CN118689112B - Self-adaptive energy-saving control method and system for fireplace equipment - Google Patents

Abstract

The invention discloses a self-adaptive energy-saving control method and a self-adaptive energy-saving control system for fireplace equipment, which relate to the technical field of intelligent control, wherein the method comprises the following steps: and determining control influence factors of fireplace equipment, and monitoring and returning factor data to the intelligent central control system. Based on the underlying regulatory logic and the element data, an autonomous regulatory strategy is determined with the demand as a guide. And (3) constructing a periodic learning module communicated with the intelligent central control system, calling a periodic history control record, mining habit characteristics of a user, performing autonomous learning, determining habit regulation and control logic, updating the intelligent central control system and determining a behavior regulation and control strategy. And combining an autonomous regulation strategy and a behavior regulation strategy to perform energy-saving optimization control on fireplace equipment. Thereby achieving the technical effects of improving the demand compliance of regulation and control and improving the energy-saving level.

Description

Self-adaptive energy-saving control method and system for fireplace equipment

Technical Field

The invention relates to the technical field of intelligent control, in particular to a self-adaptive energy-saving control method and system for fireplace equipment.

Background

The fireplace is used as a heating device and widely applied to families and commercial venues, and the existing fireplace device control method is mostly based on a preset fixed control strategy, and the indoor temperature is controlled by combining the temporary manual adjustment of a user and adjusting the heat generation intensity and the air inlet quantity of the fireplace. The operation is complicated, is difficult to adapt to different modulation and control demands of users under different environments, and the temperature fluctuation of starting and stopping control based on a fixed control strategy is large, so that energy waste is easily caused. The technical problems of low adaptability of regulation and control requirements and influence on energy saving level exist.

Disclosure of Invention

The invention provides a self-adaptive energy-saving control method and a self-adaptive energy-saving control system for fireplace equipment, which are used for solving the technical problems of low adaptability of regulation and control requirements and influence on energy-saving level in the prior art and realizing the technical effects of improving the demand compliance of regulation and control and improving the energy-saving level.

In a first aspect, the invention provides an adaptive energy saving control method for a fireplace apparatus, wherein the method comprises:

Determining control influence factors of fireplace equipment, monitoring and determining factor data and transmitting the factor data back to an intelligent central control system; based on bottom regulation logic, combining the element data, and determining an autonomous regulation strategy by taking the requirement as a guide, wherein the bottom regulation logic is an initially set basic control program; a period learning module is constructed, and communication connection is established between the period learning module and the intelligent central control system; based on the periodic learning module, a periodic history control record is called, user habit characteristics are mined, automatic learning of automatic control is performed, habit regulation and control logic is determined, and the user habit characteristics meet preset frequency; updating the intelligent central control system based on the habit regulation logic, and determining a behavior regulation strategy, wherein the autonomous regulation strategy and the behavior regulation strategy have the same timestamp identification; and combining the autonomous regulation strategy and the behavior regulation strategy to perform energy-saving optimization control on fireplace equipment, wherein the autonomous regulation strategy or the behavior regulation strategy can be an empty set.

In a second aspect, the invention also provides an adaptive energy saving control system for a fireplace apparatus, wherein the system comprises:

And the element extraction module is used for determining control influence elements of fireplace equipment, monitoring and determining element data and transmitting the element data back to the intelligent central control system.

And the regulation strategy demanding module is used for determining an autonomous regulation strategy based on the bottom regulation logic, combining the element data and taking the requirement as a guide, wherein the bottom regulation logic is an initially set basic control program.

The periodic construction learning module is used for constructing a periodic learning module, and the periodic learning module is in communication connection with the intelligent central control system.

And the habit learning module is used for calling periodic history control records based on the period learning module, mining habit characteristics of a user, performing automatic learning of automatic control, and determining habit regulation logic, wherein the habit characteristics of the user meet preset frequency.

And the strategy updating module is used for updating the intelligent central control system based on the habit regulation and control logic and determining a behavior regulation and control strategy, wherein the autonomous regulation and control strategy and the behavior regulation and control strategy have the same timestamp identification.

The control execution module is used for combining the autonomous regulation strategy and the behavior regulation strategy to perform energy-saving optimization control on fireplace equipment, wherein the autonomous regulation strategy or the behavior regulation strategy can be an empty set.

The invention discloses a self-adaptive energy-saving control method and a self-adaptive energy-saving control system for fireplace equipment, comprising the following steps: determining control influence factors of fireplace equipment, monitoring and determining factor data and transmitting the factor data back to an intelligent central control system; based on bottom regulation logic, combining the element data, and determining an autonomous regulation strategy by taking the requirement as a guide, wherein the bottom regulation logic is an initially set basic control program; a period learning module is constructed, and communication connection is established between the period learning module and the intelligent central control system; based on the periodic learning module, a periodic history control record is called, user habit characteristics are mined, automatic learning of automatic control is performed, habit regulation and control logic is determined, and the user habit characteristics meet preset frequency; updating the intelligent central control system based on the habit regulation logic, and determining a behavior regulation strategy, wherein the autonomous regulation strategy and the behavior regulation strategy have the same timestamp identification; and combining the autonomous regulation strategy and the behavior regulation strategy to perform energy-saving optimization control on fireplace equipment, wherein the autonomous regulation strategy or the behavior regulation strategy can be an empty set. The self-adaptive energy-saving control method and the self-adaptive energy-saving control system for fireplace equipment solve the technical problems of low adaptability of regulation and control requirements and influence on energy-saving level, and realize the technical effects of improving the demand compliance of regulation and control and improving the energy-saving level

Drawings

FIG. 1 is a flow chart of the adaptive energy saving control method for fireplace equipment of the present invention.

FIG. 2 is a schematic diagram of the adaptive energy saving control system for fireplace equipment of the present invention.

Reference numerals illustrate: the system comprises an element extraction module 11, a regulation strategy demanding module 12, a period construction learning module 13, a habit learning module 14, a strategy updating module 15 and a control executing module 16.

Detailed Description

The technical scheme provided by the embodiment of the invention aims to solve the technical problems of low adaptability of regulation and control requirements and influence on energy saving level in the prior art, and adopts the following overall thought:

Firstly, determining control influence factors of fireplace equipment, monitoring and determining factor data and transmitting the factor data back to an intelligent central control system.

And then, determining an autonomous regulation strategy based on the bottom regulation logic by combining the element data and taking the requirement as a guide, wherein the bottom regulation logic is an initially set basic control program.

And then, a period learning module is constructed, and the period learning module is in communication connection with the intelligent central control system.

And then, based on the periodic learning module, calling periodic history control records, mining habit characteristics of the user, performing automatic learning of automatic control, and determining habit regulation logic, wherein the habit characteristics of the user meet preset frequency.

And updating the intelligent central control system based on the habit regulation logic, and determining a behavior regulation strategy, wherein the autonomous regulation strategy and the behavior regulation strategy have the same timestamp identification.

Finally, combining the autonomous regulation strategy and the behavior regulation strategy to perform energy-saving optimization control of fireplace equipment, wherein the autonomous regulation strategy or the behavior regulation strategy can be an empty set.

The foregoing aspects will be better understood by reference to the following detailed description of the invention taken in conjunction with the accompanying drawings and detailed description. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the exemplary embodiments used only to explain the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention. It should be noted that, for convenience of description, only some, but not all of the drawings related to the present invention are shown.

Example 1

FIG. 1 is a schematic flow chart of the adaptive energy saving control method for fireplace equipment of the present invention, wherein the method comprises:

and determining control influence factors of fireplace equipment, monitoring and determining factor data and transmitting the factor data back to the intelligent central control system.

Optionally, the control influencing elements of the fireplace apparatus include indoor temperature, outdoor temperature, indoor humidity, indoor index gas concentration, etc. Specifically, the indoor temperature reflects the temperature change condition of the indoor environment and is a key factor for adjusting the output power of the fireplace.

Specifically, the outdoor temperature is a main external factor affecting the indoor temperature, the outdoor temperature affects the rate of heat dissipation from the indoor to the external environment, specifically, the lower the outdoor temperature is, the larger the corresponding indoor-outdoor temperature difference is, the larger the heat conduction gradient force is, the higher the heat loss caused by heat dissipation is, and the higher output power needs to be compensated to maintain the indoor temperature.

Specifically, the change of indoor humidity, which affects the body temperature of the human body and thus the comfort, is an important influencing element affecting the fireplace control, is preferable to be the relative humidity of the indoor environment. The body sensing temperature refers to the temperature sensed by a person under specific humidity and wind speed conditions. When the humidity is high, the human body is difficult to dissipate heat and feel hotter; when the humidity is low, the human body dissipates heat quickly and feels cooler. Alternatively, the somatosensory temperature is calculated using a Heat Index (Heat Index) formula in combination with temperature and humidity.

Specifically, the indoor index gas concentration is used for reflecting the indoor air quality condition and avoiding air pollution caused by fireplace combustion. Wherein the index gas comprises carbon monoxide, carbon dioxide, nitrogen oxides and the like.

Further, element data is monitored and determined and transmitted back to the intelligent central control system, and first, based on the control influence elements of the fireplace equipment, various monitoring sensing devices are configured, including, for example, temperature sensors (indoor and outdoor), humidity sensors, gas concentration sensors (CO sensor, CO ₂ sensor, NO _x sensor) and the like. And then, acquiring element data of various elements through the various monitoring sensors, and transmitting the data to an intelligent central control system through a wireless network or wired connection. The feedback mechanism of the element data comprises real-time transmission and periodic transmission, and when the activity level of a user in the indoor space is high, the sensor transmits the acquired data to the central control system in real time; when the activity level of the user in the indoor space is low or no one is available, the sensor packages the collected data periodically and intermittently transmits the data to the central control system. Thereby being beneficial to saving energy and reducing consumption of fireplace equipment.

Optionally, temperature sensors (indoor) and humidity sensors are distributed and installed at a plurality of indoor positions, so that accuracy and comprehensiveness of measurement results are ensured. The gas concentration sensor is installed in the vicinity of the fireplace and other critical areas. The temperature sensor (outdoor) is arranged at an outdoor light-shielding position, so that direct sunlight is avoided.

The elements are acquired, monitored and collected, and an environment element basis is provided for subsequent self-adaptive energy-saving control.

And determining an autonomous regulation strategy based on the bottom regulation logic by combining the element data and taking the requirement as a guide, wherein the bottom regulation logic is an initially set basic control program.

Specifically, the bottom regulation logic refers to basic control logic of the target fireplace equipment, and the control logic is embodied by an embedded initial set basic control program, so that the system is ensured to have basic feedback control capability, and can normally operate under basic conditions. Optionally, the underlying regulatory logic is implemented based on an initially set PID controller.

Illustratively, the underlying regulatory logic includes a number of aspects: acquiring sensor data to obtain indoor temperature and humidity data; setting initial control parameters, and setting an initial temperature target value and a humidity compensation coefficient; basic control algorithm, realizing simple on/off control or proportional control.

Optionally, the autonomous regulation strategy is used for dynamically adjusting the output of the fireplace according to the requirements of the user so as to achieve the dual purposes of energy saving and comfort. Among these, the demands for guidance include comfort demands and energy-saving demands.

In some embodiments, determining an autonomous regulatory strategy comprises:

Reading the element data, wherein the element data comprises at least one item; traversing the bottom regulation logic, matching the element data, and determining a single control strategy; and carrying out collision analysis and avoidance regulation fusion on the single control strategy, and determining the autonomous regulation strategy.

Optionally, traversing the element data, performing control matching on the basis of the plurality of elements in the bottom layer regulation logic, checking a control rule matched with the plurality of element data, performing control rule adjustment, and determining a plurality of groups of single control strategies. The single control strategy is a control strategy specified by considering only single element data, such as a regulation strategy for outdoor temperature, a regulation strategy for humidity, and the like.

Illustratively, the underlying regulatory logic includes: temperature control logic to increase fireplace output power if the indoor temperature is below the target temperature; if the room temperature is above the target temperature, the fireplace output power is reduced. The single control strategy includes an adjustment strategy for outdoor temperature (for example, if outdoor temperature is reduced, fireplace output power is increased, etc.), an adjustment strategy for indoor humidity (for example, if indoor humidity level is higher, fireplace output power is reduced, etc.), and an adjustment strategy for indoor index gas concentration (for example, if indoor index gas concentration is over-limit or rising speed is faster, fireplace output power is reduced, ventilation is enhanced, etc.).

Alternatively, in practical applications, a single control strategy may conflict. For example, temperature control strategies require increasing fireplace output, while humidity control strategies require decreasing fireplace output. Therefore, collision analysis and avoidance regulation and control are fused to the single control strategy, so as to determine the final autonomous regulation and control strategy.

The method comprises the steps of carrying out collision analysis and avoidance regulation fusion on a single control strategy, firstly analyzing and obtaining data fluctuation conditions of various element data, and combining to generate a plurality of fluctuation element groups, wherein the plurality of fluctuation element groups correspond to various element data scenes of a target environment; then, based on a plurality of fluctuation element groups, traversing a plurality of single control strategies to perform collision analysis, checking whether collision exists among the single control strategies, evaluating the influence of the collision on a target environment, and determining the severity of the collision; and then, based on a preset control priority, integrating each single control strategy to realize the fusion of avoidance regulation and control and acquire an autonomous regulation and control strategy. For example, for a factor data scenario (unnecessary collision) that does not affect safety, a weighted average method is used to determine the final fireplace output adjustment amount, and in a factor data scenario (necessary collision), a policy that preferentially ensures safety is selected for regulation.

Through the fusion of collision analysis and avoidance regulation, the potential conflict among the single control strategies is solved, and the single control strategies are ensured to realize optimal control under the condition of no mutual interference.

And constructing a period learning module, wherein the period learning module is in communication connection with the intelligent central control system.

Optionally, the periodic learning module is an autonomous learning module constructed based on a feature mining algorithm, and is used for periodically analyzing and learning manual control records of a user, so as to obtain control habits of the user, continuously optimize and update regulation and control strategies, and improve energy saving effect and user comfort level of fireplace equipment.

Optionally, the period learning module is configured on a remote server or a cloud computing module, and the period learning module is in data exchange and control strategy updating with the intelligent central control system through remote communication connection. Through remote deployment in communication connection, the device cost brought by local deployment is reduced, and meanwhile, high-efficiency data processing and learning training are realized through the strong computing capacity and storage resources of cloud computing.

Based on the periodic learning module, a periodic history control record is called, user habit characteristics are mined, automatic learning of automatic control is performed, habit regulation and control logic is determined, and the user habit characteristics meet preset frequency.

Optionally, the periodic learning module periodically acquires manual control records of the user and environmental element data (such as temperature, humidity, gas concentration, etc.) from the intelligent central control system, and stores the data in a database to acquire periodic historical control records. Wherein, the acquisition period of data is set according to the needs.

Optionally, after the periodic history control record is invoked, the periodic learning module analyzes the control habit of the user based on the data mining and machine learning technologies, so as to obtain the habit characteristics of the user, wherein the habit characteristics of the user characterize the control preference of the user under different environmental conditions.

Optionally, the user habit features are converted into specific habit control logic based on the mined user habit features. For example, if a user frequently manually adjusts the fireplace power up in a specific temperature range, the power mapping curve in the temperature range is adjusted to generate corresponding habit regulation logic, so that subsequent adaptive control meets the control expectations of the user.

Specifically, the user habit features need to meet a preset frequency, thereby eliminating sporadic manual control.

In some embodiments, invoking a periodic history control record, mining user habit features, includes:

Reading a history control record, wherein the history control record comprises an autonomous control record and a subjective control record; performing one-layer clustering on the history control records in response to homology, determining a first clustering result, performing two-layer clustering on the first clustering result in an automatic and subjective mode, and determining a second clustering result, wherein the frequency ratio of the subjective clustering clusters is higher; and mining and acquiring the habit characteristics of the user based on the second aggregation result.

Specifically, a history control record of the user is extracted, which includes an autonomous control record (control by an automation system according to a preset rule) and a subjective control record (control by manual adjustment by the user). Optionally, the history control record also includes relevant environmental data such as temperature, humidity, time, etc.

Optionally, a clustering algorithm (e.g., K-means, DBSCAN, etc.) is used to perform a layer of clustering on the history control records, and a first clustering result is determined. The first clustering result includes a plurality of preliminary clusters, each of which represents a similar control behavior pattern (i.e., response homology).

Optionally, on the basis of the first-layer clustering, further combining subjectivity and automation control to perform two-layer clustering, and determining finer user control habits. First, control records in the first layer of clustering results are classified into two types of automatic control and manual control according to control sources. And then, re-clustering the primary clustering clusters by using a hierarchical clustering algorithm, performing frequency analysis by combining subjective control frequency and automatic control record, identifying the behavior of frequent manual adjustment of a user, determining the final user control habit characteristics, and outputting the final user control habit characteristics as a second-layer clustering result. The subjective control cluster has higher frequency ratio, that is, the subjective control record with lower frequency can be fully focused to reflect the control habit of the user more accurately.

By the method, the period learning module can identify the control habit of the user more finely, continuously optimize and update the regulation and control strategy, improve the energy-saving effect of fireplace equipment and the comfort level of the user, and improve the intelligent level of the system.

In some embodiments, prior to autonomous learning of the automated control, comprising:

Setting an initial learning rate; if the user habit characteristics are cycle continuation characteristics, increasing direction adjustment is carried out on the initial learning rate, and the initial learning rate and the characteristic frequency variable are adjusted according to the ratio, so that a first learning rate is determined; if the user habit characteristics are newly added habit characteristics, based on the characteristic frequency, the initial learning rate is adjusted according to the ratio, and a second learning rate is determined; and identifying the user habit features based on the first learning rate and the second learning rate.

Optionally, before the automatic learning of the automatic control, the user control habit characteristics are analyzed, and the learning rate is dynamically adjusted. The method is helpful for the periodic learning module to adapt to new data distribution or characteristic change more quickly, and the autonomous learning efficiency and performance are improved.

Specifically, first, an initial learning rate is set, which is a basic learning rate without considering the user control habit characteristics, and then, the user habit characteristics are analyzed frequently, and whether the user habit characteristics are cycle continuation characteristics or newly added habit characteristics is determined. Where the period continuation feature represents a steady behavior pattern of the user and the newly added habit feature may represent a change or new trend of the user's behavior. And then, according to the analysis result of the feature frequency, respectively adjusting the learning rates of the cycle extension feature and the newly-added habit feature so that the cycle learning module can better capture and adapt to the features.

Optionally, for the cycle extension feature, the initial learning rate is adjusted upward in an equal ratio based on the ratio of the feature frequency increment of the cycle extension feature in the control record to the historical average feature frequency, so as to ensure that the first learning rate is higher than the initial learning rate; for the newly added habit features, the initial learning rate is converted according to the proportion of the feature frequency of the newly added habit features to the increment of all feature frequencies in the control record, so that adverse interference of the newly added habit features acquired based on the sporadic autonomous control record to the habit features of the user is avoided.

Optionally, the period continuation feature and the newly added habit feature in the habit features of the user are identified based on the adjusted first learning rate and second learning rate. The periodic learning module can accurately distinguish different types of characteristics and importance thereof, and the user behavior mode can be better understood by effectively utilizing the information in the learning process, so that accuracy and robustness of the habit regulation and control logic are improved.

And updating the intelligent central control system based on the habit regulation logic, and determining a behavior regulation strategy, wherein the autonomous regulation strategy and the behavior regulation strategy have the same timestamp identification.

Optionally, the habit regulation logic is transmitted and updated to the intelligent central control system, and the new habit regulation logic is integrated into the existing intelligent central control system, including covering or replacing the old regulation strategy, and updating the system configuration, so as to ensure that the new regulation strategy is effective. The autonomous regulation strategy is used for autonomously regulating the running state of fireplace equipment according to the real-time element data. The behavior regulation strategy is based on habit regulation logic, and the adaptability adjustment combined with the user requirement is carried out, so that the system is more in line with the control preference of the user, and the comfort level of the user is improved.

And combining the autonomous regulation strategy and the behavior regulation strategy to perform energy-saving optimization control on fireplace equipment, wherein the autonomous regulation strategy or the behavior regulation strategy can be an empty set.

In some embodiments, energy saving optimization control of fireplace equipment is performed, including:

Obtaining a subjective control instruction, wherein the subjective control instruction has the highest priority; if the subjective control instruction has strategy collision, risk judgment is carried out on the subjective control instruction; and if the risk does not exist, responding to the subjective control instruction, and if the risk exists, generating a risk popup window based on the subjective control instruction.

Specifically, first, a subjective control instruction is obtained through manual input of a user or an interactive control panel, and the subjective control instruction directly reflects real-time requirements and preferences of the user and has the highest priority, namely, is preferentially responded to manual operation of the user.

Then, whether the subjective control instruction collides with the current autonomous regulation strategy or the behavior regulation strategy is detected, if the strategy collision exists, the risk is further judged, and whether the subjective control instruction can cause safety risks, such as exceeding the concentration of index gases (carbon monoxide, carbon dioxide, nitrogen oxides and the like), is determined.

Further, if the risk does not exist, the subjective control instruction is directly responded and executed, if the risk exists, a risk popup window based on the subjective control instruction is generated, a potential risk possibly caused by the subjective control instruction is prompted for a user, and corresponding processing advice is provided. Through risk judgment and risk popup window prompt, the safety problem can not be caused when the user is controlled manually, and energy-saving optimization control is realized on the premise of ensuring the safety of the user, and the use experience of the user is improved.

Further, after the energy-saving optimization control of the fireplace equipment is performed, the method comprises the following steps:

Monitoring fireplace operating efficiency, wherein the fireplace operating efficiency comprises combustion efficiency and heat exchange efficiency, and comprises efficiency trend and efficiency vector; performing overrun judgment on the running efficiency of the fireplace, and if the running efficiency of the fireplace is lower than an efficiency threshold value, generating an energy consumption early warning instruction; and performing overrun traceability positioning and operation and maintenance adjustment of fireplace equipment based on the energy consumption early warning instruction.

Optionally, the fireplace operation efficiency is monitored, wherein the monitoring content comprises combustion efficiency and heat exchange efficiency, and further, the combustion efficiency comprises efficiency trend and efficiency vector.

The combustion efficiency refers to the ability of the fireplace to burn fuel, and the combustion efficiency directly influences the heat which can be generated by the fireplace. Combustion efficiency may be determined by quantitative analysis of combustion products such as carbon dioxide and water vapor. Efficient combustion produces less unburned fuel and harmful emissions; heat exchange efficiency refers to the ability of the fireplace to transfer heat generated by combustion to the ambient environment. Illustratively, the heat exchange efficiency is determined by measuring the temperature of the fireplace's outlet and the temperature of the ambient environment. The efficient heat exchange allows more heat to be utilized, thereby increasing the energy saving level of the fireplace.

Further, the combustion efficiency also comprises an efficiency trend and an efficiency vector, wherein the efficiency trend refers to the change condition of the fireplace operation efficiency with time. By monitoring the efficiency trend, it is possible to see if the performance of the fireplace is improving or deteriorating, so that timely maintenance and adjustment is performed. The efficiency vector refers to the variation in fireplace operating efficiency under different operating conditions. By monitoring the efficiency vector, it is helpful to understand which operating conditions can improve the operating efficiency of the fireplace, thereby optimizing specifically.

For example, the relevant data is collected in real time by the combustion monitoring sensor and the heat exchange monitoring sensor. And then analyzing and calculating the acquired data to generate an efficiency trend and an efficiency vector.

Optionally, according to design parameters and operation specifications of the fireplace equipment, setting thresholds of combustion efficiency and heat exchange efficiency, obtaining the thresholds of the efficiency, if the operation efficiency is lower than the set thresholds, judging that the operation efficiency is in an overrun state, considering that the operation energy consumption of the fireplace equipment is poor at the moment, generating an energy consumption early warning instruction with lower energy saving level, and prompting a user or an operation and maintenance personnel to pay attention. The energy consumption early warning instruction comprises overrun proportion and overrun content.

Further, after the energy-saving optimization control of the fireplace equipment is performed, the method further comprises the following steps:

Determining a risk setpoint based on a service state of fireplace equipment, the risk setpoint identifying a threshold vector; traversing the risk positioning points and configuring an emergency plan library; judging whether the service data meet the threshold vector, if so, traversing the emergency plan library, and carrying out risk emergency control and early warning.

Optionally, after energy-saving control is performed on the fireplace equipment, risk management and emergency plan configuration are performed on the fireplace equipment to ensure safety and reliability of the equipment in the service process, specifically, firstly, the position or condition where the fireplace equipment possibly has risks is determined through real-time monitoring and data analysis, wherein risk positioning points are determined according to the working state and environmental factors of the fireplace equipment, and potential safety hazards are caused.

Optionally, the fault and safety event history of the target fireplace device and the homologous fireplace device of the target fireplace device are obtained through big data, and a plurality of risk locating points are extracted.

Illustratively, the operating conditions (including combustion conditions, temperature profiles, exhaust conditions, etc.) and environmental factors (including indoor and outdoor temperatures, humidity, air circulation conditions, etc.) of the fireplace installation. And determining risk positioning points, wherein the risk categories related to the risk positioning points comprise local temperature abnormality, unsmooth exhaust, incomplete combustion and the like.

Specifically, the temperatures of the parts of the fireplace equipment are monitored in real time by the temperature sensor, a temperature threshold is set, and if the temperature threshold is exceeded, the local temperature abnormality is determined. And the exhaust flow sensor and the pressure sensor are arranged to monitor the flow and the pressure of the exhaust system to judge the unsmooth exhaust. And analyzing the combustion condition by a combustion monitoring sensor, detecting whether unburned fuel remains or the concentration of harmful gas is too high, and determining whether incomplete combustion exists.

Optionally, a corresponding threshold vector is set for each risk setpoint, the vector comprising a plurality of thresholds, such as temperature, pressure, gas concentration, etc., defining a safety range for the risk setpoint beyond which the fireplace device is considered to be at fault or at risk.

Optionally, according to the characteristics of the risk locating points and possible risk conditions, configuring a corresponding emergency plan. The emergency plan library contains countermeasures and operational flows for different risk situations in order to quickly respond in case of emergency.

Optionally, service data of the fireplace equipment is monitored in real time, and compared with a set threshold vector to judge whether the fireplace equipment is in a safe state or not, and whether emergency measures need to be taken or not. If the service data meet the threshold vector, the fireplace equipment is indicated to have risk, the emergency plan library is traversed, and corresponding risk emergency control measures are adopted. The risk emergency control measures comprise equipment parameter adjustment, standby equipment starting or equipment operation stopping and the like. Meanwhile, the method also comprises the step of starting an early warning mechanism to inform relevant personnel to take action.

Through the steps, the risk locating points are determined based on the service state of fireplace equipment, and the emergency plan library is configured, so that potential risks can be found and processed in time in the service process of the equipment, and the safe operation of the equipment is ensured.

In summary, the self-adaptive energy-saving control method for fireplace equipment provided by the invention has the following technical effects:

The data of the elements are monitored and determined by determining the control influencing factors of the fireplace equipment and are transmitted back to the intelligent central control system. Based on the bottom regulation logic, the element data is combined, the requirement is used as a guide, and an autonomous regulation strategy is determined. And constructing a period learning module, wherein the module is in communication connection with the intelligent central control system. Based on the periodic learning module, a periodic history control record is called, habit characteristics of a user are mined, automatic learning of automatic control is performed, and habit regulation and control logic is determined. The user habit features need to meet preset frequency requirements. And updating the intelligent central control system based on habit regulation logic, and determining a behavior regulation strategy. And combining an autonomous regulation strategy and a behavior regulation strategy to perform energy-saving optimization control on fireplace equipment. Thereby realizing the technical effects of improving the demand compliance of regulation and control and improving the energy-saving level.

Example two

FIG. 2 is a schematic diagram of the adaptive energy saving control system for fireplace equipment of the present invention. For example, the flow diagram of the adaptive energy saving control method for fireplace equipment of the present invention of FIG. 1 can be implemented by the structure shown in FIG. 2.

Based on the same conception as the adaptive energy saving control method for fireplace equipment in the embodiment, the adaptive energy saving control system for fireplace equipment further provided by the invention comprises:

the element extraction module 11 is used for determining control influence elements of fireplace equipment, monitoring and determining element data and transmitting the element data back to the intelligent central control system.

The regulation strategy demanding module 12 is configured to determine an autonomous regulation strategy based on the underlying regulation logic, which is an initially set basic control program, in combination with the element data and with the demand as a guide.

The period construction learning module 13 is configured to construct a period learning module, and the period learning module is in communication connection with the intelligent central control system.

The habit learning module 14 is configured to invoke a periodic history control record based on the period learning module, mine a habit feature of a user, and perform autonomous learning of automatic control to determine a habit regulation logic, where the habit feature of the user meets a preset frequency.

And a policy updating module 15, configured to update the intelligent central control system based on the habit regulation logic, and determine a behavior regulation policy, where the autonomous regulation policy and the behavior regulation policy have the same timestamp identifier.

The control execution module 16 is configured to combine the autonomous regulation strategy and the behavior regulation strategy to perform energy-saving optimization control of the fireplace device, where the autonomous regulation strategy or the behavior regulation strategy may be an empty set.

Wherein, regulation strategy demanding module 12 comprises:

And the element data acquisition unit is used for reading the element data, and the element data comprises at least one item.

And the element matching unit is used for traversing the bottom regulation logic, matching the element data and determining a single control strategy.

And the collision analysis and avoidance regulation and control unit is used for carrying out collision analysis and avoidance regulation and control fusion on the single control strategy and determining the autonomous regulation and control strategy.

Further, the habit learning module 14 includes:

And the history record unit is used for reading the history control record, wherein the history control record comprises an autonomous control record and a subjective control record.

And the hierarchical clustering unit is used for carrying out one-layer clustering on the history control records with response homology, determining a first clustering result, carrying out two-layer clustering on the first clustering result with automation and subjectivity, and determining a second clustering result, wherein the frequency ratio of the subjective clustering clusters is higher.

And the user habit mining unit is used for mining and acquiring the user habit characteristics based on the second aggregation result.

Further, the habit learning module 14 further comprises:

and the learning rate unit is used for setting the initial learning rate.

And the periodic learning rate adjusting unit is used for increasing and adjusting the initial learning rate and adjusting the ratio of the initial learning rate to the characteristic frequency variable if the user habit characteristic is a periodic continuation characteristic, so as to determine a first learning rate.

And the newly added learning rate adjusting unit is used for adjusting the initial learning rate according to the ratio based on the characteristic frequency to determine a second learning rate if the habit characteristics of the user are the newly added habit characteristics.

And the characteristic identification unit is used for identifying the habit characteristics of the user based on the first learning rate and the second learning rate.

Further, the control execution module 16 includes:

And the priority acquisition unit is used for acquiring the subjective control instruction, wherein the subjective control instruction has the highest priority.

And the risk judging unit is used for judging the risk of the subjective control instruction if the subjective control instruction has strategy collision.

And the risk processing unit is used for responding to the subjective control instruction if the risk does not exist, and generating a risk popup window based on the subjective control instruction if the risk exists.

Further, the system further comprises an energy consumption early warning unit for: the fireplace operating efficiency is monitored, including combustion efficiency and heat exchange efficiency, including efficiency trends and efficiency vectors. And performing overrun judgment on the running efficiency of the fireplace, and if the running efficiency of the fireplace is lower than an efficiency threshold value, generating an energy consumption early warning instruction. And performing overrun traceability positioning and operation and maintenance adjustment of fireplace equipment based on the energy consumption early warning instruction.

Further, the system further comprises a unit for: a risk setpoint is determined based on a service state of the fireplace device, the risk setpoint identifying a threshold vector. And traversing the risk positioning points and configuring an emergency plan library. Judging whether the service data meet the threshold vector, if so, traversing the emergency plan library, and carrying out risk emergency control and early warning.

It should be understood that the embodiments mentioned in this specification focus on the differences from other embodiments, and that the specific embodiment in the first embodiment described above is equally applicable to the adaptive energy saving control system for fireplace equipment described in the second embodiment, and is not further developed herein for brevity of description.

It is to be understood that both the foregoing description and the embodiments of the present invention enable one skilled in the art to utilize the present invention. While the invention is not limited to the embodiments described above, it should be understood that: modifications of the technical solutions described in the foregoing embodiments or equivalent substitutions of some technical features thereof may be still performed by those skilled in the art; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.

Claims (5)

1. An adaptive energy saving control method for a fireplace apparatus, the method comprising:

Determining control influence factors of fireplace equipment, monitoring and determining factor data and transmitting the factor data back to an intelligent central control system;

based on bottom regulation logic, combining the element data, and determining an autonomous regulation strategy by taking the requirement as a guide, wherein the bottom regulation logic is an initially set basic control program;

A period learning module is constructed, and communication connection is established between the period learning module and the intelligent central control system;

based on the periodic learning module, a periodic history control record is called, user habit characteristics are mined, automatic learning of automatic control is performed, habit regulation and control logic is determined, and the user habit characteristics meet preset frequency;

Updating the intelligent central control system based on the habit regulation logic, and determining a behavior regulation strategy, wherein the autonomous regulation strategy and the behavior regulation strategy have the same timestamp identification;

combining the autonomous regulation strategy and the behavior regulation strategy to perform energy-saving optimization control on fireplace equipment, wherein the autonomous regulation strategy or the behavior regulation strategy can be an empty set;

wherein the determining an autonomous regulatory strategy comprises:

Reading the element data, wherein the element data comprises at least one item;

traversing the bottom regulation logic, matching the element data, and determining a single control strategy;

Performing collision analysis and avoidance regulation fusion on the single control strategy, and determining the autonomous regulation strategy;

the step of calling the periodic history control record and mining the habit characteristics of the user comprises the following steps:

Reading a history control record, wherein the history control record comprises an autonomous control record and a subjective control record;

performing one-layer clustering on the history control records in response to homology, determining a first clustering result, performing two-layer clustering on the first clustering result in an automatic and subjective mode, and determining a second clustering result, wherein the frequency ratio of the subjective clustering clusters is higher;

mining and acquiring the habit characteristics of the user based on the second aggregation result;

wherein, the energy-saving optimization control of fireplace equipment comprises the following steps:

Obtaining a subjective control instruction, wherein the subjective control instruction has the highest priority;

if the subjective control instruction has strategy collision, risk judgment is carried out on the subjective control instruction;

And if the risk does not exist, responding to the subjective control instruction, and if the risk exists, generating a risk popup window based on the subjective control instruction.

2. An adaptive energy saving control method for fireplace equipment as claimed in claim 1, wherein prior to said autonomously learning of the automated control, comprising:

setting an initial learning rate;

if the user habit characteristics are cycle continuation characteristics, increasing direction adjustment is carried out on the initial learning rate, and the initial learning rate and the characteristic frequency variable are adjusted according to the ratio, so that a first learning rate is determined;

if the user habit characteristics are newly added habit characteristics, based on the characteristic frequency, the initial learning rate is adjusted according to the ratio, and a second learning rate is determined;

and identifying the user habit features based on the first learning rate and the second learning rate.

3. An adaptive energy saving control method for fireplace equipment as claimed in claim 1, wherein after energy saving optimization control of the fireplace equipment is performed, comprising:

Monitoring fireplace operating efficiency, wherein the fireplace operating efficiency comprises combustion efficiency and heat exchange efficiency, and comprises efficiency trend and efficiency vector;

Performing overrun judgment on the running efficiency of the fireplace, and if the running efficiency of the fireplace is lower than an efficiency threshold value, generating an energy consumption early warning instruction;

and performing overrun traceability positioning and operation and maintenance adjustment of fireplace equipment based on the energy consumption early warning instruction.

4. An adaptive energy saving control method for fireplace equipment as claimed in claim 1, wherein after energy saving optimised control of the fireplace equipment is performed, the method further comprises:

Determining a risk setpoint based on a service state of fireplace equipment, the risk setpoint identifying a threshold vector;

traversing the risk positioning points and configuring an emergency plan library;

judging whether the service data meet the threshold vector, if so, traversing the emergency plan library, and carrying out risk emergency control and early warning.

5. An adaptive energy saving control system for a fireplace apparatus, the system being for performing the adaptive energy saving control method for a fireplace apparatus as claimed in any one of claims 1 to 4, the system comprising:

The element extraction module is used for determining control influence elements of fireplace equipment, monitoring and determining element data and transmitting the element data back to the intelligent central control system;

the regulation strategy demanding module is used for determining an autonomous regulation strategy based on bottom regulation logic, combining the element data and taking the requirement as a guide, wherein the bottom regulation logic is an initially set basic control program;

the cycle building learning module is used for building a cycle learning module, and the cycle learning module is in communication connection with the intelligent central control system;

The habit learning module is used for calling periodic history control records based on the period learning module, mining habit characteristics of a user, performing automatic control autonomous learning, and determining habit regulation logic, wherein the habit characteristics of the user meet preset frequency;

the strategy updating module is used for updating the intelligent central control system based on the habit regulation logic and determining a behavior regulation strategy, wherein the autonomous regulation strategy and the behavior regulation strategy have the same timestamp identification;

The control execution module is used for combining the autonomous regulation strategy and the behavior regulation strategy to perform energy-saving optimization control on fireplace equipment, wherein the autonomous regulation strategy or the behavior regulation strategy can be an empty set.