following steps: - Provide an outdoor sensor network (12) comprising distributed sensor modules (10, 10', 10"), the sensor modules (10, 10', 10") being fixed to ropes (56) for one or more interception and / or stabilization devices for rocks, stones, avalanches, debris flows or landslides. Some or all of the sensor modules (10, 10', 10") of the sensor network (12) include: A rope force sensor (30) is used to detect rope force sensor data, which is in the form of rope tension at a rope (56) including a plurality of respective intercepting and / or stabilizing devices. The sensor network (12) may include some or all of the sensor modules (10, 10', 10"), which also include: An outdoor corrosion sensor (38) is used to determine outdoor corrosion measurement data, which is in the form of corrosiveness of the external atmosphere in the area where the corresponding interception and / or stabilization device is located. - Provide an external analysis and / or prediction unit (14), implemented separately from the sensor module (10, 10', 10") and implemented as a computer or computer network, including at least one processor (88), at least one memory unit (16), and at least one operating program that can be called by the at least one processor (88) from the at least one memory unit (16). - In the external analysis and / or prediction unit (14), an outdoor corrosion measurement dataset containing the outdoor corrosion measurement data, an impact measurement dataset containing the impact sensor data, and / or a rope force measurement dataset containing the rope force sensor data are received and collected from the distributed sensor modules (10, 10', 10''). - Capture the geographic coordinates of the corresponding interception and / or stabilization device, or load the geographic coordinates of the corresponding interception and / or stabilization device that have been pre-captured. - Receive tropospheric measurement datasets and geographically assign the received tropospheric measurement datasets to the corresponding sensor modules (10, 10', 10") in the sensor modules (10, 10', 10") of the outdoor sensor network (12), wherein the received tropospheric measurement datasets include data that allows the derivation of at least one parameter of the troposphere surrounding the corresponding sensor module (10, 10', 10"). - Geographically associate and assign each of the received outdoor corrosion measurement datasets, each of the received impact measurement datasets, and / or each of the received rope force measurement datasets with the captured or loaded geographic coordinates. -Storing the received dataset of the outdoor sensor network (12) in the memory unit (16) of the external analysis and / or prediction unit (14), and characterized by further including the method steps: - The risk of natural disasters in the area where one or more interception and / or stabilization devices are located is determined by analyzing these datasets of the outdoor sensor network (12), wherein in this analysis, pattern recognition is performed by the external analysis and / or prediction unit (14) using significant time intervals of all received datasets and the geographic coordinates of the respective sensor modules, wherein in the pattern recognition, the progression of sensor data in the datasets of each sensor of the sensor modules (10, 10', 10") of the outdoor sensor network (12) and / or the correlation of the progression of sensor data in the datasets of several sensors of the sensor modules (10, 10', 10") of the outdoor sensor network (12) are determined, thereby inferring an increase or decrease in the risk of natural disasters, and Based on the pattern recognition, the external analysis and / or prediction unit (14) determines the prediction of the natural disaster risk in the form of the probability of the impact occurring in the corresponding interception and / or stabilization device, and - The natural disaster risks determined by the external analysis and / or prediction unit (14) will be provided to the user group.

2. The analysis and / or prediction method according to claim 1, characterized in that, The interception and / or stabilization device includes metal components exposed to atmospheric corrosion, and the natural disasters provided to the user group (18) include the remaining lifespan of the interception and / or stabilization device, which has been determined based on the sensor data.

3. The analysis and / or prediction method according to claim 1 or 2, characterized in that, The natural disaster risk provided includes the rate of removal of the anti-corrosion coating on metal components with anti-corrosion coatings, which is determined based on the sensor data.

4. The analysis and / or prediction method according to claim 3, characterized in that, The corrosion level of the geographical environment of the interception and / or stabilization device is defined based on the determined corrosion layer removal rate.

5. The analysis and / or prediction method according to claim 1 or 2, characterized in that, In at least one area, the sensor modules (10, 10', 10'') of the outdoor sensor network (12) are installed before the interception and / or stabilization device, and then the modulation of the interception and / or stabilization device is performed according to the determined natural disaster risk.

6. The analysis and / or prediction method according to claim 5, characterized in that, The interception and / or stabilization device includes the installation of a wire mesh (226) and / or a wire rope (228), wherein the type and / or thickness of the anti-corrosion layer of the wire mesh (226) and / or the wire rope (228) is selected based on the determined natural disaster risk.

7. The analysis and / or prediction method according to claim 5, characterized in that, The interception and / or stabilization device includes the installation of a wire mesh (226) and / or a wire rope (228), wherein the wire thickness and / or material of the wire mesh (226) and / or the wire rope (228) are selected based on the determined natural disaster risk, and / or wherein the size of the wire mesh (226) and / or the mesh size of the wire mesh (226) are selected based on the determined natural disaster risk.

8. The analysis and / or prediction method according to claim 1 or 2, characterized in that, Based on the prediction of the natural disaster risk or based on the intensity and / or type of the impact and / or filling event determined by the external analysis and / or prediction unit (14), a maintenance command for the corresponding interception and / or stabilization device, an immediate repair command for the corresponding interception and / or stabilization device, or a dispatch of maintenance drones and / or reconnaissance drones (34) to the corresponding interception and / or stabilization device are triggered.

9. A remote monitoring sensor device (36) having a sensor module (10) for an outdoor sensor network (12), the sensor module being configured to record and provide sensor data for sensor network-based analysis and / or prediction methods, having at least one outdoor corrosion sensor (38), having at least one environmental sensor (40) for determining tropospheric measurement data, and having at least one communication unit (42) for wirelessly transmitting the sensor data to an external analysis and / or prediction unit (14), characterized in that, The sensor module (10) includes an hermetically sealed sensor module housing (44), and the sensor module (10) includes at least one rope force sensor (30), which is configured to detect the impact intensity of impactors in rockfall barriers, debris flow barriers, avalanche barriers, rockfall curtains, attenuators, slope protection, and avalanche protection by inferring the rope force pulled at the rope of the corresponding interception and / or stabilization device, and / or to detect the fill level of the debris flow barrier. The rope force sensor (30) is at least partially integrated with the connection unit (224) of the sensor module (10), and The connecting unit (224) is configured to directly fix the sensor module (10) to the rope (56, 56', 56") of the structure (24).

10. The remote monitoring sensor device (36) according to claim 9, characterized in that, The sensor module (10) includes a wireless camera interface for coupling with an external camera (46).

11. The remote monitoring sensor device (36) according to claim 9 or 10, characterized in that, Includes an external enable and / or disable element (48) configured to enable and / or disable the sensor module (10) based on the relative positioning of the external enable and / or disable element (48) relative to the sensor module housing (44) of the sensor module (10).

12. The remote monitoring sensor device (36) according to claim 9 or 10, characterized in that, The communication unit (42) is configured to transmit the sensor data directly and without detour via one or more collection points for the sensor data to the external analysis and / or prediction unit (14), which is configured to receive sensor data from multiple sensor modules (10, 10', 10'') distributed on different application areas (20, 20', 20'').

13. The remote monitoring sensor device (36) according to claim 9 or 10, characterized in that, The sensor module (10) includes at least one accelerometer (50).

14. The remote monitoring sensor device (36) according to claim 9 or 10, characterized in that, In order to measure the rope force, the rope force sensor (30) includes at least one strain gauge strip (54) arranged separately from the rope (56, 56', 56''), and the rope force of the rope is monitored by the rope force sensor (30).

15. The remote monitoring sensor device (36) according to claim 9 or 10, characterized in that, The outdoor corrosion sensor (38) is based on the measurement of corrosion current generated by corrosion, wherein the outdoor corrosion sensor (38) includes at least one charge memory (58), the at least one charge memory being charged by the corrosion current until a charging limit is reached, the charge memory (58) discharging at the charging limit, and wherein the sensor module (10) includes a galvanometer (60) configured to measure the discharge current of the charge memory (58) for determining outdoor corrosion measurement data.

16. The remote monitoring sensor device (36) according to claim 9 or 10, characterized in that, The sensor module (10) includes a pre-analysis unit (64) configured to perform pre-analysis of at least one proximity sensor on measurement data of at least one of the sensors of the sensor module (10) and / or at least one external sensor coupled to the sensor module (10).

17. The remote monitoring sensor device (36) according to claim 16, characterized in that, The pre-analysis unit (64) is configured to autonomously select which portion of the sensor's measurement dataset is transmitted by the communication unit (42), and / or whether the sensor's measurement dataset is transmitted by the communication unit (42), and / or wherein the pre-analysis unit (64) is configured to define the transmission interval of the communication unit (42) using measurement data from at least one sensor of the sensor module (10) and / or at least one external sensor coupled to the sensor module (10), and / or wherein the pre-analysis unit (64) is configured to define the standby phase and / or adjustment of the measurement interval of at least the sensor and / or at least one additional sensor based on the measurement data from at least one sensor of the sensor module (10) and / or at least one external sensor coupled to the sensor module (10).

18. The remote monitoring sensor device (36) according to claim 9 or 10, characterized in that, It includes at least one additional sensor module (10') which is assigned to the same application area (20) as the sensor module (10).

19. The remote monitoring sensor device (36) according to claim 9 or 10, characterized in that, The sensor module (10) includes a setting module (70) configured to wirelessly communicate with an external setting device (72) of the installer for the configuration of the sensor module (10), wherein the sensor module (10) includes a setting element (74) that can be read or actuated by the external setting device (72) to initiate the configuration of the sensor module (10).

20. An outdoor sensor network (12) having a plurality of remote monitoring sensor devices (36) according to any one of claims 9 to 19, comprising different application areas (20, 20', 20'') and each comprising a sensor module (10, 10', 10'') assigned to the different application areas (20, 20', 20''), and in each case wirelessly communicating with a shared external analysis and / or prediction unit (14).

21. A rockfall barrier (76), avalanche protection device (78), rockfall curtain (80), slope protection device (82) and / or debris flow barrier (84), comprising at least one rope (56, 56', 56''), and at least one sensor module (10, 10', 10'') of a remote monitoring sensor device (36) according to any one of claims 9 to 19, said sensor module (10, 10', 10'') being fixed to said rope (56, 56', 56'').

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