A power system based on a rotating door algorithm and a dead zone algorithm

Abstract

The application discloses a power system based on a rotating door algorithm and a dead zone algorithm, which comprises an environmental parameter sensing unit for real-time monitoring and uploading of environmental temperature and humidity data, a central air conditioner terminal intelligent control unit for intelligent control of starting and stopping, temperature adjustment, air speed adjustment, air direction adjustment and mode modification, a swarm intelligent computing node embedded in the central air conditioner terminal intelligent control unit, an embedded self-organizing operating system, edge computing and control signal output, and a data compression module for uploading data after sensing layer sensor data collection, which can save time, reduce cost, facilitate data compression work, and effectively solve the contradiction between low data compression precision and large data storage of air conditioner sensors by combining the dead zone algorithm and the rotating door algorithm.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system data processing, and particularly relates to a power system based on a rotating door algorithm and a dead zone algorithm. BACKGROUND

[0002] The central air conditioner intelligent power system is a full-range optimization of air conditioner operation, energy saving, safety and the like by using Internet of Things and the like. Similar to intelligent vehicle networking and industrial Internet of Things, the central air conditioner intelligent power system can perceive environmental information in the air conditioner operation process, reserve a large amount of energy saving and emission reduction related knowledge, long-term save the air conditioner operation information collected by sensors, judge various conditions in the air conditioner operation process according to the information fed back by the sensors, such as whether the air conditioner is normally operated, whether the energy consumption is too high, and whether it is comfortable and suitable for people, and can monitor the problems of high energy consumption and deviation from the comfortable temperature zone in the air conditioner operation process in real time, make favorable adjustment to the adverse situation occurring in the air conditioner operation process according to the mastered knowledge of energy saving and emission reduction and human temperature perception, accurately identify the temperature information change of the external environment in the air conditioner operation process, such as the decrease of night temperature, and actively adjust, so as to truly achieve accurate identification and scientific response. The central air conditioner intelligent power system will generate a large amount of data in the monitoring of the air conditioner operation process, and the compression efficiency of the massive data is poor, the compression time is long, and how to collect and compress the related data needs a new solution. SUMMARY

[0003] To achieve the above purpose, the technical scheme of the application is as follows: a power system based on a rotating door algorithm and a dead zone algorithm, the power system comprising

[0004] An environmental parameter perception unit for real-time monitoring and uploading of environmental temperature and humidity data;

[0005] A central air conditioner terminal intelligent control unit for intelligent control of starting and stopping, temperature adjustment, air speed adjustment, air direction adjustment and mode modification;

[0006] A group intelligent computing node embedded in the central air conditioner terminal intelligent control unit, with a self-organizing operating system, realizing edge computing and control signal output;

[0007] A data compression module for uploading data after sensor data collection and processing of the environmental perception layer.

[0008] The rotating door algorithm has the advantages of a large amount of compressed data, saving of flow cost, reduction of data collection times, reduction of data transmission times and improvement of data transmission efficiency. The rotating door algorithm fits all data into a curve, and the background can calculate the sensor data of any time node according to the rotating door algorithm.

[0009] As an improvement of the present application, a power system based on a rotating door algorithm, the power system comprises

[0010] An environmental parameter sensing unit is configured to monitor and upload environmental temperature and humidity data in real time.

[0011] A central air conditioner terminal intelligent control unit is configured to intelligently control start and stop, temperature adjustment, air speed adjustment, air direction adjustment, and mode modification.

[0012] A swarm intelligent computing node is embedded in the central air conditioner terminal intelligent control unit, and is internally provided with a self-organizing operating system to realize edge computing and control signal output.

[0013] A data compression module is configured to upload data after the environmental sensing layer sensor collects the data.

[0014] As an improvement of the present application, the data compression module fits the air conditioner sensor data into a curve through a rotating door algorithm, and the background calculates the sensor data at any time node according to the rotating door, wherein the compression ratio Compression error wherein CR is the compression ratio, which is the ratio of the number of original data to the number of data after compression, CE is the compression error, m≤n is satisfied, n is the number of sensor data before compression, m is the number of sensor data after compression, y i is the air conditioner sensor data.

[0015] As an improvement of the present application, the original air conditioner sensor data points which have a greater impact on compression accuracy are saved through the rotating door algorithm, wherein the following is satisfied: wherein ΔE is the tolerance in the rotating door algorithm, and the following is satisfied: ΔE min ≤ ΔE ≤ ΔE max .

[0016] As an improvement of the present application, the data is fitted by using the least square method, and the fitting formula is: φ = {φ0, φ1, …, φ n}.

[0017] As an improvement of the present application, function fitting is performed through a function class mathematical model, a function y = S * (x) is selected to minimize the sum of squared errors, and the calculation formula of variance is:

[0018]

[0019] δ i = |S * (i) - y i |, wherein i = 0, 1, …, n-m,

[0020] δmax To compress the maximum error, while satisfying delta max ≤delta i , the average variance formula is calculated as: Mu is the average value of the original air conditioner sensor data in the interval, when sigma = 0, delta E remains unchanged, sigma = delta E, when Then When Then

[0021] As an improvement of the application, the data compression module processes the data including the indoor temperature data of the air-conditioned room, the indoor humidity data of the air-conditioned room, and the air speed data of the air outlet of the central air conditioner.

[0022] As an improvement of the application, the dead zone algorithm is used for data compression of the air conditioner sensor data, wherein the dead zone algorithm formula is: X = [x1, x2, …, x n ] T , z = {Z a , Z b , Z c , Z d} T , wherein Za, Zb, Zc, Zd are data compression parameter limit values, and X is compressed data.

[0023] Then define R0 and R1 through the formula: Wherein: the data value at time Ti is equal to Wj, while satisfying m ≤ n, and Y = (t, w); w = (W1, W2, …, W n ), Wm+1 rule as follows = (Ai, Bi, Ci, Di), indicating that all historical data values cannot satisfy the dead zone limit value range, so the data at time Ti is marked as Wm+1, and m is updated to m+1. 1 ≤ i ≤ n, 1 ≤ j ≤ m, n is the record number of the air conditioner sensor data running, and m is the maximum number of historical data values. WAj, WBj, WCj, and WDj are data parameter values.

[0024] Compared with the prior art, the application has the advantages of being simple, easy to understand, and easy to operate, and has a wide range of applications. It can save time and reduce cost, and is conducive to the development of data compression work. The combination of the dead zone algorithm and the rotating door algorithm can effectively solve the contradiction between low air conditioner sensor data compression precision and large data storage capacity. DETAILED DESCRIPTION

[0025] The present application will be further clarified by the following examples, which should not be construed as limiting the scope of the present application.

[0026] Embodiment: A power system based on a rotating door algorithm and a dead zone algorithm, the power system comprising an environmental parameter sensing unit for real-time monitoring and uploading of environmental temperature and humidity data; a central air conditioner terminal intelligent control unit for intelligent control of starting and stopping, temperature adjustment, air speed adjustment, air direction adjustment, and mode modification; a swarm intelligent computing node embedded in the central air conditioner terminal intelligent control unit, with a self-organizing operating system, realizing edge computing and control signal output; a data compression module for uploading data after the sensing layer sensor collects data. The data compression module processes data including indoor temperature data of an air-conditioned room, indoor humidity data of the air-conditioned room, and air speed data of an air outlet of a central air conditioner.

[0027] Let ΔE be the compression accuracy parameter of the rotating door algorithm. Its compression principle is that the starting point 0 is the last stored point, and the upper and lower two branch points with a distance of ΔE from the 0 point are taken as the basis to establish two virtual doors. When there is only one sensor data node, the door is closed. With the increase of data nodes, the door will rotate to open, and the width of the door can be extended. Once it is opened, it cannot be closed again. As long as the inner angle sum of the two doors is less than 180° (the two doors are not parallel), the rotation extension can continue to operate. If the inner angle sum of the two doors is greater than or equal to 180°, the operation is stopped, and the previous data point is stored as the new starting point to start a new compression work.

[0028] ΔE is the compression accuracy. If the compression accuracy is large, it means that the compression ratio is high, and more data points will be discarded, increasing the compression error. If the compression accuracy is small, it means that the compression ratio is small, and more data points will be retained, reducing the compression error, but the compression effect will be weakened.

[0029] The starting point 0 is the starting point of the first segment, and the upper and lower two branch points with a distance of ΔE from the 0 point are taken as the basis, and the two branch points are respectively extended as rays to the outside world. When the 0 point, the rotating door is closed. When the rotating door rotates to open, the upper and lower two branch points respectively form rays with data node 1, and the intersection point of the rays is 1 point. The inner angle formed by the two rays and the y-axis is less than 180°, and at this time the rays can be further extended outward. At this time, the rotating door continues to rotate to open, and the upper and lower two branch points respectively form rays with data node 2, and the intersection point of the rays is 2 point. The inner angle formed by the two rays and the y-axis is less than 180°, and at this time the rays can be further extended outward.

[0030] When the rotating door continues to open, since the rotating door cannot be closed after it is opened, the slope of the line connecting the upper support point and the data node must be continuously expanded or remain unchanged, and the slope of the line connecting the lower support point and the data node must be continuously reduced or remain unchanged. If the line connecting the upper support point and the data node 3 forms a ray, the slope of the ray is smaller than the slope of the data node 2, which does not meet the corresponding requirement, so the ray of the data node 2 is retained. At this time, the line connecting the lower support point and the data node 3 forms a ray, the slope of the ray is smaller than the slope of the data node 2, which meets the requirement, so the ray of the data node 3 is retained. The sum of the internal angles formed by the two rays and the y-axis is less than 180°, at this time, the rays can be continuously expanded outward.

[0031] When the rotating door continues to open, if the line connecting the upper support point and the data node 4 forms a ray, the slope of the ray is smaller than the slope of the data node 2, which does not meet the requirement, so the ray of the data node 2 is retained. At this time, the line connecting the lower support point and the data node 4 forms a ray, the slope of the ray is smaller than the slope of the data node 3, which meets the requirement, so the ray of the data node 4 is retained. The sum of the internal angles formed by the two rays and the y-axis is equal to 180°, at this time, the rays cannot be continuously expanded outward according to the requirement, and the operation needs to be stopped.

[0032] The air conditioner sensor data of the first compression segment contains four data nodes, which are fitted into a curve, and the terminal sends relevant parameters to the background, thereby saving traffic fees, reducing the number of data collection, reducing the number of data transmission, and improving the data transmission efficiency.

[0033] The air conditioner sensor data of the second compression segment starts from the data node 4, and the relevant operation of the rotating door algorithm refers to the opening mode of the rotating door of the first compression segment, until the sum of the internal angles formed by the two rays and the y-axis is greater than or equal to 180°, at this time, the rays cannot be continuously expanded outward according to the requirement, and the operation needs to be stopped, thereby completing the compression work of the second data packet.

[0034] The rotating door algorithm has the advantages of compressing a large amount of data, saving traffic fees, reducing the number of data collection, reducing the number of data transmission, and improving the data transmission efficiency. The rotating door algorithm fits all data into a curve, and the background can calculate the sensor data of any time node according to the rotating door algorithm.

[0035] The power system data compression module fits the air conditioner sensor data into a curve through the rotating door algorithm, and the background calculates the sensor data of any time node according to the rotating door, wherein the compression ratio Compression error Wherein CR is the compression ratio, which is the ratio of the number of original data to the number of data after compression, CE is the compression error, m≤n, n is the number of sensor data before compression, m is the number of sensor data after compression, y iAir conditioner sensor data.

[0036] The original air conditioner sensor data points with greater impact on compression precision are saved by the rotating door algorithm, wherein the following conditions are met: where ΔE is the tolerance in the rotating door algorithm, and the following conditions are met: ΔE min ≤ ΔE ≤ ΔE max .

[0037] Data The least square method is used for fitting, and the fitting formula is: φ = {φ0, φ1, …, φ n}.

[0038] The function fitting is performed by the function class mathematical model, and the function y = S * (x) is selected to minimize the error sum of squares, and the variance formula is:

[0039]

[0040] δ i = |S * (i) - y i |, where i = 0, 1, …, n-m,

[0041] δ max is the maximum compression error, and the following conditions are met: δ max ≤ δ i , and the average variance formula is: μ is the average value of the original air conditioner sensor data in the interval, when σ = 0, ΔE remains unchanged, σ = ΔE, when then when then

[0042] Then the data compression is performed on the air conditioner sensor data by the dead zone algorithm, wherein: X = [x1, x2, …, x n ] T , z = {Z a , Z b , Z c , Z d} T , wherein Za, Zb, Zc, Zd are data compression parameter limit values.

[0043] Define an R0 and R1, through the formula: wherein: the data value at time Ti is equal to Wj, and the following conditions are met: m ≤ n, and deduce that deduce that Y = (t, w); w = (W1, W2, …, W n ), and deduce that Wm+1 rule is as follows = (Ai, Bi, Ci, Di), indicating that all historical data values cannot meet the dead zone limit value range, so the data at time Ti is marked as Wm+1, and m is updated to m+1. 1≤i≤n, 1≤j≤m, n is the number of records of the air conditioner sensor data running, m is the maximum number of historical data values. WAj, WBj, WCj, WDj are data parameter values.

[0044] The dead zone compression algorithm is suitable for continuous change data based on time series. Within the allowed change limit value (dead zone, threshold) range, if the deviation of the current data value from the last saved data value exceeds the specified dead zone range, the current data is saved, otherwise it is discarded. If point A is saved as the first appearing data point, and point A is taken as the reference point, a dead zone range is generated. If point B appears within the dead zone range, it is discarded. When point C appears outside the dead zone range, the data value of point C needs to be saved, and point C is taken as the reference point to generate a new dead zone range. When point D appears within the dead zone range, it is discarded. The subsequent data points are processed in this way. Only the last saved data point needs to be compared to determine whether the current data needs to be saved, which is convenient to operate. The two data points B and C can be restored by linear interpolation. In this case, some compression methods with slope changes need to be used to compress the data values along the slope changes.

[0045] It should be noted that the above content only illustrates the technical idea of the present application and cannot limit the protection scope of the present application. For ordinary skilled persons in the art, under the premise of not departing from the principles of the present application, a number of improvements and refinements can be made, which fall within the protection scope of the claims of the present application.

Claims

1. A power system based on a swing door algorithm and a dead zone algorithm, characterized by, The power system comprises An environmental parameter sensing unit for real-time monitoring and uploading of environmental temperature and humidity data; A central air conditioner terminal intelligent control unit for intelligent control of starting and stopping, temperature adjustment, air speed adjustment, air direction adjustment and mode modification; A swarm intelligence computing node, which is embedded in the central air conditioner terminal intelligent control unit, has a self-organizing operating system and realizes edge computing and control signal output; A data compression module for uploading data after processing of sensor data collected by the environmental sensing layer; The air conditioner sensor data is fitted into a curve by a revolving door algorithm in the data compression module, and the background calculates the sensor data at any time node according to the revolving door, wherein a compression ratio CR= , a compression error CE= , wherein CR is a compression ratio, which is a ratio of the number of original data to the number of data after compression, CE is a compression error, m≤n, n is the number of sensor data before compression, m is the number of sensor data after compression, is the air conditioner sensor data; The original air conditioner sensor data points which have greater influence on compression precision are saved by the rotating door algorithm, wherein the following conditions are met: wherein is the tolerance in the rotating door algorithm, and the following conditions are met: ; Data , , …, The least square method was used for fitting, and the fitting formula was: ; The function fitting is performed through a function class mathematical model, a function is selected, error square sum is minimized, and a variance formula is calculated as follows: ， wherein , To compress the maximum error, while satisfying The average variance formula is calculated as: , is the average value of the original air conditioning sensor data in the interval, when , Remain unchanged, When , there is When , there is .

2. A power system based on the rotating door algorithm and the dead zone algorithm according to claim 1, characterized in that, The data compression module processes air conditioner room indoor temperature data, air conditioner room indoor humidity data and air speed data of an air outlet of the central air conditioner.

3. A power system based on the rotating door algorithm and the dead zone algorithm according to claim 2, characterized in that, Data compression is performed on the sensor data of the air conditioner by a dead zone algorithm, wherein the compression formula is: , , wherein Za, Zb, Zc, and Zd are data compression parameter limit values.

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