CN115727962A - Temperature compensation method, device, equipment, medium and intelligent panel - Google Patents
Temperature compensation method, device, equipment, medium and intelligent panel Download PDFInfo
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Abstract
The invention discloses a temperature compensation method, a device, equipment, a medium and an intelligent panel, wherein the temperature compensation method comprises the following steps: reading the reading of the temperature sensor and the temperature value of the first heat source element; taking the reading of the temperature sensor and the temperature value of the first heat source element as the input of a prediction model, and outputting a temperature prediction value at the current moment; and distributing a weight ratio of the output temperature value at the last moment to the temperature predicted value at the current moment, fitting and outputting the output temperature value at the current moment, wherein the state of the second heat source element is monitored, and the weight ratio of the output temperature value at the last moment to the temperature predicted value at the current moment is adjusted according to the state change of the second heat source element. According to the method and the device, only the temperature sensor reading and the CPU temperature sensor reading are used as variables, the BP neural network is used for fitting, and then the smoothing technology is utilized, so that the situation that the device is abrupt in change in actual experience is avoided, and the prediction precision is improved.
Description
Technical Field
The invention relates to a temperature control technology, in particular to a temperature compensation method, a temperature compensation device, temperature compensation equipment, a temperature compensation medium and an intelligent panel, which are used for reducing interference, improving temperature monitoring precision and reducing oscillation under the condition that the installation environment of a temperature sensor is easily interfered by the heat value of a heat source element.
Background
The temperature monitoring is widely applied in life, most of the temperature monitoring devices adopt a temperature sensor arranged at a measured point to acquire temperature data in real time, so that equipment or people can acquire temperature information and the temperature information can be used as a reference for executing a next instruction or operation, and the temperature monitoring device plays an important role in the fields of environment monitoring, equipment operation, monitoring and the like.
For example, smart home, which is a typical scenario for temperature monitoring. Under the rapid development of the internet of things technology, the life of people is being changed by smart homes, and sensor equipment is an essential part in intelligent furniture linkage as a data acquisition node. The temperature display of some of the intelligent panels relies on sensor readings, among other things; however, since the temperature sensor is always installed inside the device, the components inside the panel device generate heat when working, which will directly affect the reading of the temperature sensor, and thus the temperature in the actual environment cannot be directly reflected, so that it is a technical problem to make the panel device display a precise ambient temperature.
To solve the problem, in terms of hardware, the relative position of the sensor can be adjusted to reduce the influence caused by the heating element, but the relative position is often limited by the size of equipment and the installation environment, and the improvement effect is limited.
Disclosure of Invention
The present invention is directed to solving, to some extent, one of the technical problems in the art described above. Therefore, the first aspect of the present invention provides a temperature compensation method, which uses the relationship correlation between the temperature value at the current time and the temperature at the previous time to fit and predict the current temperature, and flexibly adjusts the fitting weight by monitoring the states of other heat sources under the condition that the input variables obtained by the prediction model are extremely limited, so as to eliminate the phenomena of oscillation and the like caused by insufficient variables.
Specifically, the temperature compensation method comprises the following steps:
reading a temperature sensor reading and a temperature value of a first heat source element, the temperature sensor configured to measure an ambient temperature, the first heat source element and the temperature sensor being in a same installation environment;
taking the reading of the temperature sensor and the temperature value of the first heat source element as the input of a prediction model, and outputting a temperature prediction value at the current moment;
and distributing a weight ratio of the output temperature value at the last moment to the temperature predicted value at the current moment, fitting and outputting the output temperature value at the current moment, wherein the state of the second heat source element is monitored, and the weight ratio of the output temperature value at the last moment to the temperature predicted value at the current moment is adjusted according to the state change of the second heat source element.
In a further embodiment, if the states of the second heat source element at the current time and the previous time are maintained, the weight ratio is a fixed weight ratio; if the states of the second heat source element at the current moment and the previous moment are changed, reducing the fitting weight of the temperature value output at the previous moment and increasing the fitting weight of the temperature predicted value at the current moment along with the increase of the number of moments maintained after the states of the second heat source element are switched in the transition time; and when the number of the maintained time after the state of the second heat source element is switched is larger than the number of the transition time, clearing the number of the maintained time after the state of the second heat source element is switched, and enabling the output temperature value at the current time to be equal to the predicted temperature value at the current time.
In a further embodiment, the number of times the state of the second heat source element is maintained after switching is recorded by a counter C, and the initial state is set to 0; and making the temperature value output at the first moment directly equal to the reading of the temperature sensor; if the state of the second heat source element is the same at the time i-1 and the time i and C =0, Y [ i ] = a x Y [ i-1] + (1-a) x S [ i ], wherein a is a weight parameter, Y [ i-1] is an output temperature value at the last time, and S [ i ] is a predicted temperature value at the current time; if the state of the second heat source element is different from the state of the second heat source element at the time i-1, setting a counter C to be 1 and starting counting, if C is less than or equal to t, adding 1 to the counter Y [ i ] = (1- (1-a)/t × C) × Y [ i-1] + ((1-a)/t × C) × Si ]; if C > t, then Y [ i ] = S [ i ], and the counter C is reset to 0; where t is the number of transition times.
In a further embodiment, the weight parameter a =0.8 and the number of transition times t =50.
In further embodiments, humidity compensation is included; reading a reading of a humidity sensor when the computer is started as an initial value; and establishing a mapping relation between the temperature change and the humidity change so as to calculate a humidity change value at a corresponding moment according to the temperature change value at any moment and output a humidity value.
In a further embodiment, the second heat source element is a screen, and when the screen is switched between light and dark, the state of the second heat source element is determined to be changed; otherwise, the state of the second heat source element is maintained.
Based on the temperature compensation method provided by the invention, the output value of the prediction model can be corrected, the precision of the prediction value of the final output prediction value is improved under the condition that the variables obtained by the prediction model are extremely limited, the oscillation phenomenon of the final output prediction value is reduced, and the abrupt feeling of the user on the sense organ is avoided.
In order to achieve the above object, a second aspect of the present invention provides an intelligent panel, which includes: a temperature sensor configured to acquire an ambient temperature; the first heat source element and the second heat source element are positioned in the same installation environment with the temperature sensor, the first heat source element has a readable temperature value, and the second heat source element has a recognizable state change; the first heat source element comprises a processor; a memory storing a computer program that is loaded and executed by the processor to implement a method as described in the foregoing.
The intelligent panel provided by the invention is applied to an intelligent home scene, and can perform fitting regression by finding the influence rule of each internal heat source element on the temperature sensor when the temperature sensor is inevitably interfered by other heat source elements. And under the condition that the variables obtained by the prediction model are extremely limited, the accuracy of the predicted value of the finally output predicted value can be improved, the oscillation phenomenon of the finally output predicted value is reduced, and the sense of the user is prevented from being obtrusive.
In order to achieve the above object, a third aspect of the present invention provides a temperature compensation device, including: the temperature reading module is used for reading the reading of the temperature sensor and the temperature value of the first heat source element; the prediction model predicts a temperature prediction value at the current moment based on the read data of the temperature reading module; the monitoring module is used for monitoring the state change of the second heat source element; and the compensation module is used for reading the output temperature value at the last moment and the temperature predicted value at the current moment and distributing a weight ratio to fit and output the output temperature value at the current moment, wherein the weight ratio of the output temperature value at the last moment and the temperature predicted value at the current moment is adjusted according to the state change of the second heat source element.
The temperature compensation device can be carried on equipment needing temperature monitoring, so that under the condition that the installation environment of the temperature sensor is limited and is inevitably influenced by a heat source element, a more accurate predicted temperature is output through a temperature compensation mechanism. In the compensation process, the required variables are few, the problem of oscillation of the temperature predicted value possibly caused by the fact that the dependent variable is few is effectively avoided, and the monitoring effect is improved.
To achieve the above object, a fourth aspect of the present invention is to provide a computer device, which includes a processor and a memory, the memory storing a computer program, the computer program being loaded by the processor and executed to implement the method as described above
To achieve the above object, a fifth aspect of the present invention is directed to a computer-readable storage medium including one or more program instructions which, when executed, implement the method as described above.
Drawings
FIG. 1 is a prediction example of a neural network prediction model that considers only CPU temperature influencing factors;
FIG. 2 is a schematic flow chart of a temperature compensation method according to the present invention;
FIG. 3 is a schematic diagram of a BP neural network prediction model according to the present application;
FIG. 4 shows 1 embodiment of the present invention;
FIG. 5 is a schematic view of an intelligent panel according to the present invention;
FIG. 6 is a schematic view of a temperature compensation device according to the present invention;
FIG. 7 is a schematic diagram of a computer apparatus according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present invention and should not be construed as limiting the present invention.
The method aims to predict and output the actual temperature by searching the influence rule of each internal heat source element on the temperature sensor.
It should be understood that, in the present application, the heat source element refers to a component that generates heat during a working process, and the component and the temperature sensor are in the same installation environment, and the reading accuracy of the temperature sensor is affected to a certain extent by the heat generation. The same installation environment does not mean that the heat source element and the temperature sensor are necessarily located in the same cavity, and the heat source element and the temperature sensor may be located in adjacent cavities with heat conduction, and the heat source element and the temperature sensor may be regarded as being located in the same installation environment as long as the heat of the heat source element can be conducted to the position of the temperature sensor to a certain extent and sensed to influence the reading accuracy of the temperature sensor.
The influence rule of the heat source element on the temperature sensor is searched through the neural network, and specifically, the influence rule is realized by a BP neural network prediction model. The BP neural network indeed has strong fitting ability, and an ideal method is as follows: firstly, a neural network prediction model is built, then a training set is obtained by extracting various variables which can affect the temperature sensor, the numerical value of the temperature sensor and the real label of the corresponding temperature at the moment, and the neural network prediction model is trained to obtain the predicted value of the real temperature so as to eliminate the error of the temperature sensor caused by heat source interference to at least a certain extent.
However, in practical situations, there may be several heat source elements capable of affecting the temperature sensor, and the main influencing factor capable of being extracted in practice is likely to be only one variable of the CPU (processor) temperature, so that the CPU temperature and the temperature reading are directly used for fitting, and sudden changes may occur in the process of displaying the device, so that the user experience is very obtrusive.
The reason is that in the process of algorithm temperature fitting, the considered variables are often incomplete, only main influence factors such as the CPU temperature are simply captured, and other secondary factors are ignored, which may actually cause a situation that two real results appear in one state in the data. An example of the fitting can be seen in fig. 1, wherein a total of 8355 time nodes are recorded in the graph, operations such as bright and dark screen switching are performed, the operations are recorded once every minute, light gray is a predicted value temperature, dark black is a real temperature value, it can be found that a dark black curve is almost covered by an oscillating light gray curve, if the operations are directly predicted, the display of the whole device is very abrupt in fluctuation, and the operations are specifically represented by that among the 8355 time nodes, only 7941 time point errors are controlled within 2 degrees, and the oscillation error reaches 7 degrees at most, and only 6457 time points reach 1 degree.
Therefore, after the influence rule of the heat source element on the temperature sensor is searched by the prediction model, the predicted value of the prediction model is further corrected, and the oscillation value is eliminated.
In particular, the first aspect of the invention proposes a temperature compensation method that makes it possible to eliminate certain anomalous points and to prevent rapid, strange variations in temperature, and tests have shown that a certain degree of accuracy can be improved.
In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
In order to better understand the technical scheme, the technical scheme is described in detail in the following with reference to the attached drawings of the specification and specific embodiments.
First, training set data required for neural network training is obtained.
Putting the two devices into a temperature-variable box, controlling the temperature of the temperature-variable box, controlling the state of the devices, such as bright screen, and playing music; and simultaneously recording the reading of the CPU temperature sensor, the reading of the temperature sensor and the temperature of the temperature-changing box in each temperature interval and equipment state. Taking data of one device as a training set, wherein input variables of the training data are readings of a CPU temperature sensor, temperature degrees of the temperature sensor correspond to temperature readings of a temperature changing box, and a test set is data of the other device.
And secondly, building a BP neural network prediction model.
The method comprises the steps of building a BP neural network prediction model, setting an input layer as two neurons, setting two hidden layers, enabling each hidden layer to have 5 neurons, adopting a sigmoid activation function between layers, increasing nonlinear fitting capacity, enabling an output layer as one neuron and not using an activation function, and therefore, because normalization preprocessing is not used, the activation function is not needed to restrict an output interval. The structure is shown in fig. 2.
Then, a prediction model is trained. The method comprises the steps of training by using an L1 loss function, setting a certain training period, selecting an Adam gradient descent method, setting a preliminary step length, and reducing the step length after training a plurality of periods for many times to prevent the model from not converging. Through training, a neural network prediction model can be obtained, and a predicted environment temperature value can be obtained preliminarily through the prediction model only by feeding parameters of CPU temperature sensor reading and temperature sensor reading.
In one possible training mode, the training cycle number is set to 1000, the initial step size is set to 0.01, and the adjustment is made to 0.001 after 500 cycles and 0.0001 after 800 cycles.
And finally, compensating and correcting the temperature predicted value of the prediction model and outputting the temperature predicted value. Considering that the output temperature value at the present moment should be correlated with the output temperature value at the previous moment, and observing that the abrupt change range of the output temperature value in fig. 1 is abnormally large, it is usually a moment when the state of the second heat source element changes (in particular, in the example of fig. 1, it corresponds to the bright-dark switching of the screen). Therefore, the idea of using a smooth function is considered to eliminate the influence caused by oscillation due to insufficient variables;
specifically, the method comprises the following steps:
reading a temperature sensor reading and a temperature value of a first heat source element, the temperature sensor configured to measure an ambient temperature, the first heat source element and the temperature sensor being in a same installation environment;
taking the reading of the temperature sensor and the temperature value of the first heat source element as the input of a prediction model, and outputting a temperature prediction value at the current moment;
and distributing a weight ratio of the output temperature value at the last moment to the temperature predicted value at the current moment, fitting and outputting the output temperature value at the current moment, wherein the state of the second heat source element is monitored, and the weight ratio of the output temperature value at the last moment to the temperature predicted value at the current moment is adjusted according to the state change of the second heat source element.
It will be appreciated that the first heat source element is in one example of this embodiment a CPU element, but it could also be another element which affects the accuracy of the temperature sensor readings and which may enable its own temperature monitoring. The second heat source element is an element which also affects the reading accuracy of the temperature sensor, has a degree of influence on the reading of the temperature sensor which is inferior to that of the first heat source element, and has no capability of realizing self temperature monitoring, but has a state change capable of being monitored.
It should be understood that the monitorable state change means that the state change of the element can be recognized and acquired by the processor through a certain signal mode, which can include power on and power off of the element, power consumption high and low switching and the like. For example, when the element is a display screen, the change in state that can be monitored may be a bright-dark screen state of the display screen.
For different state changes, in a further embodiment:
if the states of the second heat source element at the current moment and the last moment are maintained, the weight ratio is a fixed weight ratio;
if the states of the second heat source element at the current moment and the previous moment are changed, the prediction mutation amplitude is larger at the moment, so that a transition time is set to filter the mutation value, the fitting weight of the temperature value output at the previous moment is slowly reduced along with the increase of the number of the keeping moments after the states of the second heat source element are switched in the transition time, the fitting weight of the temperature prediction value at the current moment is correspondingly increased, and the sum of weight coefficients is kept to be 1;
when the number of times maintained after the state switching of the second heat source element is greater than the number of transition times, the number of times maintained after the state switching of the second heat source element is cleared, and the output temperature value at the current time is directly equal to the predicted temperature value at the current time, so as to prevent the output temperature from being inaccurate due to overlarge error in the transition process of the temperature (at this time, the clearing of the number of times maintained after the state switching plays a role in resetting).
In a more specific embodiment, the number of times maintained after the state of the second heat source element is switched is recorded with a counter C and the initial state is 0, and the first-time output temperature is set to a temperature directly read by the apparatus. Then, when the state of the second heat source element is the same at time i-1 and time i and C =0, Y [ i ] = a × Y [ i-1] + (1-a) × S [ i ], where a is a weight parameter, Y [ i-1] is an output temperature value at the previous time, and S [ i ] is a predicted temperature value at the current time. If the state of the second heat source element is different from that at time i-1, the counter C is set to 1 and starts counting, and if C is not greater than t, Y [ i ] = (1- (1-a)/t × C) × Y [ i-1] + ((1-a)/t × C) × si ], and the counter is incremented by 1. If C > t, at this time, the counter C is reset to 0, the fitting weight of the output temperature value Y [ i-1] at the previous time is reduced to 0, and the fitting weight of the temperature predicted value Si at the current time is increased to 1, that is, Y [ i ] = Si, that is, the temperature predicted value is output as a real result. In each formula, t is the number of transition times.
More specifically, at this time, the second heat source element is a screen, and when the screen is switched between light and dark, the state of the second heat source element is determined to be changed; otherwise, the state of the second heat source element is maintained.
At the initial moment of starting the equipment, the reading of the temperature sensor is considered to be more accurate, and at the moment, the reading of the temperature sensor is output as the output temperature value at the current moment.
Please refer to fig. 3, which shows the result of an embodiment of the values a =0.8 and t =50. The light gray is still the final output temperature value, the dark black is the real environment temperature, the oscillation of the whole function image is eliminated, and after the test, 8355 time points have 8355 errors controlled within 2 degrees, 7535 control within 1 degree, and no huge sudden change is found, compared with the result shown in fig. 1 (only 7941 time points have errors controlled within 2 degrees, and the oscillation error reaches 7 degrees to the maximum, 6457 time points reach within 1 degree), the method obtains a remarkable oscillation filtering result, and the prediction precision is improved to a certain extent.
In addition, based on the temperature compensation method, humidity compensation can be achieved. Similarly, the reading of the humidity sensor is considered to be accurate at the time of starting the device, and at this time, the reading of the humidity sensor at the time of starting the device is taken as an initial value. And in the subsequent humidity change prediction process, a mapping relation between the temperature change and the humidity change is established through historical data, so that the humidity change value at the corresponding moment is calculated according to the temperature change value at any moment, and the humidity value is output.
In a specific embodiment, the fitting of the humidity value is performed according to the mapping relation that the temperature changes by 1 degree and the humidity correspondingly changes by 5 degrees.
Based on the method, only the reading of the temperature sensor and the reading of the CPU temperature sensor are used as variables, the BP neural network is used for fitting, and then the smoothing technology is utilized, so that the situation that the equipment is suddenly changed when the equipment is operated such as turning on and off a screen, playing music and the like in actual experience is avoided, the temperature error is controlled to be within 1 degree under most conditions, the error is controlled to be within 2 degrees under few conditions, and a good prediction effect is obtained.
Referring to fig. 4, a second aspect of the present invention is to provide an intelligent panel, which includes a temperature sensor 110, a processor 130, a screen 140 and a memory 150.
Wherein the temperature sensor 110 is configured to acquire an ambient temperature. The processor 130 (first heat source element) and the screen 140 (second heat source element) are in the same installation environment as the temperature sensor 110, the processor 130 has a readable temperature value, and the screen 140 has a state change recognizable by the processor 130. The memory 150 stores a computer program that is loaded and executed by the processor 130 to implement the temperature compensation method as described above.
The intelligent panel may further include a humidity sensor 120, and it should be understood that, in this embodiment, the temperature sensor 100 and the humidity sensor 120 may be of an integrated design or of a split design, and the present application does not specifically limit the same. When the intelligent panel comprises the humidity sensor 120, the memory 150 stores a corresponding computer program, and when the computer program is loaded by the processor, the humidity compensation method is further performed to realize the humidity prediction.
When the intelligent panel provided by the invention is applied to an intelligent home scene, fitting regression can be carried out by searching the influence rule of each internal heat source element on the temperature sensor when the temperature sensor is inevitably interfered by other heat source elements. And under the condition that the variables obtained by the prediction model are extremely limited, the accuracy of the predicted value of the finally output predicted value can be improved, the oscillation phenomenon of the finally output predicted value is reduced, and the sense of the user is prevented from being obtrusive.
The third aspect of the present invention is to provide a temperature compensation apparatus, which includes a temperature reading module 210, a prediction model 220, a monitoring module 230, and a compensation module 240. The temperature reading module 210 is configured to read a reading of the temperature sensor and a temperature value of the first heat source element. The prediction model 220 predicts a predicted temperature value at the current time based on the read data of the temperature reading module 210, and the prediction model 220 is obtained by the model construction and training method described above. The monitoring module 230 is configured to monitor a state change of the second heat source element, such as acquiring a bright/dark signal of the display screen, and calculate a number of times that the state of the second heat source element is maintained after being switched. And the compensation module 240 is configured to read the output temperature value at the previous time and the predicted temperature value at the current time and assign a weight ratio to fit and output the output temperature value at the current time, wherein the weight ratio between the output temperature value at the previous time and the predicted temperature value at the current time is adjusted according to the state change of the second heat source element.
The temperature compensation device can be carried on equipment needing temperature monitoring, so that under the condition that the installation environment of the temperature sensor is limited and is inevitably influenced by a heat source element, a more accurate predicted temperature is output through a temperature compensation mechanism. In the compensation process, the required variables are few, the problem of temperature predicted value oscillation possibly caused by the fact that the dependent variables are few is effectively avoided, and the monitoring effect is improved.
The temperature compensation device of the present invention may further include a humidity reading module 250, and the humidity reading module 250 reads humidity data, so that the compensation module 240 may adjust a predicted humidity value according to a temperature variation value, thereby implementing humidity control.
The specific details of the implementation process of the functions and actions of each module in the device are referred to the implementation process of the corresponding step in the method, and are not described herein again.
For the device embodiment, since it basically corresponds to the method embodiment, reference may be made to the partial description of the method embodiment for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the modules described as separate parts may or may not be physically separate, and the parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. One of ordinary skill in the art can understand and implement without inventive effort.
Accordingly, the fourth aspect of the present invention also proposes a computer device comprising a processor 310 and a memory 320, the memory 320 storing a computer program, the computer program being loaded and executed by the processor 310 to implement the method as described above.
Accordingly, the fifth aspect of the present invention also proposes a computer-readable storage medium comprising one or more program instructions which, when executed, implement the method as described above.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. Computer-usable storage media include permanent and non-permanent, removable and non-removable media, and may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of the storage medium of the computer include, but are not limited to: phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices, or any other non-transmission medium, may be used to store information that may be accessed by a computing device.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means can be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "above," and "over" a second feature may be directly on or obliquely above the second feature, or simply mean that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above should not be understood to necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and not to be construed as limiting the present invention, and that changes, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A method of temperature compensation, comprising:
reading a temperature sensor reading and a temperature value of a first heat source element, the temperature sensor configured to measure an ambient temperature, the first heat source element and the temperature sensor being in a same installation environment;
taking the reading of the temperature sensor and the temperature value of the first heat source element as the input of a prediction model, and outputting a temperature prediction value at the current moment;
and distributing a weight ratio of the output temperature value at the last moment to the temperature predicted value at the current moment, fitting and outputting the output temperature value at the current moment, wherein the state of the second heat source element is monitored, and the weight ratio of the output temperature value at the last moment to the temperature predicted value at the current moment is adjusted according to the state change of the second heat source element.
2. The temperature compensation method of claim 1, wherein:
if the states of the second heat source element at the current moment and the last moment are maintained, the weight ratio is a fixed weight ratio;
if the states of the second heat source element at the current time and the previous time are changed, in the transition time, along with the increase of the number of times maintained after the states of the second heat source element are switched, the fitting weight of the temperature value output at the previous time is reduced, and the fitting weight of the temperature predicted value at the current time is increased; and when the number of times of maintaining the second heat source element after the state switching is larger than the number of transition times, clearing the number of times of maintaining the second heat source element after the state switching, and enabling the output temperature value at the current time to be equal to the predicted temperature value at the current time.
3. The temperature compensation method of claim 2, wherein:
recording the number of times of maintaining the state of the second heat source element after switching by using a counter C, and setting the initial state to be 0; and making the temperature value output at the first moment directly equal to the reading of the temperature sensor;
if the state of the second heat source element is the same at the time i-1 and the time i and C =0, Y [ i ] = a x Y [ i-1] + (1-a) x S [ i ], wherein a is a weight parameter, Y [ i-1] is an output temperature value at the last time, and S [ i ] is a predicted temperature value at the current time;
if the state of the second heat source element is different from the state of the second heat source element at the time i-1, setting a counter C to be 1 and starting counting, if C is less than or equal to t, adding 1 to the counter Y [ i ] = (1- (1-a)/t × C) × Y [ i-1] + ((1-a)/t × C) × Si ]; if C > t, then Y [ i ] = S [ i ], and the counter C is reset to 0; where t is the number of transition times.
4. A method of temperature compensation according to claim 3, wherein: the weight parameter a =0.8 and the number of transition instants t =50.
5. The temperature compensation method of claim 1, wherein:
humidity compensation is also included;
reading the reading of the humidity sensor at start-up as an initial value
And establishing a mapping relation between the temperature change and the humidity change, calculating a humidity change value at a corresponding moment according to the temperature change value at any moment, and outputting a humidity value.
6. The temperature compensation method of claim 1, wherein:
the second heat source element is a screen, and when the screen is switched between brightness and darkness, the state change of the second heat source element is judged; otherwise, the state of the second heat source element is judged to be maintained.
7. Intelligent panel, its characterized in that includes:
a temperature sensor configured to acquire an ambient temperature;
the first heat source element and the second heat source element are positioned in the same installation environment with the temperature sensor, the first heat source element has a readable temperature value, and the second heat source element has a recognizable state change; the first heat source element comprises a processor;
a memory storing a computer program that is loaded and executed by the processor to implement the temperature compensation method according to any of claims 1-6.
8. A temperature compensation device, comprising:
the temperature reading module is used for reading the reading of the temperature sensor and the temperature value of the first heat source element;
the prediction model predicts a temperature prediction value at the current moment based on the read data of the temperature reading module;
the monitoring module is used for monitoring the state change of the second heat source element;
and the compensation module is used for reading the output temperature value at the last moment and the temperature predicted value at the current moment and distributing a weight ratio to fit and output the output temperature value at the current moment, wherein the weight ratio of the output temperature value at the last moment and the temperature predicted value at the current moment is adjusted according to the state change of the second heat source element.
9. A computer device, characterized in that the computer device comprises a processor and a memory, the memory storing a computer program which is loaded and executed by the processor to implement the temperature compensation method according to any of claims 1-6.
10. A computer readable storage medium comprising one or more program instructions that, when executed, implement the temperature compensation method of any one of claims 1-6.
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| CN116389183B (en) * | 2023-06-07 | 2023-08-29 | 深圳市华翌科技有限公司 | Intelligent home interaction data processing method based on Internet of things |
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