CN114971426B - Heating equipment constant-temperature intelligent analysis method based on multi-mode perception - Google Patents
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Abstract
The invention relates to the technical field of multi-modal perception, in particular to a heating equipment constant-temperature intelligent analysis method based on multi-modal perception. The method is a digital data processing method particularly suitable for specific functions, and realizes constant-temperature intelligent regulation and control by using internet data services such as database and cloud database services. Firstly, acquiring the temperature, the wind speed, the heating power and the personnel mobility of each area; obtaining temperature stability evaluation according to the fluctuation degree of the temperature sequence; grouping zones by heating power and personnel mobility; fitting a temperature stability function according to the wind speed and temperature stability evaluation in each group, and acquiring the reliability of the function; obtaining standard temperature stability evaluation according to the temperature stability evaluation and the reliability; and acquiring a standard wind speed corresponding to the standard temperature stability evaluation, and adjusting the wind speed according to the standard temperature stability evaluation and the standard wind speed. The embodiment of the invention collects and analyzes multi-region data, and achieves the purpose of realizing constant-temperature intelligent control by adjusting wind speed in regions.
Description
Technical Field
The invention relates to the technical field of multi-modal perception, in particular to a heating equipment constant-temperature intelligent analysis method based on multi-modal perception.
Background
The winter heating is the living demand of residents in China, is social service for solving the basic living demand of the residents in China, and common heating facilities comprise an air conditioner, a floor heating system, a central air conditioner and the like. The demand of people on floor heating and central air conditioning of the central heating system is huge and continuous, and the central heating industry has very wide development prospect.
At present, a common method for regulating and controlling temperature of a central heating system is unified regulation and control, a central air conditioner uniformly regulates and controls temperature to heat a plurality of areas, but because the indoor environments of the areas where the central air conditioner is located are different, the heat insulating performance of the heating areas is different, the mobility of personnel is different, actual heating effects of the areas are different, and constant temperature control capability of some areas is poor, so that constant temperature regulation and control are necessary by changing wind speed in different areas.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a heating equipment constant temperature intelligent analysis method based on multi-modal perception, and the adopted technical scheme is as follows:
acquiring the temperature, the real-time wind speed, the real-time heating power and the personnel mobility in unit time of different areas;
obtaining the relative heat preservation performance of the area according to the real-time heating power; obtaining temperature stability evaluation according to the fluctuation degree of the temperature sequence; constructing a temperature stability vector according to the relative heat preservation performance and the personnel mobility; based on the same real-time heating power, obtaining the stability similarity of the temperature stability vectors of any two different areas; classifying the plurality of regions according to the stability similarity of the different regions to obtain a plurality of groups;
according to real-time wind speed and temperature stability corresponding to a plurality of areas in each group, evaluating and fitting a temperature stability function; inputting a real-time wind speed to the temperature stability function to obtain a predicted temperature stability evaluation, and obtaining reliability from loss between the predicted temperature stability evaluation and a real temperature stability evaluation; obtaining relative personnel mobility of the areas according to the personnel mobility, and obtaining a wind speed threshold value by multiplying the relative personnel mobility by the maximum real-time wind speed in the real-time wind speed sequence corresponding to the areas in the group;
obtaining a standard temperature stability evaluation according to the mean value of the temperature stability evaluation and the reliability; and inputting the temperature stability function to the standard temperature stability evaluation to obtain a standard wind speed, and adjusting the real-time wind speed according to the standard temperature stability evaluation, the standard wind speed and the wind speed threshold.
Preferably, the obtaining of the relative thermal insulation performance of the area according to the real-time heating power includes:
selecting any area as a target area, and acquiring the maximum heating power and the first maximum range in a heating power sequence corresponding to the target area;
acquiring a first difference value between the maximum heating power and the real-time heating power; the ratio of the first difference to the first maximum range is the relative thermal insulation performance.
Preferably, the obtaining the stable similarity of the temperature stability vectors of any two different regions includes:
obtaining cosine similarity of the temperature stability vectors in two different areas; and taking a natural constant as a base number, and taking an exponential function with the cosine similarity as an exponent as the stable similarity.
Preferably, the classifying the plurality of regions according to the stable similarity of the different regions to obtain a plurality of groups includes:
the reciprocal of the stable similarity is taken as the corresponding difference distance between the two regions;
and classifying the plurality of regions by using a K-Means clustering algorithm according to the difference distance between different regions to obtain a plurality of groups.
Preferably, the relative human mobility of the region derived from the human mobility comprises:
constructing a person mobility sequence based on the group, the person mobility of a plurality of regions within the group; and acquiring minimum personnel mobility and a second maximum range in the personnel mobility sequence, wherein a difference value between the real-time personnel mobility and the minimum personnel mobility is used as a second difference value, and a ratio of the second difference value to the second maximum range is relative personnel mobility.
Preferably, the obtaining of the temperature stability evaluation according to the fluctuation degree of the temperature sequence includes:
and constructing a temperature difference sequence by the difference of two adjacent temperatures in the temperature sequence, wherein the variance of the temperature difference sequence is the temperature stability evaluation.
Preferably, the obtaining a standard temperature stability evaluation according to the mean value of the temperature stability evaluation and the reliability includes:
sorting the temperature stability evaluations in the temperature stability evaluation sequence according to the sizes, and calculating the average value of Top-k temperature stability evaluations in the sorted temperature stability evaluation sequence;
obtaining standard temperature stability evaluation according to the mean value and the reliability;
the calculation formula of the standard temperature stability evaluation is as follows:
wherein,
as standard temperature stabilityEvaluating;
is as follows
The normalized reliability corresponding to each group;
is the mean value.
Preferably, the adjusting the real-time wind speed according to the standard temperature stability evaluation, the standard wind speed and the wind speed threshold value comprises:
when the real-time temperature stability evaluation is smaller than the standard temperature stability evaluation, adjusting the real-time wind speed to the standard wind speed;
when the real-time temperature stability evaluation is larger than or equal to the standard temperature stability evaluation, inputting the real-time temperature stability evaluation into the temperature stability function to obtain a predicted wind speed; when the predicted wind speed is larger than the wind speed threshold value, adjusting the real-time wind speed to the wind speed threshold value.
The embodiment of the invention at least has the following beneficial effects:
the embodiment of the invention utilizes a multi-mode perception technology, the method is a digital data processing method particularly suitable for specific functions, and the constant-temperature intelligent regulation and control are realized by utilizing internet data services such as a database, cloud database service and the like. Firstly, acquiring the temperature, the real-time wind speed, the real-time heating power and the personnel mobility in unit time of different areas; obtaining the relative heat preservation performance of the area according to the real-time heating power; obtaining temperature stability evaluation according to the fluctuation degree of the temperature sequence; constructing a temperature stability vector according to the relative heat preservation performance and the personnel mobility; based on the same real-time heating power, acquiring the stability similarity of the temperature stability vectors of any two different areas; classifying the plurality of regions according to the stability similarity of the different regions to obtain a plurality of groups; according to real-time wind speed and temperature stability corresponding to a plurality of areas in each group, evaluating and fitting a temperature stability function; inputting a real-time wind speed to temperature stability function to obtain a predicted temperature stability evaluation, and obtaining reliability through loss between the predicted temperature stability evaluation and the real temperature stability evaluation; obtaining relative personnel mobility of the areas according to the personnel mobility, and obtaining a wind speed threshold value by multiplying the relative personnel mobility by the maximum real-time wind speed in the real-time wind speed sequence corresponding to the areas in the group; obtaining standard temperature stability evaluation according to the mean value and the reliability of the temperature stability evaluation; and inputting a temperature stability function to standard temperature stability evaluation to obtain a standard wind speed, and adjusting the real-time wind speed according to the standard temperature stability evaluation and the standard wind speed and a wind speed threshold. The embodiment of the invention achieves the aim of realizing intelligent control of constant temperature by regulating wind speed in different areas through collecting and analyzing multi-area data.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for intelligently analyzing a heating equipment constant temperature based on multi-modal perception according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined objects, the following detailed description of the method for intelligently analyzing the constant temperature of a heating device based on multi-modal sensing, the specific implementation, structure, features and effects thereof, which is provided by the present invention, is provided with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment of the invention provides a specific implementation method of a heating equipment constant-temperature intelligent analysis method based on multi-mode perception, and the method is suitable for an indoor heating equipment constant-temperature intelligent control scene. Indoor being divided into a plurality of regions under this scene, all placing a plurality of temperature sensor in every region, every region all has respective corresponding heating equipment. The method aims to solve the problem that the uniform regulation and control of a plurality of areas can cause poor constant temperature control capability of some areas. The embodiment of the invention relates to a digital data processing method particularly suitable for specific functions, which realizes constant-temperature intelligent control by utilizing internet data services such as a database service, a cloud database service and the like, and realizes the purpose of realizing regional wind speed regulation to realize constant-temperature intelligent control by acquiring and analyzing multi-region data and carrying out different wind speed regulation on different regions.
The specific scheme of the heating equipment constant temperature intelligent analysis method based on multi-modal perception provided by the invention is specifically described below by combining the attached drawings.
Referring to fig. 1, a flow chart of steps of a heating equipment constant temperature intelligent analysis method based on multi-modal perception according to an embodiment of the present invention is shown, where the method includes the following steps:
and S100, acquiring the temperature, the real-time wind speed, the real-time heating power and the personnel mobility in unit time of different areas.
The method comprises the steps that the current indoor temperature is measured by utilizing temperature sensors, due to the difference of the spatial positions of indoor heating areas, the temperatures of different areas have certain difference, the temperatures of different areas in a room are respectively obtained by utilizing a plurality of temperature sensors, namely, a plurality of temperature sensors are arranged in each area, and the average value of the plurality of temperature sensors in the current area is collected to be used as the temperature of the current area. In an embodiment of the invention the sampling frequency of the temperature sensor is acquired once per second.
Meanwhile, the indoor heat preservation effects of different areas are different, the heating power required for keeping the same indoor temperature constant is monitored under the condition that no person enters and exits, and the larger the required heating power is, the more easily the indoor heat is lost; otherwise, the indoor heat is not easy to lose.
And acquiring real-time heating power of heating equipment in different areas by using a power meter, wherein the real-time heating power is also the real-time heating power for stabilizing the indoor temperature.
Since the mobility of people in different areas has a large influence on the fluctuation of the indoor temperature, when the mobility of people in the room is large, the area can be regarded as a short-time stay area; when the mobility of the indoor person is small, the area is considered as a long stay area. In order to maintain the indoor temperature constancy of different areas, the areas with high personnel mobility, namely areas with short stay, can be maintained in temperature by increasing the air speed of the heating equipment.
Utilize infrared sensor to gather personnel mobility, the collection mode is for obtaining the number of times of passing in and out of personnel in the region, installs infrared sensor at the gate to detect this indoor personnel's personnel mobility. The mobility of the person in the unit time is used as an index for evaluating the mobility of the person in the room in the time zone.
And acquiring the mobility of the personnel in unit time, namely acquiring the average value of the mobility of the personnel in unit time. Wherein the unit time is 1min, the infrared sensor collects the mobility of the personnel once every 5 seconds, namely the mobility of the personnel is collected for 12 times in the unit time. Because the influence of the entering and exiting of personnel on the indoor temperature has the time delay, the abnormity on the temperature can be found on the data after the personnel enter the room for a period of time, so that the fixed-length unit time is collected to express the influence of the current personnel mobility on the temperature.
And adjusting and controlling the indoor temperature to be constant based on the wind speed of the heating equipment, and collecting the current wind speed in each room. The wind speed is automatically obtained based on the heating facility.
S200, obtaining the relative heat preservation performance of the area according to the real-time heating power; obtaining temperature stability evaluation according to the fluctuation degree of the temperature sequence; constructing a temperature stability vector according to the relative heat preservation performance and the personnel mobility; based on the same real-time heating power, acquiring the stability similarity of the temperature stability vectors of any two different areas; and classifying the plurality of regions according to the stable similarity of the different regions to obtain a plurality of groups.
And (3) carrying out constant temperature control on a plurality of areas, firstly obtaining the relative heat preservation performance of each area, and further regulating and controlling the wind speed of each area on the basis of the relative heat preservation performance.
And obtaining the relative heat preservation performance of each area according to the real-time heating power. Specifically, the method comprises the following steps: and selecting any area as a target area, and acquiring the maximum heating power and the first maximum range in the heating power sequence corresponding to the target area. And acquiring a first difference value between the maximum heating power and the real-time heating power, wherein the ratio of the first difference value to the first maximum range is the relative heat insulation performance.
Further, the temperature stability is obtained based on the fluctuation degree of the acquired temperature sequence in unit time, specifically, a temperature difference sequence is constructed by the difference between two adjacent temperatures in the temperature sequence, and the variance of the temperature difference sequence is used for temperature stability evaluation.
In different indoor environments, because the influence degrees of various factors such as the indoor area size, the indoor air mobility, the personnel mobility and the temperature stability are different, different regions are divided so as to adjust the wind speed according to actual conditions.
And analyzing the temperature change relation of different areas to realize the division of the different areas.
First, a temperature stability vector is constructed from the relative thermal insulation performance and the personnel mobility.
Based on same real-time heating power, obtain the stable similarity of the temperature stability vector in arbitrary two different regions, it is specific: and obtaining cosine similarity of the temperature stability vectors of the two different regions, wherein the cosine similarity is used as a base number, and the cosine similarity is used as an exponential function of an index to be the stability similarity of the two different regions. And analyzing the stability similarity of the wind power system under the constant heating power, judging the similar conditions of the environments of the two areas, further analyzing the different areas, and giving the adaptive constant-temperature wind speed. The greater the stability similarity of the two regions, the closer the environments of the two regions are, and conversely, the smaller the stability similarity of the two regions, the greater the difference between the environments of the two regions.
And respectively obtaining the stable similarity of every two different regions. And classifying the plurality of regions according to the stable similarity of the different regions to obtain a plurality of groups. Specifically, the method comprises the following steps:
the reciprocal of the stationary similarity is taken as the corresponding difference distance between the two regions. The difference distance
Is of the formula
Wherein
to stabilize the similarity.
And classifying the plurality of regions by using a K-Means clustering algorithm according to the difference distance between different regions to obtain a plurality of groups. In the embodiment of the present invention, the value of K is 4, and in other embodiments, an implementer may adjust the value according to actual situations. That is, the division of the plurality of regions into four groups is completed, and the environmental difference of the regions within each group is small.
Step S300, fitting a temperature stability function according to real-time wind speed and temperature stability evaluation corresponding to a plurality of areas in each group; inputting a real-time wind speed to temperature stability function to obtain a predicted temperature stability evaluation, and obtaining reliability according to loss between the predicted temperature stability evaluation and the real temperature stability evaluation; and obtaining the relative personnel mobility of the regions according to the personnel mobility, and obtaining the wind speed threshold value by the product of the relative personnel mobility and the maximum real-time wind speed in the real-time wind speed sequence corresponding to the regions in the group.
And analyzing the relation between the wind speed and the temperature stability evaluation in the similar environment of a plurality of areas in each group to realize that the proper wind speed is given according to the temperature stability evaluation value.
Acquiring wind speeds corresponding to a plurality of areas in each group, and constructing a wind speed sequence; obtaining temperature stability evaluation construction corresponding to a plurality of areas in a groupAnd (4) a temperature stability evaluation sequence, and simultaneously storing the temperature stability evaluation and the wind speed which are continuously acquired as historical data into a historical database. Fitting a plurality of wind speeds and a plurality of temperature stability evaluations in a historical database to obtain a fitting temperature stability function
Wherein, in the process,
for temperature stability evaluation;
is the wind speed;
is the slope of the fitted temperature stabilization function;
is the intercept of the fitted stabilization function. It should be noted that the slope and intercept of the fitted temperature stabilization function can be obtained by fitting.
And after the temperature stability function is obtained, inputting the current wind speed into the temperature stability function to obtain the predicted temperature stability evaluation.
The loss between the predicted temperature stability evaluation and the true temperature stability evaluation is obtained.
The loss function
Comprises the following steps:
wherein,
the number of real temperature stability evaluations in the historical database;
evaluating the real temperature stability;
to predict temperature stability evaluation.
The loss function represents the mean of the difference between the true temperature stability assessment and the fitted predicted temperature stability assessment, and the closer the loss is to 0, the higher the degree of fit of the temperature stability function.
The reliability of the temperature stabilization function is derived from the loss function. The degree of reliability
The formula of (1) is:
wherein, in the process,
is a loss function.
The reliability represents the reliability of the fitting degree of the real temperature stability evaluation and the predicted temperature stability evaluation obtained by fitting, and the lower the reliability is, the lower the fitting degree is, and the poorer the quality of the obtained temperature stability function is.
When the wind speed is adjusted based on the fitting function model, the E value directly influences the adjustment precision. And analyzing the reliability of the four groups to obtain the relative regulation precision of each group, wherein the relative regulation precision is the reliability after normalization.
First, the
Relative adjustment accuracy of individual groups
Comprises the following steps:
wherein,
is a first
Reliability of the individual group;
is as follows
Reliability of the individual group;
is the sum of the reliabilities of the four groups.
The purpose of obtaining the relative adjustment precision is as follows: when the group with larger relative adjustment precision is in a condition that the heating environment of the adjusted and controlled wind speed is closer to the ideal condition, and when the adjustment precision is poorer, a larger error may exist, and a higher ideal temperature stability evaluation threshold value is given to the group with smaller adjustment precision, so that the group with better robustness is obtained.
When the heat loss rate of the heating environment is large, a large heat transport amount should be ensured in order to maintain the sensible temperature comfort in the area. However, since increasing the wind speed may cause negative effects such as noise and discomfort to human body, the threshold value of the building heating and ventilating wind speed should be analyzed in combination with the actual environment for different areas.
The wind speed threshold is analyzed based on the personnel flow condition of the indoor environment, when the personnel flow volume is large, the wind speed can be higher, the area belongs to a personnel short stay area, and can be a public place, and when the personnel flow volume is small, the wind speed needs to be limited.
Further, the relative human mobility of the region is obtained according to the human mobility. Specifically, the method comprises the following steps: and constructing a personnel mobility sequence based on the personnel mobility of a plurality of areas in any group, acquiring the minimum personnel mobility and the second maximum range in the personnel mobility sequence, taking the difference value between the real-time personnel mobility and the minimum personnel mobility as a second difference value, and taking the ratio of the second difference value to the second maximum range as the relative personnel mobility.
And obtaining the wind speed threshold value by the product of the relative personnel mobility and the maximum wind speed in the wind speed sequence corresponding to the plurality of areas in each group. The required wind speed threshold for the temperature stability evaluation of the region was analyzed based on the relative personnel mobility of the personnel mobility. When the mobility of the person is larger, the person is more likely to be a public area, and the indoor temperature stability evaluation requirement is lower, whereas when the mobility of the person is smaller, the person is more likely to be a private space or an office area, and the temperature stability evaluation requirement of the area is higher due to the fact that the person is more sensitive to the temperature.
S400, obtaining a standard temperature stability evaluation according to the mean value and the reliability of the temperature stability evaluation; and inputting a temperature stability function to standard temperature stability evaluation to obtain a standard wind speed, and adjusting the real-time wind speed according to the standard temperature stability evaluation, the standard wind speed and a wind speed threshold.
And sorting the temperature stability evaluations in the temperature stability evaluation sequence according to the sizes, and calculating the mean value of Top-k temperature stability evaluations in the sorted temperature stability evaluation sequence. In the embodiment of the invention, k is 10, and in other embodiments, the value can be adjusted according to actual conditions. And obtaining standard temperature stability evaluation according to the mean value and the reliability.
Evaluation of Standard temperature stability
The calculation formula of (c) is:
wherein,
is as follows
The normalized reliability corresponding to each group;
is the mean value.
The reliability E is the fitting degree of the constructed temperature stability function, the larger the fitting degree is, the more reliable the fitting curve is, the better the 10 temperature stability evaluations with the temperature stability are selected, the model can finally reach the stable value range, the more valuable the sample is, the more ideal the control effect based on the fitting function is, the smaller the error of adjusting the wind speed through the function model is, the smaller margin can be given, on the contrary, under the condition that the control effect of the fitting function is relatively poor, the larger margin is given, and the range can be subsequently limited based on the personnel mobility so as to achieve the correction effect.
And inputting the standard temperature stability evaluation into the temperature stability function to obtain the standard wind speed based on the temperature stability function. And judging whether the wind speed needs to be adjusted or not based on the temperature stability evaluation of the current area. Specifically, the method comprises the following steps:
and when the real-time temperature stability evaluation is smaller than the standard temperature stability evaluation, adjusting the real-time wind speed to the standard wind speed so as to realize that the temperature stability evaluation approaches to the standard temperature stability evaluation after the wind speed is adjusted.
When the real-time temperature stability evaluation is larger than or equal to the standard temperature evaluation, inputting the real-time temperature stability evaluation into a temperature stability function to obtain a predicted wind speed, and when the predicted wind speed is larger than a wind speed threshold, adjusting the real-time wind speed to the wind speed threshold; when the predicted wind speed is less than the wind speed threshold, no adjustment is required.
The aim of regional constant-temperature intelligent regulation is achieved by regulating the wind speed of each region. The purpose when setting the wind speed threshold is that when the current region has no personnel, if the region continuously works at a larger wind speed, the phenomenon of idle consumption can occur.
In summary, the embodiment of the invention utilizes a multi-modal perception technology, and the method is a digital data processing method particularly suitable for specific functions, and utilizes internet data services such as a database and cloud database services to realize constant-temperature intelligent regulation and control. Firstly, acquiring the temperature, the real-time wind speed, the real-time heating power and the personnel mobility in unit time of different areas; obtaining the relative heat preservation performance of the area according to the real-time heating power; obtaining temperature stability evaluation according to the fluctuation degree of the temperature sequence; constructing a temperature stability vector by the relative heat preservation performance and the personnel mobility; based on the same real-time heating power, obtaining the stability similarity of the temperature stability vectors of any two different areas; classifying the areas according to the stability similarity of the different areas to obtain a plurality of groups; according to real-time wind speed and temperature stability corresponding to a plurality of areas in each group, evaluating and fitting a temperature stability function; inputting a real-time wind speed to temperature stability function to obtain a predicted temperature stability evaluation, and obtaining reliability according to loss between the predicted temperature stability evaluation and the real temperature stability evaluation; obtaining relative personnel mobility of the areas according to the personnel mobility, and obtaining a wind speed threshold value by multiplying the relative personnel mobility by the maximum real-time wind speed in the real-time wind speed sequence corresponding to the areas in the group; obtaining standard temperature stability evaluation according to the mean value and the reliability of the temperature stability evaluation; and inputting a temperature stability function to standard temperature stability evaluation to obtain a standard wind speed, and adjusting the real-time wind speed according to the standard temperature stability evaluation and the standard wind speed and a wind speed threshold. The embodiment of the invention achieves the aim of realizing intelligent control of constant temperature by regulating wind speed in different areas through collecting and analyzing multi-area data.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. The heating equipment constant-temperature intelligent analysis method based on multi-modal perception is characterized by comprising the following steps of:
acquiring the temperature, the real-time wind speed, the real-time heating power and the personnel mobility in unit time of different areas;
obtaining the relative heat preservation performance of the area according to the real-time heating power; obtaining temperature stability evaluation according to the fluctuation degree of the temperature sequence; constructing a temperature stability vector according to the relative heat preservation performance and the personnel mobility; based on the same real-time heating power, obtaining the stability similarity of the temperature stability vectors of any two different areas; classifying the areas according to the stability similarity of the different areas to obtain a plurality of groups;
according to the real-time wind speed and temperature stability evaluation fitting temperature stability functions corresponding to the multiple regions in each group; inputting a real-time wind speed to the temperature stability function to obtain a predicted temperature stability evaluation, and obtaining reliability according to loss between the predicted temperature stability evaluation and the real temperature stability evaluation; obtaining relative personnel mobility of the areas according to the personnel mobility, and obtaining a wind speed threshold value by multiplying the relative personnel mobility by the maximum real-time wind speed in the real-time wind speed sequence corresponding to the areas in the group;
obtaining a standard temperature stability evaluation according to the mean value of the temperature stability evaluation and the reliability; inputting the standard temperature stability evaluation into the temperature stability function to obtain a standard wind speed, and adjusting the real-time wind speed according to the standard temperature stability evaluation, the standard wind speed and the wind speed threshold;
the standard temperature stability evaluation obtaining method comprises the following steps: sorting the temperature stability evaluations in the temperature stability evaluation sequence according to the sizes, and calculating the mean value of Top-k temperature stability evaluations in the sorted temperature stability evaluation sequence; obtaining standard temperature stability evaluation according to the mean value and the reliability;
the calculation formula of the standard temperature stability evaluation is as follows:
wherein,
for standard temperature stability evaluation;
is as follows
Respectively corresponding normalized reliability;
is the mean value.
2. The heating equipment constant temperature intelligent analysis method based on multi-modal perception according to claim 1, wherein the obtaining of the relative heat preservation performance of the area according to the real-time heating power comprises:
selecting any area as a target area, and acquiring the maximum heating power and the first maximum range in a heating power sequence corresponding to the target area;
acquiring a first difference value between the maximum heating power and the real-time heating power; the ratio of the first difference to the first maximum range is the relative thermal insulation performance.
3. The heating equipment constant temperature intelligent analysis method based on multi-modal perception according to claim 1, wherein the obtaining of the stable similarity of the temperature stability vectors of any two different areas comprises:
obtaining cosine similarity of the temperature stability vectors of two different areas; and taking a natural constant as a base number, and taking an exponential function with the cosine similarity as an exponent as the stable similarity.
4. The intelligent analysis method for the constant temperature of the heating equipment based on the multi-modal awareness as claimed in claim 1, wherein the classifying the plurality of regions according to the stability similarities of the different regions to obtain a plurality of groups comprises:
the reciprocal of the stable similarity is taken as the corresponding difference distance between the two regions;
and classifying the plurality of regions by using a K-Means clustering algorithm according to the difference distance between different regions to obtain a plurality of groups.
5. The heating equipment constant temperature intelligent analysis method based on multi-modal perception according to claim 1, wherein the obtaining of the relative staff mobility of the area from the staff mobility comprises:
constructing a person mobility sequence based on the group, the person mobility of a plurality of regions within the group; and acquiring minimum personnel mobility and a second maximum range in the personnel mobility sequence, wherein a difference value between the real-time personnel mobility and the minimum personnel mobility is used as a second difference value, and a ratio of the second difference value to the second maximum range is relative personnel mobility.
6. The heating equipment constant temperature intelligent analysis method based on multi-modal perception according to claim 1, wherein the obtaining of the temperature stability evaluation according to the fluctuation degree of the temperature sequence comprises:
and constructing a temperature difference sequence by the difference of two adjacent temperatures in the temperature sequence, wherein the variance of the temperature difference sequence is the temperature stability evaluation.
7. The heating equipment constant temperature intelligent analysis method based on multi-modal awareness according to claim 1, wherein the adjusting the real-time wind speed according to the standard temperature stability evaluation, the standard wind speed and the wind speed threshold value comprises:
when the real-time temperature stability evaluation is smaller than the standard temperature stability evaluation, adjusting the real-time wind speed to the standard wind speed;
when the real-time temperature stability evaluation is larger than or equal to the standard temperature stability evaluation, inputting the real-time temperature stability evaluation into the temperature stability function to obtain a predicted wind speed; when the predicted wind speed is greater than the wind speed threshold, adjusting the real-time wind speed to the wind speed threshold.
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