阅读说明：本技术 出租车顶灯内多核传感器系统的轮休方法 (Sequential rest method of multi-core sensor system in taxi top lamp ) 是由 马姗姗 杜文强 程琳 寇世田 司书春 许军 于 2019-01-31 设计创作，主要内容包括：一种多核传感器系统中子传感器(100)的轮休方法,在传感器数据突然大幅变化时,能够智能判断出变化原因是传感器故障还是突发污染,提高数据可靠性。当设备发生故障时,如果能够通过自动判断修复,提高数据的在线率,对于治霾工作所需的连续监测具有重要价值。同时又可以节省设备维护保养方面的人力物力,减少社会浪费。(A method for alternate rest of a neutron sensor (100) in a multi-core sensor system can intelligently judge whether the change reason is sensor failure or sudden pollution when the sensor data is suddenly and greatly changed, and the data reliability is improved. When equipment breaks down, if can repair through automatic judgement, improve the online rate of data, have important value to the required continuous monitoring of haze work. Meanwhile, manpower and material resources in the aspect of equipment maintenance can be saved, and social waste is reduced.)

A sequential rest method of a multi-core sensor system in a taxi top lamp is characterized in that the multi-core sensor system is installed in the top lamp of a taxi and comprises a main control module and a detection module; the detection module comprises a sensor module consisting of at least four similar sub-sensor units; the method is characterized in that one or more sub-sensor units are selected for alternate rest on the premise of ensuring that at least three sub-sensor units which normally work in a sensor module are maintained, and the multi-core sensor system actively degrades to work.

A sequential rest method of a multi-core sensor system in a taxi top lamp is characterized in that the multi-core sensor system is installed in the top lamp of a taxi and comprises a main control module and a detection module; the detection module comprises a sensor module consisting of at least two similar sub-sensor units; the detection module also comprises a low-frequency calibration module consisting of at least one similar sub-sensor unit; the method is characterized in that one or more sub-sensor units are selected from the sensor module to perform alternate rest on the premise of ensuring that at least one sub-sensor unit which normally works is maintained in the sensor module, and the multi-core sensor system actively degrades to work.

A method of iterative rest as claimed in claim 2, wherein the sub-sensor units within the low frequency calibration module have an operating frequency substantially lower than the operating frequency of the sensor module.

A method of iterative rest as claimed in claim 3, wherein the ratio of the operating frequency of the sensor module to the operating frequency of the low frequency calibration module is: 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1, 10: 1, 15: 1, or 20: 1.

A method of retirement according to claim 2, wherein the data of the low frequency calibration module is incorporated into the data of the sensor module with double weight; and the method is used for screening the suspected abnormal sub-sensors from the sensor module.

A method of iterative operation according to claim 5, wherein said sub-sensors of suspected anomalies are sub-sensors of the sensor module that fluctuate most or drift most, but do not fluctuate or drift enough to be considered as sub-sensor anomalies; the judgment standard of the sub-sensor abnormity is one of the following abnormity:

1) sub-sensor abnormal fluctuations;

2) the sub-sensor drifts abnormally;

3) a sub-sensor correlation anomaly.

A round-robin method according to any one of claims 1 to 6, wherein status indicators are installed on the sub-sensor units, and when an abnormal sub-sensor is identified, the status indicator at a position corresponding thereto is changed in color to a warning color; the status indicator light corresponding to the sub-sensor in the normal working state is in continuous green; the status indicator light corresponding to the sub-sensor entering the alternate rest state is green and alternately turned on and off.

A method of iterative rest as claimed in any one of claims 1 to 6, wherein said detection module is adapted to detect the concentration of atmospheric pollutants; the main control module is used for receiving, analyzing and uploading detection data of the detection module; after receiving a group of detection data simultaneously obtained by the sensor module, the main control module analyzes and comprehensively calculates the group of detection data to obtain a result as an output numerical value; eliminating data of the abnormal sub-sensors during calculation; the comprehensive calculation is one of the following calculation methods: 1) averaging; 2) a median method; 3) a correlation coefficient method; 4) a variance method; 5) percentage method.

A method of retirement according to any of claims 1 to 6, wherein each sub-sensor in the sensor module is retired in sequence; the interval duration of the front and back alternate break is not more than T/N, wherein: n is the quantity of sub sensor unit in the sensor module, and T is the average stable operating time length of sub sensor unit.

A method for break in turn as claimed in one of claims 1 to 6, wherein a sub-sensor unit that meets the break condition is selected from the sensor module for break in turn; the alternate rest condition is one of the following conditions:

1) the sub-sensor with the longest time entering the fatigue state;

2) the sub-sensor closest to entering the fatigue state;

3) the sub-sensor with the longest accumulated working time;

4) the sub-sensor with the least number of the accumulated alternate break times;

5) under the condition that the temperature data of the sub-sensors can be obtained, the sub-sensor with the highest temperature;

6) and a suspected anomaly sub-sensor.

A method of retirement according to any of claims 1 to 6, wherein the sub-sensor unit is one of the following sensors: PM (particulate matter) ₁Sensor and PM _2.5Sensor and PM ₁₀Sensor and PM ₁₀₀A sensor, a sulfur dioxide sensor, a nitrogen oxide sensor, an ozone sensor, a carbon monoxide sensor, a VOCs sensor, or a TVOC sensor.

The iterative method of claims 1-6, wherein the sub-sensor units are laser particle sensors; the sensor system is characterized in that the accuracy of the detection data of the sensor module is improved by the following method: adding a laser power detection device and a laser power control circuit to compensate the laser power; obtaining the change relation of the particulate matter concentration values corresponding to all the laser power values by adopting an experimental method; and compensating the attenuated detection data through a laser power control circuit according to the detection result of the power detector.