Remote centralized control and fault information diagnosis method and system for high-temperature alloy heating furnace equipment

Abstract

The invention discloses a remote centralized control and fault information diagnosis method and system for heating furnace equipment for high-temperature alloy, and belongs to the field of centralized control of heating furnace equipment in the metal smelting industry, and the method comprises the steps: 1) obtaining a prediction result of the operation energy consumption of the heating furnace equipment required for completing the process according to the production scheduling information of the electronic production billboard process; step 2), obtaining an optimal recommendation result of the starting sequence of the heating furnace according to the heating type, the maximum load capacity, the equipment intact state and the equipment operation ending time of the existing heating furnace on the production site; the control system can also monitor the real-time temperature of each temperature zone of the equipment in operation, and automatically adjust the start-stop state of the heating element in the corresponding temperature zone according to the set temperature. The intelligent controller technology and the big data processing algorithm are utilized, and based on the cloud platform technology, the operation information of the heating furnace type equipment on the production site is remotely obtained in real time in the dispatching room.

Description

Technical Field

[0001] The present invention relates to the field of centralized control of heating furnace equipment in the metal smelting industry, and in particular to a remote centralized control and fault information diagnosis method and system for heating furnace equipment for high-temperature alloys. Background Art

[0002] At present, in the high-temperature alloy smelting industry, heating furnace equipment is generally required for annealing high-temperature alloy ingots. Operators start and control the heating furnace equipment in sequence according to the production kanban production schedule. However, this sequential start-up method is unreasonable. There are many types of heating furnace equipment on the production site, and the installation locations are relatively scattered. When the high-temperature alloy ingots that need to be annealed are small-tonnage, there are small heating furnaces and large heating furnaces on site. If the heating furnace equipment started in sequence at this time is a heating furnace with a large load, there will be a phenomenon of "overkill", which will not only cause the equipment to waste excess energy, but also cause the subsequent large-tonnage high-temperature alloy ingot annealing to be unavailable. In recent years, most of the control methods for heating furnace equipment have adopted logical sequence control or instrument control with PLC control as the core. Although this kind of logic control and instrument control technology is very mature, when multiple equipment are used on a large scale, there is a disadvantage of insufficient scheduling optimization. At the same time, it is impossible to summarize the patterns and make predictive diagnoses of equipment failures, and it is impossible to accurately formulate plans for major and medium equipment repairs and spare parts procurement, resulting in the inability to perform preventive maintenance on equipment, which seriously affects on-site production efficiency. Summary of the invention

[0003] The purpose of the present invention is to provide a remote centralized control and fault information diagnosis method and system for heating furnace equipment for high-temperature alloys, which can link and centrally monitor the heating furnace equipment at the production site, select suitable heating furnace equipment according to the process parameters of the electronic production board to anneal the ingot, and optimize the ignition and start-stop of the heating elements in the equipment temperature zone based on the optimization control strategy, so as to meet the process requirements and reduce energy consumption. At the same time, based on advanced fault diagnosis algorithms, the rules for causing equipment fault points are summarized, so that equipment maintenance personnel can refer to them to formulate equipment major and medium repair plans and spare parts procurement plans, which is convenient for equipment preventive maintenance. The system is simple in composition and low in cost. It will not change the original configuration structure and device of the equipment. At the same time, the system has rich interfaces, which is convenient for the inclusion of new equipment into the system at a later stage. It has a high degree of automation to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a remote centralized control and fault information diagnosis method for heating furnace equipment for high temperature alloys, comprising the following steps:

[0005] Step 1), according to the electronic production kanban process scheduling information, such as the heating ingot type, the heating target temperature and the heating curve slope, obtain the heating furnace equipment operation energy consumption prediction result required to complete the process;

[0006] Step 2), according to the heating type of the existing heating furnaces at the production site, such as natural gas, electricity, maximum load, equipment integrity, and equipment operation end time, the optimal recommendation result of the heating furnace start-up sequence is obtained;

[0007] Step 3), matching heating furnace equipment according to the heating furnace equipment operation energy consumption prediction results and the start-up sequence priority recommendation results to obtain the optimal equipment pairing information for executing the heating process;

[0008] Step 4), inputting instructions to the centralized control system based on the pairing information, issuing heating curve information, waiting for the furnace to be installed on site, starting the equipment, and starting data monitoring to collect real-time temperature information of the temperature zone of the equipment, generating a dynamic optimization control signal, inputting it into the centralized optimization control system, and obtaining an output signal;

[0009] Step 5), according to the dynamic optimization control signal and output signal, calculate and adjust the number of start and stop of the current heating elements in each temperature zone of the heating furnace equipment and the running time and other data to obtain an optimized adjustment scheme. At the same time, adjust the output signal of the equipment temperature control controller based on the optimized adjustment scheme;

[0010] Step 6), according to the feedback signal, monitor the operating status of the heating furnace equipment, capture and analyze abnormal data, give abnormal point signals, trigger early warning prompts, inform relevant personnel to handle, and the system will summarize the occurrence rules of fault points;

[0011] The step 1 specifically comprises: pre-processing the heating material information and the heating process curve information in the process information on the electronic production board to generate data for screening; performing a prediction model analysis on the above-generated data and the collected heating furnace equipment information based on a feature selection algorithm, calculating the energy consumption of different heating furnaces after completing the same process, and generating an energy consumption prediction result;

[0012] The step 2 specifically includes: using a fuzzy dynamic Bayesian network algorithm to analyze and calculate the type, maximum load, equipment integrity, and remaining time of the equipment operation of the existing heating furnace equipment at the production site to obtain comprehensive data; and prioritizing the comprehensive data according to a multi-way merge sorting algorithm to obtain a priority recommendation result for starting the heating furnace equipment;

[0013] In step 3, the energy consumption prediction result and the device startup priority recommendation result are matched based on the Rabin-Karp algorithm;

[0014] The generation process of the dynamic optimization control signal in step 4 is specifically as follows: according to the matching information, determine the equipment in the heating furnace equipment cluster that meets the requirements and start it; at the same time, start monitoring the heating status of each temperature zone of the heating furnace equipment, automatically compare it with the current temperature setting value, calculate the startup quantity and operating time requirement of the heating element in the corresponding temperature zone, and generate a dynamic optimization control signal;

[0015] The step 5 specifically includes: using an iterative learning control algorithm to calculate the dynamic optimization control signal and the output signal data, outputting the adjustment signal data, and obtaining an optimized adjustment scheme.

[0016] The step 6 is specifically as follows: based on the convolutional neural network model algorithm, modeling and analyzing the abnormal data captured by the system, giving the abnormal points of the equipment, and at the same time fusing the historical abnormal data through deep learning to give the law of occurrence of the fault points.

[0017] A remote centralized control and fault information diagnosis system for heating furnace equipment for high temperature alloys, comprising:

[0018] Equipment layer unit, the heating furnace group that needs to be included in the system on site, under the premise of not destroying the original configuration and structure of the equipment, through the Internet of Things gateway, its various parameters can be quickly and accurately connected to this system;

[0019] The collection unit is used to collect data such as material heating process information of the electronic production board and equipment operating parameters of the on-site heating furnace;

[0020] The energy consumption unit for predicting the operation of the heating furnace equipment is used to calculate the energy consumption required to complete the material heating process based on the heating material information, heating process curve information, and heating furnace equipment parameter data in the process information on the electronic production board, and generate energy consumption prediction results;

[0021] The equipment optimal recommended start-up unit is used to match the heating furnace according to the heating type, maximum load, equipment condition, remaining equipment operation time and energy consumption prediction results of the material heating process of the existing heating furnace equipment at the production site.

[0022] The centralized control temperature zone temperature control optimization unit inputs instructions to the control system based on the matching information, starts the corresponding equipment, and simultaneously starts the real-time temperature monitoring of the corresponding equipment temperature zone, calculates the start-up quantity and regular requirements of the heating elements in the corresponding temperature zone, generates an optimization control signal, inputs it into the centralized optimization control system, and obtains an output signal;

[0023] The signal adjustment unit is used to calculate the adjustment signal data according to the current input signal of the temperature zone of the monitoring heating furnace and the output signal of the next step, and automatically adjust the start and stop and operation time of the heating element of the corresponding temperature zone;

[0024] The fault diagnosis unit is used to model and analyze the abnormal data captured by monitoring, and give the fault point. At the same time, it analyzes the historical abnormal data and gives the law of causing the fault point, which is convenient for equipment maintenance personnel to estimate the equipment overhaul plan and spare parts procurement, so as to achieve preventive maintenance of equipment and increase the service life of equipment.

[0025] The benefits of the present invention are as follows: the present invention proposes a remote centralized control and fault information diagnosis method for heating furnace equipment for high-temperature alloys. By predicting and arranging data, formulating an optimization adjustment plan, and rationally selecting the start-up of the heating furnace equipment, the corresponding balance between the material tonnage and the heating furnace load can be achieved, avoiding unnecessary energy waste. The control method can also automatically adjust the start-up and stop quantity and operation time of the heating elements in the heating temperature zone according to the real-time changes in the heating temperature zone of the heating furnace to maximize the energy utilization efficiency. At the same time, the control method can also model and analyze the captured abnormal parameters of the equipment, give the equipment failure points and their triggering rules, and the equipment maintenance personnel can formulate equipment overhaul and spare parts procurement plans in a targeted manner according to the rules, so as to achieve preventive maintenance of the equipment and improve the equipment start-up rate and service life.

[0026] The present invention utilizes intelligent controller technology and big data processing algorithms, based on cloud platform technology, to remotely obtain real-time operation information of heating furnace equipment at the production site, and deeply combines the electronic production kanban production plan with advanced control strategies to more reasonably control the start-up of the heating furnace equipment at the site, thereby reducing energy consumption while improving the utilization rate of the equipment. At the same time, according to advanced fault diagnosis algorithms, abnormal data is analyzed to facilitate equipment maintenance personnel to perform preventive maintenance of equipment, thus realizing automated management and monitoring of heating furnace equipment, greatly reducing labor demand and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a remote centralized control and fault information diagnosis method for a heating furnace for high temperature alloys;

[0028] Figure 2 This is a structural diagram of a remote centralized control and fault information diagnosis system for heating furnace equipment for high-temperature alloys. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1-Figure 2 The present invention provides a technical solution: a remote centralized control and fault information diagnosis method for heating furnace equipment for high temperature alloys, comprising the following steps:

[0031] Step 1), according to the electronic production kanban process scheduling information (heating ingot type, heating target temperature and heating curve slope, obtain the heating furnace equipment operation energy consumption forecast result required to complete the process: pre-process the heating material information and heating process curve information in the process information on the electronic production kanban to generate data for screening; based on the feature selection algorithm, the above-generated data is screened and the collected heating furnace equipment information is analyzed by the prediction model, the energy consumption of different heating furnaces after completing the same process is calculated, and the energy consumption forecast result is generated.

[0032] Step 2), according to the heating type (natural gas, electric energy), maximum load, equipment integrity, and equipment operation end time of the existing heating furnaces at the production site, the optimal recommendation result for the start-up order of the heating furnaces is obtained; specifically: the type, maximum load, equipment integrity, and remaining time of the equipment operation end of the existing heating furnace equipment at the production site are analyzed and calculated using a fuzzy dynamic Bayesian network algorithm to obtain comprehensive data; the comprehensive data is prioritized according to a multi-way merge sorting algorithm to obtain a priority recommendation result for the start-up of the heating furnace equipment.

[0033] Step 3) Match the heating furnace equipment according to the energy consumption prediction results of the heating furnace equipment and the priority recommendation results of the start-up sequence to obtain the optimal equipment pairing information for executing the heating process; specifically: match the energy consumption prediction results and the equipment start-up priority recommendation results based on the Rabin-Karp algorithm.

[0034] Step 4), based on the pairing information, input instructions to the centralized control system, issue heating curve information, wait for the furnace to be installed on site, start the equipment, start data monitoring, collect real-time temperature information of the temperature zone of the equipment, generate a dynamic optimization control signal, input the signal into the centralized optimization control system, and obtain an output signal; specifically: according to the matching information, determine the equipment in the heating furnace equipment cluster that meets the requirements and start it; at the same time, start monitoring the heating conditions of each temperature zone of the heating furnace equipment, automatically compare it with the current temperature setting value, calculate the startup quantity and operating time requirements of the heating elements in the corresponding temperature zone, and generate a dynamic optimization control signal.

[0035] Step 5), according to the dynamic optimization control signal and the output signal, calculate and adjust the number of start and stop of the current heating elements in each temperature zone of the heating furnace equipment and the running time and other data to obtain an optimized adjustment scheme. At the same time, adjust the output signal of the equipment temperature control controller based on the optimized adjustment scheme; specifically: use the iterative learning control algorithm to calculate the dynamic optimization control signal and the output signal data, output the adjustment signal data, and obtain the optimized adjustment scheme;

[0036] Step 6) Based on the convolutional neural network model algorithm, the abnormal data captured by the system is modeled and analyzed to give the abnormal points of the equipment. At the same time, historical abnormal data is integrated through deep learning to give the law of occurrence of fault points.

[0037] like Figure 2 As shown, the present invention provides a remote centralized control and fault information diagnosis system for heating furnace equipment for high-temperature alloys, including an equipment layer unit, a collection unit, an energy consumption unit for predicting the operation of the heating furnace equipment, an equipment optimal recommended start-up unit, a centralized control temperature zone temperature control optimization unit, an adjustment unit, a control feedback unit, and a fault diagnosis unit.

[0038] Equipment layer unit, the heating furnace group that needs to be included in the system on site, under the premise of not destroying the original configuration and structure of the equipment, through the Internet of Things gateway, its various parameters can be quickly and accurately connected to this system;

[0039] The collection unit is used to collect the operation data of the production board and the on-site heating furnace equipment, to collect the material heating process information of the electronic production board and the equipment operation parameters of the on-site heating furnace and other data, to calculate the energy consumption when completing the material heating process, and to generate energy consumption prediction results;

[0040] The equipment optimal recommended start-up unit is used to match the heating furnace according to the heating type, maximum load, equipment integrity, remaining equipment operation time and energy consumption prediction results of the completed material heating process of the existing heating furnace equipment at the production site;

[0041] The centralized control temperature zone temperature control optimization unit inputs instructions to the control system based on the matching information, starts the corresponding equipment, and simultaneously starts the real-time temperature monitoring of the corresponding equipment temperature zone, calculates the start-up quantity and regular requirements of the heating elements in the corresponding temperature zone, generates an optimization control signal, inputs it into the centralized optimization control system, and obtains an output signal;

[0042] The signal adjustment unit is used to calculate the adjustment signal data according to the current input signal of the temperature zone of the monitoring heating furnace and the output signal of the next step, and automatically adjust the start and stop and operation time of the heating element of the corresponding temperature zone;

[0043] The control feedback unit is mainly divided into a control unit and a detection feedback unit. The control unit is used to control the programmable logic controller, contactor, relay, and frequency converter of the start and stop and operation time of the heating elements in the heating zone of the heating furnace equipment during the centralized control optimization process; the detection feedback unit is used to detect the current temperature of the temperature zone after the control unit components adjust the start and stop of the heating elements in the heating zone of the heating furnace equipment, and feed it back to the centralized control system;

[0044] The fault diagnosis unit is used to model and analyze the abnormal data captured by monitoring, and give the fault point. At the same time, it analyzes the historical abnormal data and gives the law of causing the fault point, which is convenient for equipment maintenance personnel to estimate the equipment overhaul plan and spare parts procurement, so as to achieve preventive maintenance of equipment and increase the service life of equipment.

[0045] In a specific embodiment, the priority system unit specifically includes: a screening and sorting priority unit, which is used to screen and sort the optimal recommended start-up unit of the heating furnace according to the heating type, maximum load, equipment integrity, and remaining time of the equipment operation of the existing heating furnaces on site; an adaptation priority unit is used to match the start-up of the heating furnace equipment with the predicted energy consumption data and the optimal recommended start-up unit.

[0046] The technical solution of the present invention provides a remote centralized control and fault information diagnosis device for heating furnace equipment for high-temperature alloys, which is applied to the centralized control of heating furnace equipment of multiple natural gas heating furnace equipment and multiple resistance heating furnace equipment in metal smelting plants. In the centralized control of the heating furnace equipment, the Internet of Things gateway used for data collection will timely upload the operation information of the on-site heating furnace equipment and the process information of the electronic production board to the cloud platform, and then optimize and control the heating furnace equipment at the production site through the device of the present invention, which can meet the annealing needs of ingots with different tonnages. When the heating furnace equipment is started, the intelligent controller adjusts the start and stop and operating time of the heating elements in the heating zone according to the adjustment signal given by the centralized control system. At the same time, the advanced fault diagnosis algorithm models and analyzes the captured abnormal data, and provides the equipment fault point and its triggering law.

[0047] The benefits of the present invention are mainly reflected in the following aspects:

[0048] 1. Improve the effective utilization rate of on-site heating furnace equipment: By reasonably controlling the on-site heating furnace equipment cluster, the scheduling optimization and matching balance of the heating furnace equipment can be achieved, avoiding unnecessary energy waste, saving enterprise manpower and material resources, and achieving cost reduction and efficiency improvement.

[0049] 2. Improve the stability of temperature zone control of heating furnace equipment: The remote centralized control method and device of heating furnace equipment can quickly detect abnormal conditions by real-time monitoring and analyzing the temperature changes of the heating furnace equipment in the temperature zone during operation, and quickly start the corresponding control strategy by comparing it with the set value, and adjust the start and stop and operation time of the heating elements in the corresponding temperature zone to maximize energy efficiency.

[0050] 3. Improve production efficiency: The centralized control method and device can use data processing algorithms to obtain real-time process information from the electronic production dashboard and centralized operation information of the on-site heating furnace equipment. Through iterative learning control strategies, the ingot and heating furnace equipment can be equipped more accurately to avoid the occurrence of materials and other equipment on site, thereby improving on-site production efficiency.

[0051] 4. Reduce equipment operation and maintenance costs: The remote centralized control method and device for heating furnace equipment can realize the automated management and monitoring of heating furnace equipment, greatly reducing the need for manual on-site inspections. In addition, based on advanced fault diagnosis strategies, by analyzing and processing equipment abnormal data, the triggering rules of equipment fault points are summarized. Equipment maintenance personnel can formulate equipment overhaul and spare parts procurement plans based on these rules, so as to achieve preventive maintenance of equipment and improve equipment availability and service life.

[0052] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A remote centralized control and fault information diagnosis method for heating furnace equipment for high temperature alloys, characterized in that: The steps include: Step 1), based on the electronic production kanban process scheduling information, obtain the energy consumption forecast result of the heating furnace equipment required to complete the process; Step 2), according to the heating type, maximum load, equipment integrity, and equipment operation end time of the existing heating furnaces at the production site, the optimal recommended result of the heating furnace start-up sequence is obtained; Step 3), matching heating furnace equipment according to the heating furnace equipment operation energy consumption prediction results and the start-up sequence priority recommendation results to obtain the optimal equipment pairing information for executing the heating process; Step 4), inputting instructions to the centralized control system based on the pairing information, issuing heating curve information, waiting for the furnace to be installed on site, starting the equipment, and starting data monitoring to collect real-time temperature information of the temperature zone of the equipment, generating a dynamic optimization control signal, inputting it into the centralized optimization control system, and obtaining an output signal; Step 5), according to the dynamic optimization control signal and output signal, calculate and adjust the number of start and stop of the current heating elements in each temperature zone of the heating furnace equipment and the running time and other data to obtain an optimized adjustment scheme. At the same time, adjust the output signal of the equipment temperature control controller based on the optimized adjustment scheme; Step 6), based on the feedback signal, monitor the operating status of the heating furnace equipment, capture and analyze abnormal data, give abnormal point signals, trigger early warning prompts, inform relevant personnel to handle, and the system will summarize the occurrence pattern of fault points.

2. A remote centralized control and fault information diagnosis method for heating furnace equipment for high temperature alloys according to claim 1, characterized in that: In the middle of step 1, the heating material information and the heating process curve information in the process information on the electronic production board are preprocessed to generate data for screening; After screening the above-generated data based on the feature selection algorithm and analyzing the collected heating furnace equipment information, the energy consumption of different heating furnaces after completing the same process is calculated to generate energy consumption prediction results.

3. The remote centralized control and fault information diagnosis method for heating furnace equipment for high temperature alloys according to claim 1 is characterized in that: In step 2, the fuzzy dynamic Bayesian network algorithm is used to analyze and calculate the type, maximum load, equipment integrity, and remaining time of the equipment operation of the existing heating furnace equipment at the production site to obtain comprehensive data; the comprehensive data is prioritized according to the multi-way merge sorting algorithm to obtain the priority recommendation result for the start-up of the heating furnace equipment.

4. The remote centralized control and fault information diagnosis method for heating furnace equipment for high temperature alloys according to claim 1 is characterized in that: In step 3, the energy consumption prediction result and the device startup priority recommendation result are matched based on the Rabin-Karp algorithm.

5. The remote centralized control and fault information diagnosis method for heating furnace equipment for high temperature alloys according to claim 1 is characterized in that: The process of generating the dynamic optimization control signal in step 4 is as follows: according to the matching information, determine the equipment in the heating furnace equipment cluster that meets the requirements and start it; at the same time, start monitoring the heating conditions of each temperature zone of the heating furnace equipment, automatically compare it with the current temperature setting value, calculate the startup quantity and operating time requirements of the heating elements in the corresponding temperature zone, and generate a dynamic optimization control signal.

6. The remote centralized control and fault information diagnosis method for heating furnace equipment for high temperature alloys according to claim 1, characterized in that: In step 5, an iterative learning control algorithm is used to calculate the dynamic optimization control signal and the output signal data, and the adjustment signal data is output to obtain an optimized adjustment scheme.

7. The method for remote centralized control and fault information diagnosis of heating furnace equipment for high temperature alloys according to claim 1, characterized in that: In step 6, the abnormal data captured by the system is modeled and analyzed based on the convolutional neural network model algorithm to provide the abnormal points of the equipment. At the same time, historical abnormal data is integrated through deep learning to provide the law of occurrence of fault points.

8. A remote centralized control and fault information diagnosis system for heating furnace equipment for high temperature alloys according to claim 1, characterized in that: include: Equipment layer unit, the heating furnace group that needs to be included in the system on site, under the premise of not destroying the original configuration and structure of the equipment, through the Internet of Things gateway, its various parameters can be quickly and accurately connected to this system; The collection unit is used to collect data such as material heating process information of the electronic production board and equipment operating parameters of the on-site heating furnace; The energy consumption unit for predicting the operation of the heating furnace equipment is used to calculate the energy consumption required to complete the material heating process based on the heating material information, heating process curve information, and heating furnace equipment parameter data in the process information on the electronic production board, and generate energy consumption prediction results; The equipment optimal recommended start-up unit is used to match the heating furnace according to the heating type, maximum load, equipment integrity, remaining equipment operation time and energy consumption prediction results of the completed material heating process of the existing heating furnace equipment at the production site; The centralized control temperature zone temperature control optimization unit inputs instructions to the control system based on the matching information, starts the corresponding equipment, and simultaneously starts the real-time temperature monitoring of the corresponding equipment temperature zone, calculates the start-up quantity and regular requirements of the heating elements in the corresponding temperature zone, generates an optimization control signal, inputs it into the centralized optimization control system, and obtains an output signal; The signal adjustment unit is used to calculate the adjustment signal data according to the current input signal of the temperature zone of the monitoring heating furnace and the output signal of the next step, and automatically adjust the start and stop and operation time of the heating element of the corresponding temperature zone; The fault diagnosis unit is used to model and analyze the abnormal data captured by monitoring, and give the fault point. At the same time, it analyzes the historical abnormal data and gives the law of causing the fault point, which is convenient for equipment maintenance personnel to estimate the equipment overhaul plan and spare parts procurement, so as to achieve preventive maintenance of equipment and increase the service life of equipment.

9. A remote centralized control system for heating furnace equipment as claimed in claim 8, characterized in that: It also includes a control feedback unit, which is mainly divided into a control unit and a detection feedback unit. The control unit is used to control the programmable logic controller, contactor, relay, and frequency converter of the start and stop and operation time of the heating elements in the heating zone of the heating furnace equipment during the centralized control optimization process; The detection feedback unit is used to detect the current temperature of the temperature zone and feed back to the centralized control system after the control unit component adjusts the start and stop of the heating elements in the heating zone of the heating furnace equipment.

10. A remote centralized control system for heating furnace equipment according to claim 9, characterized in that: The optimal recommended start-up unit of the equipment specifically includes: The screening and sorting priority unit is used to screen and sort the optimal recommended start-up unit of the heating furnace according to the heating type, maximum load, equipment integrity, and remaining time of the equipment operation of the existing heating furnaces on site; The optimal recommendation unit is adapted to use the predicted energy consumption data and the optimal recommendation start-up unit to match the start-up of the heating furnace equipment.

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