METHOD FOR MONITORING A TREATMENT STRUCTURE FOR RAINWATER, AND TREATMENT STRUCTURE FOR RAINWATER

Abstract

A treatment structure for rainwater has at least one receiving vessel for rainwater and an outlet for discharging treated rainwater. A monitoring device for monitoring a need for maintenance of the treatment structure is provided. The monitoring device has at least one first sensor for detecting a fill level of rainwater in the vessel, at least one second sensor for detecting an amount of precipitation in a predetermined area surrounding the treatment structure, and at least one data processing unit in which parameter values from a group comprising a predetermined target fill level and a predetermined target rate of decrease of the fill level of rainwater in the vessel are stored. Here, the at least one first and the at least one second sensor are operatively coupled to the data processing unit via an electronic data line.

Claims

1-11. (canceled)

12. A method for monitoring a treatment structure (1) for precipitation water, wherein the treatment structure (1) includes a vessel (2) for receiving the precipitation water, an outlet (5) for discharging treated precipitation water, and a monitoring device for monitoring a need for maintenance of the treatment structure (1), wherein the monitoring device has a first sensor (11) for detecting a fill level of the precipitation water in the vessel (2), a second sensor (12) for detecting an amount of precipitation in a predetermined area surrounding the treatment structure (1), and a data processing unit (10, 20), in which parameter values including a predetermined target fill level and a predetermined target rate of decrease of the fill level of the precipitation water in the vessel (2) are stored, wherein the first sensor (11) and the second sensor (12) are operatively coupled to the data processing unit (10, 20) via an electronic data line (13, 21), the method comprising: detecting values of the fill level of the precipitation water received in the vessel (2) and of the precipitation in the predetermined area surrounding the treatment structure (1) at predetermined time intervals by the first sensor (11) and the second sensor (12); transmitting detected values to the data processing unit (10, 20); evaluating the detected values by the data processing unit by comparing a current fill level to the predetermined target fill level and/or comparing a current rate of decrease of the fill level of the precipitation water in the vessel (2) to the predetermined target rate of decrease and thereby determining a level of silting of the vessel (2); and outputting the need for maintenance for the treatment structure for precipitation water as a function of the determined level of silting of the vessel (2).

13. The method according to claim 12, wherein the data processing unit (10, 20) includes a local data processing unit (10) and a central data processing unit (20), wherein detecting values of the fill level of the precipitation water and of the precipitation in the predetermined area surrounding the treatment structure (1) is performed by the local data processing unit (10), wherein transmitting the detected values to the data processing unit (10, 20) includes forwarding the detected values from the local data processing unit (10) to the central data processing unit (20), and wherein evaluating the detected values by the data processing unit is performed by the central data processing unit (20).

14. The method according to claim 12, further comprising: evaluating the detected values of the first sensor (11) and of the second sensor (12) by using a parameterized precipitation runoff model, wherein the parameterized precipitation runoff model includes predetermined parameters of the treatment structure (1) to be monitored.

15. The method according to claim 12, further comprising evaluating the detected values of the first sensor (11) and of the second sensor (12) by using artificial intelligence (AI).

16. The method according to claim 12, further comprising evaluating the detected values of the first sensor (11) and of the second sensor (12) by using a parameterized precipitation runoff model and by using artificial intelligence (AI), wherein the parameterized precipitation runoff model includes predetermined parameters of the treatment structure (1) to be monitored.

17. The method according to claim 12, further comprising evaluating the detected values of the first sensor (11) and of the second sensor (12) by using a parameterized hydraulic model, wherein the parameterized hydraulic model includes predetermined parameters of the treatment structure (1) to be monitored.

18. The method according to claim 17, wherein the predetermined parameters of the treatment structure (1) to be monitored are stored in the data processing unit (10, 20) and are selected from the group consisting of dimensions of the vessel (2), nominal width of the outlet (5), type of the treatment structure (1), separating performance of the treatment structure (1), and passage value of the treatment structure (1).

19. A treatment structure (1) for precipitation water, comprising: a receiving vessel (2) for precipitation water; an outlet (5) for discharging treated precipitation water; and a monitoring device for monitoring a need for maintenance of the treatment structure (1), including a first sensor (11), by which a fill level of the precipitation water in the vessel (2) is detected, a second sensor (12), by which an amount of precipitation in a predetermined area surrounding the treatment structure (1) is detected, and a data processing unit (10, 20), by which values detected by the first sensor (11) and the second sensor (12) are evaluated with regard to the need for maintenance of the treatment structure, wherein the first sensor (11) and the second sensor (12) are operatively coupled to the data processing unit (10, 20) via an electronic data line (13, 21).

20. The treatment structure (1) according to claim 19, wherein the monitoring device has a further sensor, selected from the group consisting of a flow measurement sensor, a load measurement sensor, an ultrasonic sensor, a radar sensor, a temperature sensor, and a moisture sensor.

21. The treatment structure (1) according to claim 19, wherein the data processing unit (10, 20) includes a local data processing unit (10) and a central data processing unit (20), wherein the local data processing unit (10) is operatively coupled electronically to the first sensor (11) and the second sensor (12) via a first data line (13), and wherein the central data processing unit (20) is operatively coupled electronically to the local data processing unit (10) via a second data line (21).

22. The treatment structure (1) according to claim 21, wherein the monitoring device has a self-sufficient energy supply, and wherein the first sensor (11) and the second sensor (12) and the local data processing unit (10) have a rechargeable battery.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG. 1 shows a schematic view of an embodiment of the treatment structure for precipitation water with the monitoring device and

[0039] FIG. 2 shows a schematic view of a further embodiment of the treatment structure for precipitation water with the monitoring device.

DETAILED DESCRIPTION

[0040] A treatment structure 1 for precipitation water, which operates on the basis of sedimentation, is illustrated in FIG. 1. The treatment structure 1 has a receiving vessel 2, which is embedded in a base 100. Precipitation water 3, which forms a fill level, collects in the vessel 2. Sediment 4, which is present in the precipitation water 3, deposits in the vessel 2. The precipitation water purified in this way can be drained off from the vessel 2 through an outlet 5 and is discharged into a seepage device 6 (such as, e.g., a drain trench).

[0041] The treatment structure 1 silts as times goes by and as precipitations occur. To be able to monitor the treatment structure 1 with regard to whether a cleaning of the vessel 2 is necessary, the treatment structure 1 is equipped with a monitoring device, which is essentially constructed of a data processing unit 10, which is electronically connected to the below-listed sensors: to determine the fill level of the precipitation water 3 in the vessel 2, the vessel 2 has a first sensor 11, which measures the fill level of the precipitation water in the vessel. So that an amount of precipitation can be detected, several second sensors 12 (figuratively illustrated by means of two sensors 12), which measure the precipitation intensity or the amount of precipitation, respectively, in a predetermined area surrounding the treatment structure 1, are arranged on an area around the vessel 2.

[0042] The sensors 11, 12 are connected to the data processing unit 10 via an electronic data line 13 (which can be realized in a wired or wireless manner via radio). The data processing unit 10 detects the values detected by the sensors 11, 12, processes them and sends them via an electronic data connection 21, which is illustrated as being wireless in the figure, to a central data processing unit 20, which takes over the evaluation of the data collected by the data processing unit 10. The actual data processing takes place in the central data processing 20, for example the precipitation intensity for the entire monitored area around the treatment structure 1 is thus determined there.

[0043] A completely underground treatment structure 1 is shown in FIG. 2, wherein the vessel 2 is sunk at floor level. Several local data processing units 10 are provided, which are each individually assigned to the sensors 11, 12. In the case of the precipitation amount sensors 12, all of them can be connected to a common local data processing unit. The local data processing units 10 communicate separately with the central data processing unit 20, electronic data lines 13 can thus be reduced.

[0044] As components, the monitoring of the treatment structure 1 essentially requires a fill level measurement and a precipitation intensity measurement, which is supported via cloud platform (corresponding to the central data processing unit 20). On the cloud platform, the overall system of the treatment structure is managed, data is processed, statements are made and parameters are changed, calibrated or adapted, respectively, in the local data processing unit, also with regard to the sensors.

[0045] The fill level sensor (corresponding to the first sensor 11) or the measurement therewith, respectively, can then be designed as follows:

[0046] The fill level sensor or sensors are installed in the treatment structure, wherein at least one daily measurement of the water level status is provided. The fill level sensor can optionally be combined with or expanded by, respectively, a flow measurement, a dirt load measurement, a conductive measurement or other measurements by means of correspondingly suitable sensors. With a use of batteries, the fill level sensor can reach long service lives (of up to 5 years) and can be parameterized to structure-and assembly-related parameters, such as, for example, assembly position, overflow sill and structure-related fade-outs. Due to the fact that space is extremely limited in the receiving vessel or the buffer tank, such a parametrization is sensible, so that the sensor can fade out optionally predetermined false reports, which can result due to the structure-and assembly-related parameters. Fill level sensors can be used, which have self-learning functions and which adjust automatically to the structure after installation. Different parameters are detected thereby, which are provided by means of the local data processing unit. A state (good/bad) of the treatment structure or also of the dynamic thereof (for example, reachable maximum rate of decrease) can be learned by the sensor. The local data processing unit ensures that the fill level sensor operates in the respective correct measuring cycle and monitors whether the sensor provides data. The local data processing unit can adapt and change the measuring cycles of the fill level sensor for this purpose. The fill level sensor itself can be connected to a cloud or via the local data processing unit, respectively (realized by means of the central data processing unit), this takes place via a data transmission via different radio standards. The parameters for the fill level sensor can also be changed or adapted via this, respectively.

[0047] The used fill level measurement technology is variable. Non-contact technologies with, e.g., ultrasound, infrared or radar or technologies with contact with, e.g., conductive contacts or float switches can thus be used. The fill level sensor can be used with different measuring programs. In a normal program, a daily measurement of the fill level of the vessel can thus take place via the respectively used fill level measurement technology. A switch-over to an event program can be made, wherein the fill level sensor is read out as to whether there is threat of an inadmissible overflow of the treatment structure, i.e. that the structure discharges unpurified precipitation water because the degree of silting has become too high and the structure abates in the case of a rain yield factor, which is too low (evaluation in the central data processing unit). Measurements for this can take place at minute intervals. Depending on the rise and the fall of the water level in the treatment structure can be checked for target state. The rise and fall of the fill level as well as the retention duration in the structure, which can be determined therefrom, are indicators for the need for maintenance or also for the state of the structure in general, respectively. The data transmission can take place at different points in time in this program, thus, for example, at the beginning of the switch-over to this program and again when switching back into the normal program, for example when the target level (max. water level status) is reached. The fill level sensor can be triggered by means of an additional sensor within the structure, in order to prompt the fill level sensor to change the measuring program, for example via a conductive contact.

[0048] The precipitation intensity or precipitation amount sensor (corresponding to the second sensor 12) or the measurement therewith, respectively, can be designed as follows:

[0049] One or several such sensors are arranged in an area/a predetermined area of the property to be monitored (corresponding to a geographic area for determining the amount of precipitation or also referred to as rain yield factor). Used technologies are: analog ombrometers, communal rain gauges, digital rain meters, optical precipitation intensity meters, radar sensors, piezoelectric sensors. They can be retrofitted or integrate already existing measuring systems, such as communal rain gauges, respectively. By means of PV/battery operation or connection to local-position energy sources, such as lampposts, the sensors can be supplied with energy in a cost-efficient manner or also so as to be independent of external energy. Via their data connection to the local data processing unit, its measuring data, such as the data of the fill level sensor, are likewise provided to the central data processing unit. Its main functionality lies in the detection of the precipitation intensities, they further transmit changed values or absolute values or can reflect changes of the precipitation intensities and amounts of rain compared to old data. These systems do not have any measuring cycles but are in continuous operation.

[0050] The data processing unit, in particular the central data processing unit 20, which, as cloud platform, manages an overall system, processes data, makes statements and sets parameters on the measuring systems, can operate as follows:

[0051] The detected sensor values/data are/is processed collectively and a decision basis, thus the need for maintenance and statements about the performance, are generated there. The sensor values for property-or system-related rain yield factors, respectively, which are measured by means of the second sensor 12, and the fill level values, which are measured by means of the first sensor 11 (from normal and event program), are provided to a cloud software for the data analysis, evaluation and subsequent use.

[0052] With regard to the evaluation of the detected sensor values and determination of the need for maintenance, measured amounts of precipitation (rain yield factors) and fill level behavior (which corresponds to the purification behavior of the treatment structure) are correlated, wherein the entry of contamination per precipitation is an unknown variable. It is estimated. Fill level amount of the vessel and the precipitation intensity, which is measured on the surrounding area, correlate differently, depending on the monitored treatment structure: a distinction must be made that there are treatment structures, which allow purification and seepage, and those, which only purify. In any case, a portion of the treatment structure is always under water, holds back solids/particles from the precipitation water and allows the purified water to seep away. This purification and/or seepage performance correlates with the intensity of the rain or rain yield factor, respectively (amount of precipitation x drainage/precipitation area) and with regard to the fill level in the structure (actual state). The correlations of the behavior can be described in a deterministic manner and can be displayed with the help of an AI, so that not every parameter, which is incorporated in the calculation, has to be adjusted by hand.

[0053] All static parameters, thus parameters typical for the structure, are parameters, which can improve a statement about the need for maintenance. For a data-reduced monitoring with faster calculating time, they can be omitted. The rate of decrease of the water level/fill level to target level in the structure decreases with each precipitation event and a subsequently occurring silting of the structure. A state, at which the functionality does no longer exist sufficiently, occurs at some point. For a needs-based maintenance interval, it is then important to determine when the behavior of the amount of precipitation to purification rate (interpretation from fill level over time as well as ground moisture . . . ) changes. In the event that the vessel is full and the rate of decrease tends to zero, it can happen that the treatment structure abates unpurified water even though it should not do that. The overflow can then be identified by means of the monitoring device via the measured water level. In such cases, an optimization can be provided for how or when the fill level sensor switches into which measuring program, in particular with switchover from daily and minute-by-minute measurement and data transmission, wherein future overflow can be prevented.

[0054] The treatment structure with monitoring device advantageously provides for a functional monitoring of the structure itself, which can reflect performance promises from the manufacturer and which makes it possible to fulfill legal requirements with regard to maintenance and cleaning of the treatment structures. The exceeding of a precipitation amount threshold can further be documented and reports can be produced about the mode of operation of the treatment structure in general. These reports can comprise property-or system-related amounts of precipitation, respectively, points in time critical water level positions are exceeded and runoff/overflow calculated therefrom. It can further be documented by means of the data collected in the central data processing unit, how fast the water level in the treatment structure normalizes when precipitation has fallen. The need for cleaning in terms of a maintenance monitoring can likewise be recorded, so that it is documented, when an emptying/cleaning of the treatment structure is necessary in order to always or again, respectively, meet the manufacturer-specific and legal requirements of a correct mode of operation.

[0055] The software program, which is used for evaluating the detected sensor values, in particular includes system-related digital twins in the form of structure-related parameterizable precipitation runoff models, which reflect the behavior of the structure and thus the correlation between variable parameters (which are measured, such as, e.g., of the fill level of the vessel) and static parameters (such as, e.g., nominal width of the vessel). If-then simulations and predictions into the future can thus be generated with the help of this digital twin and actual measuring values, and a global optimum for managing (maintaining) these structures can be determined via a plurality of structures, such as, for example, a cost-, time-and ecology-efficient maintenance sequence and maintenance tour planning.

[0056] The overall system can further realize a so-called SaaS solution (Software as a Service) for sewer cleaning companies as operator of such treatment structures, wherein these companies can set up a cost-efficient and time-efficient monitoring of their treatment structures.