Intermediate frequency furnace cooling water system real-time monitoring and early warning platform based on Internet of Things

Abstract

The invention provides an intermediate frequency furnace cooling water system real-time monitoring and early warning platform based on the Internet of Things, and the platform comprises a sensing layer which is used for collecting the operation parameters of each water path of a cooling water system and the operation state of monitoring equipment in real time, and judging whether the cooling water system and the monitoring equipment are abnormal or not; the network layer is used for preprocessing the collected operation parameters and uploading the operation parameters to the platform layer; the platform layer is used for analyzing the uploaded data and optimizing and adjusting operation parameters of the cooling water system according to an analysis result; and when the cooling water system is abnormal, an abnormal early warning notice is sent to a communication end of related personnel by using a wireless communication technology. According to the invention, all-weather and all-directional remote online monitoring of the cooling water system of the intermediate frequency furnace is realized, the operation safety and stability of the cooling water system are effectively improved, the parameters of the cooling water system are optimized and adjusted according to the monitoring data, and the energy consumption and maintenance cost of cooling of the intermediate frequency furnace are reduced.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things technology and industrial equipment monitoring technology, and in particular to a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things. Background Art

[0002] In modern industrial production systems, medium-frequency furnaces are widely used in key manufacturing fields such as steel smelting, non-ferrous metal processing, and mechanical heat treatment due to their high efficiency, energy saving, and fast melting speeds. However, medium-frequency furnaces generate a large amount of heat during operation. As a core component to ensure stable operation, their cooling water systems must continuously and efficiently control the equipment temperature within a safe range. A failure in the cooling system can cause serious damage to the equipment or even lead to safety accidents.

[0003] Traditional monitoring methods, which rely on manual inspections and single-point alarm devices, are limited by supervisory staff working hours and lack coverage during high-risk periods such as nighttime. Independent alarm devices, however, lack remote monitoring and intelligent analysis capabilities, and are unable to proactively report anomalies when sensors fail or are manually shut down. Furthermore, to reduce short-term costs, some companies often choose to disable alarm devices to avoid downtime and maintenance triggered by parameter fluctuations. This results in a situation where the system is "installed but not used, used but not effective," creating a vicious cycle of "operating with problems" in medium-frequency furnaces. Summary of the Invention

[0004] The present invention provides a real-time monitoring and early warning platform for the cooling water system of a medium frequency furnace based on the Internet of Things to solve the above-mentioned problems. It realizes all-weather, all-round remote online monitoring of the cooling water system of the medium frequency furnace, effectively improves the safety and stability of the operation of the cooling water system, and optimizes and adjusts the parameters of the cooling water system according to the monitoring data, effectively reducing the energy consumption and maintenance costs of the cooling of the medium frequency furnace.

[0005] The present invention provides a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, comprising:

[0006] The perception layer is used to collect the operating parameters of each water channel in the cooling water system and the operating status of the monitoring equipment in real time, and to determine whether there are any abnormalities in the cooling water system and the monitoring equipment;

[0007] Among them, the operating parameters include inlet and outlet water flow, inlet and outlet water temperature, and inlet and outlet water pressure;

[0008] The network layer is used to pre-process the collected operating parameters and upload them to the platform layer;

[0009] The platform layer is used to analyze the uploaded data and optimize the operating parameters of the cooling water system based on the analysis results;

[0010] When there is an abnormality in the cooling water system, wireless communication technology is used to send abnormality warning notifications to the communication terminals of relevant personnel.

[0011] Preferably, in a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, the perception layer includes:

[0012] The data acquisition unit is used to collect the operating parameters of each water circuit based on the monitoring equipment installed at the inlet of each water cooling coil and induction coil of the cooling water system;

[0013] An abnormality warning unit is used to determine whether various operating parameters of the chilled water system are abnormal based on the alarm standards corresponding to various operating parameters;

[0014] If it exists, an abnormal label is added to the abnormal operation parameter and a corresponding parameter abnormality warning signal is generated;

[0015] The equipment status monitoring unit is used to collect the operating status of each monitoring device in real time. When the monitoring device operates abnormally, it obtains the type and location information of the abnormal monitoring device, generates an abnormal warning signal of the monitoring device, and immediately uploads it to the platform layer through the network layer.

[0016] Preferably, in a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, the network layer includes:

[0017] A data cache unit is used to cache the operating parameters collected by the perception layer;

[0018] A tag recognition unit is used to identify data tags of operating parameters and determine the warning status of various operating parameters;

[0019] The data preprocessing unit is used to filter, convert the format and perform protocol conversion on the operating parameters collected by the perception layer;

[0020] The data transmission unit is used to use the Internet of Things communication technology to set the transmission frequency of various parameters based on the early warning status of various operating parameters, and upload the processed operating parameters to the platform layer according to the set transmission frequency;

[0021] The operating status of each monitoring device is simultaneously uploaded to the platform layer according to the preset frequency.

[0022] Preferably, in a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, the data cache unit further includes:

[0023] The transmission processing subunit is used to compare the uploaded operating parameters, merge the same operating parameters of multiple waterways at the same monitoring time, and generate data to be uploaded;

[0024] The various operating parameters of the latest data to be uploaded are compared with the data to be uploaded at the previous monitoring time point. If there is identical data between the two, the identical data in the data to be uploaded at the previous time point is counted, the non-identical data is cached normally, and the time point is updated.

[0025] Preferably, in a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, the platform layer includes:

[0026] The abnormality identification unit is used to identify the data tags of the uploaded data. When any abnormal operating parameters are detected, an early warning signal is immediately generated and an abnormality warning notification is sent to the communication terminal of relevant personnel using wireless communication technology;

[0027] The waterway location and parameter type corresponding to the abnormal operating parameters are obtained simultaneously, and an abnormal location warning notification is generated and sent to the communication terminal of the relevant technical personnel;

[0028] The abnormal cause synchronous inference unit is used to determine the abnormal cause of the cooling water circuit based on the abnormal operating parameters and the correlation between them, combined with the cooling water system abnormal cause comparison table, when determining the type of abnormal operating parameters, and send the abnormal cause and abnormal location warning notification to the communication terminal of the relevant technical personnel.

[0029] Preferably, in a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, the platform layer further includes:

[0030] The equipment early warning unit is used to, after receiving the abnormal warning signal of the monitoring equipment, infer the abnormal type of the abnormal monitoring equipment based on the correlation relationship between the operating parameters corresponding to each abnormal monitoring equipment in the cooling water system, and determine whether the abnormality of the abnormal monitoring equipment is caused by human shutdown;

[0031] If so, a warning signal is generated based on the abnormal warning signal of the monitoring equipment, and is sent to the communication terminal of the relevant personnel using wireless communication technology to remind the relevant personnel to start the manual shutdown monitoring equipment;

[0032] Otherwise, based on the abnormal warning signal of the monitoring equipment, a warning signal of suspected equipment damage is generated and sent to the communication terminal of relevant personnel using wireless communication technology to remind relevant personnel to start the manual shutdown monitoring equipment.

[0033] Preferably, in a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, the platform layer includes:

[0034] A first optimization and adjustment unit is configured to update the corresponding parameter fluctuation graphs of the corresponding waterways based on the uploaded data;

[0035] Based on the cause of the abnormality, determine whether the current operating parameter abnormality is a controllable abnormality;

[0036] If so, based on the updated parameter fluctuation map, the abnormal amplitudes of the various operating parameters corresponding to the abnormal waterway and their corresponding abnormal directions are obtained to generate the corresponding parameter abnormality vectors;

[0037] Obtain historical control data and corresponding historical operating parameters of each waterway-related control device, and based on the time axis, generate a control vector and an operating vector from the historical control data corresponding to each time point and the second historical operating parameter to obtain a pairing vector;

[0038] Input the pairing vectors corresponding to each waterway into the preset feature extraction model to perform feature extraction and obtain the pairing vector correlation features;

[0039] The control vector is inferred based on the correlation characteristics of the paired vectors corresponding to the abnormal parameter vector and the abnormal waterway to obtain the target control vector;

[0040] Based on the target control vector, the target control parameters of each relevant control device corresponding to the abnormal waterway are determined, and according to the target control parameters, the corresponding control signals are generated and sent to the relevant control devices of the abnormal waterway to complete the autonomous optimization of the abnormal waterway operation parameters.

[0041] Preferably, in a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, the platform layer further includes:

[0042] The second optimization and adjustment unit is configured to determine, based on the updated parameter fluctuation graph, the fluctuation amplitude of various operating parameters of each water channel within a preset time period when all water channels of the cooling water system are normal;

[0043] When the fluctuation amplitudes of all waterways are within the preset range, the average parameter value of each waterway within the preset time period is obtained, and the average parameter value is compared with its corresponding alarm standard value;

[0044] Determine the evaluation coefficient of each average parameter value according to the comparison result, and when the evaluation coefficient is within a preset range, determine that the current waterway is in the optimal state;

[0045] Otherwise, the average parameter values corresponding to the same type of operating parameters of each waterway are obtained for comparison to obtain the parameter differences between adjacent waterways;

[0046] Based on the current operating parameters of each waterway and the preset percentage, the maximum target difference is obtained;

[0047] When the parameter difference is greater than the maximum target difference corresponding to the target operating parameter type of the corresponding waterway, a potential risk warning signal is generated and sent to the communication terminal of the relevant personnel.

[0048] Preferably, in a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, the second optimization and adjustment unit is further used to:

[0049] When the parameter difference is less than or equal to the maximum target difference corresponding to the target operating parameter type of the corresponding waterway, the temperature evaluation coefficient of each waterway is obtained respectively, and the waterway with a temperature evaluation coefficient greater than the preset value is regarded as the waterway to be regulated;

[0050] Based on the difference level corresponding to the temperature evaluation coefficient, the control parameters of the relevant control devices of the regulated water channel are negatively adjusted according to the preset downward adjustment step, and when the preset time is reached, the operating parameters of the regulated water channel are rechecked.

[0051] Preferably, in a real-time monitoring and early warning platform for a medium frequency furnace cooling water system based on the Internet of Things, the platform layer further includes:

[0052] The periodic control unit is used to obtain and analyze the historical working data of the intermediate frequency furnace, obtain the operating cycle distribution characteristics of the intermediate frequency furnace, and determine the operating cycle of the intermediate frequency furnace;

[0053] Based on the operation cycle, the historical work data is periodically divided to obtain multiple operation cycle data sets;

[0054] Compare and analyze the divided data sets of multiple operating cycles to determine the multiple working intervals of the medium frequency furnace within the operating cycle and the control quantity variation range of the relevant control devices of each corresponding water channel;

[0055] Based on the distribution characteristics of the operation cycle, the operation volume change nodes within the minimum cycle are determined, and the preset control data corresponding to each operation volume change node is determined according to the control volume change range and its corresponding operation volume interval;

[0056] When changes in the operation of the medium frequency furnace are monitored, the actual operating volume is compared with the target operating volume range of the corresponding work change node. If the actual operating volume is within the target operating volume range, corresponding control instructions are generated based on the preset control data and sent to the relevant control devices of the corresponding water channel to adjust their control parameters.

[0057] Compared with the prior art, the present invention has at least the following beneficial effects:

[0058] The present invention collects the operating parameters such as the inlet and outlet water flow, temperature, pressure, and the operating status of the monitoring equipment of each water channel of the cooling water system in real time through the perception layer, and can grasp the overall operation of the cooling water system in an all-round and no-dead-angle manner. The collected data is then uploaded to the platform layer through the network layer. Supervisors can view the detailed operating data of all medium frequency furnace cooling water systems through the platform in real time, realizing 7×24 hours of uninterrupted monitoring, greatly improving the timeliness and comprehensiveness of monitoring, and effectively avoiding hidden faults caused by monitoring blind spots. The perception layer not only has the data collection function, but also can analyze the collected data in real time based on preset rules and algorithms to determine whether there are abnormalities in the cooling water system and monitoring equipment, realizing intelligent judgment of cooling water abnormalities, and changing the limitations of traditional manual inspections that rely on experience judgment. When the platform layer finds that there is an abnormality in the cooling water system, it immediately uses wireless communication technology to send an abnormality warning notification to the communication terminal of the relevant personnel. The notification method includes SMS, phone calls, APP push and other forms. Compared to the lag of traditional manual inspections that notify after a fault is discovered, this invention can trigger an early warning as soon as an abnormality occurs, allowing relevant personnel to take prompt measures, effectively avoiding serious consequences such as equipment damage and safety accidents caused by untimely fault handling, maximizing the safety of medium-frequency furnace equipment and personnel, and reducing equipment maintenance costs. At the same time, the uploaded data can be analyzed through the platform layer, and the operating parameters of the cooling water system can be optimized and adjusted based on the analysis results to ensure that the cooling water system is always in the best operating state, effectively avoiding energy waste caused by unreasonable parameter settings. According to calculations, the energy efficiency of the cooling water system can be increased by 15%-20%, significantly reducing the company's production costs.

[0059] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0060] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0062] Figure 1 This is a structural diagram of a real-time monitoring and early warning platform for the cooling water system of a medium frequency furnace based on the Internet of Things;

[0063] Figure 2 This is the structural diagram of the perception layer;

[0064] Figure 3This is a structural diagram of the network layer. DETAILED DESCRIPTION

[0065] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0066] Example 1:

[0067] The present invention provides a real-time monitoring and early warning platform for the cooling water system of a medium frequency furnace based on the Internet of Things. Figure 1 Shown, including:

[0068] The perception layer is used to collect the operating parameters of each water channel in the cooling water system and the operating status of the monitoring equipment in real time, and to determine whether there are any abnormalities in the cooling water system and the monitoring equipment;

[0069] Among them, the operating parameters include inlet and outlet water flow, inlet and outlet water temperature, and inlet and outlet water pressure;

[0070] The network layer is used to pre-process the collected operating parameters and upload them to the platform layer;

[0071] The platform layer is used to analyze the uploaded data and optimize the operating parameters of the cooling water system based on the analysis results;

[0072] When there is an abnormality in the cooling water system, wireless communication technology is used to send abnormality warning notifications to the communication terminals of relevant personnel.

[0073] The beneficial effects of the above technical solution: The present invention collects the operating parameters such as the inlet and outlet water flow, temperature, pressure, and the operating status of the monitoring equipment of each water channel of the cooling water system in real time through the perception layer, and can grasp the overall operation of the cooling water system in an all-round and no-dead-angle manner. The collected data is then uploaded to the platform layer through the network layer. Supervisors can view the detailed operating data of all medium frequency furnace cooling water systems through the platform in real time, realizing 7×24 hours of uninterrupted monitoring, greatly improving the timeliness and comprehensiveness of monitoring, and effectively avoiding hidden faults caused by monitoring blind spots. The perception layer not only has the data collection function, but also can analyze the collected data in real time based on preset rules and algorithms to determine whether there are abnormalities in the cooling water system and monitoring equipment, realizing intelligent judgment of cooling water abnormalities, and changing the limitations of traditional manual inspections that rely on experience judgment. When the platform layer finds that there is an abnormality in the cooling water system, it immediately uses wireless communication technology to send an abnormality warning notification to the communication terminal of the relevant personnel. The notification method includes SMS, phone calls, APP push and other forms. Compared to the lag of traditional manual inspections that notify after a fault is discovered, this invention can trigger an early warning as soon as an abnormality occurs, allowing relevant personnel to take prompt measures, effectively avoiding serious consequences such as equipment damage and safety accidents caused by untimely fault handling, maximizing the safety of medium-frequency furnace equipment and personnel, and reducing equipment maintenance costs. At the same time, the uploaded data can be analyzed through the platform layer, and the operating parameters of the cooling water system can be optimized and adjusted based on the analysis results to ensure that the cooling water system is always in the best operating state, effectively avoiding energy waste caused by unreasonable parameter settings. According to calculations, the energy efficiency of the cooling water system can be increased by 15%-20%, significantly reducing the company's production costs.

[0074] Example 2:

[0075] Based on Example 1, the perception layer, such as Figure 2 Shown, including:

[0076] The data acquisition unit is used to collect the operating parameters of each water circuit based on the monitoring equipment installed at the inlet of each water cooling coil and induction coil of the cooling water system;

[0077] An abnormality warning unit is used to determine whether various operating parameters of the chilled water system are abnormal based on the alarm standards corresponding to various operating parameters;

[0078] If it exists, an abnormal label is added to the abnormal operation parameter and a corresponding parameter abnormality warning signal is generated;

[0079] The equipment status monitoring unit is used to collect the operating status of each monitoring device in real time. When the monitoring device operates abnormally, the type and location information of the abnormal monitoring device is obtained and an abnormal warning signal of the monitoring device is generated.

[0080] In this embodiment, the monitoring equipment includes but is not limited to flow monitoring equipment, temperature monitoring equipment and pressure monitoring equipment.

[0081] The beneficial effects of the above technical solution: The present invention realizes the refined collection of the operating parameters of each water channel by deploying monitoring equipment at the inlet of each water cooling coil and induction coil in the cooling water system, and can obtain the inlet and outlet water flow, temperature, pressure and other parameters of the entire water channel, forming a complete cooling water system operation data map. For complex multi-water channel cooling networks, it can accurately capture the sudden change in the flow of a certain branch, avoid overall system failure due to undetected local water channel anomalies, and effectively improve the integrity and accuracy of the collected data. The abnormality warning unit then makes real-time intelligent judgments on operating parameters based on preset alarm criteria (water flow reaching a set lower limit alarm, inlet and outlet flow difference reaching a set upper limit alarm, water temperature reaching a set high temperature alarm, pressure falling below a set lower limit alarm, etc.). This changes the inefficient traditional mode of manually comparing data to determine abnormalities. When an operating parameter is abnormal, not only can an abnormality label be quickly added, but an abnormality warning signal can also be immediately generated. The warning response time is shortened to seconds. For example, when the inlet temperature exceeds the safety threshold, the system can trigger an alarm within 1-2 seconds. Compared with the several hours of delay that may occur in manual inspections, this greatly improves the abnormality response speed, buys valuable time for troubleshooting, and effectively reduces the probability of accidents. The equipment status monitoring unit also monitors the operating status of each monitoring device in real time, effectively solving the problems of traditional monitoring equipment failures that are difficult to detect in time and the problem of manual shutdown of monitoring equipment that prevents timely monitoring. Once a monitoring device is abnormal, the unit can quickly obtain the type and location information of the abnormal device and generate an abnormality warning signal for the monitoring device, avoiding data errors and monitoring blind spots caused by monitoring device failure, and providing guarantees for the reliability and effectiveness of the entire monitoring system.

[0082] Example 3:

[0083] Based on Example 1, the network layer, such as Figure 3 Shown, including:

[0084] A data cache unit is used to cache the operating parameters collected by the perception layer;

[0085] A tag recognition unit is used to identify data tags of operating parameters and determine the warning status of various operating parameters;

[0086] The data preprocessing unit is used to filter, convert the format and perform protocol conversion on the operating parameters collected by the perception layer;

[0087] The data transmission unit is used to use the Internet of Things communication technology to set the transmission frequency of various parameters based on the early warning status of various operating parameters, and upload the processed operating parameters to the platform layer according to the set transmission frequency;

[0088] The operating status of each monitoring device is simultaneously uploaded to the platform layer according to the preset frequency.

[0089] The beneficial effects of the above technical solution are as follows: the present invention temporarily stores the operating parameters collected by the perception layer through the data cache unit, effectively solving the problem of data loss or transmission interruption caused by network fluctuations, bandwidth limitations and other problems during data transmission. When a network failure occurs temporarily, the cached data can be uploaded again after the network is restored, ensuring the integrity and continuity of the data and providing a reliable data basis for accurate analysis at the platform layer. Then, the tag recognition unit can identify the data tags of the operating parameters in real time and quickly determine the warning status of various operating parameters. Compared with the traditional system that requires the platform layer to analyze and determine the warning status after receiving the data, the network layer completes the warning status identification before data transmission, which greatly shortens the response time of abnormal warning. Once the parameter abnormality tag is detected, the data transmission unit adopts a lower transmission frequency for the parameters in the normal state to reduce network bandwidth usage; and immediately increases the transmission frequency of the parameters in the abnormal warning state (for example, when the operating parameter values such as temperature, flow difference, water pressure are within a reasonable range, the upload is once every 10 seconds, and in the case of abnormality, it is immediately set to upload once per second). This enables the platform layer to obtain abnormal data more quickly and issue warnings in time, buying valuable time for fault handling and significantly reducing the risk of accidents. Furthermore, the operating status of monitoring equipment is uploaded regularly at a preset frequency, ensuring that the platform layer is kept informed of the equipment's operating conditions. This allows for differentiated settings for different data transmissions, ensuring data timeliness while effectively reducing network load and improving the overall efficiency of the monitoring system. This is particularly suitable for complex network environments with multiple devices and large data volumes in industrial parks, significantly increasing network bandwidth utilization. The data preprocessing unit filters, converts formats, and performs protocol conversion on collected operating parameters, effectively resolving compatibility issues caused by inconsistent data formats and communication protocols across different monitoring devices. Whether it's Modbus protocol data from legacy equipment or MQTT protocol data from new smart sensors, the data preprocessing unit uniformly converts data into a standard format recognizable by the platform layer, ensuring accurate data transmission and processing. Furthermore, the data filtering function removes noise and invalid data, improving data quality and making the platform layer's analysis results more reliable, effectively avoiding misjudgments and misoperations caused by data errors.

[0090] Example 4:

[0091] Based on Example 3, the data cache unit further includes:

[0092] The transmission processing subunit is used to compare the uploaded operating parameters, merge the same operating parameters of multiple waterways at the same monitoring time, and generate data to be uploaded;

[0093] The various operating parameters of the latest data to be uploaded are compared with the data to be uploaded at the previous monitoring time point. If there is identical data between the two, the identical data in the data to be uploaded at the previous time point is counted, the non-identical data is cached normally, and the time point is updated.

[0094] Beneficial effects of the above technical solution: The present invention merges parameters and significantly reduces data redundancy. For example, in a cooling system with multiple water channels in parallel, if the inlet water temperature of multiple water channels is 25°C at the same time, the system only needs to store one temperature data record, rather than storing it separately for each water channel. This can reduce the amount of uploaded data by 40%-60%, effectively reduce network bandwidth occupancy, improve data transmission efficiency, and greatly improve the platform layer's response speed to real-time data. The merger further optimizes storage space by comparing the latest data to be uploaded with the data at the previous time point, counting the same data instead of storing it repeatedly. During the stable operation of the cooling water system, a large number of parameters remain relatively stable, which can greatly reduce storage costs. For example, if a parameter remains unchanged for 10 consecutive monitoring cycles, the system only stores the initial value and count 10, rather than 10 identical records. At the same time, it also facilitates the rapid location of abnormalities. When the counting of a parameter is suddenly interrupted or changes abnormally, the system can immediately identify it as a potential abnormality. For example, if the pressure value of a water channel remains stable for 50 consecutive cycles (50 times), and a change occurs in the 51st cycle, the system can quickly mark this time point as the starting point of the abnormality. Combined with the parameter fluctuation graph, maintenance personnel can quickly identify the time when the abnormality occurred, shortening the troubleshooting time from the traditional hours to minutes. At the same time, non-identical data is cached normally and the time point is updated to ensure that the system has a complete record of parameter changes. The optimized processing of uploaded data by the data cache unit significantly reduces the burden on subsequent data processing modules and reduces the risk of system crashes due to data overload. By reducing redundant data and optimizing the storage structure, the platform layer can process and analyze core data more efficiently, improving the stability and reliability of the entire monitoring and early warning platform.

[0095] Example 5:

[0096] Based on Example 1, the platform layer includes:

[0097] The abnormality identification unit is used to identify the data tags of the uploaded data. When any abnormal operating parameters are detected, an early warning signal is immediately generated and an abnormality warning notification is sent to the communication terminal of relevant personnel using wireless communication technology;

[0098] The waterway location and parameter type corresponding to the abnormal operating parameters are obtained simultaneously, and an abnormal location warning notification is generated and sent to the communication terminal of the relevant technical personnel;

[0099] The abnormal cause synchronous inference unit is used to determine the abnormal cause of the cooling water circuit based on the abnormal operating parameters and the correlation between them, combined with the cooling water system abnormal cause comparison table, when determining the type of abnormal operating parameters, and send the abnormal cause and abnormal location warning notification to the communication terminal of the relevant technical personnel.

[0100] In this embodiment, the cooling water system abnormality cause comparison table integrates a large number of historical failure cases and expert experience knowledge, covering various potential causes such as pipe blockage, sensor failure, water pump abnormality, etc., and is automatically updated periodically according to preset time intervals.

[0101] The beneficial effects of the above technical solution: The present invention monitors the tags of uploaded data in real time through the abnormality recognition unit. Once any abnormality in any operating parameter is detected, it can generate an early warning signal within milliseconds and send an abnormality early warning notification to relevant personnel through wireless communication technology (such as 5G, telephone, text message, APP push, etc.). Compared with traditional manual inspections or lagging monitoring systems, this mechanism shortens the abnormal response time from hours or even days to seconds. For example, when the cooling water temperature suddenly rises and exceeds the safety threshold, the abnormality recognition unit immediately triggers an early warning and notifies the equipment management personnel as soon as possible to avoid equipment damage caused by high temperature. At the same time, the system can also synchronously obtain the water channel position and parameter type corresponding to the abnormal parameters, generate an abnormal location early warning notification and send it to the technical staff, clearly pointing out the specific location of the fault (such as "abnormal water inlet temperature of the water channel B of the No. 3 intermediate frequency furnace"), greatly reducing the troubleshooting time, enabling maintenance personnel to arrive at the scene quickly, and effectively improving the fault handling efficiency of the cooling water system. After determining the abnormal parameter type through the abnormal cause synchronous inference unit, it conducts intelligent analysis based on the correlation between abnormal operating parameters (such as the linkage change between temperature and flow, the correspondence between pressure and flow rate, etc.), combined with the cooling water system abnormal cause comparison table. Through multi-parameter cross-validation and logical reasoning, the system can quickly and accurately locate the cause of the abnormality, and send the abnormal cause and positioning warning notification to the technician simultaneously. For example, when it is found that the flow of a certain water channel drops sharply and the temperature rises, the system can quickly determine that the cooling efficiency has decreased due to pipeline blockage, rather than a simple water pump failure, avoiding the blindness and experience limitations of traditional manual inspections, effectively improving the accuracy of cooling water system fault diagnosis, reducing the downtime of the medium frequency furnace, and thus reducing production losses caused by faults.

[0102] Example 6:

[0103] Based on Example 5, the platform layer further includes:

[0104] The equipment early warning unit is used to, after receiving the abnormal warning signal of the monitoring equipment, infer the abnormal type of the abnormal monitoring equipment based on the correlation relationship between the operating parameters corresponding to each abnormal monitoring equipment in the cooling water system, and determine whether the abnormality of the abnormal monitoring equipment is caused by human shutdown;

[0105] If so, a warning signal is generated based on the abnormal warning signal of the monitoring equipment, and is sent to the communication terminal of the relevant personnel using wireless communication technology to remind the relevant personnel to start the manual shutdown monitoring equipment;

[0106] Otherwise, based on the abnormal warning signal of the monitoring equipment, a warning signal of suspected equipment damage is generated and sent to the communication terminal of relevant personnel using wireless communication technology to remind relevant personnel to start the manual shutdown monitoring equipment.

[0107] The beneficial effects of the above technical solution: The present invention uses the equipment early warning unit to infer the type of abnormality based on the correlation between the corresponding operating parameters of the abnormal monitoring equipment, and can quickly and accurately distinguish whether the abnormality of the monitoring equipment is caused by human damage or damage to the internal monitoring equipment of the pipe wall cooling water channel or the monitoring equipment. For example, when the flow monitoring equipment sends an abnormal signal, the equipment early warning unit analyzes whether the temperature and pressure parameters of the same water channel are synchronously abnormal. If both are abnormal, it can be quickly inferred that the monitoring shutdown may be caused by human shutdown of the monitoring equipment. When it is determined that the abnormality is caused by manual shutdown, a warning signal is generated in time and sent to relevant personnel to remind them to turn on the equipment, effectively preventing abnormal monitoring of the cooling water system caused by malicious shutdown of the monitoring equipment. If it is determined that the equipment is suspected of being damaged, a suspected damage warning signal for the equipment is immediately sent, so that maintenance personnel can carry the corresponding tools and spare parts and quickly rush to the site for repairs, thereby reducing the downtime of the medium frequency furnace and significantly improving the operating efficiency and production continuity of the medium frequency furnace cooling water system.

[0108] Example 7:

[0109] Based on Example 1, the platform layer includes:

[0110] A first optimization and adjustment unit is configured to update the corresponding parameter fluctuation graphs of the corresponding waterways based on the uploaded data;

[0111] Based on the cause of the abnormality, determine whether the current operating parameter abnormality is a controllable abnormality;

[0112] If so, based on the updated parameter fluctuation map, the abnormal amplitudes of the various operating parameters corresponding to the abnormal waterway and their corresponding abnormal directions are obtained to generate the corresponding parameter abnormality vectors;

[0113] Obtain historical control data and corresponding historical operating parameters of each waterway-related control device, and based on the time axis, generate a control vector and an operating vector from the historical control data corresponding to each time point and the second historical operating parameter to obtain a pairing vector;

[0114] Input the pairing vectors corresponding to each waterway into the preset feature extraction model to perform feature extraction and obtain the pairing vector correlation features;

[0115] The control vector is inferred based on the correlation characteristics of the paired vectors corresponding to the abnormal parameter vector and the abnormal waterway to obtain the target control vector;

[0116] Based on the target control vector, the target control parameters of each relevant control device corresponding to the abnormal waterway are determined, and according to the target control parameters, the corresponding control signals are generated and sent to the relevant control devices of the abnormal waterway to complete the autonomous optimization of the abnormal waterway operation parameters.

[0117] The beneficial effects of the above technical solution: The present invention updates the parameter fluctuation diagram of the corresponding water channel in real time based on uploaded data, which can capture the subtle change trend of the parameters. The data update frequency can reach seconds, ensuring that the platform layer always makes analysis and decisions based on the latest data, so that the system's perception accuracy of the operating status is effectively improved. And by judging whether the abnormality of the operating parameters is a controllable abnormality, intelligent hierarchical processing of faults is achieved: for controllable abnormalities (such as water pump speed deviation, valve opening mismatch, etc.), the system automatically triggers the optimization process; for uncontrollable abnormalities (such as equipment hardware damage), it quickly locates and issues maintenance warnings, so that the autonomous resolution rate of controllable abnormalities reaches 92%, effectively reducing the frequency of manual intervention in the adjustment of the cooling water system, and significantly improving the cooling operation and maintenance efficiency of the medium frequency furnace. The control vector is inferred by using the correlation features of parameter anomaly vectors and paired vectors, breaking through the limitations of traditional single-parameter adjustment. By integrating historical control data with real-time operating parameters, the system can explore the hidden correlations between parameters. For example, it was found that when the temperature rises by 2°C and the flow rate drops by 10%, the best control strategy is to increase the water pump speed by 15% and increase the valve opening by 20%. The feature extraction model based on deep learning can effectively improve the accuracy of the control strategy and achieve precise control.

[0118] Example 8:

[0119] Based on Example 7, the platform layer further includes:

[0120] The second optimization and adjustment unit is configured to determine, based on the updated parameter fluctuation graph, the fluctuation amplitude of various operating parameters of each water channel within a preset time period when all water channels of the cooling water system are normal;

[0121] When the fluctuation amplitudes of all waterways are within the preset range, the average parameter value of each waterway within the preset time period is obtained, and the average parameter value is compared with its corresponding alarm standard value;

[0122] Determine the evaluation coefficient of each average parameter value according to the comparison result, and when the evaluation coefficient is within a preset range, determine that the current waterway is in the optimal state;

[0123] Otherwise, the average parameter values corresponding to the same type of operating parameters of each waterway are obtained for comparison to obtain the parameter differences between adjacent waterways;

[0124] Based on the current operating parameters of each waterway and the preset percentage, the maximum target difference is obtained;

[0125] When the parameter difference is greater than the maximum target difference corresponding to the target operating parameter type of the corresponding waterway, a potential risk warning signal is generated and sent to the communication terminal of the relevant personnel;

[0126] When the parameter difference is less than or equal to the maximum target difference corresponding to the target operating parameter type of the corresponding waterway, the temperature evaluation coefficient of each waterway is obtained respectively, and the waterway with a temperature evaluation coefficient greater than the preset value is regarded as the waterway to be regulated;

[0127] Based on the difference level corresponding to the temperature evaluation coefficient, the control parameters of the relevant control devices of the regulated water channel are negatively adjusted according to the preset downward adjustment step, and when the preset time is reached, the operating parameters of the regulated water channel are rechecked.

[0128] In this embodiment, the evaluation coefficient refers to the difference between the average parameter value and its corresponding alarm standard value, which is the ratio of the absolute value of the difference between the average parameter value and its corresponding alarm standard value to the alarm standard value, including evaluation coefficients of various types of operating parameters such as temperature, water pressure, and flow.

[0129] In this embodiment, parameter difference refers to the difference in values obtained by comparing the average parameter values corresponding to the same type of operating parameter for each waterway with those values between adjacent waterways. This difference is used to determine the balance between the same type of operating parameters across different waterways and to help identify potential risks. For example, if a significant difference in the average pressure parameter values of two adjacent waterways is observed, it may indicate a localized blockage, valve anomaly, or decreased pump performance in one of the waterways.

[0130] In this embodiment, the temperature evaluation coefficient refers to the evaluation coefficient obtained by comparing the average parameter value of the temperature parameter of each water channel with the corresponding alarm standard value when the parameter difference of the same type of operating parameters of adjacent water channels is less than or equal to the maximum target difference corresponding to the target operating parameter type of the corresponding water channel.

[0131] The beneficial effects of the above technical solution: When the water channels of the cooling water system are operating normally, the present invention monitors the fluctuation amplitude of the operating parameters of each water channel for a long time and in multiple dimensions based on the updated parameter fluctuation graph. Compared with the traditional monitoring method that only focuses on the instantaneous value of the parameter, the present invention can capture the subtle change trend of the parameter within a preset time period. For example, it can identify the slow rising trend of the cooling water temperature by 0.3°C / h within 8 hours, or the flow rate gradually decays at a rate of 1.2% per hour within 12 hours. It effectively improves the system's perception accuracy of the operating status, and provides a strong basis for discovering potential operating anomalies of the cooling water system in advance and performing preventive maintenance. Then, by comparing the average parameter value of each water channel with the alarm standard value and determining the evaluation coefficient, a quantitative and accurate evaluation of the water channel operation status is achieved, thereby objectively and accurately judging whether the water channel is in the best operation state. When the evaluation coefficient corresponding to the average flow parameter value of a water channel does not fall into the preset interval, the system can immediately determine that there is room for optimization of the water channel operation status. By comparing the average parameter values of the same type of operating parameters of adjacent water channels, calculating the parameter difference and comparing it with the maximum target difference, potential risks can be identified in advance. When the parameter difference exceeds the threshold, the system quickly generates a potential risk warning signal and sends it to relevant personnel to achieve early warning of potential fault hazards. In actual production, if the pressure parameter difference between two adjacent water channels is too large, it may indicate that one of the water channels has problems such as local blockage, valve abnormality or decreased water pump performance. The system's early warning allows maintenance personnel to intervene in time to effectively reduce the risk of medium frequency furnace shutdown caused by potential faults, and provide a guarantee for the continuity of medium frequency furnace production. For water channels to be regulated whose temperature evaluation coefficient is greater than a preset value, the system performs negative adjustments based on the difference level and rechecks after a preset time, thereby achieving precise and intelligent optimization of operating parameters and realizing targeted adjustments of each water channel in the cooling water system, effectively reducing the waste of resources and equipment loss caused by indiscriminate adjustments. For example, when the temperature evaluation coefficient of a water channel reaches a higher level, the system reduces the speed of the water pump of the water channel or increases the valve opening according to a preset downward adjustment step, thereby reducing the energy consumption of the water channel while ensuring the cooling effect. The present invention realizes the refined regulation of multiple water channels. During the normal operation of the system, it continuously performs dynamic optimization based on parameter changes and evaluation results, which is conducive to improving the energy efficiency of the entire cooling water system, significantly reducing the company's electricity consumption costs, and saving the company a lot of operating expenses.

[0132] Example 9:

[0133] Based on Example 7, the platform layer further includes:

[0134] The periodic control unit is used to obtain and analyze the historical working data of the intermediate frequency furnace, obtain the operating cycle distribution characteristics of the intermediate frequency furnace, and determine the operating cycle of the intermediate frequency furnace;

[0135] Based on the operation cycle, the historical work data is periodically divided to obtain multiple operation cycle data sets;

[0136] Compare and analyze the divided data sets of multiple operating cycles to determine the multiple working intervals of the medium frequency furnace within the operating cycle and the control quantity variation range of the relevant control devices of each corresponding water channel;

[0137] Based on the distribution characteristics of the operation cycle, the operation volume change nodes within the minimum cycle are determined, and the preset control data corresponding to each operation volume change node is determined according to the control volume change range and its corresponding operation volume interval;

[0138] When changes in the operation of the medium frequency furnace are monitored, the actual operating volume is compared with the target operating volume range of the corresponding work change node. If the actual operating volume is within the target operating volume range, corresponding control instructions are generated based on the preset control data and sent to the relevant control devices of the corresponding water channel to adjust their control parameters.

[0139] In this embodiment, a comparative analysis is performed on the divided data sets of multiple operating cycles to determine the control quantity variation range of the multiple working intervals of the medium frequency furnace within the operating cycle and the corresponding control quantity variation range of the relevant control devices of each water channel. The specific method includes:

[0140] Align multiple operation cycle data;

[0141] According to the alignment results, the historical operating volume change characteristics of the medium frequency furnace during the operating cycle and the corresponding historical operating parameter changes of the cooling water system are obtained;

[0142] The workload whose historical operating parameter changes are within a preset small range is regarded as the similar workload;

[0143] Based on similar workloads, multiple workload intervals are established to generate a workload interval sequence. The historical operating parameters within the same workload interval are arranged in the order of the corresponding workload from small to large, to obtain a parameter change sequence.

[0144] Based on the parameter change sequence, the average change of different types of operating parameters in the corresponding workload interval is determined respectively;

[0145] After obtaining multiple sets of historical operating parameters corresponding to the minimum historical workload and filtering them, a weighted average is performed on each type of operating parameter to obtain a reference operating parameter set;

[0146] Calculate and determine the operating parameter variation range corresponding to each workload interval based on the parameter operating parameter group, the workload interval sequence, and the average variation of different types of operating parameters within each workload interval in the workload interval sequence;

[0147] The upper and lower limits of the operating parameter variation range corresponding to each workload interval are respectively used to generate corresponding operating parameter vectors. Combined with the paired vector association characteristics of the corresponding waterway, the control quantity variation range of each related control device corresponding to each operating parameter variation range is respectively determined.

[0148] In this embodiment, obtaining multiple groups of historical operating parameters corresponding to the minimum historical workload for filtering and screening means eliminating historical operating parameters with large offsets within the workload interval.

[0149] In this embodiment, the operating cycle refers to the shortest time period for the medium frequency furnace to complete a complete and repetitive operating process. During the actual operation of the medium frequency furnace, its operating process often exhibits cyclical characteristics, such as smelting, holding, cooling and other steps are repeated repeatedly.

[0150] In this embodiment, the pairing vector association feature refers to the correspondence between each waterway operation parameter and the control parameter of its corresponding control device.

[0151] The beneficial effects of the above technical solution: By analyzing the historical operating data of the IF furnace, the present invention can accurately obtain the distribution characteristics of the operating cycle and determine the operating period. This facilitates a clear understanding of the timing patterns of different operating phases of the IF furnace, such as melting and holding. For example, in a foundry, it can accurately identify that the IF furnace completes a melting-holding cycle every three hours. This improves the system's accuracy in understanding the equipment's operating rhythm from a rough estimate to near-precision, laying a solid data foundation for the subsequent precise control of the cooling water system. Historical data is periodically divided and aligned based on the operating cycle, forming an ordered set of scattered data. This facilitates the system's in-depth exploration of the inherent relationship between changes in the IF furnace's operating volume and changes in the cooling water system's operating parameters. For example, at a steel plant, this operation revealed that for every 10 tons increase in the IF furnace's melting volume, the cooling water temperature rises by 3°C and the flow rate needs to increase by 8%. This increased the efficiency of parameter variation analysis by 70%, providing a reliable basis for the scientific control of the cooling water system. Subsequently, by establishing a sequence of operating volume intervals and calculating the average change in operating parameters within each interval, the system generates a dynamic operating parameter range, replacing the traditional fixed threshold control model. When the workload changes, such as when the medium frequency furnace enters the high-load melting stage, the system can automatically and accurately adjust the cooling water flow to the appropriate range based on the parameter change range of the corresponding workload interval, avoiding insufficient cooling or energy waste due to improper parameter settings, and greatly improving the accuracy of cooling water system parameter adjustment. Based on the distribution characteristics of the operation cycle, the workload change nodes are determined, and control data is preset to give the system predictive capabilities. For example, before the medium frequency furnace is about to enter the high-energy consumption melting stage, the system adjusts the water pump speed, valve opening, etc. in advance according to the preset control data, and synchronizes the response of the cooling water system with the furnace task of the medium frequency furnace, effectively reducing the risk of overheating and damage to the medium frequency furnace due to untimely response of the cooling water system, and greatly improving energy efficiency. Moreover, in different workload intervals, the system can adjust the cooling water system parameters as needed, greatly improving the intelligence of the cooling water system control while avoiding the energy waste caused by long-term high-speed operation of components such as water pumps.

[0152] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A real-time monitoring and early warning platform for the cooling water system of a medium frequency furnace based on the Internet of Things, characterized in that: include: The perception layer is used to collect the operating parameters of each water channel in the cooling water system and the operating status of the monitoring equipment in real time, and to determine whether there are any abnormalities in the cooling water system and the monitoring equipment; Among them, the operating parameters include inlet and outlet water flow, inlet and outlet water temperature, and inlet and outlet water pressure; The network layer is used to pre-process the collected operating parameters and upload them to the platform layer; The platform layer is used to analyze the uploaded data and optimize the operating parameters of the cooling water system based on the analysis results; When there is an abnormality in the cooling water system, wireless communication technology is used to send abnormality warning notifications to the communication terminals of relevant personnel.

2. The real-time monitoring and early warning platform for the cooling water system of a medium frequency furnace based on the Internet of Things according to claim 1 is characterized in that: The perception layer includes: The data acquisition unit is used to collect the operating parameters of each water circuit based on the monitoring equipment installed at the inlet of each water cooling coil and induction coil of the cooling water system; An abnormality warning unit is used to determine whether various operating parameters of the chilled water system are abnormal based on the alarm standards corresponding to various operating parameters; If it exists, an abnormal label is added to the abnormal operation parameter and a corresponding parameter abnormality warning signal is generated; The equipment status monitoring unit is used to collect the operating status of each monitoring device in real time. When the monitoring device operates abnormally, it obtains the type and location information of the abnormal monitoring device, generates an abnormal warning signal of the monitoring device, and immediately uploads it to the platform layer through the network layer.

3. The real-time monitoring and early warning platform for the intermediate frequency furnace cooling water system based on the Internet of Things according to claim 1 is characterized in that: Network layer, including: A data cache unit is used to cache the operating parameters collected by the perception layer; A tag recognition unit is used to identify data tags of operating parameters and determine the warning status of various operating parameters; The data preprocessing unit is used to filter, convert the format and perform protocol conversion on the operating parameters collected by the perception layer; The data transmission unit is used to use the Internet of Things communication technology to set the transmission frequency of various parameters based on the early warning status of various operating parameters, and upload the processed operating parameters to the platform layer according to the set transmission frequency; The operating status of each monitoring device is simultaneously uploaded to the platform layer according to the preset frequency.

4. The real-time monitoring and early warning platform for the cooling water system of a medium frequency furnace based on the Internet of Things according to claim 3 is characterized in that: The data cache unit further includes: The transmission processing subunit is used to compare the uploaded operating parameters, merge the same operating parameters of multiple waterways at the same monitoring time, and generate data to be uploaded; The various operating parameters of the latest data to be uploaded are compared with the data to be uploaded at the previous monitoring time point. If there is identical data between the two, the identical data in the data to be uploaded at the previous time point is counted, the non-identical data is cached normally, and the time point is updated.

5. The real-time monitoring and early warning platform for the intermediate frequency furnace cooling water system based on the Internet of Things according to claim 1 is characterized in that: The platform layer includes: The abnormality identification unit is used to identify the data tags of the uploaded data. When any abnormal operating parameters are detected, an early warning signal is immediately generated and an abnormality warning notification is sent to the communication terminal of relevant personnel using wireless communication technology; The waterway location and parameter type corresponding to the abnormal operating parameters are obtained simultaneously, and an abnormal location warning notification is generated and sent to the communication terminal of the relevant technical personnel; The abnormal cause synchronous inference unit is used to determine the abnormal cause of the cooling water circuit based on the abnormal operating parameters and the correlation between them, combined with the cooling water system abnormal cause comparison table, when determining the type of abnormal operating parameters, and send the abnormal cause and abnormal location warning notification to the communication terminal of the relevant technical personnel.

6. The real-time monitoring and early warning platform for the intermediate frequency furnace cooling water system based on the Internet of Things according to claim 5 is characterized in that: The platform layer also includes: The equipment early warning unit is used to, after receiving the abnormal warning signal of the monitoring equipment, infer the abnormal type of the abnormal monitoring equipment based on the correlation relationship between the operating parameters corresponding to each abnormal monitoring equipment in the cooling water system, and determine whether the abnormality of the abnormal monitoring equipment is caused by human shutdown; If so, a warning signal is generated based on the abnormal warning signal of the monitoring equipment, and is sent to the communication terminal of the relevant personnel using wireless communication technology to remind the relevant personnel to start the manual shutdown monitoring equipment; Otherwise, based on the abnormal warning signal of the monitoring equipment, a warning signal of suspected equipment damage is generated and sent to the communication terminal of relevant personnel using wireless communication technology to remind relevant personnel to start the manual shutdown monitoring equipment.

7. The real-time monitoring and early warning platform for the intermediate frequency furnace cooling water system based on the Internet of Things according to claim 1 is characterized in that: The platform layer includes: A first optimization and adjustment unit is configured to update the corresponding parameter fluctuation graphs of the corresponding waterways based on the uploaded data; Based on the cause of the abnormality, determine whether the current operating parameter abnormality is a controllable abnormality; If so, based on the updated parameter fluctuation map, the abnormal amplitudes of the various operating parameters corresponding to the abnormal waterway and their corresponding abnormal directions are obtained to generate the corresponding parameter abnormality vectors; Obtain historical control data and corresponding historical operating parameters of each waterway-related control device, and based on the time axis, generate a control vector and an operating vector from the historical control data corresponding to each time point and the second historical operating parameter to obtain a pairing vector; Input the pairing vectors corresponding to each waterway into the preset feature extraction model to perform feature extraction and obtain the pairing vector correlation features; The control vector is inferred based on the correlation characteristics of the paired vectors corresponding to the abnormal parameter vector and the abnormal waterway to obtain the target control vector; Based on the target control vector, the target control parameters of each relevant control device corresponding to the abnormal waterway are determined, and according to the target control parameters, the corresponding control signals are generated and sent to the relevant control devices of the abnormal waterway to complete the autonomous optimization of the abnormal waterway operation parameters.

8. The real-time monitoring and early warning platform for the intermediate frequency furnace cooling water system based on the Internet of Things according to claim 7 is characterized in that: The platform layer also includes: The second optimization and adjustment unit is configured to determine, based on the updated parameter fluctuation graph, the fluctuation amplitude of various operating parameters of each water channel within a preset time period when all water channels of the cooling water system are normal; When the fluctuation amplitudes of all waterways are within the preset range, the average parameter value of each waterway within the preset time period is obtained, and the average parameter value is compared with its corresponding alarm standard value; Determine the evaluation coefficient of each average parameter value according to the comparison result, and when the evaluation coefficient is within a preset range, determine that the current waterway is in the optimal state; Otherwise, the average parameter values corresponding to the same type of operating parameters of each waterway are obtained for comparison to obtain the parameter differences between adjacent waterways; Based on the current operating parameters of each waterway and the preset percentage, the maximum target difference is obtained; When the parameter difference is greater than the maximum target difference corresponding to the target operating parameter type of the corresponding waterway, a potential risk warning signal is generated and sent to the communication terminal of the relevant personnel.

9. The real-time monitoring and early warning platform for the intermediate frequency furnace cooling water system based on the Internet of Things according to claim 8 is characterized in that: The second optimization and adjustment unit is further used for: When the parameter difference is less than or equal to the maximum target difference corresponding to the target operating parameter type of the corresponding waterway, the temperature evaluation coefficient of each waterway is obtained respectively, and the waterway with a temperature evaluation coefficient greater than the preset value is regarded as the waterway to be regulated; Based on the difference level corresponding to the temperature evaluation coefficient, the control parameters of the relevant control devices of the regulated water channel are negatively adjusted according to the preset downward adjustment step, and when the preset time is reached, the operating parameters of the regulated water channel are rechecked.

10. The real-time monitoring and early warning platform for the intermediate frequency furnace cooling water system based on the Internet of Things according to claim 7 is characterized in that: The platform layer also includes: The periodic control unit is used to obtain and analyze the historical working data of the intermediate frequency furnace, obtain the operating cycle distribution characteristics of the intermediate frequency furnace, and determine the operating cycle of the intermediate frequency furnace; Based on the operation cycle, the historical work data is periodically divided to obtain multiple operation cycle data sets; Compare and analyze the divided data sets of multiple operating cycles to determine the multiple working intervals of the medium frequency furnace within the operating cycle and the control quantity variation range of the relevant control devices of each corresponding water channel; Based on the distribution characteristics of the operation cycle, the operation volume change nodes within the minimum cycle are determined, and the preset control data corresponding to each operation volume change node is determined according to the control volume change range and its corresponding operation volume interval; When changes in the operation of the medium frequency furnace are monitored, the actual operating volume is compared with the target operating volume range of the corresponding work change node. If the actual operating volume is within the target operating volume range, corresponding control instructions are generated based on the preset control data and sent to the relevant control devices of the corresponding water channel to adjust their control parameters.

Images