CN111911444A - Intelligent fan power supply scheduling method and system - Google Patents
Intelligent fan power supply scheduling method and system Download PDFInfo
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- CN111911444A CN111911444A CN202010718172.8A CN202010718172A CN111911444A CN 111911444 A CN111911444 A CN 111911444A CN 202010718172 A CN202010718172 A CN 202010718172A CN 111911444 A CN111911444 A CN 111911444A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/004—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids by varying driving speed
Abstract
The invention relates to the technical field of power supply systems, in particular to a power supply scheduling method and system for an intelligent fan. According to the invention, when the starting signal for the portable fan is detected, the residual electric quantity data of the battery in the portable fan is collected in real time, and the current electric quantity interval where the residual electric quantity data is located is determined. And when the residual electric quantity is insufficient, all the gear switches are turned on, part of the low-gear switches are turned on when the residual electric quantity has certain loss, and when the residual electric quantity is insufficient, all the gear switches are turned off and the self-adaptive control process is started. And further, the control parameters of the self-adaptive control process are accurately and reliably adjusted through the determined passive loss curve and the determined active loss curve, so that the wind power of the portable fan is automatically adjusted through the self-adaptive control process, the output power of a battery piece is reduced, the loss of the battery piece is reduced, and the service life of the portable fan in low electric quantity is prolonged.
Description
Technical Field
The invention relates to the technical field of power supply systems, in particular to a power supply scheduling method and system for an intelligent fan.
Background
Nowadays, a portable fan is one of important articles carried by people when going out. In hot summer, the portable fan can make up for the shortcoming that air conditioner and floor fan can not be used when going out. Portable fans are typically powered by lithium batteries or dry cells for portability. However, in some extreme outdoor environments, if the battery cannot be replaced or the portable fan cannot be charged in time, the power of the portable fan is quickly exhausted. Therefore, how to prolong the service life of the portable fan when the portable fan is in a low power state is an urgent technical problem to be solved at the present stage.
Disclosure of Invention
In order to solve the technical problems in the related art, the invention provides a power supply scheduling method and system for an intelligent fan.
In a first aspect, a power supply scheduling method for an intelligent fan is provided, which includes the following steps:
when a starting signal for a portable fan is detected, acquiring residual electric quantity data of a battery piece in the portable fan in real time, and determining a current electric quantity interval where the residual electric quantity data are located;
when the current electric quantity interval is a first interval, controlling a switch key of the portable fan to be conducted with n gear switches, wherein n is a positive integer;
when the current electric quantity interval is a second interval, controlling the on-off key to be disconnected with m gear switches in the n gear switches, wherein m is a positive integer smaller than n;
when the electric quantity interval is a third interval, when the switch key is controlled to be disconnected with all gear switches of the portable fan, a self-adaptive control thread is started in parallel, and the ambient environment data of the portable fan, which is collected by a collector integrated with the portable fan, is obtained;
determining a passive loss curve of the battery device according to the ambient environment data, and generating an active loss curve of the battery device based on the residual electric quantity data; and iterating the active loss curve and the passive loss curve to obtain an iteration result, and adjusting the control parameters of the self-adaptive control process according to the iteration result so as to adjust the wind power of the portable fan according to the self-adaptive control process.
Optionally, determining the current power interval in which the remaining power data is located specifically includes:
extracting power consumption rate in the residual electric quantity data and superposition weight corresponding to the power consumption rate; wherein the superposition weight is used for representing the change degree of the power consumption rate;
calculating a relative electric quantity value of the residual electric quantity data according to the power consumption rate and the superposition weight; wherein, the relative electric quantity value is the average value of the electric quantity values of the residual electric quantity data in a set time period;
mapping the relative electric quantity value to a preset value interval to obtain a current electric quantity interval in which the residual electric quantity data is located; the preset numerical value interval is divided into a plurality of subintervals according to the set step length.
Optionally, the method further comprises:
acquiring the current output power of the portable fan;
determining a target step length corresponding to the current output power from a preset correlation distribution diagram;
and replacing the set step length with the target step length.
Optionally, determining a passive loss curve of the battery device according to the ambient environment data specifically includes:
according to a first data label and a second data label which are used for representing environment types in the surrounding environment data, determining variable directional coefficients of a plurality of pieces of environment variable data to be extracted and used for calculating the influence weight sequence of the battery device and conversion description values among different pieces of environment variable data; wherein the first data tag is used for indicating a dry-wet category of the environmental category, and the second data tag is used for indicating a temperature category of the environmental category;
based on the determined variable direction coefficients of the plurality of pieces of environment variable data and the conversion description values among different pieces of environment variable data, partially extracting the plurality of pieces of environment variable data, so that the variable direction coefficient of the extracted first piece of environment variable data is larger than a first preset value, and the conversion description value among the extracted first piece of environment variable data is smaller than a second preset value;
for any one second environment variable data except the first environment variable data in the plurality of environment variable data, determining a target conversion description value of the second environment variable data under each kind of environment variable data in the extracted first environment variable data, and distributing part of the first environment variable data to a group corresponding to the first data label and distributing part of the second environment variable data to a group corresponding to the second data label according to the target description value;
calculating a difference value between each first environment variable data under the grouping corresponding to the first data label and each second environment variable data under the grouping corresponding to the second data label, and determining a difference value sequence corresponding to the first environment variable data; calculating a difference coefficient for representing the fluctuation degree of the difference value sequence; judging whether the difference coefficient reaches a target coefficient, and if the difference coefficient reaches the target coefficient, determining the first environment variable data as target variable data with delay influence on the battery piece;
determining a first coordinate point of other variable data except the target variable data in a preset coordinate plane in a plurality of environment variable data, fitting the first coordinate point to obtain an initial loss curve, mapping the target variable data to the initial loss curve to obtain an influence factor of each target variable data relative to the initial loss curve, and smoothing the initial loss curve by using the influence factor to obtain a passive loss curve of the battery device.
Optionally, generating an active loss curve of the battery device based on the remaining capacity data includes:
acquiring a load curve corresponding to the residual electric quantity data;
calculating the electric energy receiving rate of each load node of the load curve, and determining a first accumulated value of the load nodes of which the electric energy receiving rate is less than or equal to a preset reference receiving rate based on the electric energy receiving rate of each load node;
determining the proportion between the first accumulated value of the load node and the second accumulated values corresponding to all the load nodes of the load curve to obtain the electric energy receiving loss ratio of the load curve;
determining a comprehensive transmission loss rate of the load curve; determining a current electric energy loss factor of the load curve under the residual electric quantity data according to the electric energy receiving loss ratio of the load curve and the comprehensive transmission loss rate of the load curve;
and determining a loss curve weighted value corresponding to the current electric energy loss factor of the load curve based on a pre-stored mapping list of the performance curve and the electric energy loss factor of the battery piece, and weighting a mirror image curve corresponding to the pre-stored performance curve of the battery piece by adopting the loss curve weighted value to obtain an active loss curve of the battery piece.
Optionally, iterating the active loss curve and the passive loss curve to obtain an iteration result, including:
extracting time sequence characteristics corresponding to each parameter node from a first curve parameter group corresponding to an active loss curve, acquiring first mapping characteristics of the time sequence characteristics corresponding to each parameter node in a second curve parameter group corresponding to a passive loss curve, and determining a time sequence difference coefficient between the active loss curve and the passive loss curve according to the first mapping characteristics;
while iteratively adding each parameter node in the first curve parameter set to each parameter node in the second curve parameter set based on the timing difference coefficient, acquiring an iteration error value between each parameter node in the first curve parameter set and each parameter node in the second curve parameter set;
and acquiring an iteration array generated by iterative addition of each parameter node in the first curve parameter group and each parameter node in the second curve parameter group, and correcting an array separator in the iteration array by using the iteration error value to obtain the iteration result.
In a second aspect, a power supply scheduling system for an intelligent fan is provided, which comprises a controller, a collector, a battery, an on-off key and n gear switches; the controller is respectively communicated with the collector, the battery piece, the on-off key and the n gear switches; the controller is configured to:
when a starting signal for a portable fan is detected, acquiring residual electric quantity data of a battery piece in the portable fan in real time, and determining a current electric quantity interval where the residual electric quantity data are located;
when the current electric quantity interval is a first interval, controlling a switch key of the portable fan to be conducted with n gear switches, wherein n is a positive integer;
when the current electric quantity interval is a second interval, controlling the on-off key to be disconnected with m gear switches in the n gear switches, wherein m is a positive integer smaller than n;
when the electric quantity interval is a third interval, when the switch key is controlled to be disconnected with all gear switches of the portable fan, a self-adaptive control thread is started in parallel, and the ambient environment data of the portable fan, which is collected by a collector integrated with the portable fan, is obtained;
determining a passive loss curve of the battery device according to the ambient environment data, and generating an active loss curve of the battery device based on the residual electric quantity data; and iterating the active loss curve and the passive loss curve to obtain an iteration result, and adjusting the control parameters of the self-adaptive control process according to the iteration result so as to adjust the wind power of the portable fan according to the self-adaptive control process.
Optionally, the determining, by the controller according to the ambient data, a passive loss curve of the battery device specifically includes:
according to a first data label and a second data label which are used for representing environment types in the surrounding environment data, determining variable directional coefficients of a plurality of pieces of environment variable data to be extracted and used for calculating the influence weight sequence of the battery device and conversion description values among different pieces of environment variable data; wherein the first data tag is used for indicating a dry-wet category of the environmental category, and the second data tag is used for indicating a temperature category of the environmental category;
based on the determined variable direction coefficients of the plurality of pieces of environment variable data and the conversion description values among different pieces of environment variable data, partially extracting the plurality of pieces of environment variable data, so that the variable direction coefficient of the extracted first piece of environment variable data is larger than a first preset value, and the conversion description value among the extracted first piece of environment variable data is smaller than a second preset value;
for any one second environment variable data except the first environment variable data in the plurality of environment variable data, determining a target conversion description value of the second environment variable data under each kind of environment variable data in the extracted first environment variable data, and distributing part of the first environment variable data to a group corresponding to the first data label and distributing part of the second environment variable data to a group corresponding to the second data label according to the target description value;
calculating a difference value between each first environment variable data under the grouping corresponding to the first data label and each second environment variable data under the grouping corresponding to the second data label, and determining a difference value sequence corresponding to the first environment variable data; calculating a difference coefficient for representing the fluctuation degree of the difference value sequence; judging whether the difference coefficient reaches a target coefficient, and if the difference coefficient reaches the target coefficient, determining the first environment variable data as target variable data with delay influence on the battery piece;
determining a first coordinate point of other variable data except the target variable data in a preset coordinate plane in a plurality of environment variable data, fitting the first coordinate point to obtain an initial loss curve, mapping the target variable data to the initial loss curve to obtain an influence factor of each target variable data relative to the initial loss curve, and smoothing the initial loss curve by using the influence factor to obtain a passive loss curve of the battery device.
Optionally, the controller generating an active loss curve of the battery device based on the remaining capacity data includes:
acquiring a load curve corresponding to the residual electric quantity data;
calculating the electric energy receiving rate of each load node of the load curve, and determining a first accumulated value of the load nodes of which the electric energy receiving rate is less than or equal to a preset reference receiving rate based on the electric energy receiving rate of each load node;
determining the proportion between the first accumulated value of the load node and the second accumulated values corresponding to all the load nodes of the load curve to obtain the electric energy receiving loss ratio of the load curve;
determining a comprehensive transmission loss rate of the load curve; determining a current electric energy loss factor of the load curve under the residual electric quantity data according to the electric energy receiving loss ratio of the load curve and the comprehensive transmission loss rate of the load curve;
and determining a loss curve weighted value corresponding to the current electric energy loss factor of the load curve based on a pre-stored mapping list of the performance curve and the electric energy loss factor of the battery piece, and weighting a mirror image curve corresponding to the pre-stored performance curve of the battery piece by adopting the loss curve weighted value to obtain an active loss curve of the battery piece.
Optionally, the iterating the active loss curve and the passive loss curve by the controller to obtain an iteration result includes:
extracting time sequence characteristics corresponding to each parameter node from a first curve parameter group corresponding to an active loss curve, acquiring first mapping characteristics of the time sequence characteristics corresponding to each parameter node in a second curve parameter group corresponding to a passive loss curve, and determining a time sequence difference coefficient between the active loss curve and the passive loss curve according to the first mapping characteristics;
while iteratively adding each parameter node in the first curve parameter set to each parameter node in the second curve parameter set based on the timing difference coefficient, acquiring an iteration error value between each parameter node in the first curve parameter set and each parameter node in the second curve parameter set;
and acquiring an iteration array generated by iterative addition of each parameter node in the first curve parameter group and each parameter node in the second curve parameter group, and correcting an array separator in the iteration array by using the iteration error value to obtain the iteration result.
According to the power supply scheduling method and system for the intelligent fan, provided by the embodiment of the invention, when the starting signal for the portable fan is detected, the residual electric quantity data of the battery piece in the portable fan can be acquired in real time, and the current electric quantity interval where the residual electric quantity data is located is determined. Therefore, the control strategy of the portable fan can be adjusted in a stepwise manner according to different current electric quantity intervals, and the service life of the portable fan in low electric quantity is effectively prolonged. And when the residual electric quantity is insufficient, all the gear switches are turned on, part of the low-gear switches are turned on when the residual electric quantity has certain loss, and when the residual electric quantity is insufficient, all the gear switches are turned off and the self-adaptive control process is started. And further, the control parameters of the self-adaptive control process are accurately and reliably adjusted through the determined passive loss curve and the determined active loss curve, so that the wind power of the portable fan is automatically adjusted through the self-adaptive control process, the output power of a battery piece is reduced, the loss of the battery piece is reduced, and the service life of the portable fan in low electric quantity is prolonged.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart of a power supply scheduling method for an intelligent fan according to the present invention.
Fig. 2 is a schematic diagram of an architecture of a power supply scheduling system for an intelligent fan according to the present invention.
Fig. 3 is a functional block diagram of an intelligent fan power supply scheduling device according to the present invention.
Detailed Description
After analyzing the control mode of the existing portable fan, the inventor finds that the existing portable fan usually has a plurality of wind power gears, and the plurality of wind power gears can be manually switched at any time. Thus, when the portable fan is in a low power state, if the user still uses a high wind power gear, the power of the portable fan is quickly exhausted, and the use time of the portable fan in the low power state is greatly shortened.
In view of this, in order to improve the service life of the portable fan in the low power state, embodiments of the present invention provide an intelligent fan power supply scheduling method and system, which can monitor the remaining power of the portable fan in real time, thereby implementing a stepwise adjustment of a control strategy of the portable fan, and effectively prolonging the service life of the portable fan in the low power state.
In order to achieve the above object, please refer to fig. 1, a power supply scheduling method for an intelligent fan is provided, the method is applied to a controller of a portable fan, the controller is electrically connected to a switch key and n shift switches arranged on the portable fan, the controller is also electrically connected to a battery installed in the portable fan, and in addition, the controller can also be electrically connected to a collector integrated on the portable fan. Further, the controller implements the smart fan power scheduling method by performing the following steps S110 to S150 when running.
Step S110, when a starting signal for the portable fan is detected, acquiring the residual electric quantity data of a battery piece in the portable fan in real time, and determining the current electric quantity interval where the residual electric quantity data is located.
In this embodiment, the user can perform the power-on operation through the on-off key provided on the portable fan. For example, a short press of the power-on key turns on. The electric quantity interval can be set according to the model and the power of the portable fan, and is not limited herein.
And step S120, when the current electric quantity interval is a first interval, controlling the on-off key to be conducted with n gear switches, wherein n is a positive integer.
In this embodiment, the first interval is used to indicate that the remaining power of the portable fan is sufficient, in which case the controller may activate all the range switches of the portable fan. The user can realize the switching between different wind-force gears through pressing the on-off key briefly. It will be appreciated that the sequence of switching the wind gear positions may be from small to large.
And step S130, when the current electric quantity interval is a second interval, controlling the on-off key to be disconnected with m gear switches in the n gear switches, wherein m is a positive integer smaller than n.
In this embodiment, the second interval is used to indicate that there is a certain consumption of the remaining power of the portable fan, in which case the controller may turn off the m high level switches of the portable fan. The user can switch between low wind gears. Therefore, the power consumption of the battery piece can be reduced, and the service life is prolonged.
And step S140, when the electric quantity interval is a third interval and the on-off key is controlled to be disconnected with all gear switches of the portable fan, starting an adaptive control routine in parallel and acquiring the ambient environment data of the portable fan, which is acquired by the acquisition unit integrated with the portable fan.
In this embodiment, the third interval is used to indicate that the remaining power of the portable fan is in the low power interval, in which case the controller may turn off all the switches of the portable fan and start the adaptive control routine to automatically control and adjust the wind speed of the portable fan. In one example, the collector may be a sensor and the ambient data includes, but is not limited to, temperature, humidity, lighting, ambient wind speed, and the like.
Step S150, determining a passive loss curve of the battery device according to the ambient environment data, and generating an active loss curve of the battery device based on the residual electric quantity data; and iterating the active loss curve and the passive loss curve to obtain an iteration result, and adjusting the control parameters of the self-adaptive control process according to the iteration result so as to adjust the wind power of the portable fan according to the self-adaptive control process.
In step S150, the passive loss curve is used to represent the power loss of the battery device under the influence of the environmental factors, and the active loss curve is used to represent the power loss of the battery device during use. The control parameters of the self-adaptive control process can be accurately and reliably adjusted by analyzing the passive loss curve and the active loss curve, so that the wind power of the portable fan is automatically adjusted through the self-adaptive control process, the output power of a battery piece is reduced, the loss of the battery piece is reduced, and the service life of the portable fan in low electric quantity is prolonged.
In specific implementation, by executing the contents described in the above steps S110 to S150, when a start signal for the portable fan is detected, the remaining power data of the battery device in the portable fan can be collected in real time, and the current power interval where the remaining power data is located can be determined. Therefore, the control strategy of the portable fan can be adjusted in a stepwise manner according to different current electric quantity intervals, and the service life of the portable fan in low electric quantity is effectively prolonged. And when the residual electric quantity is insufficient, all the gear switches are turned on, part of the low-gear switches are turned on when the residual electric quantity has certain loss, and when the residual electric quantity is insufficient, all the gear switches are turned off and the self-adaptive control process is started. And further, the control parameters of the self-adaptive control process are accurately and reliably adjusted through the determined passive loss curve and the determined active loss curve, so that the wind power of the portable fan is automatically adjusted through the self-adaptive control process, the output power of a battery piece is reduced, the loss of the battery piece is reduced, and the service life of the portable fan in low electric quantity is prolonged.
In a specific embodiment, the determining of the current power interval where the remaining power data is located in step S110 may specifically include what is described in step S111 to step S113 below.
Step S111, extracting power consumption rate in the residual electric quantity data and superposition weight corresponding to the power consumption rate; wherein the superposition weight is used for representing the degree of change of the power consumption rate.
In this embodiment, the value range of the superposition weight is-1 to 1. If the superposition weight is between-1 and 0, the power consumption rate is reduced, and if the superposition weight is between 0 and 1, the power consumption rate is increased.
Step S112, calculating the relative electric quantity value of the residual electric quantity data according to the power consumption rate and the superposition weight; wherein, the relative electric quantity value is the average value of the electric quantity values of the residual electric quantity data in a set time period.
In this embodiment, the set time period may be calculated by the superimposition weight, and is not limited herein.
Step S113, mapping the relative electric quantity value to a preset value interval to obtain a current electric quantity interval where the residual electric quantity data is located; the preset numerical value interval is divided into a plurality of subintervals according to the set step length.
In this embodiment, the setting step size can be adjusted according to the current output power of the portable fan. For example, the current output power of the portable fan may be obtained, a target step size corresponding to the current output power may be determined from a preset correlation distribution map, and then the set step size may be replaced with the target step size. Therefore, the accuracy of dividing the preset numerical value interval can be ensured.
It can be understood that, through the contents described in the above steps S111 to S113, the power consumption rate corresponding to the remaining power data can be taken into account, so that the current power interval in which the remaining power data is located can be accurately determined.
In a specific implementation process, in order to ensure the integrity and smoothness of the passive loss curve and avoid the occurrence of a breakpoint of the passive loss curve, not only the direct influence of the ambient data on the battery device but also the secondary influence of the ambient data on the battery device need to be considered. To achieve the above object, the determining the passive loss curve of the battery device according to the ambient data described in step S150 may specifically include the following steps S1511 to S1515.
Step S1511, determining variable direction coefficients of a plurality of environment variable data to be extracted for calculating the influence weight sequence of the battery device and conversion description values between different environment variable data according to a first data tag and a second data tag used for representing environment types in the surrounding environment data; wherein the first data tag is used for indicating a dry-wet category of the environmental category, and the second data tag is used for indicating a temperature category of the environmental category.
Step S1512, based on the determined variable direction coefficients of the multiple pieces of environment variable data and the conversion description values between different pieces of environment variable data, partially extracting the multiple pieces of environment variable data, so that the variable direction coefficient of the extracted first environment variable data is greater than a first preset value, and the conversion description value between the extracted first environment variable data is smaller than a second preset value.
Step S1513, for any one of the plurality of environment variable data except the first environment variable data, determining a target transformation description value of the second environment variable data under each of the extracted first environment variable data, and distributing part of the first environment variable data under a group corresponding to the first data tag and distributing part of the second environment variable data under a group corresponding to the second data tag according to the target description value.
Step S1514, calculating, for each first environment variable data under the group corresponding to the first data tag, a difference between the first environment variable data and each second environment variable data under the group corresponding to the second data tag, and determining a difference sequence corresponding to the first environment variable data; calculating a difference coefficient for representing the fluctuation degree of the difference value sequence; and judging whether the difference coefficient reaches a target coefficient, and if the difference coefficient reaches the target coefficient, determining the first environment variable data as target variable data with delay influence on the battery piece.
Step S1515, determining a first coordinate point of other variable data except the target variable data in a preset coordinate plane in the multiple environment variable data, fitting the first coordinate point to obtain an initial loss curve, mapping the target variable data to the initial loss curve to obtain an influence factor of each target variable data relative to the initial loss curve, and smoothing the initial loss curve by using the influence factor to obtain a passive loss curve of the battery device.
In the practical application process, based on the contents described in the above steps S1511 to S1515, the target variable data having a delay influence on the battery device can be determined, so that not only the direct influence of the ambient environment data (other variable data) on the battery device is considered, but also the secondary influence of the ambient environment data (target variable data) on the battery device is considered, so that the integrity and smoothness of the passive loss curve can be ensured, and the occurrence of a breakpoint on the passive loss curve can be avoided.
The inventor analyzes the wiring of the portable fan to find that the transmission loss of the electric energy between the battery and the lead is different under different residual capacity data of the battery, and if the problem is not considered, the slope of the generated active loss curve is lower, which affects the accuracy of the subsequent wind power adjustment, so that the battery consumes too fast. To solve this technical problem, in step S150, an active loss curve of the battery device is generated based on the remaining capacity data, which further includes the following steps S1521 to S1525.
Step 1521, a load curve corresponding to the residual capacity data is obtained.
Step S1522 of calculating an electric energy receiving rate of each load node of the load curve, and determining a first accumulated value of the load nodes having the electric energy receiving rate less than or equal to a preset reference receiving rate based on the electric energy receiving rate of each load node.
Step S1523, determining a ratio between the first accumulated values of the load nodes and the second accumulated values corresponding to all the load nodes of the load curve, so as to obtain an electric energy receiving loss ratio of the load curve.
Step S1524, determining the comprehensive transmission loss rate of the load curve; and determining the current electric energy loss factor of the load curve under the residual electric quantity data according to the electric energy receiving loss ratio of the load curve and the comprehensive transmission loss rate of the load curve.
Step S1525, based on a mapping list of pre-stored performance curves of the battery pieces and electric energy loss factors, determining loss curve weighting values corresponding to the current electric energy loss factors of the load curves, and weighting mirror curves corresponding to the pre-stored performance curves of the battery pieces by adopting the loss curve weighting values to obtain active loss curves of the battery pieces.
Through the steps S1521 to S1523, the electric energy transmission loss of the battery device under different residual capacity data can be taken into consideration, and thus the mirror curve can be weighted based on the determined weighting value of the loss curve so as to accurately obtain the active loss curve of the battery device. Therefore, the slope of the active loss curve can be ensured to be within a normal range, the accuracy of subsequent wind power adjustment is ensured, and the condition that the power consumption of the battery is too fast is avoided.
After determining the passive loss curve and the active loss curve, in order to ensure continuity of an iteration result of the loss curve and thus transitivity of the control parameter, iterating the active loss curve and the passive loss curve to obtain an iteration result as described in step S150 may exemplarily include what is described in the following steps S1531 to S1533.
Step S1531, extracting a time sequence feature corresponding to each parameter node from a first curve parameter group corresponding to an active loss curve, obtaining a first mapping feature of the time sequence feature corresponding to each parameter node in a second curve parameter group corresponding to a passive loss curve, and determining a time sequence difference coefficient between the active loss curve and the passive loss curve according to the first mapping feature.
Step S1532, when each parameter node in the first curve parameter set and each parameter node in the second curve parameter set are iteratively added based on the timing difference coefficient, acquiring an iteration error value between each parameter node in the first curve parameter set and each parameter node in the second curve parameter set.
Step S1533, obtaining an iteration array generated by iteratively adding each parameter node in the first curve parameter set and each parameter node in the second curve parameter set, and correcting the array separators in the iteration array by using the iteration error values to obtain the iteration result.
In practical implementation, based on the descriptions in step S1531 to step S1533, the set of spacers in the iteration array can be corrected to ensure the continuity of the iteration result of the loss curve, so as to ensure the transitivity of the control parameter.
In an implementation manner, the adjusting the control parameter of the adaptive control routine according to the iteration result described in step S150 to adjust the wind power of the portable fan according to the adaptive control routine may specifically include the following steps S1541 to S1544.
Step S1541, generating a wind speed change trajectory corresponding to the iteration result, and searching a plurality of groups of target control parameters meeting the control logic corresponding to the wind speed change trajectory from a thread set corresponding to the adaptive control thread according to the wind speed change trajectory.
Step S1542, determining target electric quantity data consumed by the control signals corresponding to each group of target control parameters, and calculating a first wind speed and a second wind speed corresponding to each group of target control parameters in the wind speed change trajectory based on the target electric quantity data; wherein the first wind speed is a wind speed without consideration of the target power data, and the second wind speed is a wind speed with consideration of the target power data.
Step S1543, calculating a wind speed difference between the first wind speed and the second wind speed corresponding to each group of target control parameters, and determining the target control parameter corresponding to the minimum wind speed difference as an execution parameter of the adaptive control routine under the residual electric quantity data.
Step S1544, the execution parameters are issued to the rotating motor corresponding to the portable fan through the adaptive control routine so as to adjust the rotating speed of the rotating motor.
In practical implementation, the wind power of the portable fan can be accurately adjusted through the content described in the steps S1541 to S1544.
On the basis, please refer to fig. 2 in combination, which provides an intelligent fan power supply dispatching system 200, including a controller 210, a collector 220, a battery 230, a switch 240 and a plurality of position switches 250; the controller 210 is in communication with the collector 220, the battery 230, the switch 240 and the plurality of range switches 250, respectively; the controller 210 is configured to:
when a starting signal for a portable fan is detected, acquiring residual electric quantity data of a battery piece in the portable fan in real time, and determining a current electric quantity interval where the residual electric quantity data are located;
when the current electric quantity interval is a first interval, controlling a switch key of the portable fan to be conducted with n gear switches, wherein n is a positive integer;
when the current electric quantity interval is a second interval, controlling the on-off key to be disconnected with m gear switches in the n gear switches, wherein m is a positive integer smaller than n;
when the electric quantity interval is a third interval, when the switch key is controlled to be disconnected with all gear switches of the portable fan, a self-adaptive control thread is started in parallel, and the ambient environment data of the portable fan, which is collected by a collector integrated with the portable fan, is obtained;
determining a passive loss curve of the battery device according to the ambient environment data, and generating an active loss curve of the battery device based on the residual electric quantity data; and iterating the active loss curve and the passive loss curve to obtain an iteration result, and adjusting the control parameters of the self-adaptive control process according to the iteration result so as to adjust the wind power of the portable fan according to the self-adaptive control process.
Preferably, the determining, by the controller 210, the passive loss curve of the battery device according to the ambient data specifically includes:
according to a first data label and a second data label which are used for representing environment types in the surrounding environment data, determining variable directional coefficients of a plurality of pieces of environment variable data to be extracted and used for calculating the influence weight sequence of the battery device and conversion description values among different pieces of environment variable data; wherein the first data tag is used for indicating a dry-wet category of the environmental category, and the second data tag is used for indicating a temperature category of the environmental category;
based on the determined variable direction coefficients of the plurality of pieces of environment variable data and the conversion description values among different pieces of environment variable data, partially extracting the plurality of pieces of environment variable data, so that the variable direction coefficient of the extracted first piece of environment variable data is larger than a first preset value, and the conversion description value among the extracted first piece of environment variable data is smaller than a second preset value;
for any one second environment variable data except the first environment variable data in the plurality of environment variable data, determining a target conversion description value of the second environment variable data under each kind of environment variable data in the extracted first environment variable data, and distributing part of the first environment variable data to a group corresponding to the first data label and distributing part of the second environment variable data to a group corresponding to the second data label according to the target description value;
calculating a difference value between each first environment variable data under the grouping corresponding to the first data label and each second environment variable data under the grouping corresponding to the second data label, and determining a difference value sequence corresponding to the first environment variable data; calculating a difference coefficient for representing the fluctuation degree of the difference value sequence; judging whether the difference coefficient reaches a target coefficient, and if the difference coefficient reaches the target coefficient, determining the first environment variable data as target variable data with delay influence on the battery piece;
determining a first coordinate point of other variable data except the target variable data in a preset coordinate plane in a plurality of environment variable data, fitting the first coordinate point to obtain an initial loss curve, mapping the target variable data to the initial loss curve to obtain an influence factor of each target variable data relative to the initial loss curve, and smoothing the initial loss curve by using the influence factor to obtain a passive loss curve of the battery device.
Preferably, the controller 210 generating the active loss curve of the battery device based on the remaining capacity data includes:
acquiring a load curve corresponding to the residual electric quantity data;
calculating the electric energy receiving rate of each load node of the load curve, and determining a first accumulated value of the load nodes of which the electric energy receiving rate is less than or equal to a preset reference receiving rate based on the electric energy receiving rate of each load node;
determining the proportion between the first accumulated value of the load node and the second accumulated values corresponding to all the load nodes of the load curve to obtain the electric energy receiving loss ratio of the load curve;
determining a comprehensive transmission loss rate of the load curve; determining a current electric energy loss factor of the load curve under the residual electric quantity data according to the electric energy receiving loss ratio of the load curve and the comprehensive transmission loss rate of the load curve;
and determining a loss curve weighted value corresponding to the current electric energy loss factor of the load curve based on a pre-stored mapping list of the performance curve and the electric energy loss factor of the battery piece, and weighting a mirror image curve corresponding to the pre-stored performance curve of the battery piece by adopting the loss curve weighted value to obtain an active loss curve of the battery piece.
Preferably, the controller 210 iterating the active loss curve and the passive loss curve to obtain an iteration result includes:
extracting time sequence characteristics corresponding to each parameter node from a first curve parameter group corresponding to an active loss curve, acquiring first mapping characteristics of the time sequence characteristics corresponding to each parameter node in a second curve parameter group corresponding to a passive loss curve, and determining a time sequence difference coefficient between the active loss curve and the passive loss curve according to the first mapping characteristics;
while iteratively adding each parameter node in the first curve parameter set to each parameter node in the second curve parameter set based on the timing difference coefficient, acquiring an iteration error value between each parameter node in the first curve parameter set and each parameter node in the second curve parameter set;
and acquiring an iteration array generated by iterative addition of each parameter node in the first curve parameter group and each parameter node in the second curve parameter group, and correcting an array separator in the iteration array by using the iteration error value to obtain the iteration result.
Based on the same inventive concept, there is also provided an intelligent fan power supply scheduling device 300 as shown in fig. 3, where the device is applied to the controller 210 in fig. 2, and specifically may include the following functional modules:
the data acquisition module 310 is configured to acquire remaining power data of a battery device in the portable fan in real time when a start signal for the portable fan is detected, and determine a current power interval where the remaining power data is located;
the conduction control module 320 is configured to control a switch key of the portable fan to be conducted with n gear switches when the current electric quantity interval is a first interval, where n is a positive integer;
the disconnection control module 330 is configured to control the on-off key to be disconnected from m gear switches of the n gear switches when the current electric quantity interval is a second interval, where m is a positive integer smaller than n;
the thread running module 340 is configured to, when the electric quantity interval is a third interval and the on-off key is controlled to be disconnected from all gear switches of the portable fan, start an adaptive control thread in parallel and acquire ambient environment data of the portable fan, which is acquired by an acquirer integrated with the portable fan;
an automatic adjustment module 350, configured to determine a passive loss curve of the battery device according to the ambient data, and generate an active loss curve of the battery device based on the remaining power data; and iterating the active loss curve and the passive loss curve to obtain an iteration result, and adjusting the control parameters of the self-adaptive control process according to the iteration result so as to adjust the wind power of the portable fan according to the self-adaptive control process.
For the description of the above functional modules, refer to the description of the method shown in fig. 2, and no further description is made here.
In summary, by the method, the system and the device described in this embodiment, when the start signal for the portable fan is detected, the remaining power data of the battery device in the portable fan can be collected in real time, and the current power interval where the remaining power data is located can be determined. Therefore, the control strategy of the portable fan can be adjusted in a stepwise manner according to different current electric quantity intervals, and the service life of the portable fan in low electric quantity is effectively prolonged. And when the residual electric quantity is insufficient, all the gear switches are turned on, part of the low-gear switches are turned on when the residual electric quantity has certain loss, and when the residual electric quantity is insufficient, all the gear switches are turned off and the self-adaptive control process is started. And further, the control parameters of the self-adaptive control process are accurately and reliably adjusted through the determined passive loss curve and the determined active loss curve, so that the wind power of the portable fan is automatically adjusted through the self-adaptive control process, the output power of a battery piece is reduced, the loss of the battery piece is reduced, and the service life of the portable fan in low electric quantity is prolonged.
Claims (10)
1. A power supply scheduling method for an intelligent fan is characterized by comprising the following steps:
when a starting signal for a portable fan is detected, acquiring residual electric quantity data of a battery piece in the portable fan in real time, and determining a current electric quantity interval where the residual electric quantity data are located;
when the current electric quantity interval is a first interval, controlling a switch key of the portable fan to be conducted with n gear switches, wherein n is a positive integer;
when the current electric quantity interval is a second interval, controlling the on-off key to be disconnected with m gear switches in the n gear switches, wherein m is a positive integer smaller than n;
when the electric quantity interval is a third interval, when the switch key is controlled to be disconnected with all gear switches of the portable fan, a self-adaptive control thread is started in parallel, and the ambient environment data of the portable fan, which is collected by a collector integrated with the portable fan, is obtained;
determining a passive loss curve of the battery device according to the ambient environment data, and generating an active loss curve of the battery device based on the residual electric quantity data; and iterating the active loss curve and the passive loss curve to obtain an iteration result, and adjusting the control parameters of the self-adaptive control process according to the iteration result so as to adjust the wind power of the portable fan according to the self-adaptive control process.
2. The method according to claim 1, wherein determining the current power interval in which the remaining power data is located specifically includes:
extracting power consumption rate in the residual electric quantity data and superposition weight corresponding to the power consumption rate; wherein the superposition weight is used for representing the change degree of the power consumption rate;
calculating a relative electric quantity value of the residual electric quantity data according to the power consumption rate and the superposition weight; wherein, the relative electric quantity value is the average value of the electric quantity values of the residual electric quantity data in a set time period;
mapping the relative electric quantity value to a preset value interval to obtain a current electric quantity interval in which the residual electric quantity data is located; the preset numerical value interval is divided into a plurality of subintervals according to the set step length.
3. The method of claim 2, further comprising:
acquiring the current output power of the portable fan;
determining a target step length corresponding to the current output power from a preset correlation distribution diagram;
and replacing the set step length with the target step length.
4. The method according to any one of claims 1-3, wherein determining a passive loss curve of the battery device from the ambient data comprises:
according to a first data label and a second data label which are used for representing environment types in the surrounding environment data, determining variable directional coefficients of a plurality of pieces of environment variable data to be extracted and used for calculating the influence weight sequence of the battery device and conversion description values among different pieces of environment variable data; wherein the first data tag is used for indicating a dry-wet category of the environmental category, and the second data tag is used for indicating a temperature category of the environmental category;
based on the determined variable direction coefficients of the plurality of pieces of environment variable data and the conversion description values among different pieces of environment variable data, partially extracting the plurality of pieces of environment variable data, so that the variable direction coefficient of the extracted first piece of environment variable data is larger than a first preset value, and the conversion description value among the extracted first piece of environment variable data is smaller than a second preset value;
for any one second environment variable data except the first environment variable data in the plurality of environment variable data, determining a target conversion description value of the second environment variable data under each kind of environment variable data in the extracted first environment variable data, and distributing part of the first environment variable data to a group corresponding to the first data label and distributing part of the second environment variable data to a group corresponding to the second data label according to the target description value;
calculating a difference value between each first environment variable data under the grouping corresponding to the first data label and each second environment variable data under the grouping corresponding to the second data label, and determining a difference value sequence corresponding to the first environment variable data; calculating a difference coefficient for representing the fluctuation degree of the difference value sequence; judging whether the difference coefficient reaches a target coefficient, and if the difference coefficient reaches the target coefficient, determining the first environment variable data as target variable data with delay influence on the battery piece;
determining a first coordinate point of other variable data except the target variable data in a preset coordinate plane in a plurality of environment variable data, fitting the first coordinate point to obtain an initial loss curve, mapping the target variable data to the initial loss curve to obtain an influence factor of each target variable data relative to the initial loss curve, and smoothing the initial loss curve by using the influence factor to obtain a passive loss curve of the battery device.
5. The method of claim 4, wherein generating an active loss curve for the battery device based on the remaining capacity data comprises:
acquiring a load curve corresponding to the residual electric quantity data;
calculating the electric energy receiving rate of each load node of the load curve, and determining a first accumulated value of the load nodes of which the electric energy receiving rate is less than or equal to a preset reference receiving rate based on the electric energy receiving rate of each load node;
determining the proportion between the first accumulated value of the load node and the second accumulated values corresponding to all the load nodes of the load curve to obtain the electric energy receiving loss ratio of the load curve;
determining a comprehensive transmission loss rate of the load curve; determining a current electric energy loss factor of the load curve under the residual electric quantity data according to the electric energy receiving loss ratio of the load curve and the comprehensive transmission loss rate of the load curve;
and determining a loss curve weighted value corresponding to the current electric energy loss factor of the load curve based on a pre-stored mapping list of the performance curve and the electric energy loss factor of the battery piece, and weighting a mirror image curve corresponding to the pre-stored performance curve of the battery piece by adopting the loss curve weighted value to obtain an active loss curve of the battery piece.
6. The method of claim 5, wherein iterating the active loss curve and the passive loss curve to obtain an iteration result comprises:
extracting time sequence characteristics corresponding to each parameter node from a first curve parameter group corresponding to an active loss curve, acquiring first mapping characteristics of the time sequence characteristics corresponding to each parameter node in a second curve parameter group corresponding to a passive loss curve, and determining a time sequence difference coefficient between the active loss curve and the passive loss curve according to the first mapping characteristics;
while iteratively adding each parameter node in the first curve parameter set to each parameter node in the second curve parameter set based on the timing difference coefficient, acquiring an iteration error value between each parameter node in the first curve parameter set and each parameter node in the second curve parameter set;
and acquiring an iteration array generated by iterative addition of each parameter node in the first curve parameter group and each parameter node in the second curve parameter group, and correcting an array separator in the iteration array by using the iteration error value to obtain the iteration result.
7. A power supply scheduling system of an intelligent fan is characterized by comprising a controller, a collector, a battery piece, a switch key and n gear switches; the controller is respectively communicated with the collector, the battery piece, the on-off key and the n gear switches; the controller is configured to:
when a starting signal for a portable fan is detected, acquiring residual electric quantity data of a battery piece in the portable fan in real time, and determining a current electric quantity interval where the residual electric quantity data are located;
when the current electric quantity interval is a first interval, controlling a switch key of the portable fan to be conducted with n gear switches, wherein n is a positive integer;
when the current electric quantity interval is a second interval, controlling the on-off key to be disconnected with m gear switches in the n gear switches, wherein m is a positive integer smaller than n;
when the electric quantity interval is a third interval, when the switch key is controlled to be disconnected with all gear switches of the portable fan, a self-adaptive control thread is started in parallel, and the ambient environment data of the portable fan, which is collected by a collector integrated with the portable fan, is obtained;
determining a passive loss curve of the battery device according to the ambient environment data, and generating an active loss curve of the battery device based on the residual electric quantity data; and iterating the active loss curve and the passive loss curve to obtain an iteration result, and adjusting the control parameters of the self-adaptive control process according to the iteration result so as to adjust the wind power of the portable fan according to the self-adaptive control process.
8. The system of claim 7, wherein the controller determining the passive loss profile of the battery device based on the ambient data specifically comprises:
according to a first data label and a second data label which are used for representing environment types in the surrounding environment data, determining variable directional coefficients of a plurality of pieces of environment variable data to be extracted and used for calculating the influence weight sequence of the battery device and conversion description values among different pieces of environment variable data; wherein the first data tag is used for indicating a dry-wet category of the environmental category, and the second data tag is used for indicating a temperature category of the environmental category;
based on the determined variable direction coefficients of the plurality of pieces of environment variable data and the conversion description values among different pieces of environment variable data, partially extracting the plurality of pieces of environment variable data, so that the variable direction coefficient of the extracted first piece of environment variable data is larger than a first preset value, and the conversion description value among the extracted first piece of environment variable data is smaller than a second preset value;
for any one second environment variable data except the first environment variable data in the plurality of environment variable data, determining a target conversion description value of the second environment variable data under each kind of environment variable data in the extracted first environment variable data, and distributing part of the first environment variable data to a group corresponding to the first data label and distributing part of the second environment variable data to a group corresponding to the second data label according to the target description value;
calculating a difference value between each first environment variable data under the grouping corresponding to the first data label and each second environment variable data under the grouping corresponding to the second data label, and determining a difference value sequence corresponding to the first environment variable data; calculating a difference coefficient for representing the fluctuation degree of the difference value sequence; judging whether the difference coefficient reaches a target coefficient, and if the difference coefficient reaches the target coefficient, determining the first environment variable data as target variable data with delay influence on the battery piece;
determining a first coordinate point of other variable data except the target variable data in a preset coordinate plane in a plurality of environment variable data, fitting the first coordinate point to obtain an initial loss curve, mapping the target variable data to the initial loss curve to obtain an influence factor of each target variable data relative to the initial loss curve, and smoothing the initial loss curve by using the influence factor to obtain a passive loss curve of the battery device.
9. The system of claim 8, wherein the controller generating an active loss curve for the battery device based on the remaining capacity data comprises:
acquiring a load curve corresponding to the residual electric quantity data;
calculating the electric energy receiving rate of each load node of the load curve, and determining a first accumulated value of the load nodes of which the electric energy receiving rate is less than or equal to a preset reference receiving rate based on the electric energy receiving rate of each load node;
determining the proportion between the first accumulated value of the load node and the second accumulated values corresponding to all the load nodes of the load curve to obtain the electric energy receiving loss ratio of the load curve;
determining a comprehensive transmission loss rate of the load curve; determining a current electric energy loss factor of the load curve under the residual electric quantity data according to the electric energy receiving loss ratio of the load curve and the comprehensive transmission loss rate of the load curve;
and determining a loss curve weighted value corresponding to the current electric energy loss factor of the load curve based on a pre-stored mapping list of the performance curve and the electric energy loss factor of the battery piece, and weighting a mirror image curve corresponding to the pre-stored performance curve of the battery piece by adopting the loss curve weighted value to obtain an active loss curve of the battery piece.
10. The system of claim 9, wherein the controller iterating the active loss curve and the passive loss curve to obtain an iteration result comprises:
extracting time sequence characteristics corresponding to each parameter node from a first curve parameter group corresponding to an active loss curve, acquiring first mapping characteristics of the time sequence characteristics corresponding to each parameter node in a second curve parameter group corresponding to a passive loss curve, and determining a time sequence difference coefficient between the active loss curve and the passive loss curve according to the first mapping characteristics;
while iteratively adding each parameter node in the first curve parameter set to each parameter node in the second curve parameter set based on the timing difference coefficient, acquiring an iteration error value between each parameter node in the first curve parameter set and each parameter node in the second curve parameter set;
and acquiring an iteration array generated by iterative addition of each parameter node in the first curve parameter group and each parameter node in the second curve parameter group, and correcting an array separator in the iteration array by using the iteration error value to obtain the iteration result.
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