Method for correcting monitoring data of air environment quality automatic inspection trolley

Abstract

The invention discloses a method for correcting monitoring data of an air environment quality automatic inspection trolley. S1, the upper scheduling system issues a data acquisition starting task to the environment trolley through a wireless network; s2, after receiving the data acquisition task issued by the upper system, the environment trolley automatically and sequentially travels to the monitoring point positions according to a pre-planned path; s3, after the environment reaches the detection point position, the chassis trolley sends an in-place signal to the upper-layer monitoring platform, and after the upper-layer monitoring platform receives the in-place signal, each detection instrument is started to carry out data sampling on environment parameters near the current monitoring point position; and S4, repeatedly sampling the same point location for multiple times according to process requirements. By means of the data correction method of the algorithm, data fluctuation can be effectively filtered, fluctuation deviation data can be filtered out, then mean value calculation is carried out on reserved data, peak clipping and valley leveling are further carried out on the data, and the sampled data can be closer to the actual environment.

Description

Technical Field

[0001] The invention relates to the field of photovoltaic cell workshop automation, and in particular to a method for correcting monitoring data of an automatic air environment quality inspection vehicle. Background Art

[0002] Based on the requirements of the photovoltaic cell production process for air quality in the workshop, it is necessary to monitor the environment around the host equipment of each processing step to ensure the product yield. At present, the main focus is on monitoring indoor temperature, humidity, dust diameter and particle number in the air; due to the fluidity of the air, the values ​​detected by the chassis car often fluctuate greatly, which leads to large deviations in the output monitoring results, thereby causing problems with misjudgment of the results; therefore, it is necessary to adopt an effective data correction method to make the test results reflect the actual situation in the environment as accurately as possible.

[0003] Due to the characteristics of gas flow and diffusion, similar chassis vehicles currently mostly use the conventional detection method of directly outputting results after detection. However, the detected values ​​usually fluctuate greatly due to the flow of air, resulting in misjudgment of the test results.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related art, the present invention proposes a method for correcting monitoring data of an automatic air environment quality inspection vehicle to overcome the above-mentioned technical problems existing in the existing related art.

[0006] To this end, the specific technical solution adopted by the present invention is as follows: A method for correcting monitoring data of an automatic air quality inspection vehicle comprises the following steps: S1: The upper dispatching system issues a data collection task to the chassis car through the wireless network; S2: After receiving the data collection task sent by the upper system, the chassis car automatically drives to the monitoring points in sequence according to the pre-planned path; S3: After the environment arrives at the detection point, the chassis car sends a signal of arrival to the upper monitoring platform. After receiving the signal, the upper monitoring platform starts various detection instruments to sample the environmental parameters near the current monitoring point. S4: Repeat sampling of the same point multiple times according to process requirements; S5: The data sampling control system of the detection platform compares and sorts each set of data recorded at the current point; S6: Remove small and large values ​​according to the sorting, and then calculate the mean of the remaining data: correct the sampled data; S7: The monitoring platform sends the calculated and corrected data to the chassis car, and the wireless network of the chassis car reports the data to the upper scheduling system for archiving; S8: After the data reporting is completed, the chassis car automatically drives to the next monitoring point for data collection.

[0007] Preferably, in step S4, each sampling data (A1A2A3...B1B2B3...BnC1C2C3...) is recorded.

[0008] Preferably, in step S6, relatively small values ​​(A1A2A3...) and relatively large values ​​(C1C2C3...) are removed according to the sorting, and then the mean value of the remaining data (B1B2B3...Bn) is calculated.

[0009] Preferably, the chassis trolley includes an AGV chassis trolley, a temperature sensor, a humidity sensor, a laser dust particle counter, a detection control system and an upper-level scheduling system.

[0010] Preferably, the upper-level dispatching system is connected to the monitoring platform via wireless communication.

[0011] Preferably, the detection and control system communicates internally with the AGV chassis vehicle.

[0012] Preferably, in step S8, the data collection will be combined with the actual situation on site and the data correction feedback result to adjust the parameters in the support vector machine regression model.

[0013] The beneficial effect of the present invention is that through the data correction method of the algorithm, data fluctuations can be effectively filtered, data with fluctuation deviations can be filtered out, and then the mean of the retained data can be calculated, and the data can be further smoothed to make the sampled data closer to the actual environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 Schematic diagram of a method for correcting monitoring data of an automatic air quality inspection vehicle according to an embodiment of the present invention Figure 2 It is a scheduling schematic diagram of a method for correcting monitoring data of an automatic air environment quality inspection vehicle according to an embodiment of the present invention; Figure 3 It is a schematic diagram of a chassis vehicle in a method for correcting monitoring data of an automatic air environment quality inspection vehicle according to an embodiment of the present invention.

[0016] In the figure: 1. AGV chassis; 2. Temperature sensor; 3. Humidity sensor; 4. Laser dust particle counter. DETAILED DESCRIPTION

[0017] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0018] According to an embodiment of the present invention, a method for correcting monitoring data of an automatic air environment quality inspection vehicle is provided. Example

[0019] like Figure 1-3 As shown, the method for correcting monitoring data of an automatic air quality inspection vehicle according to an embodiment of the present invention includes the following steps: S1: The upper dispatching system issues a data collection task to the chassis car through the wireless network; S2: After receiving the data collection task sent by the upper system, the chassis car automatically drives to the monitoring points in sequence according to the pre-planned path; S3: After the chassis car arrives at the monitoring point, the chassis car sends a signal of arrival to the upper monitoring platform. After receiving the signal of arrival, the upper monitoring platform starts various detection instruments to sample the environmental parameters near the current monitoring point. S4: Repeat sampling of the same point multiple times according to process requirements; S5: The data sampling control system of the detection platform compares and sorts each set of data recorded at the current point; S6: Remove small and large values ​​according to the sorting, and then calculate the mean of the remaining data: correct the sampled data; S7: The monitoring platform sends the calculated and corrected data to the chassis car, and the wireless network of the chassis car reports the data to the upper scheduling system for archiving; S8: After the data reporting is completed, the chassis car automatically drives to the next monitoring point for data collection. Example

[0020] like Figure 1-3As shown, in step S4, each sampling data (A1A2A3 ... B1B2B3 ... BnC1C2C3 ...) is recorded.

[0021] In step S6, the smaller values ​​(A1A2A3...) and the larger values ​​(C1C2C3...) are removed according to the sorting, and then the mean value of the remaining data (B1B2B3...Bn) is calculated.

[0022] The chassis trolley comprises an AGV chassis trolley 1, a temperature sensor 2, a humidity sensor 3, a laser dust particle counter 4, a detection control system and an upper-level dispatching system. Example

[0023] like Figure 1-3 As shown, the upper scheduling system is connected to the monitoring platform via wireless communication.

[0024] The detection control system communicates internally with the AGV chassis vehicle 1 .

[0025] In step S8, the data collection will be combined with the actual situation on site and the data correction feedback results to adjust the parameters in the support vector machine regression model.

[0026] To sum up, with the help of the above-mentioned technical scheme of the present invention, through the data correction method of this algorithm, data fluctuations can be effectively filtered, the data with fluctuation deviations can be filtered out, and then the mean of the retained data can be calculated, and the data can be further smoothed to make the sampled data closer to the actual environment.

[0027] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for correcting monitoring data of an automatic air quality inspection vehicle, characterized in that: The steps include: S1: The upper dispatching system issues a data collection task to the chassis car through the wireless network; S2: After receiving the data collection task sent by the upper system, the chassis car automatically drives to the monitoring points in sequence according to the pre-planned path; S3: After the chassis car arrives at the monitoring point, the chassis car sends a signal of arrival to the upper monitoring platform. After receiving the signal of arrival, the upper monitoring platform starts various detection instruments to sample the environmental parameters near the current monitoring point. S4: Repeat sampling of the same point multiple times according to process requirements; S5: The data sampling control system of the detection platform compares and sorts each set of data recorded at the current point; S6: Remove small and large values ​​according to the sorting, and then calculate the mean of the remaining data: correct the sampled data; S7: The monitoring platform sends the calculated and corrected data to the chassis car, and the wireless network of the chassis car reports the data to the upper scheduling system for archiving; S8: After the data reporting is completed, the chassis car automatically drives to the next monitoring point for data collection.

2. A method for correcting monitoring data of an automatic air quality inspection vehicle according to claim 1, characterized in that: In step S4, each sampling data (A1A2A3 ... B1B2B3 ... BnC1C2C3 ...) is recorded.

3. The method for correcting monitoring data of an automatic air quality inspection vehicle according to claim 1 is characterized in that: In step S6, the smaller values ​​(A1A2A3...) and the larger values ​​(C1C2C3...) are removed according to the sorting, and then the mean value of the remaining data (B1B2B3...Bn) is calculated.

4. A method for correcting monitoring data of an automatic air quality inspection vehicle according to claim 1, characterized in that: The chassis trolley comprises an AGV chassis trolley (1), a temperature sensor (2), a humidity sensor (3), a laser dust particle counter (4), a detection control system and an upper-level dispatching system.

5. A method for correcting monitoring data of an automatic air quality inspection vehicle according to claim 4, characterized in that: The upper dispatching system is connected to the monitoring platform via wireless communication.

6. A method for correcting monitoring data of an automatic air quality inspection vehicle according to claim 5, characterized in that: The detection control system communicates internally with the AGV chassis vehicle (1).

7. The method for correcting monitoring data of an automatic air quality inspection vehicle according to claim 1 is characterized in that: In step S8, the data collection will be combined with the actual situation on site and the data correction feedback results to adjust the parameters in the support vector machine regression model.

Images