Centralized control system and method for production equipment of thermal power plant

Abstract

The invention discloses a centralized control system and method for production equipment of a thermal power plant, relates to the technical field of power plant monitoring, and solves the problems that when an existing thermal power plant control system monitors the production equipment in real time, due to the fact that the number of the production equipment is large and the number of monitoring items is large, the data size needing to be monitored is large, and the monitoring efficiency is high. The technical problem that the safety of the thermal power station is reduced due to the fact that part of emergency data cannot be quickly processed due to the fact that congestion possibly occurs during data transmission is solved. The data acquisition module is used for acquiring corresponding monitoring data in real time; the central processing module is used for generating a priority grade score corresponding to each piece of production equipment and generating an abnormal coefficient score corresponding to each piece of production equipment; generating a real-time transmission priority of each piece of monitoring data according to the priority grade score and the abnormal coefficient score; and the data transmission module is used for transmitting the real-time monitoring data to the data supervision module according to the real-time transmission priority.

Description

Technical Field

[0001] The present application belongs to the field of power plant monitoring technology, and relates to power plant monitoring technology, and specifically to a centralized control system and method for production equipment in a thermal power plant. Background Art

[0002] At present, more than 60% of the domestic electricity is still generated by thermal power plants, and thermal power generation still plays a major role in the rapid development of the national economy. The control rooms of thermal power plants are arranged near the furnace, machinery, electricity, and chemistry according to their professional nature. With the improvement of the degree of automation, the furnace, machinery, and electricity are gradually concentrated in one control room, and are arranged on the 8-meter floor of the main plant between the boiler and the steam turbine. There are various pressure vessels and pressure pipelines interspersed around the main control room.

[0003] In existing thermal power plants, the control room often performs centralized remote control over production equipment units (boiler, steam turbine, electrical, chemical water, environmental protection, air compressor station, fuel, etc.); during the control and monitoring process, various monthly generating equipment units will generate a large amount of data, especially for such high-risk generating equipment, which requires real-time monitoring, which requires the control room to process a large amount of data. Emergency data may not be processed in time, which in turn leads to reduced safety of the thermal power plant. Therefore, a centralized control system and method for thermal power plant production equipment is needed. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a centralized control system and method for production equipment of a thermal power plant, which is used to solve the technical problem that when the existing control system of a thermal power plant performs real-time monitoring of production equipment, due to the large number of production equipment and the large number of monitoring items, the amount of data that needs to be monitored is large, which may cause congestion during data transmission, making it impossible for some emergency data to be processed quickly, thereby reducing the safety of the thermal power station.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides a centralized control system for production equipment in a thermal power plant, including: a data acquisition module, a central processing module, a data transmission module, a data supervision module, an alarm module and a database;

[0006] The data acquisition module is used to acquire corresponding monitoring data in real time through data acquisition devices installed on various production equipment; the monitoring data includes monitoring parameters and their corresponding monitoring values;

[0007] The central processing module: obtains historical usage data and basic equipment data of each production equipment through a database connected thereto; generates a priority score corresponding to each production equipment according to the historical usage data and basic equipment data; and,

[0008] Obtain a number of monitoring data from each production equipment within a set time, and generate an abnormality coefficient score corresponding to each production equipment based on the monitoring data; generate a real-time transmission priority for each monitoring data based on the priority score and the abnormality coefficient score;

[0009] The data transmission module is configured to obtain the real-time transmission priority of each monitoring data and the real-time monitoring data, and transmit the real-time monitoring data to the data supervision module according to the real-time transmission priority.

[0010] The present application obtains the historical usage data and basic equipment data of each production equipment; generates a priority score corresponding to each equipment according to the historical number of failures and usage time in the historical usage data, and the service life, hazard factor score and equipment value in the basic equipment data; obtains a number of monitoring data within a set time of each production equipment, and generates an abnormality coefficient score corresponding to each production equipment according to the monitoring data; finally generates a real-time transmission priority for each monitoring data according to the priority score and the abnormality coefficient score; transmits each real-time monitoring data according to the real-time transmission priority; generates a real-time transmission priority for the corresponding monitoring data of each production equipment based on the comprehensive value of the production equipment and the data status of the latest monitoring data, and then uploads each monitoring data in sequence according to the real-time transmission priority, so that when monitoring a number of production equipment, each monitoring data can be uploaded in sequence, thereby improving data transmission efficiency and avoiding transmission congestion, thereby enabling the monitoring data to be processed in a timely manner, thereby improving the safety of the thermal power station.

[0011] Preferably, the data supervision module is used to obtain various monitoring data, generate monitoring status and parameter abnormality alarms of various monitoring data according to the monitoring data; compare the value of the monitoring data with the corresponding abnormal threshold, if the monitoring value corresponding to the monitoring data is greater than the abnormal threshold of the corresponding monitoring parameter, mark the corresponding monitoring state as an abnormal state, and generate a corresponding monitoring parameter abnormal alarm; if the monitoring value corresponding to the monitoring data is less than the abnormal threshold of the corresponding monitoring parameter, mark the corresponding monitoring state as a normal state;

[0012] The alarm module is used to obtain abnormal alarms of monitoring parameters and issue an alarm.

[0013] Preferably, generating a priority score corresponding to each production equipment according to the historical usage data and the basic equipment data includes:

[0014] Extract the number of historical failures and usage time from the historical usage data, and extract the service life, risk factor score and equipment value from the basic equipment data.

[0015] The number of historical failures, usage time, service life, risk factor and equipment value are input into a priority scoring model to obtain a priority score corresponding to each device; the priority scoring model is obtained through artificial intelligence model training.

[0016] Preferably, the priority rating model is obtained by training an artificial intelligence model, including:

[0017] Obtain the number of historical failures, usage time, service life, risk factor, equipment value, and priority rating of a number of production equipment through a database;

[0018] Integrate the number of historical failures, usage time, service life, risk factor, equipment value, and priority ratings corresponding to the production equipment into a number of training data and inspection data;

[0019] The artificial intelligence model is trained by training data, and the trained artificial intelligence model is tested using test data, and finally a priority scoring model is obtained whose input is the number of historical failures, usage time, service life, risk factor and equipment value; and whose output is the priority score of the corresponding production equipment; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0020] Preferably, generating an abnormality coefficient score corresponding to each production equipment according to the monitoring data includes:

[0021] Extract monitoring parameters and corresponding monitoring values ​​from a number of monitoring data; fit the monitoring values ​​corresponding to the same monitoring parameter into a monitoring value curve JZ(t) according to the time when the corresponding monitoring data was acquired;

[0022] By formula The anomaly coefficient score YCP of the corresponding monitoring data is calculated; wherein SZ is the upper limit of the anomaly threshold corresponding to the monitoring parameter in the monitoring data; XZ is the lower limit of the anomaly threshold corresponding to the monitoring parameter in the monitoring data; γ1 and γ2 are adjustment coefficients, and the specific values ​​are set according to expert experience; t1 is the start time within the set time, and t2 is the end time within the set time; α1 and α2 are proportional coefficients, and the specific values ​​are set according to expert experience.

[0023] Preferably, the real-time transmission priority of each monitoring data is generated according to the priority level score and the abnormal coefficient score, including:

[0024] Obtain the priority score YDP of each production equipment and the abnormal coefficient score YCP corresponding to each monitoring data;

[0025] The comprehensive evaluation coefficient ZP of each monitoring data is calculated by the formula ZP = β1 × YDP + β2 × YCP; where β1 and β2 are weight coefficients,

[0026] The comprehensive evaluation coefficients are arranged in order from large to small, and the real-time transmission priority of each monitoring data is generated according to the arrangement order.

[0027] Preferably, transmitting the real-time monitoring data to the data supervision module according to the real-time transmission priority includes:

[0028] Obtain the real-time transmission priority of each current monitoring data, and transmit the corresponding monitoring data to the supervision module in order of real-time transmission priority from high to low.

[0029] Another aspect of the present application provides a centralized control method for production equipment in a thermal power plant, comprising the following steps:

[0030] Step 1: Obtain the historical usage data and basic equipment data of each production equipment;

[0031] Step 2: Extract the number of historical failures and usage time from the historical usage data, as well as the service life, risk factor score and equipment value from the basic equipment data;

[0032] Step 3: Input the number of historical failures, usage time, service life, risk factor and equipment value into the priority rating model to obtain the priority rating corresponding to each device;

[0033] Step 4: Obtain a number of monitoring data of each production equipment within a set time, and generate an abnormal coefficient score corresponding to each production equipment based on the monitoring data;

[0034] Step 5: Generate the real-time transmission priority of each monitoring data according to the priority score and the abnormal coefficient score;

[0035] Step 6: Transmit each real-time monitoring data according to the real-time transmission priority;

[0036] Step 7: Generate monitoring status and parameter abnormality alarms for each monitoring data based on the monitoring data;

[0037] Step 8: Issue an alarm based on parameter abnormality alarm.

[0038] Compared with the prior art, the beneficial effects of this application are:

[0039] 1. The present application obtains the historical usage data and basic equipment data of each production equipment; generates a priority score corresponding to each equipment according to the historical number of failures and usage time in the historical usage data, as well as the service life, hazard factor score and equipment value in the basic equipment data; obtains a number of monitoring data within a set time of each production equipment, and generates an abnormality coefficient score corresponding to each production equipment according to the monitoring data; finally generates a real-time transmission priority for each monitoring data according to the priority score and the abnormality coefficient score; transmits each real-time monitoring data according to the real-time transmission priority; generates a real-time transmission priority for the corresponding monitoring data of each production equipment based on the comprehensive value of the production equipment and the data status of the latest monitoring data, and then uploads each monitoring data in sequence according to the real-time transmission priority, so that when monitoring a number of production equipment, each monitoring data can be uploaded in sequence, thereby improving data transmission efficiency and avoiding transmission congestion, thereby enabling the monitoring data to be processed in a timely manner, thereby improving the safety of the thermal power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 This is a schematic diagram of the principle of the centralized control system of the production equipment in this application;

[0042] Figure 2 Schematic diagram of the steps of the centralized control method for production equipment in this application. DETAILED DESCRIPTION

[0043] The technical solution of the present application will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0044] See also Figure 1 , the first aspect of the present application provides a centralized control system for production equipment in a thermal power plant, including: a data acquisition module, a central processing module, a data transmission module, a data supervision module, an alarm module and a database;

[0045] Data acquisition module: The corresponding monitoring data is obtained in real time through the data acquisition equipment installed on each production equipment; the monitoring data includes monitoring parameters and their corresponding monitoring values; the monitoring parameters are the monitoring items of each production equipment, including temperature and vibration frequency, etc. For example, if the production equipment is a boiler, the corresponding monitoring data includes monitoring data with the monitoring parameter of temperature; the monitoring value is the value of the corresponding monitoring parameter at the time of acquisition; the data acquisition equipment includes temperature acquisition equipment and vibration frequency acquisition equipment, etc.;

[0046] Central processing module: obtains historical usage data and basic equipment data of each production equipment through the database connected to it; historical usage data refers to various data generated when the corresponding production equipment was used before, including historical failure times and usage time, etc. The historical failure times refer to the number of failures that occurred in the corresponding production equipment since its use, and the usage time refers to the usage time of the corresponding production equipment since its use; basic equipment data refers to the basic data of the corresponding production equipment, including service life, hazard factor score and equipment value; service life refers to the rated usage time of the corresponding production equipment; hazard factor score refers to the score given by experts to each production equipment according to the impact caused by the danger of each equipment, and the higher the score, the more dangerous the corresponding production equipment; equipment value refers to the normalized total value of each production equipment in the corresponding thermal power plant; priority scores corresponding to each production equipment are generated based on historical usage data and basic equipment data; excellent score is a comprehensive score given to the corresponding production equipment; the higher the priority score, the more attention the corresponding production equipment needs to pay;

[0047] Acquire a number of monitoring data within a set time for each production equipment, where the monitoring data within the set time is the monitoring data acquired within the set time before the current monitoring data is acquired; generate an abnormal coefficient score corresponding to each production equipment based on the monitoring data; the abnormal coefficient score is a score of the possibility of abnormality of the monitoring data generated based on the monitoring data within the set time; the larger the abnormal coefficient score, the greater the possibility of abnormality, and the greater the degree of attention the corresponding monitoring data needs to pay; generate the real-time transmission priority of each monitoring data based on the priority score and the abnormal coefficient score; the real-time transmission priority is the current transmission priority of each monitoring data, and the higher the real-time transmission priority, the priority that the corresponding monitoring data should be transmitted;

[0048] Data transmission module: obtains the real-time transmission priority of each monitoring data and the real-time monitoring data, and transmits the real-time monitoring data to the data supervision module according to the real-time transmission priority;

[0049] Data supervision module: used to obtain various monitoring data, generate monitoring status and parameter abnormality alarms for various monitoring data according to the monitoring data; compare the value of the monitoring data with the corresponding abnormal threshold value. If the monitoring value corresponding to the monitoring data is greater than the abnormal threshold value of the corresponding monitoring parameter, the corresponding monitoring status is marked as abnormal and the corresponding monitoring parameter abnormal alarm is generated; if the monitoring value corresponding to the monitoring data is less than the abnormal threshold value of the corresponding monitoring parameter, the corresponding monitoring status is marked as normal;

[0050] Alarm module: used to obtain abnormal alarms of monitoring parameters and issue alarms.

[0051] This embodiment obtains historical usage data and basic equipment data of each production equipment; generates a priority score corresponding to each equipment according to the historical number of failures and usage time in the historical usage data, and the service life, risk factor score and equipment value in the basic equipment data; obtains a number of monitoring data within a set time of each production equipment, and generates an abnormality coefficient score corresponding to each production equipment according to the monitoring data; finally generates a real-time transmission priority for each monitoring data according to the priority score and the abnormality coefficient score; transmits each real-time monitoring data according to the real-time transmission priority; generates a real-time transmission priority for the monitoring data corresponding to each production equipment based on the comprehensive value of the production equipment and the data status of the latest monitoring data, and then uploads each monitoring data in sequence according to the real-time transmission priority, so that when monitoring a number of production equipment, each monitoring data can be uploaded in sequence, thereby improving data transmission efficiency and avoiding transmission congestion, thereby enabling the monitoring data to be processed in time, thereby improving the safety of the thermal power station.

[0052] A priority score corresponding to each production equipment is generated according to historical usage data and basic equipment data, including: extracting the number of historical failures and usage time in the historical usage data, extracting the service life, risk factor score and equipment value in the basic equipment data, the risk factor score is a score given by experts to each production equipment according to the impact caused by the danger of each equipment, and the higher the score, the more dangerous the corresponding production equipment; the equipment value is the result of normalizing the total value of each production equipment in the corresponding thermal power plant; the number of historical failures, usage time, service life, risk factor and equipment value are input into the priority scoring model to obtain the priority score corresponding to each equipment; the priority scoring model is obtained by training an artificial intelligence model.

[0053] The priority rating model is obtained through artificial intelligence model training, including: obtaining the historical number of failures, usage time, service life, risk factor, equipment value, and priority rating corresponding to a number of production equipment through a database; the priority rating is a comprehensive rating of the corresponding production equipment by experts based on the historical number of failures, usage time, service life, risk factor, and equipment value of the current production equipment. The higher the priority rating, the higher the attention paid to monitoring the corresponding production equipment; specifically, the more failures occurred in history, the more frequent the abnormalities of the corresponding production equipment, and the higher the attention paid to the corresponding production equipment; the higher the corresponding priority rating; the closer the usage time is to the service life, the greater the probability of abnormalities of the corresponding production equipment, and the higher the attention paid to the corresponding production equipment; the higher the corresponding priority rating; the greater the risk factor, the greater the dangerous impact of abnormalities of the corresponding production equipment, and the higher the attention paid to the corresponding production equipment; the higher the corresponding priority rating; the higher the equipment value, the higher the cost of the corresponding production equipment, the more important it is to the manufacturer, and the higher the attention paid to the corresponding production equipment; the higher the corresponding priority rating;

[0054] Integrate the number of historical failures, usage time, service life, risk factor, equipment value, and priority ratings corresponding to the production equipment into a number of training data and inspection data;

[0055] The artificial intelligence model is trained by training data, and the trained artificial intelligence model is tested by test data. Specifically, the historical number of failures, usage time, service life, risk factor and equipment value in the test data are input into the trained artificial intelligence model to obtain a corresponding priority score output; when the difference between the output priority score and the priority score recorded in the test data is within an acceptable range, the next set of test data is tested; otherwise, the relevant parameters of the artificial intelligence are adjusted, and the test is performed again using the current set of test data until a set proportion of the test data passes the test, and finally a priority score model is obtained whose input is the historical number of failures, usage time, service life, risk factor and equipment value; and whose output is the priority score of the corresponding production equipment; wherein the artificial intelligence model includes a BP neural network model and a RBF neural network model.

[0056] Generate an abnormal coefficient score corresponding to each production equipment according to the monitoring data, including: extracting monitoring parameters and corresponding monitoring values ​​from a number of monitoring data; fitting the monitoring values ​​corresponding to the same monitoring parameter into a monitoring value curve JZ(t) according to the time when the corresponding monitoring data was obtained; the fitting method includes interpolation method and other curve fitting methods;

[0057] By formula The abnormal coefficient score YCP of the corresponding monitoring data is calculated; wherein SZ is the upper limit of the abnormal threshold corresponding to the monitoring parameter in the monitoring data; XZ is the lower limit of the abnormal threshold corresponding to the monitoring parameter in the monitoring data; γ1 and γ2 are adjustment coefficients, and the specific values ​​are set according to expert experience; t1 is the start time within the set time, and t2 is the end time within the set time, that is, the time for this data transmission; α1 and α2 are proportional coefficients, and the specific values ​​are set according to expert experience; it can be understood that the proportional coefficients α1 and α2 are used to adjust the abnormal coefficient score to be consistent with the range of the priority score; while taking into account the weight distribution of the two items in the formula, the specific values ​​are set according to expert experience.

[0058] In this embodiment, the abnormality coefficient score of each monitoring data is calculated by the above formula, and is obtained by analysis based on the monitoring value corresponding to the monitoring data within the set time of the corresponding monitoring data; when the difference between the maximum and minimum values ​​of the monitoring value within the set time is larger, it means that the corresponding monitoring parameter is unstable in the near future, and the possibility of abnormality is greater. At this time, its abnormality coefficient score is set to be larger; when the monitoring value within the set time is closer to the boundary of the set abnormal threshold range, that is, closer to the upper limit of the abnormal threshold, the lower limit of the abnormal threshold, or when a slight abnormality has occurred, it is more likely that an abnormality will occur subsequently, and its abnormality coefficient score is set to be larger; the larger the abnormality coefficient score, the more attention the corresponding monitoring data needs to pay.

[0059] Generate the real-time transmission priority of each monitoring data according to the priority score and the abnormal coefficient score, including: obtain the priority score YDP of each production equipment and the abnormal coefficient score YCP corresponding to each monitoring data; calculate the comprehensive evaluation coefficient ZP of each monitoring data by the formula ZP=β1×YDP+β2×YCP; wherein β1 and β2 are weight coefficients, which are used to adjust the proportional relationship between the priority score and the abnormal coefficient score, and the specific values ​​are set according to expert experience; it can be understood that the range of the priority score YDP and the abnormal coefficient score YCP are consistent;

[0060] The comprehensive evaluation coefficients are arranged in order from large to small, and the real-time transmission priority of each monitoring data is generated according to the arrangement order; the higher the ranking, the higher the corresponding real-time transmission priority; this embodiment calculates the real-time transmission priority of each monitoring data through the above formula; when the priority score of the production equipment is higher, it means that the corresponding production equipment is more important, and the corresponding transmission data is more important, so the corresponding comprehensive evaluation coefficient is higher; when the abnormality coefficient of the monitoring data is higher, it means that the corresponding monitoring data is abnormal or unstable, and it needs to be strengthened. Monitoring, so the corresponding comprehensive evaluation coefficient is higher; this embodiment arranges the real-time transmission priority of each monitoring data through the comprehensive evaluation coefficient. When the comprehensive evaluation coefficient is larger, it means that the corresponding monitoring data is more urgent, and the corresponding real-time transmission priority is higher.

[0061] It is worth noting that in another embodiment, the transmission bandwidth is obtained by obtaining the maximum data volume of the historical data packet corresponding to each monitoring data as the standard data packet of the corresponding monitoring data; the number of monitoring data transmitted each time is set according to the transmission bandwidth and the maximum standard data packet; the comprehensive evaluation coefficients are arranged in order from large to small; the monitoring data within the set number are set to the same real-time transmission priority from large to small; if the number of monitoring data transmitted each time is 5, the top five monitoring data of the comprehensive evaluation coefficient are set to the same real-time transmission priority, and the monitoring data ranked sixth to tenth are set to another real-time transmission priority, and the higher the ranking, the higher the corresponding real-time transmission priority.

[0062] The real-time monitoring data is transmitted to the data supervision module according to the real-time transmission priority, including: obtaining the real-time transmission priority of each current monitoring data, and transmitting the corresponding monitoring data to the supervision module in the order of the real-time transmission priority from high to low.

[0063] See also Figure 2 Another aspect of the present application provides a centralized control method for production equipment in a thermal power plant, comprising the following steps:

[0064] Step 1: Obtain the historical usage data and basic equipment data of each production equipment;

[0065] Step 2: Extract the number of historical failures and usage time from the historical usage data, as well as the service life, risk factor score and equipment value from the basic equipment data;

[0066] Step 3: Input the number of historical failures, usage time, service life, risk factor and equipment value into the priority rating model to obtain the priority rating corresponding to each device;

[0067] Step 4: Obtain a number of monitoring data of each production equipment within a set time, and generate an abnormal coefficient score corresponding to each production equipment based on the monitoring data;

[0068] Step 5: Generate the real-time transmission priority of each monitoring data according to the priority score and the abnormal coefficient score;

[0069] Step 6: Transmit each real-time monitoring data according to the real-time transmission priority;

[0070] Step 7: Generate monitoring status and parameter abnormality alarms for each monitoring data based on the monitoring data;

[0071] Step 8: Issue an alarm based on parameter abnormality alarm.

[0072] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0073] How this application works:

[0074] The present application obtains the historical usage data and basic equipment data of each production equipment; generates a priority score corresponding to each equipment according to the historical number of failures and usage time in the historical usage data, and the service life, hazard factor score and equipment value in the basic equipment data; obtains a number of monitoring data within a set time of each production equipment, and generates an abnormality coefficient score corresponding to each production equipment according to the monitoring data; finally generates a real-time transmission priority for each monitoring data according to the priority score and the abnormality coefficient score; transmits each real-time monitoring data according to the real-time transmission priority; generates a real-time transmission priority for the corresponding monitoring data of each production equipment based on the comprehensive value of the production equipment and the data status of the latest monitoring data, and then uploads each monitoring data in sequence according to the real-time transmission priority, so that when monitoring a number of production equipment, each monitoring data can be uploaded in sequence, thereby improving data transmission efficiency and avoiding transmission congestion, thereby enabling the monitoring data to be processed in a timely manner, thereby improving the safety of the thermal power station.

[0075] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. A centralized control system for production equipment in a thermal power plant, comprising: Data acquisition module, central processing module, data transmission module, data supervision module, alarm module and database; characterized in that, The data acquisition module is used to acquire corresponding monitoring data in real time through data acquisition devices installed on various production equipment; the monitoring data includes monitoring parameters and their corresponding monitoring values; The central processing module: obtains historical usage data and basic equipment data of each production equipment through a database connected thereto; generates a priority score corresponding to each production equipment according to the historical usage data and basic equipment data; and, Obtain a number of monitoring data from each production equipment within a set time, and generate an abnormality coefficient score corresponding to each production equipment based on the monitoring data; generate a real-time transmission priority for each monitoring data based on the priority score and the abnormality coefficient score; The data transmission module is configured to obtain the real-time transmission priority of each monitoring data and the real-time monitoring data, and transmit the real-time monitoring data to the data supervision module according to the real-time transmission priority.

2. A centralized control system for production equipment in a thermal power plant according to claim 1, characterized in that: The priority level score corresponding to each production equipment is generated according to the historical usage data and the basic equipment data, including: Extract the number of historical failures and usage time from the historical usage data, and extract the service life, risk factor score and equipment value from the basic equipment data; The number of historical failures, usage time, service life, risk factor and equipment value are input into a priority scoring model to obtain a priority score corresponding to each device; the priority scoring model is obtained through artificial intelligence model training.

3. A centralized control system for production equipment in a thermal power plant according to claim 2, characterized in that: The priority rating model is obtained through artificial intelligence model training, including: Obtain the number of historical failures, usage time, service life, risk factor, equipment value, and priority rating of a number of production equipment through a database; Integrate the number of historical failures, usage time, service life, risk factor, equipment value, and priority ratings corresponding to the production equipment into a number of training data and inspection data; The artificial intelligence model is trained by training data, and the trained artificial intelligence model is tested using test data, and finally a priority scoring model is obtained whose input is the number of historical failures, usage time, service life, risk factor and equipment value; and whose output is the priority score of the corresponding production equipment; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

4. A centralized control system for production equipment in a thermal power plant according to claim 1, characterized in that: The abnormal coefficient score corresponding to each production equipment is generated according to the monitoring data, including: Extract monitoring parameters and corresponding monitoring values ​​from a number of monitoring data; fit the monitoring values ​​corresponding to the same monitoring parameter into a monitoring value curve JZ(t) according to the time when the corresponding monitoring data was acquired; By formula The abnormal coefficient score YCP of the corresponding monitoring data is calculated; wherein SZ is the upper limit of the abnormal threshold corresponding to the monitoring parameter in the monitoring data; XZ is the lower limit of the abnormal threshold corresponding to the monitoring parameter in the monitoring data; γ1 and γ2 are adjustment coefficients; t1 is the start time within the set time, and t2 is the end time within the set time; α1 and α2 are proportional coefficients.

5. A centralized control system for production equipment in a thermal power plant according to claim 1, characterized in that: The real-time transmission priority of each monitoring data is generated according to the priority level score and the abnormal coefficient score, including: Obtain the priority score YDP of each production equipment and the abnormal coefficient score YCP corresponding to each monitoring data; The comprehensive evaluation coefficient ZP of each monitoring data is calculated by the formula ZP = β1 × YDP + β2 × YCP; where β1 and β2 are weight coefficients; The comprehensive evaluation coefficients are arranged in order from large to small, and the real-time transmission priority of each monitoring data is generated according to the arrangement order.

6. A centralized control system for production equipment in a thermal power plant according to claim 1, characterized in that: The real-time monitoring data is transmitted to the data supervision module according to the real-time transmission priority, including: Obtain the real-time transmission priority of each current monitoring data, and transmit the corresponding monitoring data to the supervision module in order of real-time transmission priority from high to low.

7. A centralized control system for production equipment in a thermal power plant according to claim 1, characterized in that: The data monitoring module is used to obtain various monitoring data and generate monitoring status and parameter abnormality alarms for various monitoring data according to the monitoring data; The alarm module is used to obtain abnormal alarms of monitoring parameters and issue an alarm.

8. A method for centralized control of production equipment in a thermal power plant, based on the centralized control system for production equipment in a thermal power plant according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Obtain the historical usage data and basic equipment data of each production equipment; Step 2: Extract the number of historical failures and usage time from the historical usage data, as well as the service life, risk factor score and equipment value from the basic equipment data; Step 3: Input the number of historical failures, usage time, service life, risk factor and equipment value into the priority rating model to obtain the priority rating corresponding to each device; Step 4: Obtain a number of monitoring data of each production equipment within a set time, and generate an abnormal coefficient score corresponding to each production equipment based on the monitoring data; Step 5: Generate the real-time transmission priority of each monitoring data according to the priority score and the abnormal coefficient score; Step 6: Transmit each real-time monitoring data according to the real-time transmission priority; Step 7: Generate monitoring status and parameter abnormality alarms for each monitoring data based on the monitoring data; Step 8: Issue an alarm based on parameter abnormality alarm.

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