VOC sensor on-site management method and system based on spatiotemporal topology and Clifford algebra

Abstract

The present invention discloses a field management method of VOC sensors based on spatiotemporal topology and Clifford algebra, and a related system for occupational disease prevention and early warning. The method includes obtaining a factory deployment site GIS model, evaluating the gas accessibility of the field space, and marking it in the factory GIS model; determining the deployment location of the omnidirectional anemometer and VOC sensor based on the key accessibility path, and converting it into Clifford spatiotemporal space; continuously collecting and recording the original data and spatiotemporal topological data of the omnidirectional anemometer and VOC sensor; monitoring the health information of the VOC sensor, and if a VOC sensor failure occurs, reconstructing it based on the spatiotemporal topological data of the sensor, and reporting the sensor failure. The present invention also discloses a VOC sensor system using the above management method.

Description

Technical Field

[0001] The present invention relates to a VOC sensor on-site management method and system based on spatiotemporal topology and Clifford Algebra, and more specifically, to a harmful gas sensor array management method and system for occupational disease prevention and treatment. Background Art

[0002] VOC sensors, or Volatile Organic Compounds (VOCs) sensors, use gas-sensitive materials that interact with VOC molecules to convert the VOC concentration in a space into measurable changes in conductivity or other physical properties. This can be used to estimate the content of harmful substances in the environment. They are widely used in air quality detection and are also widely used in the prevention and treatment of occupational diseases.

[0003] For VOC measurement, although the traditional laboratory analysis method is highly accurate and specific, it is relatively time-consuming, labor-intensive, and costly, and cannot be applied to real-time online monitoring at the factory site. Existing portable VOC sensors are not deployed in groups in most cases, requiring inspectors to carry equipment to take samples at different locations, and cannot obtain continuous monitoring data; while online VOC sensors are mostly used as a component in environmental monitoring systems. This type of multi-parameter composite monitoring system is often expensive. If it is only used for VOC data collection at the factory site, there will be a lot of additional investment, which will limit the promotion of VOC monitoring systems.

[0004] At the same time, considering the special scenarios of occupational disease prevention and control monitoring, and given that additional monitoring systems are often unrelated to production, factories are extremely sensitive to the cost of monitoring systems and usually do not have full-time management and maintenance personnel. The maintenance of such monitoring systems needs to be as simple as possible, and it is best to achieve self-maintenance of the on-site sensor network.

[0005] For specific scenarios of occupational disease prevention and control, we have designed a VOC sensor on-site management method and overall system that can reduce deployment costs, management costs, and usage costs. Summary of the invention

[0006] The present invention provides an on-site management method of VOC sensors based on spatiotemporal topology and Clifford algebra, and a VOC sensor array system deployed on-site in a factory.

[0007] A VOC sensor field management method based on spatiotemporal topology and Clifford algebra, the steps of which are:

[0008] S1: Obtain the Geographic Information System (GIS) model of the factory deployment site;

[0009] S2: Assess gas accessibility in on-site spaces and mark them in the plant GIS model;

[0010] S3: Determine the deployment locations of the omnidirectional anemometer and VOC sensor based on the critical accessibility path marked in S2;

[0011] S4: In the GIS model, the sensor locations in S3 above are converted from Euclidean space to Clifford Algebra space;

[0012] S5: Continuously collect and record the raw data of the omnidirectional anemometer and VOC sensor, and simultaneously record the spatiotemporal topological data of the VOC sensor based on the Clifford algebraic spatial relationship;

[0013] S6: Monitor the health information of the VOC sensor. If a VOC sensor failure occurs, reconstruct it based on the spatiotemporal topological data of the sensor and report the sensor failure. If the sensor works normally, continue to execute S5.

[0014] A VOC sensor field management method based on space-time topology and Clifford algebra includes a method for converting points and vectors in Euclidean space into Clifford algebra space-time space, and the steps are:

[0015] S1: The original Euclidean space is denoted as Cl(3,0), the basis vectors are denoted as e1, e2, e3, the target space-time space adopts the Minkowski metric, denoted as Cl(1,3), and the basis vectors are denoted as γ0, γ1, γ2, γ3;

[0016] S2: Using predefined parameters, define timelike as ct, c as the speed of light, t as time, and artificially agree on the metric of Cl(1,3) as (-,+,+,+);

[0017] S3: Construct the transformation operator H: x∈R^3 -> X=x+ct*γ0;

[0018] S4: Transform the points and vectors in Cl(3,0) to Cl(1,3) space through the conversion operator;

[0019] S5: Recalculate the adjacency relationship under ct.

[0020] A VOC sensor field management method based on spatiotemporal topology and Clifford algebra includes a method for recording the spatiotemporal topological relationship between sensor data, and the steps are:

[0021] S1: Select the central sensor and obtain its adjacent sensor list based on the gas accessibility path;

[0022] S2: preset sampling period t0;

[0023] S3: The adjacent sensors of this sensor are recorded as spatial coverage;

[0024] S4: If the sampling time of a sensor with which there is a spatial coverage relationship overlaps with that of the sensor, it is recorded as time intersection;

[0025] S5: Record the time-intersecting data collected by the sensors covered by the central sensor space.

[0026] A VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra includes a method for reconstructing failed sensor data based on spatiotemporal topological data, the steps of which are:

[0027] S1: Mark the sensor as failed and obtain historical data of at least 60 acquisition cycles before the sensor fails;

[0028] S2: Sort the historical data obtained in S1 by time, taking the data of 60 acquisition cycles as an example, the data farthest from the expiration time is marked as data No. 60, and the data closest to the expiration time is marked as data No. 1;

[0029] S3: Use data from No. 60 to No. 31 to build a linear interpolation model to verify the validity of data from No. 30 to No. 1. If the data deviation exceeds 3 standard deviations, it is considered invalid data and discarded;

[0030] S4: Use the historical data of the above 60 acquisition cycles to build a linear interpolation model and calculate the expected data, which is recorded as le0;

[0031] S5: Obtain the data of the same sampling period of the healthy sensors adjacent to the failed sensor, calculate the weighted average of these data, and regard them as the reconstructed data of the sensor, denoted as ts0. The data weight is calculated based on the spatiotemporal distance between the adjacent sensor and the sensor. The closer the spatiotemporal distance is, the higher the weight is.

[0032] S6: If the gap between le0 and ts0 is too large, the data prediction abnormal event should be reported at the same time.

[0033] A computer storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra.

[0034] A VOC sensor array system for occupational disease prevention and control monitoring, the device comprising a processor, a memory, a VOC sensor array deployed at different locations, and an omnidirectional anemometer, the memory storing at least one instruction, the at least one instruction being loaded and executed by the processor to implement the above-mentioned VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra.

[0035] Beneficial effects:

[0036] The present invention discloses a series of on-site management methods for VOC sensors based on spatiotemporal topology and Clifford algebra, and related systems, which can realize self-management of VOC sensor arrays at factory sites and data reconstruction when sensors fail, facilitate the implementation of the Internet of Things when deploying occupational disease prevention and control monitoring networks, and also facilitate the management of on-site sensors, thereby reducing the deployment cost of VOC sensor arrays and the management of on-site sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The figure is an overall flow chart of a VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra.

[0038] Figure 2 Flowchart of the method for converting points and vectors in Euclidean space into Clifford algebraic space-time space.

[0039] Figure 3 Flowchart of a method for recording spatiotemporal topological relationships between sensor data.

[0040] Figure 4 The present invention is a flowchart of a method for reconstructing failed sensor data based on spatiotemporal topological data. DETAILED DESCRIPTION Specific implementation method one:

[0042] The present embodiment is now described with reference to the accompanying drawings.

[0043] Figure 1 The figure is an overall flow chart of a VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra.

[0044] The specific steps of this method are:

[0045] S1: Obtain the Geographic Information System (GIS) model of the factory deployment site;

[0046] S2: Assess gas accessibility in on-site spaces and mark them in the plant GIS model;

[0047] S3: Determine the deployment locations of the omnidirectional anemometer and VOC sensor based on the critical accessibility path marked in S2;

[0048] S4: In the GIS model, the sensor locations in S3 above are converted from Euclidean space to Clifford Algebra space;

[0049] S5: Continuously collect and record the raw data of the omnidirectional anemometer and VOC sensor, and simultaneously record the spatiotemporal topological data of the VOC sensor based on the Clifford algebraic spatial relationship;

[0050] S6: Monitor the health information of the VOC sensor. If a VOC sensor failure occurs, reconstruct it based on the spatiotemporal topological data of the sensor and report the sensor failure. If the sensor works normally, continue to execute S5.

[0051] The GIS model of the factory is a three-dimensional Euclidean space model, which can be regarded as a Clifford algebraic space, denoted as Cl(3,0).

[0052] We need to evaluate the gas accessibility of the on-site space. Generally speaking, if there is a specific airtight space, we believe that the airtight facility hinders the gas accessibility and can be marked as +∞. The overall measurement can be marked with standard characteristic gases. First, the test origin needs to be determined. By continuously diffusing the standard characteristic gas in equal amounts, the gas concentration is tested at the sampling point to represent the gas accessibility index of the on-site space.

[0053] In the above tests, important inflection points of gas accessibility are recorded, and omnidirectional anemometers need to be deployed at these inflection points.

[0054] VOC sensors are deployed at key indicator equipment and important operation points of workers.

[0055] The application scenario described in this method is mainly applied to occupational disease prevention and monitoring in factories. The main characteristics of this application scenario are that it is sensitive to the cost of the sensor network, and there are often no full-time technicians to perform daily management and maintenance of the sensor network. Therefore, the main technical problems we need to solve in this scenario are to reduce the overall cost of the sensor network and minimize the on-site management and maintenance work. In the above method, we can select a better VOC sensor deployment location by analyzing the key gas accessibility path on the factory site, which can avoid monitoring unimportant locations and reduce the number of sensors deployed, thereby reducing the cost of the system. By introducing health monitoring of VOC sensors, the daily management work of the sensor network can be reduced, especially allowing the use of adjacent sensor spatiotemporal data to reconstruct the data of failed sensors, which allows the overall network to continue to operate when a small number of sensors fail, which can improve the overall redundancy of the system and reduce maintenance costs.

[0056] Figure 2 Flowchart of the method for converting points and vectors in Euclidean space into Clifford algebraic space-time space.

[0057] A VOC sensor field management method based on space-time topology and Clifford algebra includes a method for converting points and vectors in Euclidean space into Clifford algebra space-time space, and the steps are:

[0058] S1: The original Euclidean space is denoted as Cl(3,0), the basis vectors are denoted as e1, e2, e3, the target space-time space adopts the Minkowski metric, denoted as Cl(1,3), and the basis vectors are denoted as γ0, γ1, γ2, γ3;

[0059] S2: Using predefined parameters, define timelike as ct, c as the speed of light, t as time, and artificially agree on the metric of Cl(1,3) as (-,+,+,+);

[0060] S3: Construct the transformation operator H: x∈R^3 -> X=x+ct*γ0;

[0061] S4: Transform the points and vectors in Cl(3,0) to Cl(1,3) space through the conversion operator;

[0062] S5: Recalculate the adjacency relationship under ct.

[0063] In the original Cl(3,0) space, the adjacency relationship is expressed as the gas accessibility, that is, we regard the marked points and / or spaces that can be affected by gas diffusion as adjacent, and their distance is derived by introducing the norm of the gas diffusion capacity. Considering Fick's law, the adjacency distance is proportional to the concentration gradient.

[0064] For ease of calculation, in general, we can directly select (γ1, γ2, γ3) = (e1, e2, e3). Only when the original Cl(3,0) model is too complex, we only use the adjacency matrix that ignores unreachable paths to construct (γ1, γ2, γ3).

[0065] In the transformed Cl(1,3) space, the above adjacency relationship adds a time-like dimension, that is, the adjacency relationship under ct. Considering Fick's second law, the diffusion on ct should also be included in the calculation, so we can simultaneously derive the time-like dimension and the original Euclidean space distance to obtain the concentration gradient in space and time, and the adjacency distance is proportional to this gradient.

[0066] Figure 3 Flowchart of a method for recording spatiotemporal topological relationships between sensor data.

[0067] A VOC sensor field management method based on spatiotemporal topology and Clifford algebra includes a method for recording the spatiotemporal topological relationship between sensor data, and the steps are:

[0068] S1: Select the central sensor and obtain its adjacent sensor list based on the gas accessibility path;

[0069] S2: preset sampling period t0;

[0070] S3: The adjacent sensors of this sensor are recorded as spatial coverage;

[0071] S4: If the sampling time of a sensor with which there is a spatial coverage relationship overlaps with that of the sensor, it is recorded as time intersection;

[0072] S5: Record the time-intersecting data collected by the sensors covered by the central sensor space.

[0073] Figure 4 The present invention is a flowchart of a method for reconstructing failed sensor data based on spatiotemporal topological data.

[0074] A VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra includes a method for reconstructing failed sensor data based on spatiotemporal topological data, the steps of which are:

[0075] S1: Mark the sensor as failed and obtain historical data of at least 60 acquisition cycles before the sensor fails;

[0076] S2: Sort the historical data obtained in S1 by time, taking the data of 60 acquisition cycles as an example, the data farthest from the expiration time is marked as data No. 60, and the data closest to the expiration time is marked as data No. 1;

[0077] S3: Use data from No. 60 to No. 31 to build a linear interpolation model to verify the validity of data from No. 30 to No. 1. If the data deviation exceeds 3 standard deviations, it is considered invalid data and discarded;

[0078] S4: Use the historical data of the above 60 acquisition cycles to build a linear interpolation model and calculate the expected data, which is recorded as le0;

[0079] S5: Obtain the data of the same sampling period of the healthy sensors adjacent to the failed sensor, calculate the weighted average of these data, and regard them as the reconstructed data of the sensor, denoted as ts0. The data weight is calculated based on the spatiotemporal distance between the adjacent sensor and the sensor. The closer the spatiotemporal distance is, the higher the weight is.

[0080] S6: If the gap between le0 and ts0 is too large, the data prediction abnormal event should be reported at the same time.

[0081] The historical data collected should generally not be less than 30 sampling periods in order to form a normal distribution.

[0082] Consider some sensors with special sampling cycles, mainly sensors with longer collection intervals, such as once a day. The characteristics of this type of data are relatively stable, and the sampling scale can be appropriately reduced.

[0083] The data standard deviation is generally based on the data samples No. 60 to No. 31. The error judgment criteria of le0 and ts0 also refer to this standard deviation. Specific implementation method 2:

[0085] This embodiment is a storage medium, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement the VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra.

[0086] It should be understood that any method described in the present invention may be provided as a computer program product, software or computerized method, which may include a non-transitory machine-readable medium having instructions stored thereon, which may be used to program a computer system, or other electronic device, to perform the process. The storage medium may include, but is not limited to, magnetic storage media, optical storage media; magneto-optical storage media include: read-only memory ROM, random access memory RAM, erasable programmable memory (e.g., EPROM and EEPROM) and flash memory layers; or other types of media suitable for storing electronic instructions. Specific implementation method three:

[0088] This embodiment is a VOC sensor array system that can be used for occupational disease prevention and control monitoring, and the device includes a processor, a memory, a VOC sensor array deployed at different locations, and an omnidirectional anemometer. It should be understood that including any device including a processor and a memory described in the present invention, the device may also include other units and modules that perform display, interaction, processing, control, etc. and other functions through signals or instructions.

[0089] At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra.

[0090] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A VOC sensor field management method based on spatiotemporal topology and Clifford algebra, Its characteristics include the following steps: S1: Obtain the GIS model of the factory deployment site; S2: Assess gas accessibility in on-site spaces and mark them in the plant GIS model; S3: Based on the critical accessibility path marked in S2, omnidirectional anemometers are deployed at the inflection points of gas accessibility, and VOC sensors are deployed at key indicator equipment and important operation points of workers; S4: In the GIS model, the sensor locations in S3 above are converted from Euclidean space to Clifford algebraic space. The target space is denoted as Cl(1,3). The steps are: S41: The original Euclidean space is denoted as Cl(3,0), the basis vectors are denoted as e1, e2, e3, the target space-time space adopts the Minkowski metric, denoted as Cl(1,3), and the basis vectors are denoted as γ0, γ1, γ2, γ3; S42: Using predefined parameters, define the timelike as ct, c as the speed of light, t as time, and artificially agree on the metric of Cl(1,3) as (-,+,+,+); S43: Construct the transformation operator H: x∈R^3 -> X=x+ct* γ0; S44: transform the points and vectors in Cl(3,0) to Cl(1,3) space through the conversion operator; S45: recalculate the adjacency relationship under ct; S5: Continuously collect and record the raw data of the omnidirectional anemometer and VOC sensor, and simultaneously record the spatiotemporal topological data of the VOC sensor based on the Cl(1,3) spatial relationship; S6: Monitor the health information of the VOC sensor. If a VOC sensor failure occurs, reconstruct the sensor based on the spatiotemporal topological data of the faulty sensor and report the sensor failure. If the sensor works normally, continue to execute S5.

2. The VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra as claimed in claim 1, characterized in that: The method for recording the spatiotemporal topological data of the VOC sensor comprises the following steps: S51: Select a central sensor and obtain a list of its adjacent sensors based on the gas accessibility path; S52: preset sampling period t0; S53: The adjacent sensors of the sensor are recorded as spatial coverage; S54: If the sampling time of a sensor with a spatial coverage relationship overlaps with that of the sensor, it is recorded as time intersection; S55: Record the time-intersecting data collected by the sensors covered by the central sensor space.

3. The VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra as claimed in claim 1, characterized in that: The method for reconstructing based on the spatiotemporal topological data of the faulty sensor comprises the following steps: S61: Mark the sensor as failed, and obtain historical data of at least 60 acquisition cycles before the sensor fails; S62: Sort the historical data obtained in S1 by time, taking the data of 60 acquisition cycles as an example, the data farthest from the expiration time is marked as data No. 60, and the data closest to the expiration time is marked as data No. 1; S63: Use data No. 60 to No. 31 to build a linear interpolation model to verify the validity of data No. 30 to No.

1. If the data deviation exceeds 3 standard deviations, it is considered invalid data and discarded; S64: Use the historical data of the above 60 acquisition cycles to build a linear interpolation model and calculate the expected data, which is recorded as le0; S65: obtaining data of the same sampling period of the healthy sensor adjacent to the failed sensor, calculating the weighted average of these data, and treating them as the reconstructed data of the sensor, denoted as ts0, and the data weight is calculated according to the spatiotemporal distance between the adjacent sensor and the sensor, and the closer the spatiotemporal distance is, the higher the weight is; S66: If the difference between le0 and ts0 is too large, the data prediction abnormal event should be reported at the same time.

4. A computer storage medium, characterized in that: The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra as described in any one of claims 1 to 3.

5. A VOC sensor array system for occupational disease prevention and control monitoring, characterized in that: The device includes a processor, a memory, a VOC sensor array deployed at different locations, and an omnidirectional anemometer. The memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement a VOC sensor on-site management method based on spatiotemporal topology and Clifford algebra as described in any one of claims 1 to 3.

Images