TWI498553B - Wireless air quality monitoring system and air quality predicting methods - Google Patents
Wireless air quality monitoring system and air quality predicting methods Download PDFInfo
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Description
本發明係關於一種無線空氣品質監控系統，特別是一種依據該等感測傳送板之電氣特性以及頻率響應特性，判斷該等感測傳送板所量測之污染源種類，進而調整該等污染源之濃度值之量測範圍之無線空氣品質監控系統。 The invention relates to a wireless air quality monitoring system, in particular to determining the types of pollution sources measured by the sensing transmission boards according to the electrical characteristics and frequency response characteristics of the sensing transmission boards, and thereby adjusting the concentration of the pollution sources. A wireless air quality monitoring system that measures the range of values.
在目前每人每天約有80%至90%的時間處於室內環境中(包括在住家、辦公室或其他建築物內)，室內空氣品質的良窳，直接影響工作品質及效率，因此室內空氣污染物對人體健康影響應當受到重視。近年來室內空氣健康危害的議題逐漸被大家所重視，尤其是最近二、三十年來大眾生活型態的改變，使得人們在密閉的居住空間或是辦公空間裏享受空調系統帶來的舒適便利之餘，「病態建築物症候群」(Sick Building Syndrome)也應運而生。在密閉的建築物內，如果室內通氣量不足時，污染物就容易蓄積而導致室內空氣品質惡化。 At present, about 80% to 90% of the time per person per day is in an indoor environment (including in a home, office or other building). The quality of indoor air quality directly affects work quality and efficiency, so indoor air pollutants The impact on human health should be taken seriously. In recent years, the issue of indoor air health hazards has gradually been valued by everyone. In particular, the changes in the way of life in the past two or three decades have made people enjoy the comfort and convenience of air-conditioning systems in confined living spaces or office spaces. Yu, "Sick Building Syndrome" (Sick Building Syndrome) also came into being. In a closed building, if the indoor ventilation is insufficient, the pollutants will easily accumulate and the indoor air quality will deteriorate.
另外，室外的污染物也有可能是影響室內空氣品質的因素，包括戶外汽機車、工廠排放的廢氣，或是因中央空調冷氣系統的外氣進氣口或濾網未定期清理而孳生的微生物等。台灣地處亞熱帶，屬於長年潮濕高溫的氣候型態，黴菌及細菌尤其容易孳生，因此必須更注意空調通風系統的定期維護。室內空氣品質對於經常在室內的兒童、孕婦、老人和慢性病人更是特別重要。 In addition, outdoor pollutants may also be factors affecting indoor air quality, including outdoor steam locomotives, exhaust emissions from factories, or microorganisms that are generated by the external air intake or filter screen of the central air-conditioning system. . Taiwan is located in the subtropical zone and belongs to the climatic pattern of long-term humid and high temperature. Molds and bacteria are particularly prone to twinning, so it is necessary to pay more attention to the regular maintenance of air-conditioning ventilation systems. Indoor air quality is especially important for children, pregnant women, the elderly and chronic patients who are often indoors.
有關空氣品質無線監測系統研究包括監測硬體和軟體設備，其功能為 將硬體介面的整合，藉由監測軟體加以配合的系統，其中重點工作內容包括以下四項：1.氣體感測器整合；2.資料擷取模組及監測軟體開發；3.無線通訊模組信號傳輸；以及4.空氣品質無線監測平台架構設計。 Research on air quality wireless monitoring systems includes monitoring hardware and software devices, the function of which is The integration of the hardware interface, through monitoring the software to cooperate with the system, the key work includes the following four items: 1. gas sensor integration; 2. data acquisition module and monitoring software development; 3. wireless communication mode Group signal transmission; and 4. Air quality wireless monitoring platform architecture design.
上述四項系統即完成室內空氣品質無線監測模組，並應用於實地進行長時間連續監測且設計動態指標系統提供使用者即時瞭解目前所處建築物之室內室外空氣品質現況，由於使用單點量測方式，並未考量到室內環境地區大小，因此可能無法表達某地區室內空氣品質指標數據分析，加上感測器種類是固定而不可更換，因此僅能感測一種污染源，且無法預測未來之空氣品質，而不利於監測人員使用。 The above four systems complete the indoor air quality wireless monitoring module, and are applied to the field for long-term continuous monitoring and design dynamic indicator system to provide users with instant access to the current indoor and outdoor air quality status of the current building, due to the use of single point quantities. The measurement method does not consider the size of the indoor environment. Therefore, it may not be able to express the analysis of indoor air quality indicators in a certain area. In addition, the sensor type is fixed and cannot be replaced, so it can only sense one source of pollution and cannot predict the future. Air quality is not conducive to the use of monitoring personnel.
因此，如何設計出一可感測複數種污染源，且可預測未來之空氣品質之無線空氣品質監控系統，便成為相關廠商以及相關研發人員所共同努力的目標。 Therefore, how to design a wireless air quality monitoring system that can detect multiple pollution sources and predict future air quality has become the goal of related manufacturers and related R&D personnel.
本發明人有鑑於習知之無線空氣品質監控系統無法感測複數種污染源，且不能預測未來之空氣品質之缺失，乃積極著手進行開發，以期可以改進上述既有之缺點，經過不斷地試驗及努力，終於開發出本發明。 The present inventors have actively developed the wireless air quality monitoring system in view of the inability to sense multiple sources of pollution and cannot predict the lack of future air quality, in order to improve the above-mentioned shortcomings, and to continuously test and work hard. Finally, the invention has been developed.
本發明之第一目的，係為提供一可感測複數種污染源之無線空氣品質監控系統。 A first object of the present invention is to provide a wireless air quality monitoring system that senses a plurality of sources of pollution.
為了達成上述之目的，本發明之無線空氣品質監控系統，係包括： 一遠端伺服器，具有一接收資料庫，並連接一雲端平台進行運算；複數感測器節點，係用以偵測污染源之濃度值；複數感測傳送板，係與該等感測器節點以無線方式連接，且各感測傳送板偵測一種污染源之濃度值；一智慧感測模組，係與該等感測傳送板連接，並包括：一無線通訊模組，係具有無線通訊能力，並接收來自該等感測傳送板所偵測之該等污染源之濃度值；一電性資料表單(Target Electric Data Sheet)，係儲存該等感測傳送板之電氣特性以及頻率響應特性；以及一可調整前置電路，依據該等感測傳送板之電氣特性以及頻率響應特性，判斷該等感測傳送板所量測之污染源種類，進而調整該等污染源之濃度值之量測範圍；以及一閘道器，係與該等感測傳送板、該智慧感測模組、該遠端伺服器以及該等感測器節點連接，並藉由該智慧感測模組收集該等污染源之濃度值，並將該等污染源之濃度值傳送至該遠端伺服器之接收資料庫。 In order to achieve the above object, the wireless air quality monitoring system of the present invention comprises: a remote server having a receiving database and connected to a cloud platform for computing; a plurality of sensor nodes for detecting a concentration value of a pollution source; a plurality of sensing transmission boards, and the sensor nodes Connected wirelessly, and each sensing transmitting board detects a concentration value of a pollution source; a smart sensing module is connected to the sensing transmitting boards, and includes: a wireless communication module having wireless communication capabilities And receiving concentration values of the pollution sources detected by the sensing transmission boards; a Target Electric Data Sheet storing electrical characteristics and frequency response characteristics of the sensing transmission boards; An adjustable front circuit, determining the type of the pollution source measured by the sensing transmission board according to the electrical characteristics and the frequency response characteristic of the sensing transmission board, and adjusting the measurement range of the concentration values of the pollution sources; a gateway device connected to the sensing transmission board, the smart sensing module, the remote server, and the sensor nodes, and collecting the dirt by the smart sensing module The concentration values of the source are transmitted, and the concentration values of the sources are transmitted to the receiving database of the remote server.
透過上述之裝置，本發明可依據該等感測傳送板之電氣特性以及頻率響應特性，判斷該等感測傳送板所量測之污染源種類，進而調整該等污染源之濃度值之量測範圍，因此相對於習知之裝置具有可同時感測複數種污染源之優點。 Through the above device, the present invention can determine the types of pollution sources measured by the sensing transmission boards according to the electrical characteristics and frequency response characteristics of the sensing transmission boards, and thereby adjust the measurement range of the concentration values of the pollution sources. Therefore, the device has the advantage of simultaneously sensing a plurality of sources of contamination with respect to conventional devices.
本發明之第二目的，係為提供一預測未來之空氣品質之空氣品質預測方法。 A second object of the present invention is to provide an air quality prediction method for predicting future air quality.
為了達成上述之目的，本發明之空氣品質預測方法包括一第一實施例 以及一第二實施例，其中該第一實施例包括步驟：每隔一監測時間至少記錄污染源之濃度值；以監測污染源之濃度值數列作為一樣本，並作為自我相關函數圖確認該污染源之濃度值是否為平穩型數列；若該污染源之濃度值不是平穩型數列，則利用差分程序讓該污染源之濃度值成為平穩型數列；以該自我相關函數圖和一偏自我相關函數圖(Sample Partial Autocorrelation Function,PACF)來決定一ARIMA(p.d,q)模式，其中該自我相關函數圖係決定AR(p)係數，d為差分，該偏自我相關函數圖係決定MA(q)係數，在辨認(p,q)時係依據辨認準則為該自我相關函數圖以及該偏自我相關函數圖之型式，先檢驗是否為單純AR(p)或單純MA(q)模式，若二者皆不是，便判定模式為ARMA(p,q)；模式為ARMA(p,q)時，用試誤法(Try and Error)將所有可能的模式分別進行分析，最後由模式診斷來判定何者較為適合短期預測；以及驗證模式以Box-Ljung檢定以及殘差自我相關函數需落於95%信賴區間等方式進行模式診斷，確認預測是否正確。 In order to achieve the above object, the air quality prediction method of the present invention includes a first embodiment And a second embodiment, wherein the first embodiment comprises the steps of: recording at least the concentration value of the pollution source every other monitoring time; monitoring the concentration value series of the pollution source as the same, and confirming the concentration of the pollution source as a self-correlation function graph Whether the value is a stationary series; if the concentration value of the pollution source is not a stationary series, the difference value is used to make the concentration value of the pollution source into a stationary series; the self-correlation function graph and a partial autocorrelation function map (Sample Partial Autocorrelation) Function, PACF) determines an ARIM(pd, q) mode, where the autocorrelation function map determines the AR(p) coefficient, and d is the difference. The partial autocorrelation function map determines the MA(q) coefficient, which is identified ( p, q) is based on the recognition criterion for the self-correlation function graph and the partial self-correlation function graph, first check whether it is a simple AR (p) or simple MA (q) mode, if neither is, then determine The mode is ARMA(p,q); when the mode is ARMA(p,q), all possible modes are analyzed separately by Try and Error. Finally, the mode diagnosis determines which one is more suitable for short. Prediction; and the Box-Ljung verification test mode, and a residual autocorrelation function for an 95% confidence interval falls, etc. for the diagnosis mode, to confirm whether the prediction is correct.
該第二實施例係利用質量平衡法及微分方程式建立之二氧化碳推估式，以實際室內人數、通風量預測未來任何時間點的室內二氧化碳濃度，該二氧化碳推估式為C(t)=B*P*[1-EXP(-Q*t/V)]/Q+COUT，其中C(t)=室內二氧化碳濃度，COUT=室外二氧化碳濃度，B=人體二氧化碳呼出量30(L/人．h)，P=人數，V=室內體積，Q=室內外通風量，t=時間間隔，該第二實施例包括步驟： 監測至少一日之室內外二氧化碳濃度值，並記錄室內人數；取各小時之室外二氧化碳小時平均濃度，代表該場所未來每小時的固定室外二氧化碳小時平均濃度；決定相同通風條件下之室內外通風量；以及將即時監測的室內外二氧化碳濃度、相同通風條件下之室內外通風量、預測目標時間之人數，代入二氧化碳推估式，推估本日後續之各個小時，或未來任何時間點的室內二氧化碳濃度。 In the second embodiment, the carbon dioxide estimation formula established by the mass balance method and the differential equation is used to predict the indoor carbon dioxide concentration at any time point in the future by the actual indoor number and the ventilation amount, and the carbon dioxide estimation formula is C (t) = B*. P*[1-EXP(-Q*t/V)]/Q+C OUT , where C (t) = indoor carbon dioxide concentration, C OUT = outdoor carbon dioxide concentration, B = human body carbon dioxide exhaled amount 30 (L / person. h), P = number of people, V = indoor volume, Q = indoor and outdoor ventilation, t = time interval, the second embodiment includes the steps of: monitoring the indoor and outdoor carbon dioxide concentration values of at least one day, and recording the number of people in the room; The hourly outdoor carbon dioxide hourly average concentration represents the hourly average outdoor carbon dioxide hourly concentration of the site in the future; determines the indoor and outdoor ventilation under the same ventilation conditions; and the indoor and outdoor carbon dioxide concentration that will be monitored immediately, indoors and outdoors under the same ventilation conditions The amount of ventilation and the number of people who predict the target time are substituted into the carbon dioxide estimation formula to estimate the indoor carbon dioxide concentration at each subsequent hour of the day or at any future point in time.
透過上述之方法，本發明可預測未來之空氣品質，因此相對於習知之方法可事先預測出污染濃度將超過法定標準值，而可以提早啟動空氣污染改善措施。 Through the above method, the present invention can predict the air quality in the future, so that the pollution concentration can be predicted in advance to exceed the legal standard value, and the air pollution improvement measure can be started earlier than the conventional method.
為使熟悉該項技藝人士瞭解本發明之目的，兹配合圖式將本發明之較佳實施例詳細說明如下。 The preferred embodiments of the present invention are described in detail below with reference to the drawings.
請參考第一圖所示，本發明之無線空氣品質監控系統(1)包括：一遠端伺服器(10)，具有一接收資料庫(100)，並連接一雲端平台(20)進行運算；複數感測器節點(11)，係用以偵測污染源之濃度值；複數感測傳送板(12)，係與該等感測器節點(11)以無線方式連接，且各感測傳送板(12)偵測一種污染源之濃度值；一智慧感測模組(13)，係與該等感測傳送板(12)連接，並包括：一無線通訊模組(130)，係具有無線通訊能力，並接收來自該等感測傳送板(12)所偵測之該等污染源之濃度值； 一電性資料表單(131)，係儲存該等感測傳送板(12)之電氣特性以及頻率響應特性；以及一可調整前置電路(132)，依據該等感測傳送板(12)之電氣特性以及頻率響應特性，判斷該等感測傳送板(12)所量測之污染源種類，進而調整該等污染源之濃度值之量測範圍；以及一閘道器(14)，係與該等感測傳送板(12)、該智慧感測模組(13)、該遠端伺服器(10)以及該等感測器節點(11)連接，並藉由該智慧感測模組(13)收集該等污染源之濃度值，並將該等污染源之濃度值傳送至該遠端伺服器(10)之接收資料庫(100)。 Referring to the first figure, the wireless air quality monitoring system (1) of the present invention comprises: a remote server (10) having a receiving database (100) connected to a cloud platform (20) for calculation; a plurality of sensor nodes (11) for detecting a concentration value of the pollution source; a plurality of sensing transmission boards (12) connected to the sensor nodes (11) in a wireless manner, and each sensing transmission board (12) detecting a concentration value of a pollution source; a smart sensing module (13) is connected to the sensing transmission board (12), and includes: a wireless communication module (130) having wireless communication Ability to receive concentration values of the sources of pollution detected by the sensing transfer boards (12); An electrical data sheet (131) for storing electrical characteristics and frequency response characteristics of the sensing transmitting plates (12); and an adjustable front circuit (132) according to the sensing transmitting plates (12) Electrical characteristics and frequency response characteristics, determining the types of pollution sources measured by the sensing transmission boards (12), and thereby adjusting the measurement range of the concentration values of the pollution sources; and a gateway (14), and the like The sensing transmission board (12), the smart sensing module (13), the remote server (10), and the sensor nodes (11) are connected by the smart sensing module (13) The concentration values of the pollution sources are collected and the concentration values of the pollution sources are transmitted to the receiving database (100) of the remote server (10).
其中該無線空氣品質監控系統(1)更包括一數據服務系統(21)，該數據服務系統(21)係與該雲端平台(20)連結，並接收與儲存經由該雲端平台(20)處理後之該等污染源之濃度值。使用者可透過該數據服務系統(21)查詢室內空氣品質數據。 The wireless air quality monitoring system (1) further includes a data service system (21) connected to the cloud platform (20), and received and stored by the cloud platform (20). The concentration values of such sources. The user can query the indoor air quality data through the data service system (21).
該污染源為一氧化碳、二氧化碳、揮發性有機污染物、甲醛或懸浮微粒。 The source of pollution is carbon monoxide, carbon dioxide, volatile organic pollutants, formaldehyde or aerosols.
請參考第二圖所示，該智慧感測模組(13)更包括：一類比轉數位轉換器(133)，係與該可調整前置電路(132)連接，並用以將從該可調整前置電路(132)接收之類比訊號轉成數位訊號；以及一微控制器(134)，係與該無線通訊模組(130)、該電性資料表單(131)、該可調整前置電路(132)以及該類比轉數位轉換器(133)連接，係用以處理從該無線通訊模組(130)、該電性資料表單(131)以及該類比轉數位轉換器(133)接收之資訊，並將處理後之資訊傳送至該可調整前置電路(132)。 Referring to the second figure, the smart sensing module (13) further includes: an analog-to-digital converter (133) connected to the adjustable pre-circuit (132) and used for adjustment from the The analog signal received by the pre-circuit (132) is converted into a digital signal; and a microcontroller (134) is connected to the wireless communication module (130), the electrical data sheet (131), and the adjustable pre-circuit (132) and the analog-to-digital converter (133) are connected to process information received from the wireless communication module (130), the electrical data form (131), and the analog-to-digital converter (133) And transmitting the processed information to the adjustable pre-circuit (132).
該無線通訊模組(130)係具有IEEE 802.11b/g之Wi-Fi通訊能力。 The wireless communication module (130) has Wi-Fi communication capability of IEEE 802.11b/g.
請參考第三以及四圖所示，本發明之空氣品質預測方法包括一第一實施例(3)以及一第二實施例(4)，該第一實施例(3)係包括步驟：步驟300：每隔一監測時間至少記錄污染源之濃度值；步驟301：以監測污染源之濃度值數列作為一樣本，並作為自我相關函數圖確認該污染源之濃度值是否為平穩型數列；步驟302：若該污染源之濃度值不是平穩型數列，則利用差分程序讓該污染源之濃度值成為平穩型數列；步驟303：以該自我相關函數圖和一偏自我相關函數圖(Sample Partial Autocorrelation Function,PACF)來決定一ARIMA(p.d,q)模式，其中該自我相關函數圖係決定AR(p)係數，d為差分，該偏自我相關函數圖係決定MA(q)係數，在辨認(p,q)時係依據辨認準則為該自我相關函數圖以及該偏自我相關函數圖之型式，先檢驗是否為單純AR(p)或單純MA(q)模式，若二者皆不是，便判定模式為ARMA(p,q)；步驟304：模式為ARMA(p,q)時，用試誤法(Try and Error)將所有可能的模式分別進行分析，最後由模式診斷來判定何者較為適合短期預測；以及步驟305：驗證模式以Box-Ljung檢定以及殘差自我相關函數需落於95%信賴區間等方式進行模式診斷，確認預測是否正確。 Referring to the third and fourth figures, the air quality prediction method of the present invention includes a first embodiment (3) and a second embodiment (4). The first embodiment (3) includes the steps: step 300 : recording at least the concentration value of the pollution source every other monitoring time; Step 301: using the concentration value series of the monitoring pollution source as the same, and confirming whether the concentration value of the pollution source is a stationary type as a self-correlation function graph; Step 302: If the concentration value of the pollution source is not a stationary series, the difference value is used to make the concentration value of the pollution source into a stationary series; step 303: determining the self-correlation function map and a partial partial autocorrelation function (PACF) An ARIMA (pd, q) mode in which the autocorrelation function map determines the AR(p) coefficient, and d is a difference. The partial autocorrelation function map determines the MA(q) coefficient, and when identifying (p, q) According to the identification criterion, the self-correlation function graph and the partial self-correlation function graph are first tested whether it is a simple AR(p) or simple MA(q) mode. If neither is, the mode is determined to be ARMA (p, q) Step 304: When the mode is ARMA(p, q), all possible modes are separately analyzed by Try and Error, and finally, mode diagnosis is used to determine which is more suitable for short-term prediction; and step 305: verification mode is The Box-Ljung test and the residual self-correlation function need to be in the 95% confidence interval for mode diagnosis to confirm whether the prediction is correct.
在本發明之一較佳實施例中，該監測時間係為1分鐘。 In a preferred embodiment of the invention, the monitoring time is 1 minute.
該第一實施例(3)係使用時間序列(ARIMA)法預測短時間(如一小時)以內之室內二氧化碳濃度，其為一短期預測模式，並只需要輸入監測濃度， 而不需要輸入其他物理參數，如人數、通風量或室內體積等。若欲預測未來半小時以內之濃度，則須將過去半小時以內之監測資料代入該第一實施例(3)；若欲預測未來一小時以內之濃度，則須將過去一小時以內之監測資料代入該第一實施例(3)，以此類推。 The first embodiment (3) uses the time series (ARIMA) method to predict the indoor carbon dioxide concentration within a short time (eg, one hour), which is a short-term prediction mode, and only needs to input the monitoring concentration. There is no need to enter other physical parameters such as number of people, ventilation or indoor volume. If you want to predict the concentration within the next half hour, you should substitute the monitoring data within the past half hour into the first embodiment (3); if you want to predict the concentration within the next hour, you must use the monitoring data within the past hour. Substituting the first embodiment (3), and so on.
該第二實施例(4)係利用質量平衡法及微分方程式建立之二氧化碳推估式，以實際室內人數、通風量預測未來任何時間點的室內二氧化碳濃度，該二氧化碳推估式為C(t)=B*P*[1-EXP(-Q*t/V)]/Q+COUT，其中C(t)=室內二氧化碳濃度，COUT=室外二氧化碳濃度，B=人體二氧化碳呼出量30(L/人．h)，P=人數，V=室內體積，Q=室內外通風量，t=時間間隔，該第二實施例(4)係包括步驟：步驟400：監測至少一日之室內外二氧化碳濃度值，並記錄室內人數；步驟401：取各小時之室外二氧化碳小時平均濃度，代表該場所未來每小時的固定室外二氧化碳小時平均濃度；步驟402：決定相同通風條件下之室內外通風量；以及步驟403：將即時監測的室內外二氧化碳濃度、相同通風條件下之室內外通風量、預測目標時間之人數，代入二氧化碳推估式，推估本日後續之各個小時，或未來任何時間點的室內二氧化碳濃度。 The second embodiment (4) is a carbon dioxide estimation formula established by the mass balance method and the differential equation, and the indoor carbon dioxide concentration at any time point in the future is predicted by the actual indoor number and the ventilation amount, and the carbon dioxide estimation formula is C (t). =B*P*[1-EXP(-Q*t/V)]/Q+C OUT , where C (t) = indoor carbon dioxide concentration, C OUT = outdoor carbon dioxide concentration, B = human body carbon dioxide exhalation 30 (L) / person.h), P = number of people, V = indoor volume, Q = indoor and outdoor ventilation, t = time interval, the second embodiment (4) includes the steps: step 400: monitoring at least one day of indoor and outdoor carbon dioxide Concentration value, and record the number of indoors; Step 401: take the hourly outdoor carbon dioxide hourly average concentration, representing the hourly fixed outdoor carbon dioxide hourly average concentration of the site in the future; Step 402: determine the indoor and outdoor ventilation under the same ventilation conditions; Step 403: Substituting the indoor and outdoor carbon dioxide concentration monitored immediately, the indoor and outdoor ventilation under the same ventilation condition, and the number of people predicting the target time into the carbon dioxide estimation formula, and estimating each hour of the current day, or any time in the future Indoor carbon dioxide concentration points.
在本發明之一較佳實施例中，該第一實施例(3)以及該第二實施例(4)更包括一感測傳送板正確性自動檢驗步驟(5)，該感測傳送板正確性自動檢驗步驟(5)係用於自動檢驗複數感測傳送板之正確性，該感測傳送板正確性自動檢驗步驟(5)包括子步驟： 步驟500：取該等感測傳送板裝設初始二週之小時平均值，前z%低之小時平均值之算數平均百萬分(ppm)之X0即為低標濃度，前z%高之小時平均值之算數平均Y0 ppm即為高標濃度；以及步驟501：每週檢驗最近二週之小時平均值，前z%低之小時平均值之算數平均為X ppm，前z%高之小時平均值之算數平均為Y ppm。 In a preferred embodiment of the present invention, the first embodiment (3) and the second embodiment (4) further comprise a step (5) of sensing the correctness of the transfer board, the sensing transfer board is correct. The automatic automatic inspection step (5) is for automatically verifying the correctness of the complex sensing transmission board. The sensing transmission board correctness automatic verification step (5) includes the sub-steps: Step 500: taking the sensing transmission board installation The hourly average of the initial two weeks, the hourly average of the previous z% is the average of the millionth (ppm) of the X 0, which is the low standard concentration, and the arithmetic mean of the hourly average of the previous z% is Y 0 ppm. High standard concentration; and step 501: weekly average of the last two weeks of the test, the average of the hourly average of the previous z% low is X ppm, and the average of the hourly average of the previous z% is averaged Y ppm.
其中，若Y<90% Y0代表span點漂移，若Y>110%Y0代表span點漂移，若連續發生漂移二次，則執行自動校正C校正後=C校正前*Y/Y0；若Y>120% Y0或Y<80%Y0則代表異常，若連續發生漂移二次異常，則更換該等感測傳送板，另若Y>120%Y0代表環境空氣相較該等感測傳送板裝設初期發生持續性惡化，通知使用者了解該項空氣污染源；若X>110% X0代表zero點漂移，執行自動校正C校正後=C校正前-(X-X0)；若X>120% X0代表異常，則維修及更換該等感測傳送板。 Wherein, if Y<90% Y 0 represents span drift, if Y>110%Y 0 represents span drift, if drift occurs twice continuously, then perform automatic correction C correction =C correction before *Y/Y 0 ; If Y>120% Y 0 or Y<80%Y 0, it means abnormality. If the drift secondary abnormality occurs continuously, replace the sensing transmission plates, and if Y>120%Y 0 represents the ambient air compared with these In the initial stage of the sensor transmission board installation, the user is informed of the air pollution source; if X>110% X 0 represents zero point drift, perform automatic correction C correction = C correction before - (XX 0 ); X>120% X 0 represents an abnormality, and the sensing transfer plates are repaired and replaced.
在本發明之一較佳實施例中，z係為5，且該感測傳送板正確性自動檢驗步驟(5)係分別執行於該步驟305以及該步驟403之後。 In a preferred embodiment of the present invention, the z-series is 5, and the sensing transfer board correctness automatic verification step (5) is performed after the step 305 and the step 403, respectively.
請參考第五至六圖所示，利用本發明之空氣品質預測方法所預測二氧化碳濃度之即時預測結果(虛線)與實測結果(實線)，其平均誤差係在10%之內。 Referring to Figures 5 to 6, the instantaneous prediction results (dashed line) and the measured results (solid line) of the predicted carbon dioxide concentration using the air quality prediction method of the present invention have an average error of 10%.
請參考第七圖所示，利用本發明之空氣品質預測方法，每十分鐘預測決定是否開啟空調，490分鐘內僅開啟220分鐘，其節能比率約為270/490=0.55，約55%左右。 Referring to the seventh figure, the air quality prediction method of the present invention predicts whether to turn on the air conditioner every ten minutes, and only turns on 220 minutes in 490 minutes, and the energy saving ratio is about 270/490=0.55, about 55%.
本發明係依據該等感測傳送板之電氣特性以及頻率響應特性，判斷該等感測傳送板所量測之污染源種類，進而調整該等污染源之濃度值之量測 範圍，因此相對於習知之裝置具有可同時感測複數種污染源之優點，且本發明可預測未來之空氣品質，因此相對於先前技術可事先預測出污染濃度將超過法定標準值，而可以提早啟動空氣污染改善措施；再者，其結構型態並非所屬技術領域中之人士所能輕易思及而達成者，實具有新穎性以及進步性無疑。 The invention determines the types of pollution sources measured by the sensing transmission boards according to the electrical characteristics and frequency response characteristics of the sensing transmission boards, and further adjusts the concentration values of the pollution sources. Scope, therefore, has the advantage of being able to simultaneously sense a plurality of pollution sources with respect to conventional devices, and the present invention can predict future air quality, and thus it is possible to predict in advance that the pollution concentration will exceed the legal standard value, and can be started earlier than the prior art. Air pollution improvement measures; in addition, its structural form is not easily understood by those in the technical field, and it is novel and progressive.
透過上述之詳細說明，即可充分顯示本發明之目的及功效上均具有實施之進步性，極具產業之利用性價值，且為目前市面上前所未見之新發明，完全符合發明專利要件，爰依法提出申請。唯以上所述著僅為本發明之較佳實施例而已，當不能用以限定本發明所實施之範圍。即凡依本發明專利範圍所作之均等變化與修飾，皆應屬於本發明專利涵蓋之範圍內，謹請 貴審查委員明鑑，並祈惠准，是所至禱。 Through the above detailed description, it can fully demonstrate that the object and effect of the present invention are both progressive in implementation, highly industrially usable, and are new inventions not previously seen on the market, and fully comply with the invention patent requirements. , 提出 apply in accordance with the law. The above is only the preferred embodiment of the present invention, and is not intended to limit the scope of the invention. All changes and modifications made in accordance with the scope of the invention shall fall within the scope covered by the patent of the invention. I would like to ask your review committee to give a clear explanation and pray for it.
(1)‧‧‧無線空氣品質監控系統 (1)‧‧‧Wireless Air Quality Monitoring System
(10)‧‧‧遠端伺服器 (10) ‧‧‧Remote Server
(100)‧‧‧接收資料庫 (100) ‧‧‧Receiving database
(11)‧‧‧感測器節點 (11) ‧‧‧ sensor nodes
(12)‧‧‧感測傳送板 (12)‧‧‧Sensing transfer board
(13)‧‧‧智慧感測模組 (13)‧‧‧Smart Sensor Module
(130)‧‧‧無線通訊模組 (130)‧‧‧Wireless communication module
(131)‧‧‧電性資料表單 (131)‧‧‧Electrical data sheet
(132)‧‧‧可調整前置電路 (132)‧‧‧Adjustable front circuit
(133)‧‧‧類比轉數位轉換器 (133)‧‧‧ Analog to digital converter
(134)‧‧‧微控制器 (134)‧‧‧Microcontrollers
(14)‧‧‧閘道器 (14) ‧‧‧ gateways
(20)‧‧‧雲端平台 (20) ‧‧‧Cloud Platform
(21)‧‧‧數據服務系統 (21) ‧‧‧Data Service System
(3)‧‧‧第一實施例 (3) ‧‧‧First embodiment
300‧‧‧步驟 300‧‧‧Steps
301‧‧‧步驟 301‧‧‧Steps
302‧‧‧步驟 302‧‧‧Steps
303‧‧‧步驟 303‧‧ steps
304‧‧‧步驟 304‧‧‧Steps
305‧‧‧步驟 305‧‧‧Steps
(4)‧‧‧第二實施例 (4) ‧‧‧Second embodiment
400‧‧‧步驟 400‧‧‧ steps
401‧‧‧步驟 401‧‧‧ steps
402‧‧‧步驟 402‧‧‧Steps
403‧‧‧步驟 403‧‧‧Steps
(5)‧‧‧感測傳送板正確性自動檢驗步驟 (5) ‧‧‧Automatic inspection steps for the correctness of the transmission plate
500‧‧‧步驟 500‧‧‧ steps
501‧‧‧步驟 501‧‧‧Steps
第一圖係為本發明之無線空氣品質監控系統之系統架構圖。 The first figure is a system architecture diagram of the wireless air quality monitoring system of the present invention.
第二圖係為本發明之無線空氣品質監控系統之系統架構圖之另一實施例。 The second figure is another embodiment of the system architecture diagram of the wireless air quality monitoring system of the present invention.
第三圖係為本發明之空氣品質預測方法之第一實施例。 The third figure is the first embodiment of the air quality prediction method of the present invention.
第四圖係為本發明之空氣品質預測方法之第二實施例。 The fourth figure is a second embodiment of the air quality prediction method of the present invention.
第五圖係為本發明之感測傳送板正確性自動檢驗步驟之方法流程圖。 The fifth figure is a flow chart of the method for automatically checking the correctness of the sensing transfer board of the present invention.
第六圖係為利用本發明之空氣品質預測方法之二氧化碳濃度預測圖之第一實施例。 The sixth drawing is a first embodiment of a carbon dioxide concentration prediction map using the air quality prediction method of the present invention.
第七圖係為利用本發明之空氣品質預測方法之二氧化碳濃度預測圖之第二實施例。 The seventh drawing is a second embodiment of the carbon dioxide concentration prediction map using the air quality prediction method of the present invention.
第八圖係為利用本發明之空氣品質預測方法之二氧化碳濃度預測而調整空調開關之關係圖。 The eighth figure is a relationship diagram in which the air conditioner switch is adjusted by the carbon dioxide concentration prediction of the air quality prediction method of the present invention.
(1)‧‧‧無線空氣品質監控系統 (1)‧‧‧Wireless Air Quality Monitoring System
(10)‧‧‧遠端伺服器 (10) ‧‧‧Remote Server
(100)‧‧‧接收資料庫 (100) ‧‧‧Receiving database
(11)‧‧‧感測器節點 (11) ‧‧‧ sensor nodes
(12)‧‧‧感測傳送板 (12)‧‧‧Sensing transfer board
(13)‧‧‧智慧感測模組 (13)‧‧‧Smart Sensor Module
(130)‧‧‧無線通訊模組 (130)‧‧‧Wireless communication module
(131)‧‧‧電性資料表單 (131)‧‧‧Electrical data sheet
(132)‧‧‧可調整前置電路 (132)‧‧‧Adjustable front circuit
(14)‧‧‧閘道器 (14) ‧‧‧ gateways
(20)‧‧‧雲端平台 (20) ‧‧‧Cloud Platform
(21)‧‧‧數據服務系統 (21) ‧‧‧Data Service System
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| Publication number | Priority date | Publication date | Assignee | Title |
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| TWI607324B (en) * | 2015-12-29 | 2017-12-01 | Chunghwa Telecom Co Ltd | Data feature selection and data grouping system and method for vehicle travel time |
| CN106338586A (en) * | 2016-10-21 | 2017-01-18 | 广东顺德西安交通大学研究院 | Air sensor provided with sensing module |
| TWI808564B (en) * | 2021-12-10 | 2023-07-11 | 財團法人工業技術研究院 | Fluid quality tracing method and system |
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| TW201337262A (en) | 2013-09-16 |
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