Energy device intelligent control method and system

Abstract

The application discloses an energy equipment intelligent control method and system, and belongs to the technical field of electric energy storage systems. The method comprises the following steps: acquiring environmental parameter information of an energy equipment according to a preset period, and determining demand load change information of the energy equipment based on the environmental parameter information; acquiring a target efficiency cost factor, and determining a target control parameter group based on the demand load change information and the target efficiency cost factor; determining a target controller coefficient corresponding to each control parameter based on a target setting value of each control parameter in the target control parameter group and the target efficiency cost factor; and generating a power adjustment instruction corresponding to each control parameter based on the target setting value and the target controller coefficient. According to the technical scheme, the real-time change of the demand load of the energy equipment is dynamically matched, global adaptive control of the energy equipment is realized, and the energy consumption cost and the equipment control efficiency are accurately balanced, so that the long-term operation economy and stability of the energy equipment under a complex environment are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electric energy storage systems, and particularly relates to an energy equipment intelligent control method and system. BACKGROUND

[0002] Energy equipment is the core facility of various industrial production processes and livelihood infrastructure operation, and its operation state is directly related to energy consumption cost and demand service quality. Due to dynamic changes in user demand and real-time changes in external environment and other factors, the actual demand load of energy equipment is always in dynamic change. Therefore, it is necessary to dynamically control the energy equipment to make the operation state meet the actual demand.

[0003] The current dynamic control mode of energy equipment is generally feedback control based on fixed preset values. For example, for a heat exchanger, according to the deviation of the real-time monitoring value of the return water temperature from the fixed preset value, the compressor power of the heat exchanger is adjusted, that is, the input state of the compressor (such as input current and input voltage) is adjusted to maintain stable working conditions. However, feedback control has a lag, and it is difficult to achieve precise real-time response, which may cause overshoot or insufficient adjustment. In addition, due to the complex working conditions of energy equipment and strong coupling of multiple variables, only single component or single parameter control may also cause unnecessary energy consumption.

[0004] Therefore, how to realize the global collaborative control of energy equipment and balance energy consumption cost and equipment control efficiency is a problem that needs to be solved by those skilled in the art. SUMMARY

[0005] The embodiments of the application provide an energy equipment intelligent control method and system, which aims to dynamically match the real-time changes of the demand load of energy equipment, realize global adaptive control of energy equipment, and accurately balance energy consumption cost and equipment control efficiency, so as to improve the long-term operation economy and stability of energy equipment in complex environments.

[0006] In a first aspect, the embodiments of the application provide an energy equipment intelligent control method, which comprises:

[0007] Obtaining environment parameter information of energy equipment according to a preset period, and determining demand load change information of the energy equipment based on the environment parameter information;

[0008] Obtaining a target efficiency cost factor, and determining a target control parameter group based on the demand load change information and the target efficiency cost factor; wherein the target efficiency cost factor represents the trade-off degree between control efficiency and energy consumption cost, and the target control parameter group includes target set values of at least two control parameters;

[0009] determining a target controller coefficient corresponding to each control parameter based on the target setting value and the target performance cost factor;

[0010] generating a power adjustment instruction corresponding to each control parameter based on the target setting value and the target controller coefficient, so as to adjust the running power of the associated component of each control parameter in the energy equipment.

[0011] Optionally, determining a target control parameter group based on the demand load change information and the target performance cost factor comprises:

[0012] determining a candidate control parameter group based on the demand load change information;

[0013] obtaining the current running power of the associated component of each control parameter in the candidate control parameter group, and determining a performance cost factor of each candidate control parameter group based on the current running power;

[0014] determining the candidate control parameter group whose performance cost factor is closest to the target performance cost factor as the target control parameter group.

[0015] Optionally, determining a performance cost factor of each candidate control parameter group based on the current running power comprises:

[0016] obtaining the current setting value of each control parameter included in each candidate control parameter group;

[0017] determining a control efficiency index of each candidate control parameter group based on the current setting value and the target setting value of the control parameter;

[0018] determining an energy consumption cost index of each candidate control parameter group based on the current setting value and the target setting value of the control parameter and the current running power;

[0019] determining a performance cost factor of each candidate control parameter group based on the control efficiency index and the energy consumption cost index.

[0020] Optionally, determining a control efficiency index of each candidate control parameter group based on the current setting value and the target setting value of the control parameter comprises:

[0021] calculating the absolute value of the deviation between the current setting value and the target setting value of each control parameter, and performing normalization processing on the absolute value of the deviation to obtain a normalized absolute value of the deviation;

[0022] determining a comprehensive setting deviation value of each candidate control parameter group based on the preset influence weight of each control parameter on the demand load and the normalized absolute value of the deviation;

[0023] determine a maximum value of the comprehensive setting deviation values as a reference comprehensive setting deviation value;

[0024] determine a control efficiency index of each candidate control parameter group based on the comprehensive setting deviation value of each candidate control parameter group and the reference comprehensive setting deviation value.

[0025] Optionally, determine an energy consumption cost index of each candidate control parameter group based on the current setting value and the target setting value of the control parameter and the current running power, including:

[0026] determine an energy consumption amount of each candidate control parameter group based on the current setting value and the target setting value of the control parameter and the current running power, and determine a minimum value of the energy consumption amount as a reference energy consumption amount;

[0027] determine an energy consumption cost index of each candidate control parameter group based on the energy consumption amount of each candidate control parameter group and the reference energy consumption amount.

[0028] Optionally, the target efficiency cost factor is obtained by:

[0029] obtain real-time electricity price information of an area where the energy equipment is located, and obtain running time length and running scenario information of the energy equipment;

[0030] determine a target efficiency cost factor based on the real-time electricity price information, the running time length and the running scenario information.

[0031] Optionally, determine a target controller coefficient corresponding to each control parameter based on the target setting value and the target efficiency cost factor, including:

[0032] obtain a current setting value of the control parameter and a current controller coefficient corresponding to the control parameter;

[0033] determine a correction deviation corresponding to the control parameter based on the target efficiency cost factor, the current controller coefficient, the current setting value and the target setting value;

[0034] optimize the current controller coefficient corresponding to the control parameter based on the correction deviation;

[0035] repeat the above steps until the current controller coefficient meets an optimization stop condition, to obtain a target controller coefficient corresponding to each control parameter.

[0036] In a second aspect, an embodiment of the present application provides an energy equipment intelligent control system, and the system includes:

[0037] The load change determination module is configured to acquire environmental parameter information of the energy equipment according to a preset period, and determine demand load change information of the energy equipment based on the environmental parameter information.

[0038] The control parameter determination module is configured to acquire a target performance cost factor, and determine a target control parameter group based on the demand load change information and the target performance cost factor; the target performance cost factor represents a trade-off degree between control efficiency and energy consumption cost, and the target control parameter group includes target set values of at least two control parameters.

[0039] The controller coefficient determination module is configured to determine target controller coefficients corresponding to the control parameters based on the target set values and the target performance cost factor.

[0040] The adjustment instruction generation module is configured to generate power adjustment instructions corresponding to the control parameters based on the target set values and the target controller coefficients, so as to adjust operating powers of associated components of the control parameters in the energy equipment.

[0041] Optionally, the control parameter determination module is specifically configured to:

[0042] determine candidate control parameter groups based on the demand load change information.

[0043] acquire current operating powers of associated components of the control parameters in the candidate control parameter groups, and determine performance cost factors of the candidate control parameter groups based on the current operating powers.

[0044] determine the candidate control parameter group whose performance cost factor is closest to the target performance cost factor as the target control parameter group.

[0045] Optionally, the control parameter determination module is specifically configured to:

[0046] acquire current set values of the control parameters included in the candidate control parameter groups.

[0047] determine control efficiency indicators of the candidate control parameter groups based on the current set values and target set values of the control parameters.

[0048] determine energy consumption cost indicators of the candidate control parameter groups based on the current set values and target set values of the control parameters and the current operating powers.

[0049] determine performance cost factors of the candidate control parameter groups based on the control efficiency indicators and the energy consumption cost indicators.

[0050] Optionally, the control parameter determination module is specifically configured to:

[0051] compute an absolute value of a deviation between the current setting value and the target setting value of each control parameter, and normalize the absolute value of the deviation to obtain a normalized absolute value of the deviation;

[0052] determine a comprehensive setting deviation value of each candidate control parameter group based on a preset influence weight of each control parameter on the demand load and the normalized absolute value of the deviation;

[0053] determine a maximum value of the comprehensive setting deviation value as a reference comprehensive setting deviation value;

[0054] determine a control efficiency index of each candidate control parameter group based on the comprehensive setting deviation value of each candidate control parameter group and the reference comprehensive setting deviation value.

[0055] Optionally, the control parameter determination module is specifically configured to:

[0056] determine an energy consumption of each candidate control parameter group based on the current setting value and the target setting value of the control parameter and the current running power, and determine a minimum value of the energy consumption as a reference energy consumption;

[0057] determine an energy consumption cost index of each candidate control parameter group based on the energy consumption of each candidate control parameter group and the reference energy consumption.

[0058] Optionally, the control parameter determination module is specifically configured to:

[0059] obtain real-time electricity price information of an area where the energy equipment is located, and obtain an already running duration and running scene information of the energy equipment;

[0060] determine a target efficiency cost factor based on the real-time electricity price information, the already running duration and the running scene information.

[0061] Optionally, the controller coefficient determination module is specifically configured to:

[0062] obtain a current setting value of the control parameter and a current controller coefficient corresponding to the control parameter;

[0063] determine a corrected deviation corresponding to the control parameter based on the target efficiency cost factor, the current controller coefficient, the current setting value and the target setting value;

[0064] optimize the current controller coefficient corresponding to the control parameter based on the corrected deviation;

[0065] repeat the above steps until the current controller coefficient meets an optimization stop condition, to obtain a target controller coefficient corresponding to each control parameter.

[0066] In a third aspect, an electronic device is provided, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method according to the first aspect.

[0067] In a fourth aspect, a readable storage medium is provided, which stores a program or instructions, and the program or instructions, when executed by a processor, implement the steps of the method according to the first aspect.

[0068] In the embodiments of the present application, the environmental parameter information of the energy equipment is acquired according to a preset period, and the demand load change information of the energy equipment is determined based on the environmental parameter information; a target efficiency cost factor is acquired, and a target control parameter group is determined based on the demand load change information and the target efficiency cost factor; wherein the target efficiency cost factor represents the trade-off degree between control efficiency and energy consumption cost, and the target control parameter group includes target set values of at least two control parameters; based on the target set values and the target efficiency cost factor, target controller coefficients corresponding to each control parameter are determined; based on the target set values and the target controller coefficients, power adjustment instructions corresponding to each control parameter are generated to adjust the operating power of the associated components of each control parameter in the energy equipment. The above-mentioned energy equipment intelligent control method dynamically matches the real-time changes of the demand load of the energy equipment, realizes global adaptive control of the energy equipment, and accurately balances the energy consumption cost and the equipment control efficiency, thereby improving the long-term operation economy and stability of the energy equipment under complex environment. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 is a flowchart of an energy equipment intelligent control method provided by the embodiments of the present application;

[0070] Figure 2 is a flowchart of another energy equipment intelligent control method provided by the embodiments of the present application;

[0071] Figure 3 is a flowchart of still another energy equipment intelligent control method provided by the embodiments of the present application;

[0072] Figure 4 is a structural schematic diagram of an energy equipment intelligent control system provided by the embodiments of the present application;

[0073] Figure 5 is a structural schematic diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0074] In order to make the purposes, technical solutions and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only parts related to the present application are shown in the drawings, but not all. Before discussing the example embodiments in more detail, it should be mentioned that some example embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The processes can be terminated when the operations are completed, but can also have additional steps not included in the drawings. The processes can correspond to methods, functions, procedures, subroutines, etc.

[0075] The technical solutions in the embodiments of the present application will be described clearly in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the present application.

[0076] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally a class, not limited to the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally indicates that the front and rear associated objects are in an "or" relationship.

[0077] The energy equipment intelligent control method and system provided by the embodiments of the present application will be described in detail below in conjunction with the drawings and specific embodiments and their application scenarios.

[0078] First, the present application is applicable to the scenario based on the output demand load of energy equipment. Based on the above use scenario, it can be understood that the execution subject of the present application can be a terminal device, such as a desktop computer, a notebook computer, a mobile phone, a tablet computer and an interactive multimedia, etc., which is not limited here. Specifically, the determination of the demand load change information, the determination of the target control parameter group and the determination of the target controller coefficient, etc. can be executed by the terminal device, to realize the dynamic adjustment and closed-loop control of the demand load of the energy equipment output.

[0079] The energy equipment can be various types of equipment that implement functions such as energy production, conversion, storage, transmission, or consumption, for example, a thermal power generator set, a wind power generator, a photovoltaic inverter, an energy storage battery pack, a transformer, a high-voltage switch cabinet, a gas boiler, an electric heating device, a central air conditioning chiller, an industrial kiln, a charging pile, a smart electricity meter, a waste heat recovery device, a heat pump unit, a biomass energy conversion device, and a fuel cell system.

[0080] The demand load of the energy equipment can be a specific quantitative index of energy or energy conversion capacity that needs to be output by the energy equipment to meet the energy demand in a specific scenario. The type of the demand load is directly related to the functional attributes and application scenarios of the energy equipment. For example, the demand load of a central air conditioning chiller is the cooling load, that is, the total amount of heat that needs to be removed from the air conditioning area in a specific time period, and the unit is kilowatts (kW).

[0081] Figure 1 is a flowchart of an energy equipment intelligent control method provided by an embodiment of the present application. As shown in Figure 1 , the method specifically includes the following steps:

[0082] S101, obtaining environment parameter information of the energy equipment according to a preset period, and determining demand load change information of the energy equipment based on the environment parameter information.

[0083] The preset period can be a repetition time interval of the event of determining the demand load change information of the energy equipment based on the environment parameter information.

[0084] The environment parameter information can include external environment data, device self-data, and demand load side associated data that directly or indirectly affect the demand load of the energy equipment. For example, the external environment data of a central air conditioning chiller includes outdoor dry-bulb temperature, wet-bulb temperature, and solar radiation intensity, the device self-data of the central air conditioning chiller includes the operating frequency, current, suction and discharge pressure of the compressor, and the heat exchange temperature difference of the condenser and evaporator, and the demand load side associated data of the central air conditioning chiller includes the cooling water supply and return water temperature and flow.

[0085] In an embodiment, the way of obtaining the environment parameter information of the energy equipment according to the preset period can be to collect the environment parameter information of the energy equipment by a professional sensor corresponding to the environment parameter information according to the preset period.

[0086] The demand load change information can be the demand load of the energy equipment that changes over time within a future preset period.

[0087] In an embodiment, the manner of determining the demand load change information of the energy equipment based on the environmental parameter information can comprise inputting the environmental parameter information into a pre-trained autoregressive integrated moving average model to obtain the demand load change information of the energy equipment in a future preset period. The autoregressive integrated moving average model can be a time series prediction model that integrates three layers of autoregressive, difference, and moving average structures. In this solution, the autoregressive integrated moving average model can be trained based on historical demand load change information and historical environmental parameter information of the energy equipment.

[0088] In S102, a target performance cost factor is obtained, and a target control parameter group is determined based on the demand load change information and the target performance cost factor. The target performance cost factor represents the trade-off degree between control efficiency and energy consumption cost, and the target control parameter group includes target set values of at least two control parameters.

[0089] The target performance cost factor can represent the trade-off degree between control efficiency and energy consumption cost, and can be a quantitative coefficient with a value range of 0-1. When the value of the target performance cost factor is closer to 0, it means that the control logic needs to prioritize control efficiency, i.e., quickly respond to demand load changes, minimize the deviation between actual load and demand load, and even cause a certain increase in energy consumption cost. When the value of the target performance cost factor is closer to 1, the control logic prioritizes reducing energy consumption cost, i.e., minimizing fuel or power consumption, and can accept a moderate slowdown in control response speed.

[0090] In an embodiment, the target performance cost factor can be obtained by obtaining real-time electricity price information of the region where the energy equipment is located, and determining the target performance cost factor based on the real-time electricity price information. For example, the minimum electricity price information and the maximum electricity price information of the region where the energy equipment is located are obtained, and the target performance cost factor is determined according to the relative proportional relationship of the real-time electricity price information between the minimum electricity price information and the maximum electricity price information.

[0091] Optionally, the obtaining of the target performance cost factor comprises:

[0092] The real-time electricity price information of the region where the energy equipment is located is obtained, and the running time length and running scenario information of the energy equipment are obtained.

[0093] The target performance cost factor is determined based on the real-time electricity price information, the running time length, and the running scenario information.

[0094] The real-time electricity price information of the region where the energy equipment is located can be the actual electricity price per unit at the current time in the administrative region or power grid power supply area where the energy equipment is located.

[0095] In an embodiment, the manner of obtaining the real-time electricity price information of the area where the energy equipment is located can adopt an open interface of a power company in the area where the energy equipment is located to pull the real-time electricity price information according to a preset period.

[0096] The running time length of the energy equipment can be the time length accumulated in the normal running state of the energy equipment from the last start (or factory) to the current time, which is a core index reflecting the aging degree and energy consumption efficiency attenuation of the equipment.

[0097] In an embodiment, the manner of obtaining the running time length of the energy equipment can adopt a current sensor and a running state relay deployed in the control loop of the energy equipment to judge whether the energy equipment is in the normal running state in real time, and when it is monitored that the energy equipment is in the normal running state, the time length is accumulated at a frequency of 1 second per second to obtain the running time length of the energy equipment.

[0098] The running scene information of the energy equipment can be a set of external environment and user demand characteristics that can indirectly indicate the control demand priority of the energy equipment. For example, the running scene information is a hospital, which indicates that the control efficiency requirement of the energy equipment is extremely high; the running scene information is a night shift in a shopping mall, which indicates that the control efficiency requirement of the energy equipment is low.

[0099] In an embodiment, the manner of obtaining the running scene information of the energy equipment can adopt that the running scene information of the energy equipment can be pre-set by the user.

[0100] The process of determining the target efficiency cost factor based on the real-time electricity price information, the running time length and the running scene information can include: obtaining the minimum electricity price information and the maximum electricity price information of the area where the energy equipment is located, determining a first efficiency cost factor according to the relative proportional relationship of the real-time electricity price information between the minimum electricity price information and the maximum electricity price information; calculating the ratio of the running time length to the service life of the energy equipment as a second efficiency cost factor; determining a third efficiency cost factor according to the pre-set association relationship between the running scene information and the third efficiency cost factor; and performing weighted summation calculation on the first efficiency cost factor, the second efficiency cost factor and the third efficiency cost factor to obtain the target efficiency cost factor.

[0101] The advantage of this scheme is that by simultaneously considering the real-time electricity price information, the running time length and the running scene information of the energy equipment, the limitation of a single electricity price dimension can be broken through, and the running state of the energy equipment and the adaptation scene demand are considered at the same time, so that more accurate, more adaptive and more intelligent control priority trade-off can be realized.

[0102] The target control parameter group can be a set of target setting values of the control parameters set for meeting the demand load change of the energy equipment and matching the target efficiency cost factor.

[0103] The control parameters can be key variables directly affecting the energy equipment demand load change. One demand load can correspond to at least two control parameters, that is, the target control parameter group can include target set values of at least two control parameters. For example, the control parameters of a central air conditioning water chiller unit can include cooling water supply temperature and cooling water return temperature.

[0104] The target set values can be optimal values of the control parameters calculated based on the demand load change information and the target performance cost factor.

[0105] In one embodiment, the way of determining the target control parameter group based on the demand load change information and the target performance cost factor can include determining candidate control parameter groups based on the demand load change information, obtaining current operating powers of associated components of each control parameter in the candidate control parameter groups, and determining performance cost factors of each candidate control parameter group based on the current operating powers, and determining the candidate control parameter group with the performance cost factor closest to the target performance cost factor as the target control parameter group.

[0106] S103, determining target controller coefficients corresponding to each control parameter based on the target set values and the target performance cost factor.

[0107] The controller can be a core algorithm unit for realizing closed-loop control of the energy equipment, and outputs an adjustment instruction to reduce the deviation by receiving a deviation between a current set value and a target set value of the control parameter, to ensure that the energy equipment stably operates in a target state. In the present scheme, the controller can be a PID (proportional-integral-derivative) controller.

[0108] The controller coefficients can be core parameters for realizing deviation adjustment of the controller, and directly determine control response speed, stability and precision; the controller coefficients can include a proportional coefficient, an integral coefficient and a differential coefficient. Correspondingly, the target controller coefficients are controller coefficients corresponding to the current preset period.

[0109] Specifically, the proportional coefficient can be an amplification coefficient of the controller for the current deviation, and the role is to quickly respond to the deviation; the integral coefficient can be a regulation coefficient of the controller for accumulated deviation, and the role is to eliminate static deviation; the differential coefficient can be a prediction coefficient of the controller for the rate of change of the deviation, and the role is to predict the deviation trend in advance and suppress overshoot.

[0110] In one embodiment, the manner of determining the target controller coefficient corresponding to each control parameter based on the target set value and the target performance cost factor can adopt the following steps: obtaining the current set value of the control parameter and the current controller coefficient corresponding to the control parameter, determining the correction bias corresponding to the control parameter based on the target performance cost factor, the current controller coefficient, the current set value and the target set value, optimizing the current controller coefficient corresponding to the control parameter based on the correction bias, and repeating the above steps until the current controller coefficient meets the optimization stop condition to obtain the target controller coefficient corresponding to each control parameter.

[0111] In S104, a power adjustment instruction corresponding to each control parameter is generated based on the target set value and the target controller coefficient to adjust the operating power of the associated component of each control parameter in the energy equipment.

[0112] The power adjustment instruction corresponding to the control parameter can be a quantitative control signal calculated according to the target set value and the target controller coefficient, which is used to directly adjust the operating power of the associated component.

[0113] The associated component corresponding to the control parameter can be a core component of the energy equipment that directly executes the control parameter adjustment and whose operating power can be accurately controlled. For example, the associated component corresponding to the cooling water supply temperature includes a compressor, and the associated component corresponding to the cooling water return temperature includes a cooling tower fan and a cooling water pump.

[0114] The operating power of the associated component can be a quantitative value of the ability of the associated component to consume energy and convert it into effective output in a unit of time. Adjusting the operating power of the associated component can achieve accurate approximation of the current set value of the control parameter to the target set value, thereby dynamically matching the demand load change of the energy equipment.

[0115] In one embodiment, the manner of generating a power adjustment instruction corresponding to each control parameter based on the target set value and the target controller coefficient to adjust the operating power of the associated component of each control parameter in the energy equipment can adopt the following steps: sending the target set value and the target controller coefficient to the controller corresponding to each control parameter, the controller collecting the current set value of the control parameter in real time, calculating the deviation between the current set value and the target set value, and then performing operation and processing based on the deviation and the target controller coefficient through a PID algorithm, outputting the power adjustment amount of each associated component of the corresponding control parameter, finally converting the power adjustment amount into an electrical signal (such as a voltage, current or digital pulse signal) conforming to the control interface standard of the associated component, generating the power adjustment instruction and delivering it to the execution unit (such as a frequency converter, a regulating valve or a relay) of the associated component to adjust the operating power of the associated component of each control parameter in the energy equipment.

[0116] In the embodiment of the present application, the environmental parameter information of the energy equipment is acquired according to a preset period, and the demand load change information of the energy equipment is determined based on the environmental parameter information; a target efficiency cost factor is acquired, and a target control parameter group is determined based on the demand load change information and the target efficiency cost factor; wherein the target efficiency cost factor represents the trade-off degree between control efficiency and energy consumption cost, and the target control parameter group includes target set values of at least two control parameters; based on the target set values and the target efficiency cost factor, target controller coefficients corresponding to each control parameter are determined; based on the target set values and the target controller coefficients, power adjustment instructions corresponding to each control parameter are generated to adjust the operating power of the associated components of each control parameter in the energy equipment. The above energy equipment intelligent control method dynamically matches the real-time changes of the demand load of the energy equipment, realizes global adaptive control of the energy equipment, and accurately balances the energy consumption cost and the equipment control efficiency, thereby improving the long-term operation economy and stability of the energy equipment in a complex environment.

[0117] Figure 2 is a flowchart of another energy equipment intelligent control method provided by the embodiment of the present application. As shown in Figure 2 , the embodiment specifically includes the following steps:

[0118] S201, the environmental parameter information of the energy equipment is acquired according to a preset period, and the demand load change information of the energy equipment is determined based on the environmental parameter information.

[0119] S202, a target efficiency cost factor is acquired; wherein the target efficiency cost factor represents the trade-off degree between control efficiency and energy consumption cost.

[0120] S203, a candidate control parameter group is determined based on the demand load change information.

[0121] Wherein, the candidate control parameter group can be a plurality of potential control schemes generated to meet the demand load change of the energy equipment. Each candidate control parameter group includes the same control parameters and different target set values of the control parameters.

[0122] In one embodiment, the way of determining the candidate control parameter group based on the demand load change information can be to decompose the demand load change amount into different demand load change component groups (the demand load change component group includes the demand load change components corresponding to each control parameter) by taking the demand load change information and the safe operation range of the equipment as constraint conditions, and to calculate the target set values of each control parameter based on the demand load change components corresponding to each control parameter in the demand load change component group, that is, to obtain the candidate control parameter group.

[0123] S204, acquire current running power of the associated component of each control parameter in the candidate control parameter group, and determine the performance cost factor of each candidate control parameter group based on the current running power.

[0124] The current running power of the associated component is the running power of the associated component at the current time.

[0125] In an embodiment, the manner of acquiring the current running power of the associated component of each control parameter in the candidate control parameter group can be to acquire the current current and current voltage of the associated component of each control parameter in the candidate control parameter group by using a current sensor and a voltage sensor, and calculate the current running power based on the current current and current voltage.

[0126] In an embodiment, the manner of determining the performance cost factor of each candidate control parameter group based on the current running power can be to acquire the current setting value of each control parameter included in each candidate control parameter group, determine the control efficiency index of each candidate control parameter group based on the current setting value and target setting value of the control parameter, determine the energy consumption cost index of each candidate control parameter group based on the current setting value and target setting value of the control parameter and the current running power, and determine the performance cost factor of each candidate control parameter group based on the control efficiency index and the energy consumption cost index.

[0127] Optionally, determining the performance cost factor of each candidate control parameter group based on the current running power comprises:

[0128] acquiring the current setting value of each control parameter included in each candidate control parameter group;

[0129] determining the control efficiency index of each candidate control parameter group based on the current setting value and target setting value of the control parameter;

[0130] determining the energy consumption cost index of each candidate control parameter group based on the current setting value and target setting value of the control parameter and the current running power;

[0131] determining the performance cost factor of each candidate control parameter group based on the control efficiency index and the energy consumption cost index.

[0132] The current setting value of each control parameter is the actual value of each control parameter at the current time.

[0133] In an embodiment, the manner of acquiring the current setting value of each control parameter included in the candidate control parameter group can be to set a corresponding sensor for each control parameter, and monitor the actual value of each control parameter through the sensors.

[0134] The control efficiency index of the candidate control parameter group can be an index for quantifying the comprehensive evaluation of the control accuracy and response speed of the candidate control parameter group in response to the demand load change.

[0135] In one embodiment, based on the current setting value and the target setting value of the control parameter, the control efficiency index of each candidate control parameter group can be determined by calculating the absolute value of the deviation between the current setting value and the target setting value of each control parameter, and normalizing the absolute value of the deviation to obtain a normalized absolute value of the deviation, determining a comprehensive setting deviation value of each candidate control parameter group based on the preset influence weight of each control parameter on the demand load and the normalized absolute value of the deviation, determining the maximum value of the comprehensive setting deviation value as a reference comprehensive setting deviation value, and determining the control efficiency index of each candidate control parameter group based on the comprehensive setting deviation value of each candidate control parameter group and the reference comprehensive setting deviation value.

[0136] Optionally, based on the current setting value and the target setting value of the control parameter, the control efficiency index of each candidate control parameter group can be determined by:

[0137] calculating the absolute value of the deviation between the current setting value and the target setting value of each control parameter, and normalizing the absolute value of the deviation to obtain a normalized absolute value of the deviation;

[0138] determining a comprehensive setting deviation value of each candidate control parameter group based on the preset influence weight of each control parameter on the demand load and the normalized absolute value of the deviation;

[0139] determining the maximum value of the comprehensive setting deviation value as a reference comprehensive setting deviation value;

[0140] determining the control efficiency index of each candidate control parameter group based on the comprehensive setting deviation value of each candidate control parameter group and the reference comprehensive setting deviation value.

[0141] The absolute value of the deviation between the current setting value and the target setting value of each control parameter can be the absolute value of the difference between the current setting value and the target setting value of each control parameter.

[0142] The normalized absolute value of the deviation is the absolute value of the deviation after normalization, and its core function is to eliminate the unit difference and numerical range difference of different control parameters.

[0143] In one embodiment, the normalized absolute value of the deviation can be obtained by calculating the length of the variable interval of the control parameter, and dividing the normalized absolute value of the deviation of the control parameter by the length of the variable interval to obtain the normalized absolute value of the deviation of the control parameter.

[0144] The preset influence weight of the control parameter on the demand load can be a weight coefficient preset according to an actual influence degree of the control parameter on the demand load of the energy equipment. For example, the preset influence weight of the cooling water supply temperature is 0.6, and the preset influence weight of the cooling water return temperature is 0.4.

[0145] The comprehensive setting deviation value of the candidate control parameter group can be an overall setting deviation value calculated by considering different influence degrees of different control parameters on the demand load.

[0146] In an embodiment, based on the preset influence weight of each control parameter on the demand load and the normalized deviation absolute value, the manner of determining the comprehensive setting deviation value of the candidate control parameter group can adopt weighted summation calculation of the normalized deviation absolute value of each control parameter based on the preset influence weight of the control parameter on the demand load, to obtain the comprehensive setting deviation value of the candidate control parameter group.

[0147] The reference comprehensive setting deviation value can be a reference for calculation of the control efficiency index, to ensure that the control efficiency index of all candidate control parameter groups is mapped into the interval of 0-1. The maximum value of the comprehensive setting deviation value is determined as the reference comprehensive setting deviation value, that is, the maximum comprehensive setting deviation value (i.e., the maximum control efficiency) is mapped to the performance cost factor of 0.

[0148] In an embodiment, based on the comprehensive setting deviation value of the candidate control parameter group and the reference comprehensive setting deviation value, the manner of determining the control efficiency index of the candidate control parameter group can adopt calculation of the difference between the comprehensive setting deviation value of the candidate control parameter group and the reference comprehensive setting deviation value, and calculation of the ratio of the difference to the reference comprehensive setting deviation value as the control efficiency index of the candidate control parameter group.

[0149] The advantage of this scheme is that by calculating the deviation absolute value between the current setting value and the target setting value of each control parameter, and obtaining the normalized deviation absolute value by normalizing the deviation absolute value, determining the comprehensive setting deviation value of each candidate control parameter group based on the preset influence weight of each control parameter on the demand load and the normalized deviation absolute value, determining the maximum value of the comprehensive setting deviation value as the reference comprehensive setting deviation value, and determining the control efficiency index of each candidate control parameter group based on the comprehensive setting deviation value of each candidate control parameter group and the reference comprehensive setting deviation value, the control parameter difference can be eliminated to realize standardized evaluation, and the core control parameter can be highlighted to ensure objective evaluation.

[0150] The energy consumption cost index of the candidate control parameter group can be a comprehensive evaluation index for quantifying the energy consumption economy of the candidate control parameter group in adjusting the control parameter from the current setting value to the target setting value.

[0151] In one embodiment, based on the manner of determining the energy consumption cost index of each candidate control parameter group according to the current setting value and the target setting value of the control parameter and the current running power, the energy consumption of each candidate control parameter group can be determined according to the current setting value and the target setting value of the control parameter and the current running power, the minimum value of the energy consumption is determined as the benchmark energy consumption, and the energy consumption cost index of each candidate control parameter group is determined according to the energy consumption of each candidate control parameter group and the benchmark energy consumption.

[0152] Optionally, the determining of the energy consumption cost index of each candidate control parameter group according to the current setting value and the target setting value of the control parameter and the current running power comprises:

[0153] determining the energy consumption of each candidate control parameter group according to the current setting value and the target setting value of the control parameter and the current running power, and determining the minimum value of the energy consumption as the benchmark energy consumption;

[0154] determining the energy consumption cost index of each candidate control parameter group according to the energy consumption of each candidate control parameter group and the benchmark energy consumption.

[0155] The energy consumption of the candidate control parameter group can be a quantitative value of the energy consumption required for adjusting each control parameter in the candidate control parameter group from the current setting value to the target setting value.

[0156] In one embodiment, based on the manner of determining the energy consumption of the candidate control parameter group according to the current setting value and the target setting value of the control parameter and the current running power, the target running power of each associated component can be determined according to the target setting value of the control parameter, the average value of the current running power and the target running power is calculated, the average value is multiplied by a preset period to obtain the energy consumption corresponding to the control parameter, the energy consumptions corresponding to each control parameter in the candidate control parameter group are added to obtain the energy consumption of the candidate control parameter group.

[0157] The benchmark energy consumption can provide a benchmark reference for the calculation of the energy consumption cost index, and ensure that the energy consumption cost index of all candidate control parameter groups is mapped into the interval of 0-1. Determining the minimum value of the energy consumption as the benchmark energy consumption is to map the minimum energy consumption (i.e. the minimum energy consumption cost) to the efficiency cost factor of 1.

[0158] In an embodiment, the manner of determining the energy consumption cost index of the candidate control parameter set based on the energy consumption amount of the candidate control parameter set and the benchmark energy consumption amount can comprise calculating the difference between the energy consumption amount of the candidate control parameter set and the benchmark energy consumption amount, calculating the ratio of the difference to the benchmark energy consumption amount, and subtracting the ratio from 1 to obtain the energy consumption cost index of the candidate control parameter set.

[0159] The advantage of this scheme is that by determining the energy consumption amount of each candidate control parameter set based on the current setting value and the target setting value of the control parameter and the current operating power, and determining the minimum value of the energy consumption amount as the benchmark energy consumption amount, and determining the energy consumption cost index of each candidate control parameter set based on the energy consumption amount of each candidate control parameter set and the benchmark energy consumption amount, the accuracy, comparability and scenario of the energy consumption cost evaluation can be realized, and the problem that only power is considered without considering the process and result in traditional cost evaluation, which cannot be compared horizontally, can be solved.

[0160] In an embodiment, the manner of determining the energy consumption cost index of the candidate control parameter set based on the energy consumption amount of the candidate control parameter set and the benchmark energy consumption amount can comprise calculating the difference between the energy consumption amount of the candidate control parameter set and the benchmark energy consumption amount, calculating the ratio of the difference to the benchmark energy consumption amount, and subtracting the ratio from 1 to obtain the energy consumption cost index of the candidate control parameter set.

[0161] The advantage of this scheme is that by determining the energy consumption amount of each candidate control parameter set based on the current setting value and the target setting value of the control parameter and the current operating power, and determining the minimum value of the energy consumption amount as the benchmark energy consumption amount, and determining the energy consumption cost index of each candidate control parameter set based on the energy consumption amount of each candidate control parameter set and the benchmark energy consumption amount, the accuracy, comparability and scenario of the energy consumption cost evaluation can be realized, and the problem that only power is considered without considering the process and result in traditional cost evaluation, which cannot be compared horizontally, can be solved.

[0162] S205, determining the candidate control parameter set whose energy performance cost factor is closest to the target energy performance cost factor as the target control parameter set; wherein the target control parameter set comprises target setting values of at least two control parameters.

[0163] Among them, the candidate control parameter set whose energy performance cost factor is closest to the target energy performance cost factor is determined as the target control parameter set, that is, the control scheme that best matches the target balance relationship between control efficiency and energy consumption cost is selected.

[0164] S206, determining the target controller coefficient corresponding to each control parameter based on the target setting value and the target energy performance cost factor.

[0165] S207, generate a power adjustment instruction corresponding to each control parameter based on the target set value and the target controller coefficient, to adjust the running power of the associated component of each control parameter in the energy equipment.

[0166] The advantage of this scheme is that by determining the candidate control parameter group based on the demand load change information, and determining the performance cost factor of each candidate control parameter group based on the current running power of the associated component, the candidate control parameter group closest to the target performance cost factor is finally determined as the target control parameter group, which can realize accurate matching of demand load change, performance priority and cost control, solve the drawbacks of one-size-fits-all adjustment in traditional control, and improve the adaptability of the control scheme to actual dynamic scenarios.

[0167] Figure 3 is a flowchart of another energy equipment intelligent control method provided by the embodiment of the present application. As shown in Figure 3 , the method comprises the following steps:

[0168] S301, obtain the environmental parameter information of the energy equipment according to a preset period, and determine the demand load change information of the energy equipment based on the environmental parameter information.

[0169] S302, obtain a target performance cost factor, and determine a target control parameter group based on the demand load change information and the target performance cost factor; wherein the target performance cost factor represents the trade-off degree between control efficiency and energy consumption cost, and the target control parameter group includes target set values of at least two control parameters.

[0170] S303, obtain the current set value of the control parameter and the current controller coefficient corresponding to the control parameter.

[0171] The current controller coefficient is the controller coefficient at the current time.

[0172] In one embodiment, the current controller coefficient corresponding to the control parameter can be obtained by reading the log record of the controller corresponding to the control parameter.

[0173] S304, determine the correction deviation corresponding to the control parameter based on the target performance cost factor, the current controller coefficient, the current set value and the target set value.

[0174] The correction deviation corresponding to the control parameter can be a quantitative value for quantifying the gap between the actual control effect and the ideal effect required by the target performance cost factor in the process of adjusting the control parameter from the current set value to the target set value under the current controller coefficient.

[0175] In an embodiment, the manner of determining the correction deviation corresponding to the control parameter based on the target efficiency cost factor, the current controller coefficient, the current set value and the target set value can comprise: calculating an absolute value of a deviation between the current set value of the control parameter and the target set value, calculating a theoretical response time and a theoretical energy consumption based on the absolute value of the deviation and the current controller coefficient, calculating a theoretical efficiency cost factor based on the theoretical response time and the theoretical energy consumption, and calculating a square value of an efficiency cost factor deviation between the theoretical efficiency cost factor and the target efficiency cost factor as the correction deviation corresponding to the control parameter.

[0176] S305, optimizing the current controller coefficient corresponding to the control parameter based on the correction deviation.

[0177] In an embodiment, the manner of optimizing the current controller coefficient corresponding to the control parameter based on the correction deviation can comprise: adjusting the current controller coefficient in a negative gradient direction by calculating a gradient of the correction deviation on the current controller coefficient based on a gradient descent algorithm.

[0178] S306, identifying whether the current controller coefficient satisfies an optimization stop condition; if yes, executing S307; if no, executing S304.

[0179] The current controller coefficient satisfying the optimization stop condition can mean that the correction deviation corresponding to the current controller coefficient has reached a minimum value, and the correction deviation cannot be further reduced by optimizing the current controller coefficient, for example, an optimization gradient is less than a preset gradient threshold.

[0180] S307, obtaining a target controller coefficient corresponding to each control parameter, and generating a power adjustment instruction corresponding to each control parameter based on the target set value and the target controller coefficient, to adjust the operating power of the associated components of each control parameter in the energy equipment.

[0181] The advantages of the scheme are that: by determining the correction deviation corresponding to the control parameter based on the target efficiency cost factor, the current controller coefficient, the current set value and the target set value, and optimizing the current controller coefficient corresponding to the control parameter based on the correction deviation to obtain the target controller coefficient corresponding to the control parameter, the precise, dynamic and scenario-based optimization of the controller coefficient can be realized, and the problems of poor adaptability, unbalanced efficiency cost and response lag of the traditional fixed controller coefficient can be solved.

[0182] Figure 4 is a structural schematic diagram of an energy equipment intelligent control system provided by an embodiment of the present application. As shown in Figure 4 the system comprises:

[0183] The load change determination module 410 is configured to acquire environmental parameter information of the energy equipment according to a preset period, and determine demand load change information of the energy equipment based on the environmental parameter information.

[0184] The control parameter determination module 420 is configured to acquire a target performance cost factor, and determine a target control parameter group based on the demand load change information and the target performance cost factor; the target performance cost factor represents a trade-off degree between control efficiency and energy consumption cost, and the target control parameter group includes target set values of at least two control parameters.

[0185] The controller coefficient determination module 430 is configured to determine a target controller coefficient corresponding to each control parameter based on the target set value and the target performance cost factor.

[0186] The adjustment instruction generation module 440 is configured to generate a power adjustment instruction corresponding to each control parameter based on the target set value and the target controller coefficient, so as to adjust the operating power of an associated component of each control parameter in the energy equipment.

[0187] Optionally, the control parameter determination module 420 is specifically configured to:

[0188] determine a candidate control parameter group based on the demand load change information.

[0189] acquire a current operating power of an associated component of each control parameter in the candidate control parameter group, and determine a performance cost factor of each candidate control parameter group based on the current operating power.

[0190] determine the candidate control parameter group whose performance cost factor is closest to the target performance cost factor as the target control parameter group.

[0191] Optionally, the control parameter determination module 420 is specifically configured to:

[0192] acquire a current set value of each control parameter included in each candidate control parameter group.

[0193] determine a control efficiency index of each candidate control parameter group based on the current set value and the target set value of the control parameter.

[0194] determine an energy consumption cost index of each candidate control parameter group based on the current set value and the target set value of the control parameter and the current operating power.

[0195] determine a performance cost factor of each candidate control parameter group based on the control efficiency index and the energy consumption cost index.

[0196] Optionally, the control parameter determination module 420 is specifically configured to:

[0197] calculate an absolute value of a deviation between the current setting value and the target setting value of each control parameter, and perform normalization processing on the absolute value of the deviation to obtain a normalized absolute value of the deviation;

[0198] determine a comprehensive setting deviation value of each candidate control parameter group based on a preset influence weight of each control parameter on the demand load and the normalized absolute value of the deviation;

[0199] determine a maximum value of the comprehensive setting deviation value as a reference comprehensive setting deviation value;

[0200] determine a control efficiency index of each candidate control parameter group based on the comprehensive setting deviation value of each candidate control parameter group and the reference comprehensive setting deviation value.

[0201] Optionally, the control parameter determination module 420 is specifically configured to:

[0202] determine an energy consumption of each candidate control parameter group based on the current setting value and the target setting value of the control parameter and the current operating power, and determine a minimum value of the energy consumption as a reference energy consumption;

[0203] determine an energy consumption cost index of each candidate control parameter group based on the energy consumption of each candidate control parameter group and the reference energy consumption.

[0204] Optionally, the control parameter determination module 420 is specifically configured to:

[0205] obtain real-time electricity price information of an area where the energy equipment is located, and obtain an operating duration and operating scenario information of the energy equipment;

[0206] determine a target performance cost factor based on the real-time electricity price information, the operating duration, and the operating scenario information.

[0207] Optionally, the controller coefficient determination module 430 is specifically configured to:

[0208] obtain a current setting value of the control parameter and a current controller coefficient corresponding to the control parameter;

[0209] determine a correction deviation corresponding to the control parameter based on the target performance cost factor, the current controller coefficient, the current setting value, and the target setting value;

[0210] optimize the current controller coefficient corresponding to the control parameter based on the correction deviation;

[0211] The above steps are repeatedly performed until the current controller coefficients meet an optimization stop condition, to obtain target controller coefficients corresponding to each control parameter.

[0212] In the embodiment of the present application, the load change determination module is configured to acquire environmental parameter information of the energy equipment according to a preset period, and determine demand load change information of the energy equipment based on the environmental parameter information; the control parameter determination module is configured to acquire a target performance cost factor, and determine a target control parameter group based on the demand load change information and the target performance cost factor; the target performance cost factor represents a trade-off degree between control efficiency and energy consumption cost, and the target control parameter group includes target set values of at least two control parameters; the controller coefficient determination module is configured to determine target controller coefficients corresponding to each control parameter based on the target set values and the target performance cost factor; and the adjustment instruction generation module is configured to generate power adjustment instructions corresponding to each control parameter based on the target set values and the target controller coefficients, to adjust the operating power of the associated components of each control parameter in the energy equipment. The above energy equipment intelligent control system dynamically matches the real-time changes of the demand load of the energy equipment, realizes global adaptive control of the energy equipment, and accurately balances the energy consumption cost and the equipment control efficiency, thereby improving the long-term operation economy and stability of the energy equipment in a complex environment.

[0213] The energy equipment intelligent control system in the embodiment of the present application can be a system, or a component, an integrated circuit, or a chip in a terminal. The system can be a mobile electronic device, or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc., and the embodiment of the present application is not limited in this regard.

[0214] The energy equipment intelligent control system in the embodiment of the present application can be a system with an operating system. The operating system can be an Android operating system, an IOS operating system, or other possible operating systems, and the embodiment of the present application is not limited in this regard.

[0215] The energy equipment intelligent control system provided by the embodiments of the present application can realize the processes realized by the embodiments described above, and thus repeated description is omitted here.

[0216] Figure 5 is a structural schematic diagram of an electronic device provided by the embodiments of the present application. As shown in Figure 5 the embodiments of the present application also provide an electronic device 500, which includes a processor 501, a memory 502, a program or instruction stored in the memory 502 and executable on the processor 501. When the program or instruction is executed by the processor 501, the processes of the energy equipment intelligent control method embodiments described above are realized, and the same technical effects are achieved. Thus, repeated description is omitted here.

[0217] It should be noted that the electronic device in the embodiments of the present application includes the mobile electronic device and the non-mobile electronic device described above.

[0218] The embodiments of the present application also provide a readable storage medium, which stores a program or instruction. When the program or instruction is executed by a processor, the processes of the energy equipment intelligent control method embodiments described above are realized, and the same technical effects are achieved. Thus, repeated description is omitted here.

[0219] The processor is the processor in the electronic device described in the embodiments above. The readable storage medium includes a computer readable storage medium, such as a computer readable memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0220] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or systems including a series of elements not only include those elements, but also include other elements not explicitly listed, or further include elements inherent in such processes, methods, articles or systems. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or system including the element. In addition, it should be pointed out that the scope of the methods and systems in the embodiments of the present application is not limited to the order of performing the functions shown or discussed, but can also include performing the functions in a substantially simultaneous manner or in a reverse order, for example, the described method can be performed in an order different from that described, and various steps can also be added, omitted or combined. In addition, the features described with reference to some examples can be combined in other examples.

[0221] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned example method can be realized by means of software and the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a computer software product in essence or in the form of a computer software product that contributes to the prior art. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disc, optical disc), and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, or network device, etc.) execute the method described in each embodiment of the present application.

[0222] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above-mentioned specific embodiments, and the above-mentioned specific embodiments are only illustrative and not restrictive. Those skilled in the art can make many forms without departing from the scope of the present application and the protection scope of the claims under the inspiration of the present application, which all belong to the protection scope of the present application.

[0223] The above are only the preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein. Various obvious changes, readjustments and substitutions made by those skilled in the art without departing from the scope of the present application and the protection scope of the claims also belong to the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application. The scope of the present application is determined by the scope of the claims.

Claims

1. An energy device intelligent control method, characterized by, The method comprises: acquiring environmental parameter information of the energy equipment according to a preset period, and determining demand load change information of the energy equipment based on the environmental parameter information; acquiring a target efficiency cost factor, and determining a target control parameter group based on the demand load change information and the target efficiency cost factor; wherein the target efficiency cost factor represents a trade-off degree between control efficiency and energy consumption cost, and the target control parameter group comprises target set values of at least two control parameters; the acquiring of the target efficiency cost factor comprises: acquiring real-time electricity price information of a region where the energy equipment is located, and acquiring an operating time length and an operating scene information of the energy equipment; and determining the target efficiency cost factor based on the real-time electricity price information, the operating time length and the operating scene information; the determining of the target efficiency cost factor based on the real-time electricity price information, the operating time length and the operating scene information comprises: acquiring minimum electricity price information and maximum electricity price information of the region where the energy equipment is located, determining a first efficiency cost factor according to a relative proportional relationship between the real-time electricity price information and the minimum electricity price information and the maximum electricity price information; calculating a ratio of the operating time length to a service life of the energy equipment as a second efficiency cost factor; determining a third efficiency cost factor according to a pre-set association relationship between the operating scene information and the third efficiency cost factor; and performing weighted summation calculation on the first efficiency cost factor, the second efficiency cost factor and the third efficiency cost factor to obtain the target efficiency cost factor; determining target controller coefficients corresponding to each control parameter based on the target set values and the target efficiency cost factor; generating power adjustment instructions corresponding to each control parameter based on the target set values and the target controller coefficients, so as to adjust operating power of associated components of each control parameter in the energy equipment.

2. The energy plant intelligent control method according to claim 1, wherein The determining of the target control parameter group based on the demand load change information and the target efficiency cost factor comprises: determining a candidate control parameter group based on the demand load change information; acquiring current operating power of associated components of each control parameter in the candidate control parameter group, and determining efficiency cost factors of each candidate control parameter group based on the current operating power; determining the candidate control parameter group whose efficiency cost factor is closest to the target efficiency cost factor as the target control parameter group.

3. The energy plant intelligent control method according to claim 2, wherein The determining of the efficiency cost factors of each candidate control parameter group based on the current operating power comprises: acquiring current set values of each control parameter included in each candidate control parameter group; determining control efficiency indicators of each candidate control parameter group based on the current set values and target set values of the control parameters; determining energy consumption cost indicators of each candidate control parameter group based on the current set values and target set values of the control parameters and the current operating power; determining efficiency cost factors of each candidate control parameter group based on the control efficiency indicators and the energy consumption cost indicators.

4. The energy plant intelligent control method according to claim 3, wherein The determining of the control efficiency indicators of each candidate control parameter group based on the current set values and target set values of the control parameters comprises: An absolute value of a deviation between the current setting value and the target setting value of each control parameter is calculated, and the absolute value of the deviation is normalized to obtain a normalized absolute value of the deviation; Based on a preset influence weight of each control parameter on the demand load and the normalized absolute value of the deviation, a comprehensive setting deviation value of each candidate control parameter group is determined; A maximum value of the comprehensive setting deviation values is determined as a reference comprehensive setting deviation value; Based on the comprehensive setting deviation values of each candidate control parameter group and the reference comprehensive setting deviation value, a control efficiency index of each candidate control parameter group is determined.

5. The energy device intelligent control method of claim 3, wherein, Based on the current setting value and the target setting value of the control parameter and the current running power, an energy consumption cost index of each candidate control parameter group is determined, including: Based on the current setting value and the target setting value of the control parameter and the current running power, an energy consumption amount of each candidate control parameter group is determined, and a minimum value of the energy consumption amount is determined as a reference energy consumption amount; Based on the energy consumption amount of each candidate control parameter group and the reference energy consumption amount, an energy consumption cost index of each candidate control parameter group is determined. 6.The method of Claim 1-5, wherein Based on the target setting value and the target efficiency cost factor, a target controller coefficient corresponding to each control parameter is determined, including: The current setting value of the control parameter and the current controller coefficient corresponding to the control parameter are obtained; Based on the target efficiency cost factor, the current controller coefficient, the current setting value and the target setting value, a correction deviation corresponding to the control parameter is determined; The current controller coefficient corresponding to the control parameter is optimized based on the correction deviation; The above steps are repeatedly executed until the current controller coefficient meets an optimization stop condition, and a target controller coefficient corresponding to each control parameter is obtained.

7. An intelligent control system for energy devices, characterized by, The system includes: A load change determination module is configured to obtain environmental parameter information of an energy equipment according to a preset period, and determine demand load change information of the energy equipment based on the environmental parameter information. The control parameter determination module is configured to obtain a target performance cost factor, and determine a target control parameter group based on the demand load change information and the target performance cost factor; wherein the target performance cost factor represents a trade-off degree between control efficiency and energy consumption cost, and the target control parameter group includes target set values of at least two control parameters; wherein the target performance cost factor is obtained by: obtaining real-time electricity price information of a region where the energy equipment is located, and obtaining an already-operated time length and operation scene information of the energy equipment; determining a target performance cost factor based on the real-time electricity price information, the already-operated time length, and the operation scene information; wherein the target performance cost factor is determined based on the real-time electricity price information, the already-operated time length, and the operation scene information, including: obtaining minimum electricity price information and maximum electricity price information of the region where the energy equipment is located, determining a first performance cost factor according to a relative proportional relationship between the real-time electricity price information and the minimum electricity price information and the maximum electricity price information; calculating a ratio of the already-operated time length to a service life of the energy equipment as a second performance cost factor; determining a third performance cost factor according to a pre-set association relationship between the operation scene information and the third performance cost factor; and performing weighted summation calculation on the first performance cost factor, the second performance cost factor, and the third performance cost factor to obtain the target performance cost factor; The controller coefficient determination module is configured to determine a target controller coefficient corresponding to each control parameter based on the target set value and the target performance cost factor. The adjustment instruction generation module is configured to generate a power adjustment instruction corresponding to each control parameter based on the target set value and the target controller coefficient, so as to adjust an operation power of an associated component of each control parameter in the energy equipment.

8. An electronic device, comprising: The processor, the memory, and the program or instructions stored in the memory and executable on the processor are included, and the program or instructions are executed by the processor to implement the steps of the energy equipment intelligent control method according to any one of claims 1-6.

9. A readable storage medium, characterized by, The program or instructions are stored on the readable storage medium, and the program or instructions are executed by the processor to implement the steps of the energy equipment intelligent control method according to any one of claims 1-6.

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