Electric power unit operation state optimization method and device, medium, equipment and product

Abstract

The invention relates to a power unit operation state optimization method and device, a medium, equipment and a product. The method comprises the following steps: acquiring operation information of a current power unit; preprocessing the operation information; determining a current performance parameter according to the preprocessed operation information; if the performance parameter exceeds the corresponding performance reference range, analyzing the operation state of the power unit through a performance analysis model to obtain a performance analysis result, the performance analysis result being used for indicating the reason for the performance parameter abnormality; and determining a target optimization strategy according to the performance analysis result. Therefore, the operation state of the unit can be monitored in real time, the target optimization strategy adaptive to the actual operation state of the unit can be provided, a clear working reference is provided for related personnel, the adjustment capability of the related personnel on the unit is optimized, and the operation efficiency of the unit and the economic performance of the unit are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of measurement and control for thermal power generation, and in particular, to a method, device, medium, equipment, and product for optimizing the operating state of a power unit. Background Art

[0002] With the deepening of the power system reform and the intensification of market competition, as well as the rising cost of raw materials, power enterprises are urgently in need of optimizing the production process to make full use of the unit performance to reduce costs and improve market competitiveness. The traditional operation performance evaluation system lacks real-time performance and refined management, resulting in the inability to effectively improve the operation level of operators and the economic performance of the unit. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a method, device, medium, equipment, and product for optimizing the operating state of a power unit to effectively improve the operation level of operators and the economic performance of the unit.

[0004] To achieve the above purpose, the first aspect of the present disclosure provides a method for optimizing the operating state of a power unit, including:

[0005] Obtaining the operating information of the current power unit;

[0006] Preprocessing the operating information;

[0007] Determining the current performance parameters according to the preprocessed operating information, where the performance parameters include at least one of a power generation efficiency parameter, a load parameter, an environmental protection parameter, an energy consumption and resource utilization parameter, a heat balance parameter, and a unit safety performance parameter;

[0008] If the performance parameters exceed the corresponding performance reference ranges, analyzing the operating state of the power unit through a performance analysis model to obtain a performance analysis result, where the performance analysis result is used to indicate the reason for the abnormal performance parameters;

[0009] Determining a target optimization strategy according to the performance analysis result.

[0010] Optionally, the preprocessing of the operating information includes:

[0011] Filling in the missing values in the operating information and / or correcting the abnormal values in the operating information.

[0012] Optionally, the performance parameters include a power generation efficiency parameter; the performance analysis model includes a power generation efficiency deviation analysis sub-model; and the analyzing the operating state of the power unit through the performance analysis model to obtain a performance analysis result includes:

[0013] Input the fuel quality, equipment condition, operation records, and environmental conditions into the power generation efficiency deviation analysis sub-model to obtain the reasons for the power generation efficiency deviation;

[0014] Based on the performance analysis results, determine the target optimization strategy, including:

[0015] According to the reasons for the power generation efficiency deviation, determine the first target optimization strategy for improving the power generation efficiency, where the power generation efficiency parameters include at least one of the net power generation efficiency, thermal efficiency, and cycle efficiency.

[0016] Optionally, the performance parameters include load parameters; the performance analysis model includes a load deviation analysis sub-model; analyzing the operating state of the power unit through the performance analysis model to obtain the performance analysis results, including:

[0017] Input the historical load data, equipment operation records, and maintenance logs into the load deviation analysis sub-model to obtain the reasons for the load deviation;

[0018] Based on the performance analysis results, determine the target optimization strategy, including:

[0019] According to the reasons for the load deviation, determine the second target optimization strategy for achieving the matching of the load demand and the power generation capacity, where the load parameters include at least one of the base load rate and the peak load rate.

[0020] Optionally, the performance parameters include environmental protection parameters; the performance analysis model includes an environmental protection deviation analysis sub-model; analyzing the operating state of the power unit through the performance analysis model to obtain the performance analysis results, including:

[0021] Input the equipment operation records, maintenance logs, and operation records into the environmental protection deviation analysis sub-model to obtain the reasons for the environmental protection deviation;

[0022] Based on the performance analysis results, determine the target optimization strategy, including:

[0023] According to the reasons for the environmental protection deviation, determine the third target optimization strategy for improving the environmental protection effect, where the environmental protection parameters include at least one of the pollutant emissions, waste treatment volume, and waste disposal volume.

[0024] Optionally, the performance parameters include energy consumption and resource utilization rate parameters; the performance analysis model includes an energy consumption and resource utilization rate deviation analysis sub-model; analyzing the operating state of the power unit through the performance analysis model to obtain the performance analysis results, including:

[0025] Input data related to energy consumption and resource utilization rate, performance status data of the first target device, loss records, operation records, maintenance logs, and environmental conditions into the energy consumption and resource utilization deviation analysis sub-model to obtain the reasons for energy consumption and resource utilization deviation;

[0026] Determine the target optimization strategy according to the performance analysis result, including:

[0027] Determine the fourth target optimization strategy for improving resource utilization rate according to the reasons for energy consumption and resource utilization deviation, where the energy consumption and resource utilization parameters include at least one of fuel consumption rate and resource utilization rate.

[0028] Optionally, the performance parameters include heat balance parameters; the performance analysis model includes a heat balance deviation analysis sub-model; analyzing the operation status of the power unit through the performance analysis model to obtain the performance analysis result, including:

[0029] Input heat balance parameters, operation information of the second target device, maintenance logs, operation records, and environmental conditions into the heat balance deviation analysis sub-model to obtain the reasons for heat balance deviation;

[0030] Determine the target optimization strategy according to the performance analysis result, including:

[0031] Determine the fifth target optimization strategy for reducing heat loss according to the reasons for heat balance deviation, where the heat balance parameters include input energy, output energy, and loss energy.

[0032] Optionally, the performance parameters include unit safety performance parameters; the performance analysis model includes a unit safety deviation analysis sub-model; analyzing the operation status of the power unit through the performance analysis model to obtain the performance analysis result, including:

[0033] Input device operation records, maintenance logs, fault records, operation records, and personnel management training status records into the unit safety deviation analysis sub-model to obtain the reasons for unit accidents;

[0034] Determine the target optimization strategy according to the performance analysis result, including:

[0035] Determine the sixth target optimization strategy for improving the operation safety of the unit according to the reasons for unit accidents, where the unit safety performance parameters include failure rate and single repair duration.

[0036] Optionally, the method further includes:

[0037] Generate a performance report and a trend chart according to the performance analysis result.

[0038] Optionally, the method further includes:

[0039] If the current performance parameter exceeds the corresponding performance reference range, a corresponding performance exception prompt message is generated.

[0040] Optionally, the operation information includes at least one of the device vibration frequency, device temperature, device pressure, device current, device voltage, and device sound information; the method further includes:

[0041] If the operation information exceeds the corresponding operation reference range, a corresponding operation exception prompt message is generated.

[0042] Optionally, the method further includes:

[0043] In response to receiving an operation instruction issued by a user, perform identity verification on the user;

[0044] If the verification is passed, obtain the operation information of the power unit.

[0045] A second aspect of the present disclosure provides a power unit operation status optimization device, including:

[0046] An acquisition module, configured to acquire the operation information of the current power unit;

[0047] A preprocessing module, configured to preprocess the operation information;

[0048] A first determination module, configured to determine the current performance parameter according to the preprocessed operation information, where the performance parameter includes at least one of a power generation efficiency parameter, a load parameter, an environmental protection parameter, an energy consumption and resource utilization parameter, a heat balance parameter, and a unit safety performance parameter;

[0049] An analysis module, configured to, if the performance parameter exceeds the corresponding performance reference range, analyze the operation status of the power unit through a performance analysis model to obtain a performance analysis result, where the performance analysis result is used to indicate the reason for the abnormal performance parameter;

[0050] A second determination module, configured to determine a target optimization strategy according to the performance analysis result.

[0051] A third aspect of the present disclosure provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method provided in the first aspect of the present disclosure are implemented.

[0052] A fourth aspect of the present disclosure provides an electronic device, including:

[0053] A memory, on which a computer program is stored;

[0054] A processor for executing a computer program in a memory to implement the steps of the method provided in the first aspect of the present disclosure.

[0055] The fifth aspect of the present disclosure provides a computer program product including a computer program which, when executed by a processor, implements the steps provided in the first aspect of the disclosure.

[0056] In the above technical solution, the operating information of the current power unit is acquired; the operating information is preprocessed; according to the preprocessed operating information, the current performance parameters are determined, where the performance parameters include at least one of a power generation efficiency parameter, a load parameter, an environmental protection parameter, an energy consumption and resource utilization parameter, a heat balance parameter, and a unit safety performance parameter; if the performance parameter exceeds the corresponding performance reference range, the operating state of the power unit is analyzed through a performance analysis model to obtain a performance analysis result, and the performance analysis result is used to indicate the reason for the abnormal performance parameter; according to the performance analysis result, a target optimization strategy is determined. In this way, the operating state of the unit can be monitored in real time, a target optimization strategy adapted to the actual operating state of the unit can be provided, so as to provide a clear work reference for relevant personnel, and further optimize the adjustment ability of relevant personnel for the unit, improve the operating efficiency of the unit and the economic performance of the unit.

[0057] Other features and advantages of the present disclosure will be described in detail in the subsequent specific implementation part. Description of the Drawings

[0058] The drawings are used to provide a further understanding of the present disclosure, and constitute a part of the specification, and are used to explain the present disclosure together with the following specific implementation manners, but do not constitute a limitation to the present disclosure. In the drawings:

[0059] Figure 1 is a flowchart of a method for optimizing the operating state of a power unit provided by an exemplary embodiment of the present disclosure.

[0060] Figure 2 is a block diagram of a device for optimizing the operating state of a power unit provided by an exemplary embodiment of the present disclosure.

[0061] Figure 3 is a block diagram of an electronic device provided by an exemplary embodiment of the present disclosure. Specific Embodiments

[0062] The following details the specific implementation manners of the present disclosure with reference to the drawings. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0063] It should be noted that all actions of obtaining signals, information, or data in this disclosure are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where the location is located and with the authorization given by the owner of the corresponding device.

[0064] Figure 1 It is a flowchart of a method for optimizing the operating state of a power unit provided by an exemplary embodiment of the present disclosure. As Figure 1 shown, the method may include S101 to S105.

[0065] S101, obtain the operating information of the current power unit.

[0066] Exemplarily, the operating information of the power unit may include the output power and voltage of the power unit, the flow rate, pressure, and temperature of steam, water, and gas, the flow rate and temperature of lubricating oil and cooling water, the equipment vibration frequency, equipment temperature, equipment pressure, equipment current, equipment voltage, equipment sound information, etc. of each device in the power unit, which is not limited here. The status of each device can be monitored in real time through pre-installed cameras, intelligent sensors, and Internet of Things (IoT) technologies.

[0067] Exemplarily, the above operating information can be obtained through control systems such as DCS (Distributed Control System), real-time databases, relational databases, Erlang soft real-time databases, XML files, web services, and manually input handwritten data. In this disclosure, cross and complementary methods can be applied to combine manually entered data and statistically obtained data and other means to basically achieve complete process collection and ensure data integrity.

[0068] S102, preprocess the operating information.

[0069] In one embodiment, the operating information can be preprocessed in the following ways:

[0070] Fill in the missing values in the operating information, and / or correct the abnormal values in the operating information.

[0071] Exemplarily, the obtained operating information can be input into three models: a missing value processing model, an outlier detection model, and a data integration and merging model to achieve preprocessing of the operating information. In this way, the data quality can be improved, and further the accuracy of the performance parameters determined subsequently and the data processing efficiency can be enhanced.

[0072] S103, determine the current performance parameters according to the preprocessed operating information.

[0073] Among them, the performance parameters may include at least one of a power generation efficiency parameter, a load parameter, an environmental protection parameter, an energy consumption and resource utilization parameter, a heat balance parameter, and a unit safety performance parameter.

[0074] S104, if the performance parameter exceeds the corresponding performance reference range, analyze the operating state of the power unit through the performance analysis model to obtain the performance analysis result.

[0075] Among them, the performance analysis result is used to indicate the reason for the abnormality of the performance parameter.

[0076] Exemplarily, the performance reference range is the standard range where the performance parameter is located when the power unit operates without abnormality. The performance reference range corresponding to each performance parameter can be preset or generated in real time based on the operating information of the current power unit. If the performance parameter exceeds the corresponding performance reference range, it can be determined that the operation of the power unit may be abnormal. At this time, the preprocessed operating information can be input into the pre-trained performance analysis model to obtain the corresponding performance analysis result. Based on this performance analysis result, the reason for the abnormality of the performance parameter can be clarified, providing a reliable basis for determining the subsequent target optimization strategy.

[0077] S105, determine the target optimization strategy according to the performance analysis result.

[0078] Exemplarily, based on the corresponding relationship between the pre-determined performance analysis result and the optimization strategy, the target optimization strategy corresponding to the current performance analysis result can be determined simply and quickly.

[0079] In the above technical solution, obtain the operating information of the current power unit; preprocess the operating information; determine the current performance parameter according to the preprocessed operating information, where the performance parameter includes at least one of a power generation efficiency parameter, a load parameter, an environmental protection parameter, an energy consumption and resource utilization parameter, a heat balance parameter, and a unit safety performance parameter; if the performance parameter exceeds the corresponding performance reference range, analyze the operating state of the power unit through the performance analysis model to obtain the performance analysis result, and the performance analysis result is used to indicate the reason for the abnormality of the performance parameter; determine the target optimization strategy according to the performance analysis result. In this way, the operating state of the unit can be monitored in real time, and a target optimization strategy adapted to the actual operating state of the unit can be provided, providing a clear work reference for relevant personnel, thereby optimizing the adjustment ability of relevant personnel to the unit and improving the operating efficiency and economic performance of the unit.

[0080] In an optional embodiment, the performance parameter includes a power generation efficiency parameter; the performance analysis model includes a power generation efficiency deviation analysis sub-model. In S104, analyzing the operating state of the power unit through the performance analysis model to obtain the performance analysis result includes:

[0081] Input the fuel quality, equipment condition, operation record, and environmental conditions into the power generation efficiency deviation analysis sub-model to obtain the reason for the power generation efficiency deviation.

[0082] Exemplarily, the power generation efficiency parameter includes at least one of net power generation efficiency, thermal efficiency, and cycle efficiency.

[0083] For example, the net power generation efficiency η net can be determined by formula (1):

[0084]

[0085] where Q net is the net power generation (unit: kWh), and Q in is the fuel input energy (unit: kWh).

[0086] For example, the thermal efficiency η th can be determined by formula (2):

[0087]

[0088] where W gross is the gross power generation (unit: kWh).

[0089] The cycle efficiency may refer to the efficiency of a steam power cycle, such as a Rankine cycle. The cycle efficiency can represent the efficiency at which the input thermal energy is converted into mechanical energy or electrical energy in a complete heat cycle. Specifically, the cycle efficiency can evaluate the utilization efficiency of the heat generated by the boiler in the steam turbine (unit), and can reflect the performance of the steam cycle system. For example, the cycle efficiency η cycle can be determined by formula (3):

[0090]

[0091] where W cycle is the cycle power (unit: kWh), and Q boiler is the boiler input energy (unit: kWh).

[0092] The power generation efficiency deviation analysis sub-model can be a model trained by machine learning. Inputting fuel quality, equipment condition, operation records, and environmental conditions into the power generation efficiency deviation analysis sub-model, the reasons for the power generation efficiency deviation output by the power generation efficiency deviation analysis sub-model can be obtained. The reasons for the power generation efficiency deviation may be that the fuel calorific value may be lower than expected, the equipment maintenance is poor, the operation parameters deviate from the optimal working conditions, or the environmental conditions are unfavorable, etc. The specific deviation reasons can be determined through the power generation efficiency deviation analysis sub-model, and further precision can be made on this deviation reason by combining on-site inspections and experimental tests, etc.

[0093] Correspondingly, in S105, according to the performance analysis results, the target optimization strategy is determined, including:

[0094] Determine a first target optimization strategy for improving power generation efficiency according to the reasons for the deviation of power generation efficiency.

[0095] Exemplarily, the first target optimization strategy may be any one or more of improving fuel quality, strengthening equipment maintenance, optimizing operation parameters, and improving environmental control. The specific values of the optimized operation parameters can also be output by the power generation efficiency deviation analysis sub-model, or can also be determined and output by other analysis models, which is not limited here. For example, the first target optimization strategy corresponding to the current reason for the deviation of power generation efficiency can be simply and quickly determined through the pre-determined correspondence between the reason for the deviation of power generation efficiency and the first target optimization strategy, so as to improve the power generation efficiency of the unit.

[0096] In an optional embodiment, the performance parameters include load parameters, and the performance analysis model includes a load deviation analysis sub-model. In S104, the operation state of the power unit is analyzed through the performance analysis model to obtain a performance analysis result, including:

[0097] Input historical load data, equipment operation records, and maintenance logs into the load deviation analysis sub-model to obtain the reasons for load deviation.

[0098] Exemplarily, the load parameters include at least one of a base load rate and a peak load rate.

[0099] For example, the base load rate LF can be determined by formula (4) base :

[0100]

[0101] where P base is the base load power (unit: kW), and P rated is the rated load power (unit: kWh).

[0102] For example, the peak load rate LF can be determined by formula (5) peak :

[0103]

[0104] where P peak is the peak load power (unit: kW).

[0105] Among them, the load rate is an important indicator for evaluating the operation efficiency of the power system and equipment. By calculating and analyzing the load rate, the utilization rate, operation strategy, and economy of the equipment can be optimized to ensure the reliability and stability of the system operation.

[0106] Historical load data, equipment operation records, and maintenance logs can be input into the load deviation analysis sub-model, and the specific reasons for load deviation can be determined through the load deviation analysis sub-model. The load deviation analysis sub-model can be a model trained in a machine learning manner. The reasons for load deviation may be equipment failures, improper maintenance, or external power demand fluctuations, etc.

[0107] Correspondingly, in S105, according to the performance analysis results, determine the target optimization strategy, including:

[0108] According to the reasons for load deviation, determine the second target optimization strategy for achieving the matching of load demand and power generation capacity.

[0109] Exemplarily, the second target optimization strategy may include any one or more of preferentially enabling high-efficiency units, adjusting the equipment maintenance plan to avoid peak load periods, and using the load prediction model for pre-scheduling. For example, through the correspondence between the pre-determined reasons for load deviation and the second target optimization strategy, the second target optimization strategy corresponding to the current reasons for load deviation can be determined simply and quickly to ensure the matching of load demand and power generation capacity and improve the overall operation efficiency of the units.

[0110] In an optional embodiment, the performance parameters include environmental protection parameters; the performance analysis model includes an environmental protection deviation analysis sub-model. In S104, through the performance analysis model, analyze the operating state of the power unit to obtain the performance analysis results, including:

[0111] Input the equipment operation records, maintenance logs, and operation records into the environmental protection deviation analysis sub-model to obtain the reasons for environmental protection deviation.

[0112] Among them, the environmental protection parameters include at least one of pollutant emissions, waste treatment volume, and waste disposal volume.

[0113] Among them, pollutant emissions can include sulfur dioxide emissions, nitrogen oxide emissions, particulate matter emissions, and carbon monoxide emissions. Emissions are usually expressed as the mass of pollutants emitted per unit time (such as per hour or per day). These data are different from the pollutant concentration in the air and refer to the mass concentration of pollutants in the air (such as μg / m 3 or ppm). These emission data are important bases for environmental monitoring and emission control, and can be used to evaluate and manage the environmental impact of power plants to ensure that their emissions comply with environmental protection regulations and standards.

[0114] Waste can refer to solid waste. The waste treatment volume can refer to the amount of solid waste treated in the power plant, and the waste treatment volume E can be determined by formula (6) waste_treated :

[0115] Ewaste_treated = V waste *treatment_efficiency (6)

[0116] wherein, V waste is the amount of waste generated (unit: kg), and treatment_efficiency is the treatment efficiency (unit: %).

[0117] The amount of waste disposal can refer to the amount of waste finally discharged or utilized after treatment. The amount of waste treatment E can be determined by formula (7) waste_disposed :

[0118] E waste_disposed = V waste *disposal_rate (7)

[0119] wherein, disposal_rate is the disposal rate (unit: %).

[0120] The equipment operation records, maintenance logs and operation records can be input into the environmental protection deviation analysis sub-model, and the specific reasons for environmental protection deviations can be determined through the environmental protection deviation analysis sub-model. The environmental protection deviation analysis sub-model can be a model trained by machine learning. The reasons for environmental protection deviations may be equipment failures, untimely maintenance or improper operations, etc.

[0121] Correspondingly, in S105, according to the performance analysis results, the target optimization strategies are determined, including:

[0122] According to the reasons for environmental protection deviations, the third target optimization strategy for improving environmental protection effects is determined.

[0123] Exemplarily, the third target optimization strategy may include repairing or replacing control equipment, improving the combustion process, strengthening operator training and optimizing the operation process, etc. For example, through the correspondence relationship between the pre-determined reasons for environmental protection deviations and the third target optimization strategy, the third target optimization strategy corresponding to the current reasons for environmental protection deviations can be determined simply and quickly to reduce pollutant emissions, ensure compliance with standard requirements, and improve environmental protection effects.

[0124] In an optional embodiment, the performance parameters include energy consumption and resource utilization rate parameters; the performance analysis model includes an energy consumption and resource utilization rate deviation analysis sub-model. In S104, the operation status of the power unit is analyzed through the performance analysis model, and the performance analysis results include:

[0125] The data related to energy consumption and resource utilization rate, the performance status data of the first target equipment, loss records, operation records, maintenance logs and environmental conditions are input into the energy consumption and resource utilization rate deviation analysis sub-model to obtain the reasons for energy consumption and resource utilization rate deviations.

[0126] Among them, the first target device can be a device in the unit that has a greater impact on the changes in energy consumption and resource utilization rate, or it can be understood as its key device. The energy consumption and resource utilization rate parameters can include cooling water consumption, recirculated water usage rate, fuel consumption rate, resource utilization rate, etc.

[0127] The cooling water consumption can be used to evaluate the amount of water used by the power plant during the cooling process and can be calculated by measuring the water flow rate in and out of the cooling system. For example, the cooling water consumption W can be determined by formula (8) cooling :

[0128] W cooling = m * Δt (8)

[0129] m is the cooling water flow rate (unit: m 3 / s), and Δt is the operating time (unit: s).

[0130] The recirculated water usage rate can refer to the ratio of the amount of water reused in the cooling system to the total water consumption and can be used to measure the recycling of water resources. For example, the recirculated water usage rate R of the cooling water consumption can be determined by formula (9) recycle :

[0131]

[0132] Among them, W recycle is the recirculated water volume (unit: m 3 ), and W total is the total water consumption (unit: m 3 ).

[0133] The fuel consumption rate can represent the amount of fuel consumed by the power plant for each unit of generated electricity. For example, the fuel consumption rate R can be determined by formula (10) fuel :

[0134]

[0135] Among them, W gross is the gross power generation (unit: kWh), and F is the fuel consumption (unit: kg or g).

[0136] The resource utilization rate can represent the ratio of the amount of resources actually utilized by the power plant to the amount of available resources and can be used to measure the effective utilization of resources. For example, the resource utilization rate U can be determined by formula (11) resource :

[0137]

[0138] Among them, Resource Used is the actual amount of resources used, and Resource Available is the available amount of resources.

[0139] Data related to energy consumption and resource utilization rate, performance status data of the first target device, loss records, operation records, maintenance logs, and environmental conditions can be input into the energy consumption and resource utilization rate deviation analysis sub-model. Through the energy consumption and resource utilization rate deviation analysis sub-model, the reasons for the deviation of energy consumption and resource utilization rate can be determined. In this way, the energy consumption and resource utilization rate deviation analysis sub-model can collect detailed energy consumption and resource usage data, compare with best practices, check the performance and status of key devices, pay attention to equipment aging, loss, and failure records, analyze whether operators follow best operation practices, review maintenance and service logs, and evaluate the impact of environmental factors on equipment efficiency, so as to locate the specific reasons for the decline in resource utilization rate. The energy consumption and resource utilization rate deviation analysis sub-model can be a model trained by machine learning. The reasons for the deviation of energy consumption and resource utilization rate can be low energy efficiency, equipment aging, etc.

[0140] Correspondingly, in S105, according to the performance analysis results, determine the target optimization strategy, including:

[0141] According to the reasons for the deviation of energy consumption and resource utilization rate, determine the fourth target optimization strategy for improving resource utilization rate.

[0142] Exemplarily, the fourth target optimization strategy may include energy-saving and resource management measures such as optimizing equipment operation, improving maintenance strategies, and enhancing operator skills. In this way, energy efficiency and resource utilization rate can be improved. For example, through the correspondence relationship between the pre-determined reasons for the deviation of energy consumption and resource utilization rate and the fourth target optimization strategy, the fourth target optimization strategy corresponding to the current reasons for the deviation of energy consumption and resource utilization rate can be determined simply and quickly to improve energy efficiency and resource utilization rate.

[0143] In an optional embodiment, the performance parameter includes a heat balance parameter; the performance analysis model includes a heat balance deviation analysis sub-model; in S104, analyze the operation status of the power unit through the performance analysis model to obtain the performance analysis results, including:

[0144] Input the heat balance parameter, operation information of the second target device, maintenance log, operation record, and environmental conditions into the heat balance deviation analysis sub-model to obtain the reasons for the heat balance deviation.

[0145] Among them, the second target device can be a device in the unit that has a greater impact on the change of the thermal balance parameters, or it can be understood as its key device. The thermal balance parameters include input energy, output energy, and loss energy. The input energy can include fuel energy, air energy, feed water energy, etc.; the output energy can include power generation energy, steam energy, flue gas energy, and other output energies. Among them, other outputs can include the energy of the discharged waste heat, the energy of the discharged cooling water, etc. The loss energy can include flue gas loss energy, heat dissipation loss energy, incomplete combustion loss energy, and other loss energies. Among them, other loss energies can include the heat carried away by wastewater and waste.

[0146] Instruments such as flow meters, thermocouples, and gas analyzers can be used to obtain data such as fuel consumption, steam output, flue gas composition, and equipment temperature. Based on these data, a thermal balance table as shown in Table 1 can be constructed to facilitate the effective monitoring and optimization of the performance of the power generation system.

[0147] Table 1

[0148]

[0149]

[0150] Among them, heat loss can refer to the heat dissipated without being utilized during the power generation process, and can include flue gas loss, heat dissipation loss, incomplete combustion loss, and cooling water loss.

[0151] The heat carried away by the flue gas can be determined by formula (12), that is, the flue gas loss Q 排烟损失 :

[0152] Q 排烟损失 = m 烟气 * c 烟气 *(T 烟气 - T 环境 ) (12)

[0153] Among them, m 烟气 is the flue gas mass flow rate, c 烟气 is the specific heat capacity of the flue gas, T 烟气 is the flue gas temperature, and T 环境 is the ambient temperature.

[0154] The heat dissipated from the surface of the equipment through convection and radiation can be determined by formula (13), that is, the heat dissipation loss Q 散热损失 :

[0155] Q 散热损失 = ∑(Q 对流损失 + Q 辐射损失 ) (13)

[0156] Among them, Q 对流损失 is the convection loss, and Q辐射损失 For radiative losses, the specific values of convective losses and radiative losses can be determined based on parameters such as the surface temperature of the device, the ambient temperature, the surface volume, and the heat transfer coefficient.

[0157] The heat loss of unburned fuel can be determined by formula (14), i.e., the unburned loss Q 未完全燃烧损失 :

[0158] Q 未完全燃烧损失 = m 未燃烧燃料 * calorific value (14)

[0159] where m 未燃烧燃料 is the mass of unburned fuel, and the calorific value is the calorific value of this fuel.

[0160] The heat carried away by the cooling water can be determined by formula (15), i.e., the cooling water loss Q 冷却水损失 :

[0161] Q 冷却水损失 = m 冷却水 * c 冷却水 * (T 冷却水出 - T 冷却水进 ) (15)

[0162] where m 冷却水 is the mass flow rate of the cooling water, c 冷却水 is the specific heat capacity of the cooling water, T 冷却水出 is the discharge temperature of the cooling water, and T 冷却水进 is the inlet temperature of the cooling water.

[0163] The thermal balance parameters, the operation information of the second target device, the maintenance log, the operation record, and the environmental conditions can be input into the thermal balance deviation analysis sub-model. Through the thermal balance deviation analysis sub-model, the specific reasons for the thermal balance deviation can be determined. In this way, the thermal balance deviation analysis sub-model can calculate and compare the energy input and output, check the flue gas emissions and heat dissipation losses, evaluate the incomplete combustion situation, evaluate the efficiency and operation status of key equipment, check the operation record and maintenance log, and evaluate the impact of environmental conditions on the equipment efficiency to obtain the reasons for the thermal balance deviation. The thermal balance deviation analysis sub-model can be a model trained in a machine learning manner. The reasons for the thermal balance deviation may be thermal energy losses, low equipment efficiency, etc.

[0164] Correspondingly, in S105, according to the performance analysis results, the target optimization strategy is determined, including:

[0165] According to the reasons for the thermal balance deviation, the fifth target optimization strategy for reducing heat loss is determined.

[0166] Exemplarily, the fifth target optimization strategy may include any one or more of improving combustion efficiency, enhancing equipment maintenance, optimizing operation processes, and controlling environmental factors. For example, based on the correspondence between the pre-determined causes of heat balance deviation and the fifth target optimization strategy, the fifth target optimization strategy corresponding to the current cause of heat balance deviation can be determined simply and quickly to reduce heat loss and thereby improve energy utilization efficiency.

[0167] In an optional embodiment, the performance parameter includes the unit safety performance parameter; the performance analysis model includes a unit safety deviation analysis sub-model. In S104, the operation state of the power unit is analyzed through the performance analysis model to obtain a performance analysis result, including:

[0168] Inputting the equipment operation records, maintenance logs, fault records, operation records, and personnel management training status records into the unit safety deviation analysis sub-model to obtain the causes of unit accidents.

[0169] Among them, the unit safety performance parameters include the failure rate and the average repair time per time.

[0170] The failure rate can represent the frequency of equipment failures per unit time. For example, the failure rate λ can be determined by formula (16):

[0171]

[0172] where N faiiures is the number of failures within a preset time, and T operation is the total operation time of the equipment.

[0173] The average repair time per time can represent the average time required to repair the equipment after each failure. For example, the average repair time per time MTTR can be determined by formula (17):

[0174]

[0175] where T repair is the total repair time.

[0176] Device operation records, maintenance logs, fault records, operation records, and personnel management training status records can be input into the unit safety deviation analysis sub-model. Through the unit safety deviation analysis sub-model, specific causes of unit accidents can be determined. The causes of unit accidents may be equipment failures, untimely maintenance, improper operations, etc. In this way, the unit safety deviation analysis sub-model can collect and verify data, calculate and compare actual and expected data, analyze the types and causes of safety accidents, evaluate equipment performance and fault records, check operation and maintenance records, evaluate the impact of environmental factors, and review personnel training and management measures to locate the specific causes leading to safety problems or a decline in unit reliability. The unit safety deviation analysis sub-model can be a model trained using machine learning. In one embodiment, the content output by the unit safety deviation analysis sub-model may further include the type of accident, enabling relevant personnel to more accurately understand the operating status of the unit.

[0177] Correspondingly, in S105, according to the performance analysis results, determine the target optimization strategy, including:

[0178] According to the causes of unit accidents, determine the sixth target optimization strategy for improving the operating safety of the unit. Exemplarily, the sixth target optimization strategy may include measures such as strengthening equipment maintenance, optimizing operation processes, improving the working environment, strengthening personnel training, and improving management systems. In this way, the safety and reliability of the unit can be ensured. For example, through the corresponding relationship between the pre-determined causes of unit accidents and the sixth target optimization strategy, the sixth target optimization strategy corresponding to the current causes of unit accidents can be determined simply and quickly to improve the safety and reliability of the unit.

[0179] The deviation cause analysis implemented through the performance analysis model can achieve continuous improvement and performance optimization, helping relevant personnel identify problems, improve operations, increase efficiency, and ensure operational compliance. Based on the performance analysis model, various factors can be comprehensively considered, including equipment status, operation processes, maintenance practices, environmental impacts, etc., to find and solve the root causes of performance deviations.

[0180] In an alternative embodiment, the power unit operating status optimization method provided by the present disclosure may further include: generating a performance report and a trend chart according to the performance analysis results.

[0181] In this way, relevant personnel can more intuitively understand the relevant information of the unit performance.

[0182] In an alternative embodiment, the power unit operating status optimization method provided by the present disclosure may further include: if the current performance parameter exceeds the corresponding performance reference range, generating a corresponding performance anomaly prompt message.

[0183] In this way, it is possible to timely notify relevant personnel for handling in the case of abnormal unit performance, so as to improve the safety of unit operation and reduce the possibility of accidents.

[0184] In an optional embodiment, the power unit operation status optimization method provided by the present disclosure may further include:

[0185] If the operation information exceeds the corresponding operation reference range, a corresponding operation abnormality prompt message is generated.

[0186] Among them, the operation information includes at least one of the equipment vibration frequency, equipment temperature, equipment pressure, equipment current, equipment voltage, and equipment sound information. The operation reference ranges corresponding to different operation information can be preset. The operation reference range is the standard range in which the operation information is located when the power unit operates without abnormality.

[0187] Exemplarily, a vibration sensor can be used to monitor the vibration frequency of rotating equipment (such as generators, pumps, fans, etc.). If the vibration frequency exceeds the corresponding frequency range, it can be determined that the equipment may have abnormal problems such as mechanical failures or imbalances. A temperature sensor can be used to monitor the temperature of the equipment, including the temperature of components such as generators, transformers, and bearings. If the equipment temperature exceeds the corresponding temperature range, it can be determined that the equipment may be abnormal. A pressure sensor can be used to monitor the pressure of boilers, steam pipelines, cooling systems, etc. If the equipment pressure exceeds the corresponding pressure range, it can be determined that the equipment may have abnormal problems such as leaks. The current and voltage of motors, generators, and electrical equipment can be monitored. If the equipment current exceeds the corresponding current range, and / or the equipment voltage exceeds the corresponding voltage range, it can be determined that the equipment may have abnormal problems such as current imbalance, overload, short circuit, and voltage fluctuation. The sound information of the unit equipment operation can be collected through a microphone. If the equipment sound information exceeds the corresponding sound information range, it can be determined that there may be abnormal problems such as mechanical problems or bearing failures. Among them, the equipment sound information can be information such as loudness. When the operation information exceeds the corresponding operation reference range, by generating the corresponding operation abnormality prompt message, relevant personnel can be timely notified for handling, so as to improve the safety of unit operation and reduce the possibility of accidents.

[0188] In an optional embodiment, the power unit operation status optimization method provided by the present disclosure may further include:

[0189] In response to receiving an operation instruction issued by a user, perform identity verification on the user;

[0190] If the verification is passed, obtain the operation information of the power unit.

[0191] Exemplarily, the user identity can be verified through an account and password, or through technologies such as fingerprint recognition and face recognition. In the case of successful verification, the operation information of the power unit can be obtained. In this way, it can be ensured that authorized personnel access sensitive data, thereby improving the security of unit control.

[0192] To implement the above method for optimizing the operating state of a power unit, the present disclosure also provides an operating performance evaluation system, which includes a data source layer, a data processing layer, a business application layer, and a display layer. In the data source layer, various data collection methods can be adopted, including sensor data collected automatically and operation data entered manually, ensuring wide coverage and diversity of data. The data processing layer is used to execute the steps of S101 and S102 in the above text. The business application layer can be used to execute the steps of S103 to S105 in the above text, as well as generate operation anomaly prompt information, performance anomaly prompt information, performance reports, and trend charts, so as to provide in-depth performance analysis and real-time operation guidance for relevant personnel. In the display layer, through a user-friendly interface design and interaction functions, efficient display of data and convenient operation of users can be achieved. The overall architecture of this system can ensure an efficient process from data collection to decision support, improving the real-time performance and accuracy of operating performance evaluation. A firewall and security devices, as well as communication devices for remote communication with terminal devices, can also be set in this system. This system can also have functions of data backup and disaster recovery, as well as functions of providing training for operators.

[0193] Based on the same inventive concept, the present disclosure also provides a device for optimizing the operating state of a power unit. Figure 2 It is a block diagram of a device 200 for optimizing the operating state of a power unit provided by an exemplary embodiment of the present disclosure.

[0194] Referring to Figure 2 , the device 200 for optimizing the operating state of a power unit may include:

[0195] An acquisition module 201, configured to acquire the operation information of the current power unit;

[0196] A preprocessing module 202, configured to preprocess the operation information;

[0197] A first determination module 203, configured to determine the current performance parameters according to the preprocessed operation information, where the performance parameters include at least one of a power generation efficiency parameter, a load parameter, an environmental protection parameter, an energy consumption and resource utilization parameter, a heat balance parameter, and a unit safety performance parameter;

[0198] An analysis module 204, configured to analyze the operating state of the power unit through a performance analysis model if the performance parameter exceeds the corresponding performance reference range, so as to obtain a performance analysis result, where the performance analysis result is used to indicate the reason for the abnormal performance parameter;

[0199] A second determination module 205, configured to determine a target optimization strategy according to the performance analysis result.

[0200] In the above technical solution, the operating information of the current power unit is obtained; the operating information is preprocessed; according to the preprocessed operating information, the current performance parameter is determined, where the performance parameter includes at least one of a power generation efficiency parameter, a load parameter, an environmental protection parameter, an energy consumption and resource utilization parameter, a heat balance parameter, and a unit safety performance parameter; if the performance parameter exceeds the corresponding performance reference range, the operating state of the power unit is analyzed through a performance analysis model to obtain a performance analysis result, and the performance analysis result is used to indicate the reason for the abnormal performance parameter; according to the performance analysis result, a target optimization strategy is determined. In this way, the operating state of the unit can be monitored in real time, and a target optimization strategy adapted to the actual operating state of the unit can be provided, so as to provide a clear work reference for relevant personnel, and further optimize the adjustment ability of relevant personnel to the unit, improve the operating efficiency of the unit and the economic performance of the unit.

[0201] Optionally, the preprocessing module 202 is configured to preprocess the operating information in the following manner:

[0202] Filling in the missing values in the operating information, and / or correcting the abnormal values in the operating information.

[0203] Optionally, the performance parameter includes a power generation efficiency parameter; the performance analysis model includes a power generation efficiency deviation analysis sub-model; the analysis module 204 includes:

[0204] A first analysis sub-module, configured to input fuel quality, equipment condition, operation records, and environmental conditions into the power generation efficiency deviation analysis sub-model to obtain the reason for the power generation efficiency deviation;

[0205] The second determination module 205 includes:

[0206] A first determination sub-module, configured to determine a first target optimization strategy for improving the power generation efficiency according to the reason for the power generation efficiency deviation, where the power generation efficiency parameter includes at least one of a net power generation efficiency, a thermal efficiency, and a cycle efficiency.

[0207] Optionally, the performance parameter includes a load parameter; the performance analysis model includes a load deviation analysis sub-model; the analysis module 204 includes:

[0208] The second analysis sub-module is used to input historical load data, equipment operation records, and maintenance logs into the load deviation analysis sub-model to obtain the reasons for load deviation.

[0209] The second determination module 205 includes:

[0210] The second determination sub-module is used to determine a second target optimization strategy for achieving the matching of load demand and power generation capacity according to the reasons for load deviation, where the load parameters include at least one of the base load rate and the peak load rate.

[0211] Optionally, the performance parameters include environmental protection parameters; the performance analysis model includes an environmental protection deviation analysis sub-model; the analysis module 204 includes:

[0212] The third analysis sub-module is used to input equipment operation records, maintenance logs, and operation records into the environmental protection deviation analysis sub-model to obtain the reasons for environmental protection deviation.

[0213] The second determination module 205 includes:

[0214] The third determination sub-module is used to determine a third target optimization strategy for improving environmental protection effects according to the reasons for environmental protection deviation, where the environmental protection parameters include at least one of pollutant emissions, waste treatment volume, and waste disposal volume.

[0215] Optionally, the performance parameters include energy consumption and resource utilization rate parameters; the performance analysis model includes an energy consumption and resource utilization rate deviation analysis sub-model; the analysis module 204 includes:

[0216] The fourth analysis sub-module is used to input energy consumption and resource utilization rate-related data, performance status data of the first target equipment, loss records, operation records, maintenance logs, and environmental conditions into the energy consumption and resource utilization rate deviation analysis sub-model to obtain the reasons for energy consumption and resource utilization rate deviation.

[0217] The second determination module 205 includes:

[0218] The fourth determination sub-module is used to determine a fourth target optimization strategy for improving resource utilization rate according to the reasons for energy consumption and resource utilization rate deviation, where the energy consumption and resource utilization rate parameters include at least one of the fuel consumption rate and the resource utilization rate.

[0219] Optionally, the performance parameters include thermal balance parameters; the performance analysis model includes a thermal balance deviation analysis sub-model; the analysis module 204 includes:

[0220] The fifth analysis sub-module is configured to input the thermal balance parameters, the operation information of the second target device, the maintenance log, the operation record, and the environmental conditions into the thermal balance deviation analysis sub-model to obtain the reasons for thermal balance deviation;

[0221] The second determination module 205 includes:

[0222] The fifth determination sub-module is configured to determine a fifth target optimization strategy for reducing heat loss according to the reasons for thermal balance deviation, wherein the thermal balance parameters include input energy, output energy, and loss energy.

[0223] Optionally, the performance parameters include unit safety performance parameters; the performance analysis model includes a unit safety deviation analysis sub-model; the analysis module 204 includes:

[0224] The sixth analysis sub-module is configured to input the device operation record, the maintenance log, the fault record, the operation record, and the personnel management training status record into the unit safety deviation analysis sub-model to obtain the reasons for unit accidents;

[0225] The second determination module 205 includes:

[0226] The sixth determination sub-module is configured to determine a sixth target optimization strategy for improving the operation safety of the unit according to the reasons for unit accidents, wherein the unit safety performance parameters include the failure rate and the single repair duration.

[0227] Optionally, the power unit operation status optimization device 200 may further include:

[0228] The first generation module is configured to generate a performance report and a trend chart according to the performance analysis result.

[0229] Optionally, the power unit operation status optimization device 200 may further include:

[0230] The second generation module is configured to generate a corresponding performance anomaly prompt message if the current performance parameter exceeds the corresponding performance reference range.

[0231] Optionally, the operation information includes at least one of the device vibration frequency, the device temperature, the device pressure, the device current, the device voltage, and the device sound information; the power unit operation status optimization device 200 may further include:

[0232] The third generation module is configured to generate a corresponding operation anomaly prompt message if the operation information exceeds the corresponding operation reference range.

[0233] Optionally, the power unit operation status optimization device 200 may include:

[0234] The verification module is used to verify the identity of the user in response to receiving the operation instruction issued by the user; if the verification is passed, the acquisition module 201 will execute the step of acquiring the operation information of the power unit.

[0235] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0236] Figure 3 It is a block diagram of an electronic device 1900 provided by an exemplary embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server. Referring to Figure 3 , the electronic device 1900 includes a processor 1922, the number of which may be one or more, and a memory 1932 for storing computer programs executable by the processor 1922. The computer programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processor 1922 may be configured to execute the computer program to perform the above-mentioned power unit operation status optimization method.

[0237] In addition, the electronic device 1900 may further include a power supply component 1926 and a communication component 1950. The power supply component 1926 may be configured to perform power management of the electronic device 1900, and the communication component 1950 may be configured to implement communication of the electronic device 1900, for example, wired or wireless communication. In addition, the electronic device 1900 may further include an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932.

[0238] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned power unit operation status optimization method are implemented. For example, the non-transitory computer-readable storage medium may be the above-mentioned memory 1932 including program instructions, and the above program instructions may be executed by the processor 1922 of the electronic device 1900 to complete the above-mentioned power unit operation status optimization method.

[0239] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for performing the above-mentioned power unit operation status optimization method when executed by the programmable device.

[0240] The preferred embodiments of the present disclosure have been described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0241] In addition, it should be noted that, in the various specific technical features described in the above specific embodiments, they can be combined in any suitable manner without conflict. To avoid unnecessary repetition, the present disclosure will not separately describe various possible combination manners.

[0242] Furthermore, any combination can be made between various different embodiments of the present disclosure, as long as it does not violate the idea of the present disclosure, and it should also be regarded as the content disclosed by the present disclosure.

Claims

1. A method for optimizing the operating state of a power unit, characterized in that: include: Obtain the current operation information of the power unit; Preprocessing the operation information; Determine the current performance parameters according to the preprocessed operation information, wherein the performance parameters include at least one of power generation efficiency parameters, load parameters, environmental protection parameters, energy consumption and resource utilization parameters, thermal balance parameters, and unit safety performance parameters; If the performance parameter exceeds the corresponding performance reference range, the operating state of the power unit is analyzed by a performance analysis model to obtain a performance analysis result, which is used to indicate the cause of the abnormal performance parameter; According to the performance analysis results, a target optimization strategy is determined.

2. The method according to claim 1, characterized in that: The preprocessing of the operation information includes: Perform data filling for missing values ​​in the operation information, and / or perform data correction for abnormal values ​​in the operation information.

3. The method according to claim 1, characterized in that The performance parameter includes a power generation efficiency parameter; the performance analysis model includes a power generation efficiency deviation analysis sub-model; the performance analysis result obtained by analyzing the operating state of the power unit through the performance analysis model includes: Inputting fuel quality, equipment status, operation records and environmental conditions into the power generation efficiency deviation analysis sub-model to obtain the cause of the power generation efficiency deviation; Determining a target optimization strategy according to the performance analysis result includes: According to the power generation efficiency deviation cause, a first target optimization strategy for improving power generation efficiency is determined, wherein the power generation efficiency parameter includes at least one of net power generation efficiency, thermal efficiency and cycle efficiency.

4. The method according to claim 1, characterized in that: The performance parameters include load parameters; the performance analysis model includes a load deviation analysis sub-model; the performance analysis results obtained by analyzing the operating state of the power unit through the performance analysis model include: Inputting historical load data, equipment operation records and maintenance logs into the load deviation analysis sub-model to obtain the cause of load deviation; Determining a target optimization strategy according to the performance analysis result includes: According to the load deviation cause, a second objective optimization strategy for achieving matching of load demand with power generation capacity is determined, wherein the load parameter includes at least one of a base load rate and a peak load rate.

5. The method according to claim 1, characterized in that The performance parameters include environmental parameters; The performance analysis model includes an environmental deviation analysis sub-model; The operating state of the power unit is analyzed by the performance analysis model to obtain the performance analysis results, including: Inputting equipment operation records, maintenance logs and operation records into the environmental deviation analysis sub-model to obtain the causes of environmental deviations; Determining a target optimization strategy according to the performance analysis result includes: According to the environmental deviation cause, a third target optimization strategy for improving environmental protection effect is determined, wherein the environmental protection parameter includes at least one of pollutant emission, waste treatment and waste disposal.

6. The method according to claim 1, characterized in that The performance parameters include energy consumption and resource utilization parameters; the performance analysis model includes energy consumption and resource utilization deviation analysis sub-models; The operating state of the power unit is analyzed by the performance analysis model to obtain the performance analysis results, including: Inputting energy consumption and resource utilization related data, performance status data of the first target device, loss records, operation records, maintenance logs and environmental conditions into the energy consumption and resource utilization deviation analysis sub-model to obtain the causes of energy consumption and resource utilization deviation; Determining a target optimization strategy according to the performance analysis result includes: According to the causes of the energy consumption and resource utilization deviations, a fourth objective optimization strategy for improving resource utilization is determined, wherein the energy consumption and resource utilization parameters include at least one of a fuel consumption rate and a resource utilization rate.

7. The method according to claim 1, characterized in that The performance parameters include heat balance parameters; the performance analysis model includes a heat balance deviation analysis sub-model; the performance analysis results obtained by analyzing the operating state of the power unit through the performance analysis model include: Inputting thermal balance parameters, operation information of the second target device, maintenance logs, operation records and environmental conditions into the thermal balance deviation analysis sub-model to obtain the cause of the thermal balance deviation; Determining a target optimization strategy according to the performance analysis result includes: According to the cause of the thermal balance deviation, a fifth target optimization strategy for reducing heat loss is determined, wherein the thermal balance parameters include input energy, output energy and loss energy.

8. The method according to claim 1, characterized in that The performance parameters include unit safety performance parameters; the performance analysis model includes a unit safety deviation analysis sub-model; the performance analysis results obtained by analyzing the operating state of the power unit through the performance analysis model include: Inputting equipment operation records, maintenance logs, fault records, operation records and personnel management training status records into the unit safety deviation analysis sub-model to obtain the cause of the unit accident; Determining a target optimization strategy according to the performance analysis result includes: According to the cause of the unit accident, a sixth target optimization strategy for improving the unit operation safety is determined, wherein the unit safety performance parameters include failure rate and single maintenance time.

9. The method according to any one of claims 1 to 8, characterized in that The method further comprises: Generate a performance report and trend graph based on the performance analysis results.

10. The method according to any one of claims 1 to 8, characterized in that The method further comprises: If the current performance parameter exceeds the corresponding performance reference range, a corresponding performance abnormality prompt message is generated.

11. The method according to claim 1, characterized in that: The operation information includes at least one of equipment vibration frequency, equipment temperature, equipment pressure, equipment current, equipment voltage, and equipment sound information; the method further includes: If the operation information exceeds the corresponding operation reference range, corresponding operation abnormality prompt information is generated.

12. The method according to claim 1, characterized in that The method further comprises: In response to receiving an operation instruction issued by a user, performing identity verification on the user; If the verification passes, the operation information of the power unit is obtained.

13. A device for optimizing the operating state of a power unit, characterized in that: include: An acquisition module is used to obtain the current operation information of the power unit; A preprocessing module, used for preprocessing the operation information; A first determination module is used to determine the current performance parameters according to the preprocessed operation information, wherein the performance parameters include at least one of power generation efficiency parameters, load parameters, environmental protection parameters, energy consumption and resource utilization parameters, thermal balance parameters, and unit safety performance parameters; An analysis module, for analyzing the operating state of the power unit through a performance analysis model to obtain a performance analysis result if the performance parameter exceeds the corresponding performance reference range, wherein the performance analysis result is used to indicate the cause of the abnormal performance parameter; The second determination module is used to determine a target optimization strategy according to the performance analysis result.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

15. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 12.

16. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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