Health degree assessment method for non-flow monitoring of operation state of Internet of Things equipment

Abstract

The invention discloses a health degree assessment method for non-flow monitoring of the operation state of Internet of Things equipment, and the method comprises the steps: collecting index data, collecting the operation data and environment data of the Internet of Things equipment within T time, enabling the operation data to comprise the data set of the equipment, the operation time of the equipment, the response time, the electric quantity of a battery, the signal intensity and the number of abnormal events, the environment data comprises temperature data, humidity data, vibration data and electromagnetic interference data outside the equipment; evaluating the overall quality of the data set through the collected data set of the equipment to obtain a data quality score; obtaining an environmental adaptability score through the collected temperature data, humidity data, vibration data and electromagnetic interference data outside the equipment; carrying out normalization processing; and constructing a health degree evaluation model, and performing health degree evaluation on the Internet of Things equipment. The method is suitable for the Internet of Things equipment which cannot acquire the flow data in real time or is difficult to acquire the flow data, and the application range of equipment running state monitoring is expanded.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a health assessment method for non-flow monitoring of the operating status of an Internet of Things device. Background Art

[0002] Traditional IoT device operation status monitoring mainly relies on traffic monitoring, which determines whether the device is working properly by analyzing the device's traffic data. However, in actual applications, many IoT devices are deployed in scenarios with complex network environments and difficult traffic collection, such as multi-layered and complex physical environments, and scenarios where private networks and public networks are reused, making it difficult to achieve real-time traffic monitoring. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a non-flow monitoring IoT device operating status health assessment method, which can evaluate the operating status of the device without relying on the device flow data.

[0004] The present invention adopts the following technical solutions:

[0005] A method for evaluating the health of an IoT device operating status without flow monitoring, characterized in that it comprises the following steps:

[0006] Step 1: Collect index data, collect the operation data and environmental data of the IoT device within time T. The operation data includes the device data set, device operation time, response time, battery power, signal strength and number of abnormal events. The environmental data includes the temperature data, humidity data, vibration data and electromagnetic interference data outside the device.

[0007] Step 2: Evaluate the overall quality of the data set through the collected data set of the device to obtain a data quality score; obtain the environmental adaptability score through the collected temperature data, humidity data, vibration data, and electromagnetic interference data outside the device;

[0008] Step 3: Normalize the data quality score, environmental adaptability score, device operating time, response time, battery power, signal strength, and number of abnormal events;

[0009] Step 4: Build a health assessment model to evaluate the health of IoT devices.

[0010] Preferably, the number of abnormal events is obtained by collecting data records containing timestamps and abnormal flags, filtering out events marked as abnormal, and counting these events within a time T, and finally obtaining the total number of abnormal events.

[0011] Preferably, the calculation process of the data quality score is:

[0012] Set the data quality dimensions of the data set. The numerical quality dimensions include data completeness, data accuracy, data consistency, data uniqueness, and data timeliness;

[0013] The score for data integrity is Sc, the score for data accuracy is Sa, the score for data consistency is Sco, the score for data uniqueness is Su, and the score for data timeliness is St;

[0014] The data quality score calculation formula is:

[0015] Q=wc×Sc+wa×Sa+wco×Sco+wu×Su+wt×St;

[0016] Among them, wc is the weight of the data completeness score, wa is the weight of the data accuracy score, wco is the weight of the data consistency score, wu is the weight of the data uniqueness score, and wt is the weight of the data timeliness score.

[0017] Preferably, the calculation process of the environmental adaptability score is:

[0018] Calculate the score Ste of the temperature data, the score Sh of the humidity data, the score Sv of the vibration data, and the score Se of the electromagnetic interference data of the IoT device according to the performance data of the IoT device;

[0019] The calculation formula for environmental adaptability score is:

[0020] A=wte×Ste+wh×Sh+wv×Sv+we×Se

[0021] Among them, wte is the weight of the score under temperature data, wh is the weight of the score under humidity data, wv is the weight of the score under vibration data, and we is the weight of the score under electromagnetic interference data.

[0022] Preferably, the health assessment model is:

[0023] D=100*(w1*f1(T)+w2*f2(R)+w3*f3(B)+w4*f4(S)+w5*f5(E)+w6*f6(Q)+w7*f7(A));

[0024] Where D is the health score, T1 is the device operating time, TZ is the set optimal device operating time; f2(R)=max(0,1-R / RZ), R is the response time, RZ is the set maximum response time; f3(B)=B, B is the battery power, f4(S)=S / SZ, S is the signal strength, SZ is the set maximum signal strength; f5(E)=max(0,1-E / EZ), E is the number of abnormal events, EZ is the set maximum number of abnormal events; f6(Q)=Q, f7(A)=A; w1, w2, w3, w4, w5, w6 and w7 are weights.

[0025] The present invention has the following beneficial effects:

[0026] The non-flow monitoring IoT device operating status health assessment method proposed in the present invention does not need to rely on flow data, and is suitable for IoT devices that cannot obtain flow data in real time or have difficulty in collecting flow data, thereby expanding the application scope of device operating status monitoring. Different weights can be set according to different types of IoT devices to improve the accuracy and pertinence of the assessment results. Compared with the traditional method of monitoring the operating status of IoT devices that relies on flow data, the present invention combines the operating data and environmental data of IoT devices, utilizes multi-dimensional and multi-data to judge the monitoring status of IoT devices, and quantifies the health of IoT devices, thereby more intuitively monitoring the health of IoT devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The flowchart of the method for evaluating the operating status health of non-flow monitoring IoT devices. DETAILED DESCRIPTION

[0028] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0029] Embodiment 1: In this embodiment, the IoT device is a smart door lock, and the health evaluation method of the smart door lock includes the following steps:

[0030] Combination Figure 1 ,Step 1: Collect index data, collect the operation data and environmental data of the smart door lock within T time. The operation data includes the data set of the device, the device operation time, the response time, the battery power, the signal strength and the number of abnormal events. The environmental data includes the temperature data, humidity data, vibration data and electromagnetic interference data outside the device.

[0031] The number of abnormal events is obtained by collecting data records containing timestamps and abnormal flags, filtering out events marked as abnormal, and counting these events within time T to finally obtain the total number of abnormal events.

[0032] Step 2: Evaluate the overall quality of the data set through the collected data set of the device to obtain a data quality score; obtain the environmental adaptability score through the collected temperature data, humidity data, vibration data, and electromagnetic interference data outside the device.

[0033] The calculation process of data quality score is:

[0034] Set the data quality dimensions of the data set. The numerical quality dimensions include data completeness, data accuracy, data consistency, data uniqueness, and data timeliness.

[0035] Completeness – The integrity of the data.

[0036] Data Accuracy – The correctness of data values.

[0037] Data consistency (Consistency)-the coordination of data between different data sources.

[0038] Data uniqueness – Duplicates in the data.

[0039] Data timeliness - the frequency and timeliness of data updates.

[0040] The calculation data completeness score is Sc, the calculation data accuracy score is Sa, the calculation data consistency score is Sco, the calculation data uniqueness score is Su, and the calculation data timeliness score is St.

[0041] The score range for Sc, Sa, Sco, Su, and St is [0,1].

[0042] The data integrity score Sc is calculated according to the proportion of data integrity. For example, if the data integrity is 100%, the data integrity score Sc is 1; if the data integrity is 80%, the data integrity score Sc is 0.8.

[0043] The data accuracy score Sa is calculated according to the proportion of data accuracy. For example, if the data accuracy is 100%, the data accuracy score Sa is 1; if the data accuracy is 75%, the data accuracy score Sa is 0.75.

[0044] The data consistency score Sco is calculated based on the proportion of data consistency. For example, if the data consistency is 100%, the data consistency score Sco is 1, and if the data consistency is 50%, the data accuracy score Sa is 0.5.

[0045] The calculation process of the data uniqueness score Su is as follows: if the data meets the uniqueness requirement, the data uniqueness score Su is 1; if the data does not meet the uniqueness requirement, the data uniqueness score Su is 0.

[0046] The calculation process of the data timeliness score St is as follows: if the data transmission is completed within time t, the data timeliness score St is 1; if the data transmission is completed within time 1.5t, the data timeliness score St is 0.8; if the data transmission is completed within time 2t, the data timeliness score St is 0.6; if the data transmission is completed within time 2.5t, the data timeliness score St is 0.4; if the data transmission is completed within time 3t, the data timeliness score St is 0.2; if the data transmission is completed within more than time 3t, the data timeliness score St is 0.

[0047] The data quality score calculation formula is:

[0048] Q=wc×Sc+wa×Sa+wco×Sco+wu×Su+wt×St;

[0049] wc+wa+wco+wu+wt=1.

[0050] Among them, wc is the weight of the data completeness score, wa is the weight of the data accuracy score, wco is the weight of the data consistency score, wu is the weight of the data uniqueness score, and wt is the weight of the data timeliness score. The values ​​of the above weights are adjustable.

[0051] In this embodiment, the data integrity score Sc is 0.9, the data accuracy score Sa is 0.85, the calculated data consistency score Sco is 0.8, the calculated data uniqueness score Su is 1, and the calculated data timeliness score St is 0.8.

[0052] wc=0.3, wa=0.25, wco=0.2, wu=0.15, wt=0.1.

[0053] Then Q = 0.3*0.9+0.25*0.85+0.2*0.8+0.15*1+0.1*0.8 = 0.8725. (Full score is 1)

[0054] The calculation process of environmental adaptability score is:

[0055] According to the performance data of the IoT device, the score Ste of the IoT device's temperature data, the score Sh of its humidity data, the score Sv of its vibration data, and the score Se of its electromagnetic interference data are calculated.

[0056] Temperature data (Temperature) - How the device performs in temperature fluctuations.

[0057] Humidity data (Humidity) - How the device performs in changing humidity.

[0058] Vibration data (Vibration)-the equipment's ability to resist vibration.

[0059] Electromagnetic interference data (EMI) - how the device performs under electromagnetic interference.

[0060] The calculation process of the temperature data score Ste is as follows: when the external environment temperature is set to be within the range of 20°C to 40°C, the temperature data score Ste is 1; when the temperature rises or falls by 5 degrees Celsius outside the range of 20°C to 40°C, the temperature data score Ste is subtracted by 0.2.

[0061] The calculation process of the humidity data score Sh is as follows: when the external environment humidity is set to be within the range of 20% to 80%, the humidity data score Sh is 1; when the humidity increases or decreases by 5% outside the range of 20% to 80%, the humidity data score Sh is subtracted by 0.2.

[0062] The calculation process of the score Sv under vibration data is as follows: the vibration range of the external environment is set to 0 to 50Hz. When the vibration data is within the range of 0 to 50Hz, the score Sv of the vibration data is 1. When the vibration frequency exceeds this range, the score Sv of the vibration data decreases by 0.2 for every increase or decrease of 10Hz. For example, when the vibration frequency is 60Hz, Sv = 0.8; when the vibration frequency is 70Hz, Sv = 0.6

[0063] The calculation process of the score Se under the electromagnetic interference data is to set the electromagnetic interference intensity (EMI) in the range of 0 to 200V / m (volts per meter). When the electromagnetic interference intensity is within this range, the score Se is 1. When the electromagnetic interference intensity exceeds 200V / m, the score Se decreases by 0.2 for every 50V / m increase. For example, when the electromagnetic interference is 250V / m, Se = 0.8; when the electromagnetic interference is 300V / m, Se = 0.6.

[0064] The calculation formula for environmental adaptability score is:

[0065] A=wte×Ste+wh×Sh+wv×Sv+we×Se;

[0066] wte+wh+wv+we=1.

[0067] Among them, wte is the weight of the score under temperature data, wh is the weight of the score under humidity data, wv is the weight of the score under vibration data, and we is the weight of the score under electromagnetic interference data. The values ​​of the above weights are adjustable.

[0068] The following scores were obtained by testing the device of this embodiment in different environments:

[0069] The score Ste of the temperature data is 0.8, the score Sh of the humidity data is 1, the score Sv of the vibration data is 1, and the score Se of the electromagnetic interference data is 1.

[0070] wte=0.4, wh=0.2, wv=0.2, we=0.2.

[0071] Then A=0.4*0.8+0.2*1+0.2*1+0.2*1=0.92. (Full score is 1)

[0072] Step 3: Normalize the data quality score, environmental adaptability score, device operating time, response time, battery power, signal strength, and number of abnormal events. Normalization belongs to the prior art and will not be described in detail here.

[0073] Step 4: Build a health assessment model to evaluate the health of IoT devices.

[0074] The health assessment model is:

[0075] D=100*(w1*f1(T)+w2*f2(R)+w3*f3(B)+w4*f4(S)+w5*f5(E)+w6*f6(Q)+w7*f7(A));

[0076] Where D is the health score, T1 is the device operating time, TZ is the set optimal device operating time; f2(R)=max(0,1-R / RZ), R is the response time, RZ is the set maximum response time; f3(B)=B, B is the battery power, f4(S)=S / SZ, S is the signal strength, SZ is the set maximum signal strength; f5(E)=max(0,1-E / EZ), E is the number of abnormal events, EZ is the set maximum number of abnormal events; f6(Q)=Q, f7(A)=A; w1, w2, w3, w4, w5, w6 and w7 are weights.

[0077] In this embodiment, the device operating time T1 is 800 hours, TZ is 1000 hours, then f1(T)=0.8, the device response time R is 2 seconds, RZ is 5 seconds, then f2(R)=0.6, the device battery power is 60%, f3(B)=0.6, the device signal strength is 75, SZ is 100, then f4(S)=0.75, 2 abnormal events occurred during the operation of the device, E is 2, the maximum number of abnormal events EZ is 5, then f5(E)=0.6, f6(Q)=0.8765, f7(A)=0.92.

[0078] w1=0.2, w2=0.15, w3=0.1, w4=0.1, w5=0.1, w6=0.2, w7=0.15.

[0079] Then D=100*(0.2*0.8+0.15*0.6+0.1*0.6+0.1*0.75+0.1*0.6+0.2*0.8765+0.15*0.92)=75.83.

[0080] Health level classification: Excellent: 90-100, Good: 80-89, Average: 70-79, Poor: 60-69, Dangerous: 0-59.

[0081] The final device health is 73.85, indicating that the overall status of the device is relatively general.

[0082] The present invention can also set the warning threshold to 70, and trigger a warning when the health level is lower than 70. The above process is completed in the host computer.

[0083] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for evaluating the health of an IoT device operating status without monitoring the flow rate, characterized in that: The following steps are involved: Step 1: Collect index data, collect the operation data and environmental data of the IoT device within time T. The operation data includes the device data set, device operation time, response time, battery power, signal strength and number of abnormal events. The environmental data includes the temperature data, humidity data, vibration data and electromagnetic interference data outside the device. Step 2: Evaluate the overall quality of the data set through the data set of the collected device to obtain a data quality score; The environmental adaptability score is obtained by collecting the temperature data, humidity data, vibration data and electromagnetic interference data outside the equipment; Step 3: Normalize the data quality score, environmental adaptability score, device operating time, response time, battery power, signal strength, and number of abnormal events; Step 4: Build a health assessment model to evaluate the health of IoT devices.

2. According to claim 1, a method for evaluating the health of a non-flow monitoring IoT device operating status is characterized in that: The number of abnormal events is obtained by collecting data records containing timestamps and abnormal flags, filtering out events marked as abnormal, and counting these events within a time T, and finally obtaining the total number of abnormal events.

3. According to claim 1, a method for evaluating the health of a non-flow monitoring IoT device operating status is characterized in that: The calculation process of data quality score is: Set the data quality dimensions of the data set. The numerical quality dimensions include data completeness, data accuracy, data consistency, data uniqueness, and data timeliness; The score for data integrity is Sc, the score for data accuracy is Sa, the score for data consistency is Sco, the score for data uniqueness is Su, and the score for data timeliness is St; The data quality score calculation formula is: Q=wc×Sc+wa×Sa+wco×Sco+wu×Su+wt×St; Among them, wc is the weight of the data completeness score, wa is the weight of the data accuracy score, wco is the weight of the data consistency score, wu is the weight of the data uniqueness score, and wt is the weight of the data timeliness score.

4. According to claim 1, a method for evaluating the health of a non-flow monitoring IoT device operating status is characterized in that: The calculation process of environmental adaptability score is: Calculate the score Ste of the temperature data, the score Sh of the humidity data, the score Sv of the vibration data, and the score Se of the electromagnetic interference data of the IoT device according to the performance data of the IoT device; The calculation formula for environmental adaptability score is: A=wte×Ste+wh×Sh+wv×Sv+we×Se; Among them, wte is the weight of the score under temperature data, wh is the weight of the score under humidity data, wv is the weight of the score under vibration data, and we is the weight of the score under electromagnetic interference data.

5. According to claim 1, a method for evaluating the health of a non-flow monitoring IoT device operating status is characterized in that: The health assessment model is: D=100*(w1*f1(T)+w2*f2(R)+w3*f3(B)+w4*f4(S)+w5*f5(E)+w6*f6(Q)+w7*f7(A)); Where D is the health score, T1 is the device operating time, TZ is the set optimal device operating time; f2(R)=max(0,1-R / RZ), R is the response time, RZ is the set maximum response time; f3(B)=B, B is the battery power, f4(S)=S / SZ, S is the signal strength, SZ is the set maximum signal strength; f5(E)=max(0,1-E / EZ), E is the number of abnormal events, EZ is the set maximum number of abnormal events; f6(Q)=Q, f7(A)=A; w1, w2, w3, w4, w5, w6 and w7 are weights.

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