Iot-based field device state analysis method and system, and storage medium

Abstract

This application relates to a method, system, and storage medium for field equipment status analysis based on the Internet of Things (IoT), belonging to the technical field of intelligent manufacturing. The method includes: collecting preset environmental monitoring data of feed processing equipment; controlling preset pollution treatment components to clean preset initial equipment monitoring devices based on the environmental monitoring data, and collecting actual equipment data of the feed processing equipment; collecting actual operating conditions of the feed processing equipment; correcting preset standard equipment data based on the actual operating conditions of the feed processing equipment to generate actual standard equipment data; and analyzing the actual equipment data and the actual standard equipment data to determine the equipment detection results and equipment fault types. This application improves the timeliness and accuracy of equipment monitoring.

Description

Technical Field

[0001] This application relates to the technical field of intelligent manufacturing, and in particular to methods, systems and storage media for field equipment status analysis based on the Internet of Things. Background Technology

[0002] Feed processing equipment refers to various types of machinery and auxiliary facilities used in feed processing plants for raw material handling and processing into finished products.

[0003] Among related technologies, the Internet of Things (IoT) refers to a technical system that connects physical devices, sensors, and objects through a network to achieve data transmission, remote control, and intelligent interaction. In the feed processing process of feed mills, IoT technology can realize digital control of the production process and ensure the safety of processing equipment by monitoring equipment data in real time.

[0004] Regarding the aforementioned technologies, when conducting safety testing on feed processing equipment, the presence of a large amount of dust in feed raw materials and the dust generated during the crushing process leads to dust accumulation on the surface of the monitoring equipment. Furthermore, the humidity of the feed raw materials themselves and the temperature and humidity changes required during processing cause the ambient humidity and temperature of the feed mill to rise during processing, resulting in the accumulated dust condensing into clumps. This increases the error in the data collected by the testing equipment, leading to delays in the safety monitoring of feed processing equipment. There is still room for improvement. Summary of the Invention

[0005] To improve the timeliness and accuracy of equipment monitoring, this application provides a method, system, and storage medium for field equipment status analysis based on the Internet of Things.

[0006] Firstly, this application provides a method for analyzing the status of field devices based on the Internet of Things, employing the following technical solution:

[0007] A method for analyzing the status of field devices based on the Internet of Things (IoT), comprising:

[0008] Collect pre-set monitoring environmental data for feed processing equipment;

[0009] Based on the environmental data monitored by the equipment, the preset pollution treatment components are controlled to clean the preset initial equipment monitoring device and collect the actual equipment data of the feed processing equipment.

[0010] Collect data on the actual operating conditions of feed processing equipment;

[0011] The preset standard equipment data is corrected according to the actual operating conditions of the feed processing equipment to generate actual standard equipment data;

[0012] Analyze actual equipment data and actual standard equipment data to determine equipment test results and equipment failure types.

[0013] Optionally, the steps of controlling a preset pollution treatment component to clean a preset initial equipment monitoring device based on equipment monitoring environmental data, and collecting actual equipment data of the feed processing equipment include:

[0014] Determine whether the environmental data monitored by the equipment meets the preset equipment cleaning requirements;

[0015] If the conditions are not met, the initial equipment monitoring device will collect and summarize data from the feed processing equipment to generate actual equipment data.

[0016] If the conditions are met, the environmental data monitored by the equipment will be analyzed to determine the cleaning parameters of the pollution treatment components.

[0017] The pollution treatment components are controlled to clean the initial equipment monitoring device according to the cleaning parameters, and the operating data of the monitoring device is collected.

[0018] The actual equipment monitoring device is determined based on the operating data of the monitoring device, and the actual equipment monitoring device is controlled to collect the actual equipment data of the feed processing equipment.

[0019] Optionally, the steps of determining the actual equipment monitoring device based on the monitoring device's operating data and controlling the actual equipment monitoring device to collect actual equipment data from the feed processing equipment include:

[0020] Determine whether the operating data of the monitoring device meets the preset requirements for normal operation of the monitoring device;

[0021] If the conditions are met, the initial equipment monitoring device will be identified as the actual equipment monitoring device, and the actual equipment monitoring device will be controlled to collect and summarize data from the feed processing equipment to generate actual equipment data.

[0022] If the conditions are not met, then the location of the feed processing equipment will be collected;

[0023] Based on the location of the feed processing equipment, the locations of adjacent feed processing equipment are found in the preset feed processing equipment location relationship, and the monitoring devices of adjacent equipment corresponding to the locations of adjacent feed processing equipment are determined as the actual equipment monitoring devices;

[0024] The actual equipment monitoring device collects data from adjacent feed processing equipment and corrects the data from adjacent feed processing equipment to generate actual equipment data.

[0025] Optionally, the step of controlling the actual equipment monitoring device to collect data from adjacent feed processing equipment and correcting the data from adjacent feed processing equipment to generate actual equipment data includes:

[0026] Calculate the distance between the location of the feed processing equipment and the locations of adjacent feed processing equipment to generate the distance between adjacent equipment;

[0027] Find the temperature and pressure of adjacent feed processing equipment in the data of adjacent equipment;

[0028] Calculate the difference between the product of the distance between adjacent devices and the preset temperature distance attenuation parameter and the preset reference parameter to generate the distance correction parameter;

[0029] Calculate the product of the distance correction parameter, the preset environmental correction parameter, and the temperature of adjacent devices to generate the actual device temperature;

[0030] Calculate the product between the pressure of adjacent equipment and the environmental correction parameters to generate the actual equipment pressure;

[0031] The data of adjacent feed processing equipment are corrected based on the actual equipment temperature and pressure to generate the actual equipment data.

[0032] Optionally, the step of modifying the preset standard equipment data according to the actual operating conditions of the feed processing equipment to generate actual standard equipment data includes:

[0033] Identify the workload and load rate of the feed processing equipment under actual operating conditions.

[0034] Based on the workload, the equipment operation intensity correction parameters are found in the preset equipment operation workload correction relationship;

[0035] Based on the workload rate, find the equipment operating load correction parameters in the preset equipment operating load correction relationship;

[0036] The standard equipment data is corrected based on the equipment operating load correction parameters and the equipment operating intensity correction parameters to generate actual standard equipment data.

[0037] Optionally, the step of correcting the standard equipment data based on the equipment operating load correction parameters and the equipment operating intensity correction parameters to generate actual standard equipment data includes:

[0038] Find the rated maximum feed processing capacity, rated maximum feed processing temperature, and rated equipment safety pressure in the standard equipment data;

[0039] Calculate the product between the rated maximum feed processing capacity, the equipment operating load correction parameter, and the equipment operating intensity correction parameter to generate the actual standard feed processing capacity;

[0040] The sum of the product of the rated maximum feed processing temperature, the equipment operating load correction parameter, and the equipment operating intensity correction parameter, and the preset ambient temperature correction value is calculated to generate the actual maximum feed processing temperature.

[0041] Calculate the product between the rated equipment safety pressure and the equipment operating load correction parameters to generate the actual equipment safety pressure;

[0042] The standard equipment data is corrected based on the actual standard feed processing capacity, the actual maximum feed processing temperature, and the actual equipment safety pressure to generate actual standard equipment data.

[0043] Optionally, the steps of analyzing actual equipment data and actual standard equipment data to determine equipment test results and equipment failure types include:

[0044] Determine whether the actual equipment data matches the actual standard equipment data;

[0045] If they match, the preset normal equipment result will be determined as the equipment test result, and the preset no-fault equipment type will be determined as the equipment fault type;

[0046] If there is a discrepancy, the preset equipment anomaly result will be determined as the equipment detection result;

[0047] The equipment fault type is determined by searching the preset historical equipment monitoring database based on actual equipment data.

[0048] Secondly, this application provides a field device status analysis system based on the Internet of Things, which adopts the following technical solution:

[0049] An Internet of Things (IoT) based field device status analysis system includes:

[0050] The data acquisition module is used to collect environmental data, actual equipment data, and actual operating conditions of the feed processing equipment.

[0051] A memory for storing programs for IoT-based field device status analysis methods as described in any of the preceding claims;

[0052] The processor and the program in the memory can be loaded and executed by the processor to implement the IoT-based field device status analysis method as described in any of the above.

[0053] Thirdly, this application provides a smart terminal, which adopts the following technical solution:

[0054] A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the preceding IoT-based field device status analysis methods.

[0055] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates improvements in the timeliness and accuracy of equipment monitoring, and adopts the following technical solution:

[0056] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the above-described IoT-based field device status analysis methods.

[0057] In summary, this application includes at least one of the following beneficial technical effects:

[0058] 1. By collecting pre-set equipment monitoring environmental data of feed processing equipment, the pollution treatment component is controlled to clean the initial equipment monitoring device based on the equipment monitoring environmental data. The standard equipment data is corrected according to the actual working conditions of the feed processing equipment to obtain the actual standard equipment data. This allows the working status of the feed processing equipment to be judged based on the working conditions. Then, the actual equipment data and the actual standard equipment data are analyzed to determine the equipment detection results and equipment fault types, thereby improving the timeliness of equipment monitoring and the accuracy of data.

[0059] 2. By determining whether the environmental data monitored by the equipment meets the equipment cleaning requirements, if not, the initial equipment monitoring device is controlled to collect data from the feed processing equipment and summarize the actual equipment data. If the requirements are met, the environmental data monitored by the equipment is analyzed to determine the cleaning parameters of the contamination treatment component. Based on the cleaning parameters, the contamination treatment component is controlled to clean the initial equipment monitoring device and collect the monitoring device's operating data. Based on the monitoring device's operating data, the actual equipment monitoring device is determined, and the actual equipment monitoring device is controlled to collect the actual equipment data of the feed processing equipment. This provides data support for subsequent judgment of the working status of the feed processing equipment, thereby improving the timeliness and accuracy of equipment monitoring.

[0060] 3. By identifying the working intensity and workload rate of the feed processing equipment under actual operating conditions, and then finding the equipment operating intensity correction parameter in the equipment operating intensity correction relationship based on the working intensity, and finding the equipment operating load correction parameter in the equipment operating load correction relationship based on the workload rate, the standard equipment data is corrected based on the equipment operating load correction parameter and the equipment operating intensity correction parameter to obtain the actual standard equipment data. This provides a basis for subsequent judgment on whether the feed processing equipment is faulty, thereby improving the accuracy of the judgment results. Attached Figure Description

[0061] Figure 1 This is a flowchart of a field device status analysis method based on the Internet of Things (IoT) in an embodiment of this application.

[0062] Figure 2This is a flowchart illustrating the steps in this application embodiment of controlling a preset pollution treatment component to clean a preset initial equipment monitoring device based on equipment monitoring environmental data, and collecting actual equipment data of the feed processing equipment.

[0063] Figure 3 This is a flowchart illustrating the steps in this application embodiment of determining the actual equipment monitoring device based on the monitoring device's operating data and controlling the actual equipment monitoring device to collect actual equipment data from the feed processing equipment.

[0064] Figure 4 This is a flowchart illustrating the steps in this application embodiment of controlling the actual equipment monitoring device to collect data from adjacent feed processing equipment and correct the data from adjacent feed processing equipment to generate actual equipment data.

[0065] Figure 5 This is a flowchart of the steps in this application embodiment to modify preset standard equipment data according to the actual operating conditions of the feed processing equipment in order to generate actual standard equipment data.

[0066] Figure 6 This is a flowchart of the steps in this application embodiment to modify standard equipment data according to equipment operating load correction parameters and equipment operating intensity correction parameters to generate actual standard equipment data.

[0067] Figure 7 This is a flowchart illustrating the steps in this application embodiment to analyze actual equipment data and actual standard equipment data to determine equipment test results and equipment fault types. Detailed Implementation

[0068] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 7 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0069] This application discloses an IoT-based method for analyzing the status of field equipment. This method primarily addresses the status analysis of feed mill processing equipment. Specifically, it discloses feed mill processing equipment, feed mill environmental monitoring equipment, equipment monitoring devices, pollution treatment components, and a processing terminal. The processing terminal is communicatively connected to the equipment monitoring device, pollution treatment components, and feed mill environmental monitoring equipment to achieve data interaction and control. After the feed mill environmental monitoring equipment sends the collected equipment monitoring environmental data to the processing terminal, the processing terminal compares the equipment monitoring environmental data with normal environmental data. When the equipment monitoring environmental data is inconsistent with the normal environmental data, the processing terminal controls the pollution treatment components to clean the equipment monitoring device based on the equipment monitoring environmental data. After cleaning, the processing terminal controls the equipment monitoring device to collect the working data of the feed processing equipment. The processing terminal then compares the working data with the normal working data to determine whether the feed processing equipment is malfunctioning and the type of malfunction. This method aims to quickly and reasonably collect and determine the working status of feed mill processing equipment, thereby improving the timeliness and accuracy of equipment monitoring.

[0070] Reference Figure 1 This application discloses a method for analyzing the status of field devices based on the Internet of Things, including the following steps:

[0071] Step S100: Collect the preset equipment monitoring environment data of the feed processing equipment.

[0072] Feed processing equipment refers to various types of machinery and auxiliary facilities in feed processing plants that need to be monitored for raw material handling and processing.

[0073] Equipment monitoring environmental data refers to the data set of dust adhesion thickness and dust agglomeration thickness on the equipment monitoring device. The dust adhesion thickness is obtained by subtracting the actual measured distance from the ultrasonic ranging sensor in the feed mill's environmental monitoring equipment. The actual light intensity is then collected by a light intensity sensor, and the actual light intensity is divided by the rated light intensity when there is no dust adhesion to obtain the actual transmittance. The ambient humidity around the equipment monitoring device is also collected by a humidity sensor. Finally, the processing terminal determines whether the dust adhesion thickness is no greater than 0.05 mm. The light transmittance is not less than 0.9. If the processing terminal determines that both data are met simultaneously, it means that there is no dust adhering to the equipment monitoring device, and the dust adhesion thickness and dust agglomeration thickness are both 0. If the processing terminal determines that either of the two data is not met, it means that there is dust adhering to the equipment monitoring device, and the dust adhesion thickness can be obtained. After determining that there is dust adhering, it is determined whether the ambient humidity exceeds 0.6. If it exceeds, it means that the dust has agglomerated, and the dust adhesion thickness is determined as the dust agglomeration thickness. If it does not exceed, it means that the dust has not agglomerated, and the dust agglomeration thickness is 0. Finally, the collected dust adhesion thickness and dust agglomeration thickness are summarized to obtain the equipment monitoring environment data.

[0074] Equipment monitoring devices refer to devices installed on various feed processing equipment to collect working data during the feed processing process and to monitor the processing status of the feed processing equipment.

[0075] Environmental monitoring devices for feed mills refer to devices installed around various feed processing equipment and monitoring devices to collect environmental data and assess dust adhesion to the equipment. These devices include ultrasonic ranging sensors, light intensity sensors, humidity sensors, temperature sensors, and communication modules.

[0076] Step S101: Based on the equipment monitoring environment data, control the preset pollution treatment components to clean the preset initial equipment monitoring device, and collect the actual equipment data of the feed processing equipment.

[0077] After the processing terminal determines the equipment monitoring environment data, it controls the pollution treatment component to clean the initial equipment monitoring device based on the data. The specific method is described in [reference needed]. Figure 2 This process provides equipment support for the subsequent collection of actual equipment data from feed processing equipment.

[0078] Actual equipment data refers to the collection of relevant working data of feed processing equipment during the feed processing process. Specific methods are detailed in [reference needed]. Figure 2This process provides data support for subsequent determination of whether feed processing equipment is faulty and the type of fault.

[0079] The pollution treatment component refers to a cleaning module used to clean dust adhering to the monitoring device of the cleaning equipment. It includes a purging module and a drying module. The purging module is a device used to blow away the dust particles adhering to the monitoring device, and consists of a fan whose power and working time can be controlled. The drying module is a device used to perform high-temperature dehydration on the lumpy dust adhering to the monitoring device, so as to facilitate the purging module to blow away the lumpy dust. It consists of a miniature PTC heating element whose power and working time can be controlled.

[0080] An initial equipment monitoring device refers to a device installed on feed processing equipment to collect working data during the feed processing process and to monitor the processing status of the feed processing equipment.

[0081] Step S102: Collect actual operating conditions of feed processing equipment.

[0082] The actual operating condition of feed processing equipment refers to the data set of the working intensity and workload rate that the feed processing equipment should achieve in the current time period. The actual operating condition of the feed processing equipment can be obtained by summarizing the working intensity and workload rate through the processing terminal.

[0083] Work intensity refers to a physical quantity reflecting the level of processing activity that feed processing equipment should achieve within the current time period. It is reflected by working hours and the number of rests. Work intensity is directly proportional to working hours and inversely proportional to the number of rests. The longer the working hours and the fewer the rests, the higher the work intensity, indicating that the feed processing equipment is operating busier. Workload rate refers to a physical quantity reflecting the level of effort that feed processing equipment should achieve within the current time period. It is reflected by working current and feed flow rate. Workload rate is directly proportional to working current and feed flow rate. The higher the working current and feed flow rate, the higher the workload rate, indicating that the feed processing equipment is working harder. Both work intensity and workload rate can be obtained by looking up the processing schedule table at the processing terminal based on the current time.

[0084] The processing schedule refers to the schedule of the work intensity and workload of the feed processing equipment during different time periods of the day. In one embodiment, the operator obtains the schedule by mapping each time period to the corresponding work intensity and workload based on the actual situation.

[0085] Step S103: Correct the preset standard equipment data according to the actual working conditions of the feed processing equipment to generate actual standard equipment data.

[0086] After the processing terminal determines the actual operating conditions of the feed processing equipment, it corrects the standard equipment data based on these conditions to obtain the actual standard equipment data. The specific method is described in [reference needed]. Figure 5 This process provides data support for subsequent assessments of whether feed processing equipment is malfunctioning.

[0087] Standard equipment data refers to the set of rated operating data for feed processing equipment. In one embodiment, it is obtained by the operator by searching for the corresponding technical manual according to the model of the feed processing equipment and then summarizing it through a processing terminal.

[0088] Step S104: Analyze the actual equipment data and the actual standard equipment data to determine the equipment test results and equipment fault types.

[0089] After the processing terminal determines the actual equipment data and the actual standard equipment data, it analyzes these data. The specific method is described in [reference needed]. Figure 7 The steps are used to determine the equipment test results and the type of equipment failure.

[0090] Equipment test results refer to the signals generated after monitoring the working status of feed processing equipment to determine whether the feed processing equipment is malfunctioning, including normal equipment results and abnormal equipment results.

[0091] A normal equipment result refers to the signal output when the actual equipment data of the feed processing equipment matches the actual standard equipment data; an abnormal equipment result refers to the signal output when the actual equipment data of the feed processing equipment does not match the actual standard equipment data.

[0092] Equipment failure type refers to the type of failure that occurs when the feed processing equipment malfunctions after the equipment's working status is monitored.

[0093] Reference Figure 2 The steps of controlling the preset pollution treatment components to clean the preset initial equipment monitoring device based on the equipment monitoring environmental data, and collecting actual equipment data of the feed processing equipment include:

[0094] Step S200: Determine whether the equipment monitoring environment data meets the preset equipment cleaning requirements.

[0095] Among them, the equipment cleaning requirement means that when the equipment monitoring device needs to be cleaned, no data in the monitoring environment data should be zero.

[0096] After the processing terminal determines the equipment environment data, it judges whether the equipment monitoring environment data meets the equipment cleaning requirements, thereby determining whether the initial equipment monitoring device needs to be cleaned.

[0097] Step S2001: If not satisfied, control the initial equipment monitoring device to collect and summarize data from the feed processing equipment to generate actual equipment data.

[0098] If the processing terminal determines that the equipment monitoring environment data does not meet the equipment cleaning requirements, it means that the initial equipment monitoring device does not need to be cleaned. Therefore, by controlling the initial equipment monitoring device to collect data from the feed processing equipment through the processing terminal, and summarizing the collected data, the actual equipment data can be obtained.

[0099] Step S2002: If satisfied, analyze the environmental monitoring data of the equipment to determine the cleaning parameters of the pollution treatment components.

[0100] If the processing terminal determines that the equipment monitoring environment data meets the equipment cleaning requirements, it indicates that the initial equipment monitoring device needs to be cleaned. Therefore, the processing terminal analyzes the equipment monitoring environment data to determine the cleaning parameters of the contamination treatment components.

[0101] Cleaning parameters refer to the set of parameters controlling the cleaning of the initial equipment monitoring device by the pollution treatment components, including the operating parameters of the purging module and the drying module. The processing terminal determines whether the dust adhesion thickness or the dust agglomeration thickness in the equipment monitoring environmental data is non-zero. If the processing terminal determines that the dust adhesion thickness is non-zero, it means that only dust adheres to the initial equipment monitoring device, and the empty set is determined as the drying module operating parameter, and the purging module operating parameters are calculated and summarized. If the processing terminal determines that the dust agglomeration thickness is non-zero, it means that only agglomerated dust adheres to the initial equipment monitoring device, and the dust agglomeration thickness is multiplied by 0.8 to obtain the dried dust adhesion thickness, and the drying module operating parameters are calculated and summarized, as well as the purging module operating parameters are calculated based on the dried dust adhesion thickness.

[0102] The working parameters of the purging module refer to the data set used to store the working power and working time of the fan in the purging module. The working power of the fan is obtained by multiplying the dust density, dust adhesion surface area, dust thickness and the minimum wind speed of the purging module by the processing terminal; the working time of the fan is obtained by dividing the dust thickness by the dust purging speed.

[0103] Dust density refers to the mass of dust per unit volume, with a dust density of 1 g / cm³. 3 For example; the minimum wind speed of the blowing module refers to the minimum fan speed required to blow away dust, taking a minimum wind speed of 2m / s as an example; the dust blowing speed refers to the thickness of dust blown away per unit time, taking a dust blowing speed of 0.1mm / s as an example.

[0104] The dust-attached surface area refers to the area covered by dust on the equipment monitoring device. In one embodiment, a miniature image sensor is used to capture an image of the initial location of the equipment monitoring device where dust is attached. The image is then compared with an image without dust to obtain the pixel ratio of the attached area. The dust-attached surface area is obtained by multiplying the pixel ratio of the attached area by the area of ​​that area.

[0105] The working parameters of the drying module refer to the data set used to store the working power and working time of the drying module in the purging module. The working power of the drying module is obtained by multiplying the specific heat capacity of the agglomerated dust, the density of the agglomerated dust, the surface area of ​​the dust adhesion, the drying temperature difference, the thickness of the dust agglomeration, and the working efficiency of the drying module by the processing terminal. The working time of the drying module is obtained by multiplying the thickness of the dust agglomeration, the density of the agglomerated dust, and the moisture content of the agglomerated dust by the processing terminal, and then dividing the product by the product of the heat utilization rate of the drying module and the latent heat of vaporization of moisture.

[0106] The specific heat capacity of agglomerated dust refers to the amount of heat required for the temperature of the agglomerated dust to rise by 1°C. Taking an agglomerated dust specific heat capacity of 1.2 kJ / (kg·°C) as an example; the density of agglomerated dust refers to the mass per unit volume of the agglomerated dust. Taking an agglomerated dust density of 1.5 g / cm³ as an example... 3 For example; the drying temperature difference refers to the temperature increase required for the drying module to dry the agglomerated dust, with a drying temperature difference of 25℃ as an example; the drying efficiency refers to the working efficiency of the drying module, with a drying efficiency of 0.7 as an example; the moisture content of the agglomerated dust refers to the ratio of water content in the agglomerated dust to the total dust content, with a moisture content of 15% as an example; the heat utilization rate of the drying module refers to the ratio between the actual heat used by the drying module and the heat provided, with a heat utilization rate of 0.6 as an example; the latent heat of vaporization of moisture refers to the heat that can be absorbed per unit mass of moisture in the agglomerated dust, with a latent heat of vaporization of moisture of 2260kJ / kg as an example.

[0107] Step S20021: Control the pollution treatment component to clean the initial equipment monitoring device according to the cleaning parameters, and collect the monitoring device operation data.

[0108] After the cleaning parameters are determined by the processing terminal, the processing terminal controls the pollution treatment component to clean the initial equipment monitoring device according to the cleaning parameters, and collects the operating data of the monitoring device, thereby providing data support for the subsequent collection of actual equipment data.

[0109] The monitoring device operation data refers to the data set of its own operating parameters when the initial equipment monitoring device monitors the feed processing equipment. In one embodiment, voltage and current data in the power supply circuit of the initial equipment monitoring device are collected by voltage and current sensors and then summarized by a processing terminal.

[0110] Step S20022: Determine the actual equipment monitoring device based on the monitoring device's operating data, and control the actual equipment monitoring device to collect the actual equipment data of the feed processing equipment.

[0111] In this process, after the processing terminal determines the operating data of the monitoring device, it identifies the actual equipment monitoring device based on the operating data and controls the actual equipment monitoring device to collect the actual equipment data of the feed processing equipment. The specific method is described in [reference needed]. Figure 3 This process provides equipment support for the subsequent collection of actual equipment data.

[0112] Actual equipment monitoring devices refer to devices that are actually used to collect working data during the processing of feed processing equipment and to monitor the processing status of feed processing equipment.

[0113] Reference Figure 3 The steps of determining the actual equipment monitoring device based on the operating data of the monitoring device, and controlling the actual equipment monitoring device to collect the actual equipment data of the feed processing equipment include:

[0114] Step S300: Determine whether the operating data of the monitoring device meets the preset requirements for normal operation of the monitoring device.

[0115] Among them, the normal operation requirement of the monitoring device means that when the equipment monitoring device is working normally, the set of its own working parameters must be within the normal working parameter range.

[0116] The normal operating parameter range refers to the set of parameter ranges of relevant data when the equipment monitoring device is operating normally, including the rated operating current range and the rated power supply voltage range. In one embodiment, the rated power supply voltage range is 100-500mA and the rated operating current range is 20-26V.

[0117] After the processing terminal determines the operating data of the monitoring device, it judges whether the operating data of the monitoring device meets the normal operating requirements of the monitoring device, thereby determining whether the initial equipment monitoring device can work normally.

[0118] Step S3001: If satisfied, the initial equipment monitoring device is determined as the actual equipment monitoring device, and the actual equipment monitoring device is controlled to collect and summarize data from the feed processing equipment to generate actual equipment data.

[0119] If the processing terminal determines that the operating data of the monitoring device meets the requirements for normal operation of the monitoring device, it means that the initial equipment monitoring device can work normally. Therefore, the processing terminal identifies the initial equipment monitoring device as the actual equipment monitoring device, and controls the actual equipment monitoring device to collect and summarize data from the feed processing equipment, thereby obtaining the actual equipment data, which provides data support for determining the working status of the feed processing equipment.

[0120] Step S3002: If not satisfied, then collect the location of the feed processing equipment.

[0121] If the processing terminal determines that the operating data of the monitoring device does not meet the requirements for normal operation of the monitoring device, it indicates that the initial equipment monitoring device is not working properly. Therefore, the location of the feed processing equipment is collected to provide data support for the subsequent collection of actual equipment data of the feed processing equipment using adjacent equipment monitoring devices.

[0122] The location of feed processing equipment refers to the coordinates of the feed processing equipment in the feed mill. In one embodiment, the processing terminal maps a plane rectangular coordinate system onto the feed mill and sets the midpoint of the bottom surface of the feed mill gate as the origin of the coordinate system. Then, the center point of the bottom surface of the feed processing equipment is measured using a total station to obtain the location of the feed processing equipment.

[0123] Step S30021: Based on the location of the feed processing equipment, find the location of the adjacent feed processing equipment in the preset feed processing equipment location relationship, and determine the adjacent equipment monitoring device corresponding to the location of the adjacent feed processing equipment as the actual equipment monitoring device.

[0124] The location of adjacent feed processing equipment refers to the coordinates of the closest feed processing equipment of the same type. The location of adjacent feed processing equipment can be obtained by searching and comparing the feed processing equipment location and model in the feed processing equipment location relationship through the processing terminal.

[0125] The location relationship of feed processing equipment refers to the correspondence between the model of feed processing equipment and the location of the equipment. In one embodiment, the operator obtains the mapping table by matching the feed processing equipment of the same model with the location of the feed processing equipment according to the actual situation.

[0126] After the processing terminal determines the location of adjacent feed processing equipment, it identifies the adjacent equipment monitoring device corresponding to the location of the adjacent feed processing equipment as the actual equipment monitoring device, thereby providing support for the subsequent collection of actual equipment data of the feed processing equipment.

[0127] Step S30022: Control the actual equipment monitoring device to collect data from adjacent feed processing equipment and correct the data of adjacent feed processing equipment to generate actual equipment data.

[0128] Among them, the data of adjacent feed processing equipment refers to the collection of relevant working data of adjacent feed processing equipment during the feed processing process. After the actual equipment monitoring device is determined by the processing terminal, the data of adjacent feed processing equipment can be obtained by controlling the actual equipment monitoring device to collect and summarize the data through the processing terminal.

[0129] After the processing terminal determines the data of adjacent feed processing equipment, it corrects this data to obtain the actual equipment data. For specific methods, please refer to [link / reference needed]. Figure 4 The steps.

[0130] Reference Figure 4 The steps of controlling the actual equipment monitoring device to collect data from adjacent feed processing equipment and correcting the data from adjacent feed processing equipment to generate actual equipment data include:

[0131] Step S400: Calculate the distance between the location of the feed processing equipment and the locations of adjacent feed processing equipment to generate the distance between adjacent equipment.

[0132] The distance between adjacent equipment refers to the straight-line distance between feed processing equipment and adjacent feed processing equipment. The distance between adjacent equipment can be obtained by substituting the location of the feed processing equipment and the location of the adjacent feed processing equipment into the straight-line distance calculation formula through the processing terminal.

[0133] Step S401: Locate the temperature and pressure of adjacent equipment in the data of adjacent feed processing equipment.

[0134] Among them, the temperature of adjacent equipment refers to the temperature of the adjacent feed processing equipment itself; the pressure of adjacent equipment refers to the pressure of the adjacent Celio processing equipment itself. The temperature and pressure of adjacent equipment can be obtained by searching the data of adjacent feed processing equipment through the processing terminal.

[0135] Step S402: Calculate the difference between the product of the distance between adjacent devices and the preset temperature distance attenuation parameter and the preset reference parameter to generate a distance correction parameter.

[0136] Among them, the distance correction parameter refers to the correction parameter that corrects the error between the temperature collected by the monitoring device of the adjacent device and the temperature of the feed processing equipment caused by the distance between adjacent devices. The distance correction parameter can be obtained by multiplying the distance between adjacent devices and the temperature distance attenuation parameter by the processing terminal, and then subtracting the calculated product from the reference parameter.

[0137] The reference parameter refers to the ratio between the data collected by the adjacent device acquisition device and the data collected by the initial device acquisition device under ideal conditions. In one embodiment, the reference parameter is 1.

[0138] The temperature distance attenuation parameter refers to the attenuation coefficient used to describe the correlation between the temperature collected by the monitoring device of adjacent equipment and the actual temperature of the feed processing equipment due to the distance between the adjacent equipment. In one embodiment, the temperature distance attenuation parameter is 0.02m. -1 .

[0139] Step S403: Calculate the product between the distance correction parameter, the preset environmental correction parameter and the temperature of adjacent equipment to generate the actual equipment temperature.

[0140] The actual equipment temperature refers to the temperature of the feed processing equipment itself. It can be obtained by multiplying the distance correction parameter, the ambient temperature correction parameter in the environment correction parameter, and the temperature of the adjacent equipment by the processing terminal.

[0141] Environmental correction parameters refer to the deviation correction parameters for measurement data deviations caused by differences in the environment between adjacent equipment monitoring devices and the initial equipment monitoring device. These include environmental temperature correction parameters and environmental raw material congestion correction parameters.

[0142] The ambient temperature correction parameter refers to the deviation correction parameter for measurement data caused by the temperature difference between the environment of adjacent monitoring devices and the initial monitoring device. The adjacent temperature of the adjacent monitoring device and the initial temperature of the initial monitoring device are measured by the temperature sensor in the feed mill's environmental equipment monitoring device. The initial temperature is then subtracted from the adjacent temperature by the processing terminal, and the difference is divided by 20°C to obtain the relative difference of ambient temperature. The relative difference of ambient temperature is then multiplied by 0.8, and finally the product is added to the reference parameter to obtain the ambient temperature correction parameter.

[0143] The environmental raw material congestion correction parameter refers to the deviation correction parameter caused by the difference in raw material congestion between adjacent feed processing equipment. It is obtained by measuring the adjacent raw material congestion height and the initial raw material congestion height of the feed processing equipment through the ultrasonic ranging sensor in the feed mill's environmental equipment monitoring device. Then, the processing terminal subtracts the adjacent raw material congestion height from the initial raw material congestion height, divides the difference by the normal raw material congestion height to obtain the relative difference of environmental raw material congestion, multiplies the relative difference of environmental raw material congestion by 0.8, and finally adds the product to the baseline parameter to obtain the environmental raw material congestion correction parameter.

[0144] Normal raw material congestion height refers to the maximum height of the feed processing equipment when there is no congestion. It is obtained by multiplying the actual height of the feed processing equipment by 0.6 through the processing terminal.

[0145] Step S404: Calculate the product between the pressure of adjacent equipment and the environmental correction parameters to generate the actual equipment pressure.

[0146] The actual equipment pressure refers to the equipment pressure of the feed processing equipment itself. The actual equipment pressure can be obtained by multiplying the pressure of adjacent equipment by the environmental raw material congestion correction parameter in the environmental correction parameters through the processing terminal.

[0147] Step S405: Correct the data of adjacent feed processing equipment based on the actual equipment temperature and actual equipment pressure to generate actual equipment data.

[0148] In this process, after the processing terminal determines the actual equipment temperature and pressure, it replaces the adjacent equipment temperature and pressure data in the adjacent feed processing equipment data with the actual equipment temperature and pressure data to obtain the actual equipment data.

[0149] Reference Figure 5 The steps for generating actual standard equipment data by correcting preset standard equipment data based on the actual operating conditions of feed processing equipment include:

[0150] Step S500: Identify the working intensity and workload rate in the actual operating conditions of the feed processing equipment.

[0151] The workload and workload rate in this step are the same as those in step S102 above. The workload and workload rate can be obtained by searching in the actual working conditions of the feed processing equipment through the processing terminal.

[0152] Step S501: Find the equipment operation intensity correction parameter in the preset equipment operation intensity correction relationship according to the workload.

[0153] Among them, the equipment operation intensity correction parameter refers to the correction parameter that corrects the standard equipment data of feed processing equipment according to the working intensity. The equipment operation intensity correction parameter is obtained by the processing terminal by looking up the mapping table of equipment operation intensity correction relationship according to the working intensity.

[0154] The equipment operating intensity correction relationship refers to the correspondence between equipment intensity and equipment operating intensity correction parameters. It is obtained by the operator by mapping the two types of equipment intensity to the corresponding equipment operating intensity correction parameters one by one according to the actual situation. In one embodiment, when the equipment intensity is low, the corresponding equipment operating intensity correction parameter is 0.92, and when the equipment intensity is high, the corresponding equipment operating intensity correction parameter is 1.15.

[0155] Step S502: Find the equipment operating load correction parameters in the preset equipment operating load correction relationship based on the workload rate.

[0156] Among them, the equipment operating load correction parameter refers to the correction parameter that corrects the standard equipment data of the feed processing equipment based on the working load rate. The equipment operating load correction parameter is obtained by the processing terminal by looking up the mapping table of equipment operating load correction relationship according to the working load rate.

[0157] The equipment operating load correction relationship refers to the correspondence between the workload rate and the equipment operating load correction parameter. It is obtained by the operator by mapping the two workload rates to the corresponding equipment operating load correction parameters one by one according to the actual situation. In one embodiment, when the equipment load rate is low, the corresponding equipment operating load correction parameter is 0.86, and when the equipment intensity is high, the corresponding equipment operating load correction parameter is 1.2.

[0158] Step S503: Correct the standard equipment data according to the equipment operating load correction parameters and the equipment operating intensity correction parameters to generate actual standard equipment data.

[0159] In this process, after the processing terminal determines the equipment operating load correction parameters and the equipment operating intensity correction parameters, the actual standard equipment data can be obtained by correcting the standard equipment data based on these parameters. The specific method is described in [reference needed]. Figure 6 This process provides data support for subsequent assessments of whether feed processing equipment is malfunctioning.

[0160] Reference Figure 6 The steps for correcting standard equipment data based on equipment operating load correction parameters and equipment operating intensity correction parameters to generate actual standard equipment data include:

[0161] Step S600: Locate the rated maximum feed processing capacity, rated maximum feed processing temperature, and rated equipment safety pressure in the standard equipment data.

[0162] Among them, the rated maximum feed processing capacity refers to the maximum amount of raw materials that the feed processing equipment can process at the same time; the rated maximum feed processing temperature refers to the maximum temperature that the feed processing equipment can withstand during the processing; and the rated equipment safety pressure refers to the maximum pressure that the feed processing equipment can withstand to ensure the safety of the processing process and minimize the wear and tear on the feed processing equipment. The rated maximum feed processing capacity, rated maximum feed processing temperature, and rated equipment safety pressure can all be found in the standard equipment data through the processing terminal.

[0163] Step S601: Calculate the product between the rated maximum feed processing capacity, the equipment operating load correction parameter, and the equipment operating intensity correction parameter to generate the actual standard feed processing capacity.

[0164] The actual standard feed processing capacity refers to the maximum amount of raw materials that the corrected feed processing equipment can process at the same time under the current operating conditions. The actual standard feed processing capacity can be obtained by multiplying the rated maximum feed processing capacity, the equipment operating load correction parameters, the equipment operating intensity correction parameters, and the feed processing correction coefficient by the processing terminal.

[0165] Step S602: Calculate the sum between the product of the rated maximum feed processing temperature, the equipment operating load correction parameter, and the equipment operating intensity correction parameter, and the preset ambient temperature correction value, to generate the actual maximum feed processing temperature.

[0166] The actual maximum feed processing temperature refers to the maximum temperature that the feed processing equipment can withstand during processing under the current operating conditions. The actual maximum feed processing temperature is obtained by multiplying the rated maximum feed processing temperature, the equipment operating load correction parameter, and the equipment operating intensity correction parameter by the processing terminal, and then adding the product to the ambient temperature correction value.

[0167] The ambient temperature correction value refers to the correction temperature used to adjust the degree of impact on the maximum feed processing temperature under the current operating conditions due to changes in ambient temperature. It is obtained by collecting the ambient temperature around the feed processing equipment through temperature sensors in the environmental equipment monitoring device of the feed mill control terminal, and looking up the ambient temperature in the mapping table of ambient temperature compensation relationship.

[0168] The ambient temperature compensation relationship refers to the correspondence between ambient temperature and compensation temperature. It is obtained by the operator by mapping the two ambient temperature ranges to the corresponding compensation temperature one by one according to the actual situation. In one embodiment, when the ambient temperature is less than 15°C, the corresponding compensation temperature is -4°C, and when the ambient temperature is greater than 30°C, the corresponding compensation temperature is +6°C.

[0169] Step S603: Calculate the product between the rated equipment safety pressure and the equipment operating load correction parameter to generate the actual equipment safety pressure.

[0170] The actual equipment safety pressure refers to the maximum pressure that the feed processing equipment can withstand under the current operating conditions to ensure the safety of the processing process and minimize the wear and tear on the feed processing equipment. The actual equipment safety pressure can be obtained by multiplying the rated equipment safety pressure by the equipment operating load correction parameter through the processing terminal.

[0171] Step S604: Correct the standard equipment data based on the actual standard feed processing capacity, the actual maximum feed processing temperature, and the actual equipment safety pressure to generate actual standard equipment data.

[0172] In this process, after the processing terminal determines the actual standard feed processing capacity, the actual maximum feed processing temperature, and the actual equipment safety pressure, the rated maximum feed processing capacity, the rated maximum feed processing temperature, and the rated equipment safety pressure are replaced with the corresponding actual standard feed processing capacity, the actual maximum feed processing temperature, and the actual equipment safety pressure to obtain the actual standard equipment data.

[0173] Reference Figure 7The steps for analyzing actual equipment data and actual standard equipment data to determine equipment test results and equipment fault types include:

[0174] Step S700: Determine whether the actual equipment data is consistent with the actual standard equipment data.

[0175] In this process, after the processing terminal determines the actual equipment data and the actual standard equipment data, it judges whether the actual equipment data is consistent with the actual standard equipment data, thereby determining whether the feed processing equipment has malfunctioned.

[0176] Step S7001: If they match, the preset normal equipment result is determined as the equipment test result, and the preset no-fault equipment type is determined as the equipment fault type.

[0177] If the processing terminal determines that the actual equipment data is consistent with the actual standard equipment data, it means that the feed processing equipment has not malfunctioned. Therefore, the processing terminal determines the normal equipment result as the equipment test result and the no-fault type of the equipment as the equipment fault type.

[0178] The normal equipment result in this step is consistent with the normal equipment result in step S104 above, and will not be repeated here.

[0179] The fault-free type refers to the signal used to send the judgment result to the processing terminal when the feed processing equipment does not malfunction.

[0180] Step S7002: If there is a discrepancy, the preset equipment anomaly result shall be determined as the equipment detection result.

[0181] If the processing terminal determines that the actual equipment data is inconsistent with the actual standard equipment data, it indicates that the feed processing equipment has malfunctioned. Therefore, the abnormal equipment result is determined as the equipment detection result through the processing terminal.

[0182] The equipment malfunction result in this step is the same as the equipment malfunction result in step S104 above, and will not be repeated here.

[0183] Step S70021: Find the equipment fault type in the preset historical equipment monitoring database based on the actual equipment data.

[0184] In this process, after the processing terminal determines the actual equipment data, it searches and compares the actual equipment data in the historical equipment monitoring database and identifies the fault type with the highest similarity to the actual equipment data as the equipment fault type.

[0185] The historical equipment monitoring database refers to a data set used to store the historical fault types of all feed processing equipment in the entire Internet of Things system. It is obtained by matching the fault type obtained from each analysis with the corresponding equipment data through the processing terminal and storing it.

[0186] Based on the same inventive concept, embodiments of this application provide a method for analyzing the status of field devices based on the Internet of Things, including:

[0187] The data acquisition module is used to collect data on the equipment monitoring environment, actual equipment data, actual operating conditions of feed processing equipment, operating data of monitoring devices, location of feed processing equipment, and data of adjacent feed processing equipment.

[0188] Memory, used to store programs for IoT-based field device status analysis methods;

[0189] The processor can load and execute programs in memory to implement IoT-based field device status analysis methods.

[0190] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0191] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a field device status analysis method based on the Internet of Things.

[0192] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0193] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor for a field device status analysis method based on the Internet of Things.

[0194] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0195] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for analyzing the status of field devices based on the Internet of Things, characterized in that, include: Collect pre-set monitoring environmental data for feed processing equipment; Based on the environmental data monitored by the equipment, the preset pollution treatment components are controlled to clean the preset initial equipment monitoring device and collect the actual equipment data of the feed processing equipment. Collect data on the actual operating conditions of feed processing equipment; The preset standard equipment data is corrected according to the actual operating conditions of the feed processing equipment to generate actual standard equipment data; Analyze actual equipment data and actual standard equipment data to determine equipment test results and equipment failure types; The steps of controlling the preset pollution treatment components to clean the preset initial equipment monitoring device based on the equipment monitoring environmental data, and collecting actual equipment data of the feed processing equipment include: Determine whether the environmental data monitored by the equipment meets the preset equipment cleaning requirements; If the conditions are not met, the initial equipment monitoring device will collect and summarize data from the feed processing equipment to generate actual equipment data. If the conditions are met, the environmental data monitored by the equipment will be analyzed to determine the cleaning parameters of the pollution treatment components. The pollution treatment components are controlled to clean the initial equipment monitoring device according to the cleaning parameters, and the operating data of the monitoring device is collected. The actual equipment monitoring device is determined based on the operating data of the monitoring device, and the actual equipment monitoring device is controlled to collect the actual equipment data of the feed processing equipment; The steps of determining the actual equipment monitoring device based on the operating data of the monitoring device, and controlling the actual equipment monitoring device to collect actual equipment data of the feed processing equipment include: Determine whether the operating data of the monitoring device meets the preset requirements for normal operation of the monitoring device; If the conditions are met, the initial equipment monitoring device will be identified as the actual equipment monitoring device, and the actual equipment monitoring device will be controlled to collect and summarize data from the feed processing equipment to generate actual equipment data. If the conditions are not met, then the location of the feed processing equipment will be collected; Based on the location of the feed processing equipment, the locations of adjacent feed processing equipment are found in the preset feed processing equipment location relationship, and the monitoring devices of adjacent equipment corresponding to the locations of adjacent feed processing equipment are determined as the actual equipment monitoring devices; The actual equipment monitoring device collects data from adjacent feed processing equipment and corrects the data from adjacent feed processing equipment to generate actual equipment data; The steps of controlling the actual equipment monitoring device to collect data from adjacent feed processing equipment and correcting the data from adjacent feed processing equipment to generate actual equipment data include: Calculate the distance between the location of the feed processing equipment and the locations of adjacent feed processing equipment to generate the distance between adjacent equipment; Find the temperature and pressure of adjacent feed processing equipment in the data of adjacent equipment; Calculate the difference between the product of the distance between adjacent devices and the preset temperature distance attenuation parameter and the preset reference parameter to generate the distance correction parameter; Calculate the product of the distance correction parameter, the preset environmental correction parameter, and the temperature of adjacent devices to generate the actual device temperature; Calculate the product between the pressure of adjacent equipment and the environmental correction parameters to generate the actual equipment pressure; The data of adjacent feed processing equipment are corrected based on the actual equipment temperature and pressure to generate the actual equipment data.

2. The method for analyzing the status of field devices based on the Internet of Things according to claim 1, characterized in that, The steps for correcting the preset standard equipment data based on the actual operating conditions of the feed processing equipment to generate actual standard equipment data include: Identify the workload and load rate of the feed processing equipment under actual operating conditions. Based on the workload, the equipment operation intensity correction parameters are found in the preset equipment operation workload correction relationship; Based on the workload rate, find the equipment operating load correction parameters in the preset equipment operating load correction relationship; The standard equipment data is corrected based on the equipment operating load correction parameters and the equipment operating intensity correction parameters to generate actual standard equipment data.

3. The method for analyzing the status of field devices based on the Internet of Things according to claim 2, characterized in that, The steps for correcting standard equipment data based on equipment operating load correction parameters and equipment operating intensity correction parameters to generate actual standard equipment data include: Find the rated maximum feed processing capacity, rated maximum feed processing temperature, and rated equipment safety pressure in the standard equipment data; Calculate the product between the rated maximum feed processing capacity, the equipment operating load correction parameter, and the equipment operating intensity correction parameter to generate the actual standard feed processing capacity; The sum of the product of the rated maximum feed processing temperature, the equipment operating load correction parameter, and the equipment operating intensity correction parameter, and the preset ambient temperature correction value is calculated to generate the actual maximum feed processing temperature. Calculate the product between the rated equipment safety pressure and the equipment operating load correction parameters to generate the actual equipment safety pressure; The standard equipment data is corrected based on the actual standard feed processing capacity, the actual maximum feed processing temperature, and the actual equipment safety pressure to generate actual standard equipment data.

4. The method for analyzing the status of field devices based on the Internet of Things according to claim 1, characterized in that, The steps for analyzing actual equipment data and actual standard equipment data to determine equipment test results and equipment failure types include: Determine whether the actual equipment data is consistent with the actual standard equipment data; If they match, the preset normal equipment result will be determined as the equipment test result, and the preset no-fault equipment type will be determined as the equipment fault type. If there is a discrepancy, the preset equipment anomaly result will be determined as the equipment detection result; The equipment fault type is determined by searching the preset historical equipment monitoring database based on actual equipment data.

5. A field device status analysis system based on the Internet of Things, characterized in that, include: The data acquisition module is used to collect environmental data, actual equipment data, and actual operating conditions of the feed processing equipment. A memory for storing the program of the IoT-based field device status analysis method as described in any one of claims 1 to 4; The processor and the program in the memory can be loaded and executed by the processor to implement the IoT-based field device status analysis method as described in any one of claims 1 to 4.

6. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 4, which is based on the Internet of Things for field device status analysis.

7. A computer-readable storage medium, characterized in that, The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1 to 4, which is based on the Internet of Things (IoT) field device status analysis method.

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