Water plant equipment state analysis method and system based on Internet of Things

Abstract

The invention relates to the technical field of water plant equipment monitoring, and discloses a water plant equipment state analysis method and system based on the Internet of Things, and the system comprises a lifting water pump monitoring unit which comprises a vibration sensor and a thermal infrared imager, the monitoring module is used for monitoring the state of the lifting water pump according to the vibration information acquired by the vibration sensor, the temperature information acquired by the thermal infrared imager and the operation information of the lifting water pump; the ultraviolet sterilizer monitoring unit comprises a plurality of groups of ultraviolet intensity sensors and is used for monitoring the state of the ultraviolet sterilizer according to a monitoring strategy; the linkage unit is used for adjusting the monitoring strategy of the ultraviolet sterilizer according to the state of the lifting water pump; and the equipment state analysis platform is used for performing early warning judgment according to the state of the lifting water pump and the state of the ultraviolet sterilizer. According to the invention, the accuracy and sensitivity of abnormal problem monitoring can be improved, and the problems of misjudgment and erroneous judgment caused by too small alarm value setting can be reduced at the same time.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of water plant equipment monitoring, and in particular to a water plant equipment state analysis method and system based on Internet of Things. BACKGROUND

[0002] With the development of Internet of Things technology, its application in water plant equipment monitoring is also more and more extensive. The monitoring process of the equipment is converted from manual point inspection to real-time monitoring by intelligent equipment, and then unified maintenance is performed through a water plant intelligent management platform, so that predictive maintenance can be performed on the faulty equipment in a timely manner, and the loss caused by equipment failure can be reduced.

[0003] The existing analysis process of the state of the water plant equipment based on Internet of Things mainly judges the monitoring operation parameters of each device. For example, the existing water plant equipment management platform adopts a supervisory control and data acquisition (SCADA) system, which is used as the “brain” of the water plant. The SCADA system collects the data of all sensors in real time, centrally displays, alarms and records in the central control room, sets upper and lower limit alarm values for each monitoring parameter, and immediately alarms in the central control room in case of abnormality. The relevant personnel can be pushed through short messages and Apps, so as to realize the judgment process of the abnormality of the water plant equipment.

[0004] The alarm process of the existing SCADA system is a single parameter alarm value judgment. For a single parameter, if the set alarm value range is large, the monitoring accuracy and sensitivity of the SCADA system are low, and if the set alarm value range is small, the SCADA system is prone to misjudgment and wrong judgment. Therefore, if the water plant equipment monitoring accuracy and sensitivity are ensured while avoiding the occurrence of misjudgment and wrong judgment, it is the basic problem to be solved by the application. SUMMARY

[0005] In order to ensure the water plant equipment monitoring accuracy and sensitivity while avoiding the occurrence of misjudgment and wrong judgment, the application provides a water plant equipment state analysis method and system based on Internet of Things.

[0006] In the first aspect, the application provides a water plant equipment state analysis system based on Internet of Things, which adopts the following technical scheme: A water plant equipment state analysis system based on Internet of Things, comprising: A booster pump monitoring unit, comprising a vibration sensor and an infrared thermal imager, for monitoring the state of the booster pump according to vibration information obtained by the vibration sensor, temperature information obtained by the infrared thermal imager and booster pump operation information; A UV sterilizer monitoring unit, comprising a plurality of groups of ultraviolet intensity sensors, for monitoring the state of the UV sterilizer according to a monitoring strategy; The linkage unit is used to adjust the monitoring strategy of the ultraviolet sterilizer according to the status of the booster pump. The equipment status analysis platform is used to make early warning judgments based on the status of the booster pump and the ultraviolet sterilizer.

[0007] Optionally, the process of monitoring the booster pump includes: Obtain the ambient temperature value and the real-time speed of the booster pump from the booster pump operation information; The overall vibration value and amplitude variation curve of the booster pump at the current time point are obtained by vibration sensors. The vibration risk value of the booster pump is determined based on the overall vibration value, amplitude variation curve and real-time speed of the booster pump. The predicted temperature value is obtained based on the ambient temperature value and the real-time rotation speed of the booster pump; the temperature information obtained by the infrared thermal imager is compared with the predicted temperature value, and the temperature risk value of the booster pump is determined based on the comparison result. The real-time risk value of the booster pump is determined based on the vibration risk value and temperature risk value, and the cumulative risk value of the booster pump is determined based on the changing trend of the real-time risk value over historical periods. The booster pump is monitored based on real-time and cumulative risk values.

[0008] Optionally, the process of obtaining the vibration risk value of the water pump includes: Get The amplitude variation curve A(t) over the time period is obtained. Obtain all the peak points of the amplitude variation curve A(t) over the time period. Among all peaks in the time period The number of wave crests, n; Through formula Calculate the vibration risk value of the booster pump. ; Where t is the current time point, For the preset first time interval, i represents The order of all peak points in the time period Let be the amplitude of the i-th wave crest. for Average amplitude over time period This is the preset error tolerance coefficient. ≥1.2, This represents the overall vibration value. For real-time speed, for The corresponding vibration threshold, The vibration value is a reference value, and f(x) is a judgment function. When x > 0, f(x) = x, and when x ≤ 0, f(x) = 0. Here is the weight adjustment coefficient, and n is... The number of peak points in the time period , The amplitude average of the n peak points.

[0009] Optionally, the temperature risk value obtaining process of the booster water pump comprises: According to the relationship model Solving, the predicted temperature value is obtained. Wherein, C is the heat capacity of the booster water pump, is the ambient temperature, R is the thermal resistance of the booster water pump, is The corresponding heat production; The temperature risk value of the booster water pump is obtained by calculating the equation ; Wherein, is the highest temperature value in the temperature information, is the temperature unit quantity, =1℃, is the area of the region in the temperature information , is the adjustment coefficient, is the warning area value.

[0010] Optionally, the real-time risk value obtaining process of the booster water pump comprises: The real-time risk value R(t) of the booster water pump is obtained by calculating the formula ; Wherein, , are the weight values of vibration risk and temperature risk respectively; The cumulative risk value obtaining process of the booster water pump comprises: The cumulative risk value Rg(t) of the booster water pump is obtained by calculating the formula ; Wherein, is The real-time risk value average in the time period, R1 is the real-time risk value comparison threshold, is The standard deviation of the real-time risk value corresponding to a plurality of time points selected according to a preset second time interval in the time period, the second time interval is smaller than the first time interval, is the standard deviation reference value, is the slope of the real-time risk value in the time period ; The process of monitoring the booster water pump according to the real-time risk value and the cumulative risk value comprises: The real-time risk value R(t) and the cumulative risk value Rg(t) are compared with the corresponding threshold values respectively: If the real-time risk value R(t) < R1 and Rg(t) < R2, then the booster pump is judged to be of low risk. Otherwise, the booster pump is deemed high-risk; Where R2 is the threshold corresponding to the cumulative risk value Rg(t).

[0011] Optionally, the process of monitoring the status of the ultraviolet sterilizer includes: The intensity of ultraviolet emission and the intensity of ultraviolet transmission are obtained by several sets of ultraviolet intensity sensors. The intensity of ultraviolet radiation was compared with the dose threshold ranges: When the intensity of ultraviolet radiation is outside the dose threshold range, the ultraviolet generator in the ultraviolet sterilizer should be replaced. When the intensity of ultraviolet emission is within the dose threshold range, the status of the ultraviolet sterilizer is analyzed based on the difference between the ultraviolet emission intensity and several sets of ultraviolet transmission intensities.

[0012] Optionally, the process of performing a condition analysis on the UV sterilizer includes: The differences between the ultraviolet emission intensity and several sets of ultraviolet transmission intensity were obtained respectively. , the difference Compare with the preset threshold interval [Z1, Z2]: If all differences satisfy If so, it is determined that the water quality entering the UV sterilizer is abnormal; If some differences satisfy... If so, it is determined that the transparent quartz sleeve in the ultraviolet sterilizer needs to be cleaned immediately; If all differences satisfy Through formula The consistency coefficient u at different locations of the transparent quartz sleeve is calculated and compared with the preset value u1. When the consistency coefficient u > the preset value u1, it is determined that the transparent quartz sleeve in the UV sterilizer needs to be cleaned immediately. Where q is the number of monitoring groups for ultraviolet transmission intensity, and k∈[1,q], Group q The mean.

[0013] Optionally, the process by which the linkage unit adjusts the monitoring strategy of the ultraviolet sterilizer according to the status of the booster pump includes: The monitoring frequency of the UV sterilizer is adjusted according to the difference R2-Rg(t) between R2 and the cumulative risk value Rg(t), and the monitoring frequency is proportional to R2-Rg(t).

[0014] Secondly, this application provides a method for analyzing the status of water plant equipment based on the Internet of Things, employing the following technical solution: A method for analyzing the status of water plant equipment based on the Internet of Things (IoT), wherein the method is executed by an IoT-based water plant equipment status analysis system as described above, comprising: Vibration information is obtained by the vibration sensor in the lifting pump monitoring unit, and temperature information is obtained by the infrared thermal imager in the lifting pump monitoring unit. The status of the lifting pump is monitored based on the vibration information, temperature information and lifting pump operation information. The status of the ultraviolet sterilizer is monitored by several sets of ultraviolet intensity sensors in the ultraviolet sterilizer monitoring unit according to the monitoring strategy. The monitoring strategy of the ultraviolet sterilizer is adjusted by the linkage unit according to the status of the booster pump; The equipment status analysis platform makes early warning judgments based on the status of the booster pump and the ultraviolet sterilizer.

[0015] In summary, this application includes at least one of the following beneficial technical effects: This invention can adjust the monitoring strategy through the linkage between monitoring units, which can improve the monitoring effect while reducing the resource occupation caused by high-frequency monitoring of the ultraviolet sterilizer. In addition, based on the existing technology, it can predictively analyze the overall operating status of the booster pump and the ultraviolet sterilizer by integrating multiple data, which can identify potential abnormal problems in the booster pump and the ultraviolet sterilizer, improve the accuracy and sensitivity of abnormal problem monitoring, and reduce the problem of misjudgment caused by the alarm value being set too low. Attached Figure Description

[0016] Figure 1 This is a logical block diagram of the Internet of Things-based water plant equipment status analysis system in this invention.

[0017] Figure 2 This is a flowchart of the water plant equipment status analysis method based on the Internet of Things in this invention. Detailed Implementation

[0018] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0019] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0020] This application discloses an Internet of Things-based water plant equipment status analysis system, referring to... Figure 1 The system includes a booster pump monitoring unit, an ultraviolet sterilizer monitoring unit, a linkage unit, and an equipment status analysis platform. The booster pump monitoring unit includes vibration sensors and an infrared thermal imager, used to monitor the booster pump's status based on vibration information acquired by the vibration sensors, temperature information acquired by the infrared thermal imager, and booster pump operating information. The ultraviolet sterilizer monitoring unit includes several sets of ultraviolet intensity sensors, used to monitor the status of the ultraviolet sterilizer according to a monitoring strategy. The linkage unit adjusts the ultraviolet sterilizer monitoring strategy based on the booster pump's status. The equipment status analysis platform makes early warning judgments based on the status of the booster pump and the ultraviolet sterilizer. Through the above technical methods... The system in this case can adjust the monitoring strategy through the linkage between monitoring units. Since the status of the booster pump affects the stability of water flow, adjusting the monitoring strategy of the UV sterilizer according to the status of the booster pump can improve the monitoring effect while reducing the resource occupation caused by high-frequency monitoring of the UV sterilizer. In addition, based on the existing technology, this embodiment performs predictive analysis on the overall operating status of the booster pump and the UV sterilizer by integrating multiple data, which can identify potential abnormal problems in the booster pump and the UV sterilizer, improve the accuracy and sensitivity of abnormal problem monitoring, and reduce the problem of misjudgment caused by setting the alarm value too low.

[0021] In one embodiment, a process for monitoring a lift pump is provided, including: acquiring an ambient temperature value; acquiring the real-time rotational speed of the lift pump from lift pump operation information; obtaining the overall vibration value and amplitude variation curve of the lift pump at the current time point through a vibration sensor; and determining the vibration risk value of the lift pump based on the overall vibration value, amplitude variation curve, and real-time rotational speed of the lift pump. This process includes: acquiring... The amplitude variation curve A(t) over a time period, where To preset the first time interval, the monitoring process of the booster pump is based on the settings of the management personnel. The subsequent acquisition Obtain all the peak points of the amplitude variation curve A(t) over the time period. Among all peaks in the time period The number of wave crests, n; This is the preset error tolerance coefficient. ≥1.2, a preset error tolerance coefficient, set based on empirical data, is used to identify abnormal peaks in the amplitude variation curve, through a formula. Calculate the vibration risk value of the booster pump. Where t is the current time point, and i represents... The order of all peak points in the time period Let be the amplitude of the i-th wave crest. for Average amplitude over time period The total vibration value is the root mean square value of the vibration velocity, expressed in mm / s. For real-time speed, for The corresponding vibration threshold is established based on the test data corresponding to different real-time velocity ranges to establish a corresponding comparison relationship, and then through... The corresponding vibration value threshold is determined by the interval in which it is located. The vibration value is a reference value of 1 mm / s. f(x) is a judgment function. When x > 0, f(x) = x, and when x ≤ 0, f(x) = 0. This is the weight adjustment coefficient, which is set based on the fitting of the test data, where n is... Among all peaks in the time period The number of peaks, The average amplitude of n wave peaks is used to assess the potential risk of pump vibration by considering the magnitude of the overall vibration value exceeding the corresponding vibration threshold, the number of abnormal wave peaks in the amplitude variation curve, and the range of deviations. Then, a predicted temperature value is obtained based on the ambient temperature and the real-time speed of the pump. Finally, a relational model is used to determine the predicted temperature. Solve to obtain the predicted temperature value. Where C represents the heat capacity of the booster pump. R represents the ambient temperature, and R represents the thermal resistance of the booster pump. for The corresponding heat generation, the heat capacity C of the booster pump, and the thermal resistance R of the booster pump are all obtained from the parameters of the booster pump. Based on the test data of the booster pump, the system is configured to predict the real-time temperature of the booster pump. The temperature information acquired by the infrared thermal imager is compared with the predicted temperature value, and the temperature risk value of the booster pump is determined based on the comparison result. This process includes: using equations... Calculate the temperature risk value of the booster pump. ;in, This is the highest temperature value in the temperature information. For temperature units, =1℃, For temperature information The area of ​​the region To adjust the coefficient, The warning area value is set based on the heat source area of ​​the water pump in the test data, and the adjustment coefficient is adjusted accordingly. Based on the fitting settings of the test data, a predictive judgment is made on the temperature anomaly risk of the booster pump by comprehensively considering the highest temperature value and temperature distribution. The real-time risk value of the booster pump is determined based on the vibration risk value and temperature risk value, and the cumulative risk value is determined based on the changing trend of the real-time risk value over historical periods. The process of obtaining the real-time risk value of the booster pump includes: using the formula... The real-time risk value R(t) of the booster pump is calculated; where, , These are the weighted values ​​for vibration risk and temperature risk, respectively. They are set based on the numerical range of vibration risk and temperature risk values ​​in the test data and their impact on the judgment of abnormalities in the booster pump. Therefore, the magnitude of the real-time risk value R(t) can comprehensively consider multiple factors to achieve anomaly judgment of the booster pump. The process of obtaining the cumulative risk value of the booster pump includes: using the formula... The cumulative risk value Rg(t) of the booster pump is calculated; where, for The average real-time risk value for the time period, with R1 being the real-time risk value comparison threshold, which is set according to the corresponding standard based on the test data. for The time period is determined by selecting the standard deviation of real-time risk values ​​at several time points corresponding to a preset second time interval, wherein the second time interval is shorter than the first time interval. This is a reference value for the standard deviation, which is set based on the data fitted to the maximum allowable error. For real-time risk values The slope of the time period, therefore, is determined by the real-time risk value. The cumulative risk of the booster pump is assessed by analyzing its changing state, data distribution, and overall data size. The booster pump is monitored based on real-time and cumulative risk values. The real-time risk value R(t) and the cumulative risk value Rg(t) are compared with corresponding thresholds, where R2 is the threshold for the cumulative risk value Rg(t), set according to empirical data. If the real-time risk value R(t) < R1 and Rg(t) < R2, the booster pump is considered low-risk; otherwise, it is considered high-risk. This comparison process allows for predictive assessment of potential operational risks of the booster pump, improving the accuracy and sensitivity of water plant equipment monitoring.

[0022] In one embodiment, a process for monitoring the status of an ultraviolet (UV) sterilizer is provided, comprising: acquiring UV emission intensity and UV transmission intensity using several sets of UV intensity sensors; comparing the UV emission intensity with a dose threshold range, wherein the dose threshold range is set according to the specifications of the UV sterilizer; when the UV emission intensity is not within the dose threshold range, it indicates that the UV generator has aging or other problems, and therefore the UV generator in the UV sterilizer is replaced; when the UV emission intensity is within the dose threshold range, performing a status analysis of the UV sterilizer based on the difference between the UV emission intensity and the several sets of UV transmission intensities, which includes: acquiring the difference between the UV emission intensity and the several sets of UV transmission intensities. , the difference The results are compared with a preset threshold range [Z1, Z2], which is set based on test data from a transparent quartz sleeve of the same specification in the UV sterilizer. If all differences meet the requirements... If the water quality entering the UV sterilizer is abnormal, resulting in low UV transmission intensity, then the problem is considered resolved. If the value is positive, it indicates that scaling or other issues have occurred at certain points in the transparent quartz sleeve of the UV sterilizer, requiring immediate cleaning; if all differences meet the requirements... Through formula The consistency coefficient u at different locations on the transparent quartz sleeve was calculated, where q is the number of monitoring groups for ultraviolet transmission intensity, and k∈[1,q]. Group q The mean value of the UV transmission coefficient u reflects the consistency of UV transmission through the transparent quartz sleeve at different locations. When the transmission level at a certain location deviates from that at other locations, it indicates that scaling has just occurred at that location. Therefore, the consistency coefficient u is compared with the preset value u1, which is set based on empirical data. When the consistency coefficient u > the preset value u1, it is determined that the transparent quartz sleeve in the UV sterilizer needs to be cleaned immediately. This process can improve the timeliness of cleaning the UV sterilizer and thus predictively ensure the accuracy of UV sterilizer monitoring.

[0023] In addition, the process of the linkage unit adjusting the monitoring strategy of the ultraviolet sterilizer according to the status of the booster pump includes: adjusting the monitoring frequency of the ultraviolet sterilizer according to the difference between R2 and the cumulative risk value Rg(t), R2-Rg(t), and the monitoring frequency is proportional to R2-Rg(t). Therefore, when the abnormal risk of the booster pump is high, by increasing the monitoring frequency of the ultraviolet sterilizer, the cleaning effect of the ultraviolet sterilizer can be adaptively improved with relatively low resource consumption.

[0024] This application also discloses an IoT-based water plant equipment status analysis method, which is executed by an IoT-based water plant equipment status analysis system as described in any of the above embodiments, with reference to... Figure 2 This includes: acquiring vibration information through vibration sensors in the lift pump monitoring unit; acquiring temperature information through an infrared thermal imager in the lift pump monitoring unit; monitoring the status of the lift pump based on the vibration information, temperature information, and lift pump operation information; monitoring the status of the ultraviolet sterilizer according to a monitoring strategy using several sets of ultraviolet intensity sensors in the ultraviolet sterilizer monitoring unit; adjusting the monitoring strategy of the ultraviolet sterilizer based on the status of the lift pump through a linkage unit; and making early warning judgments based on the status of the lift pump and the status of the ultraviolet sterilizer through an equipment status analysis platform.

[0025] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A water plant equipment status analysis system based on the Internet of Things, characterized in that, include: The booster pump monitoring unit includes a vibration sensor and an infrared thermal imager, which are used to monitor the status of the booster pump based on the vibration information obtained by the vibration sensor, the temperature information obtained by the infrared thermal imager, and the operating information of the booster pump. The UV sterilizer monitoring unit includes several sets of UV intensity sensors, which are used to monitor the status of the UV sterilizer according to the monitoring strategy. The linkage unit is used to adjust the monitoring strategy of the ultraviolet sterilizer according to the status of the booster pump. The equipment status analysis platform is used to make early warning judgments based on the status of the booster pump and the ultraviolet sterilizer.

2. The water plant equipment status analysis system based on the Internet of Things according to claim 1, characterized in that, The process of monitoring the booster pump includes: Obtain the ambient temperature value and the real-time speed of the booster pump from the booster pump operation information; The overall vibration value and amplitude variation curve of the booster pump at the current time point are obtained by vibration sensors. The vibration risk value of the booster pump is determined based on the overall vibration value, amplitude variation curve and real-time speed of the booster pump. The predicted temperature value is obtained based on the ambient temperature value and the real-time rotation speed of the booster pump; the temperature information obtained by the infrared thermal imager is compared with the predicted temperature value, and the temperature risk value of the booster pump is determined based on the comparison result. The real-time risk value of the booster pump is determined based on the vibration risk value and temperature risk value, and the cumulative risk value of the booster pump is determined based on the changing trend of the real-time risk value over historical periods. The booster pump is monitored based on real-time and cumulative risk values.

3. The water plant equipment status analysis system based on the Internet of Things according to claim 2, characterized in that, The process of obtaining the vibration risk value of the booster pump includes: Get The amplitude variation curve A(t) over the time period is obtained. Obtain all the peak points of the amplitude variation curve A(t) over the time period. Among all peaks in the time period The number of wave crests, n; Through formula Calculate the vibration risk value of the booster pump. ; Where t is the current time point, For the preset first time interval, i represents The order of all peak points in the time period Let be the amplitude of the i-th wave crest. for Average amplitude over time period This is the preset error tolerance coefficient. ≥1.2, This represents the overall vibration value. For real-time speed, for The corresponding vibration threshold, The vibration value is a reference value, and f(x) is a judgment function. When x > 0, f(x) = x, and when x ≤ 0, f(x) = 0. Here is the weight adjustment coefficient, and n is... Among all peaks in the time period The number of peaks, Let be the average amplitude of the n wave crests.

4. The water plant equipment status analysis system based on the Internet of Things according to claim 3, characterized in that, The process of obtaining the temperature risk value of the booster pump includes: According to the relational model Solve to obtain the predicted temperature value. ; Where C represents the heat capacity of the booster pump. R represents the ambient temperature, and R represents the thermal resistance of the booster pump. for The corresponding heat production; Through equations Calculate the temperature risk value of the booster pump. ; in, This is the highest temperature value in the temperature information. For temperature units, =1℃, For temperature information The area of ​​the region To adjust the coefficient, This represents the area value for early warning.

5. The water plant equipment status analysis system based on the Internet of Things according to claim 4, characterized in that, The process of obtaining the real-time risk value of the booster pump includes: Through formula The real-time risk value R(t) of the booster pump is calculated. in, , These are the weight values ​​for vibration risk and temperature risk, respectively. The process of obtaining the cumulative risk value of the booster pump includes: Through formula The cumulative risk value Rg(t) of the booster pump is calculated. in, for The average real-time risk value over a given period, with R1 being the threshold for comparing real-time risk values. for The time period is determined by selecting several time points corresponding to the standard deviation of real-time risk values ​​according to a preset second time interval. The second time interval is shorter than the first time interval. This is the reference value for the standard deviation. For real-time risk values The slope of the time period; The process of monitoring the booster pump based on real-time and cumulative risk values ​​includes: The real-time risk value R(t) and the cumulative risk value Rg(t) are compared with their corresponding thresholds: If the real-time risk value R(t) < R1 and Rg(t) < R2, then the booster pump is judged to be of low risk. Otherwise, the booster pump is deemed high-risk; Where R2 is the threshold corresponding to the cumulative risk value Rg(t).

6. The water plant equipment status analysis system based on the Internet of Things according to claim 5, characterized in that, The process of monitoring the status of the ultraviolet sterilizer includes: The intensity of ultraviolet emission and the intensity of ultraviolet transmission are obtained by several sets of ultraviolet intensity sensors. The intensity of ultraviolet radiation was compared with the dose threshold ranges: When the intensity of ultraviolet radiation is outside the dose threshold range, the ultraviolet generator in the ultraviolet sterilizer should be replaced. When the intensity of ultraviolet emission is within the dose threshold range, the status of the ultraviolet sterilizer is analyzed based on the difference between the ultraviolet emission intensity and several sets of ultraviolet transmission intensities.

7. The water plant equipment status analysis system based on the Internet of Things according to claim 6, characterized in that, The process of performing a condition analysis on a UV sterilizer includes: The differences between the ultraviolet emission intensity and several sets of ultraviolet transmission intensity were obtained respectively. , the difference Compare with the preset threshold interval [Z1, Z2]: If all differences satisfy If so, it is determined that the water quality entering the UV sterilizer is abnormal; If some differences satisfy... If so, it is determined that the transparent quartz sleeve in the ultraviolet sterilizer needs to be cleaned immediately; If all differences satisfy Through formula The consistency coefficient u at different locations of the transparent quartz sleeve is calculated and compared with the preset value u1. When the consistency coefficient u > the preset value u1, it is determined that the transparent quartz sleeve in the UV sterilizer needs to be cleaned immediately. Where q is the number of monitoring groups for ultraviolet transmission intensity, and k∈[1,q], Group q The mean.

8. The water plant equipment status analysis system based on the Internet of Things according to claim 7, characterized in that, The process by which the linkage unit adjusts the monitoring strategy of the ultraviolet sterilizer according to the status of the booster pump includes: The monitoring frequency of the UV sterilizer is adjusted according to the difference R2-Rg(t) between R2 and the cumulative risk value Rg(t), and the monitoring frequency is proportional to R2-Rg(t).

9. A method for analyzing the status of water plant equipment based on the Internet of Things, characterized in that, The method is executed by an IoT-based water plant equipment status analysis system as described in any one of claims 1-8, including: Vibration information is obtained by the vibration sensor in the lifting pump monitoring unit, and temperature information is obtained by the infrared thermal imager in the lifting pump monitoring unit. The status of the lifting pump is monitored based on the vibration information, temperature information and lifting pump operation information. The status of the ultraviolet sterilizer is monitored by several sets of ultraviolet intensity sensors in the ultraviolet sterilizer monitoring unit according to the monitoring strategy. The monitoring strategy of the ultraviolet sterilizer is adjusted by the linkage unit according to the status of the booster pump; The equipment status analysis platform makes early warning judgments based on the status of the booster pump and the ultraviolet sterilizer.

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