CN113587172A - Water supply temperature delay time prediction method and device and electronic equipment - Google Patents
Water supply temperature delay time prediction method and device and electronic equipment Download PDFInfo
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Abstract
The application discloses a method and a device for predicting delay time of water supply temperature and electronic equipment, wherein the method comprises the following steps: calculating a water supply temperature change value within a preset time width according to the preset time width; when the water supply temperature change value meets a preset threshold value, obtaining a working condition point of water supply temperature change; respectively acquiring a water supply temperature sequence and a return water temperature sequence from the beginning of the working point to the end of the time width according to the working point and the time width of the water supply temperature change; constructing a backwater temperature time lag matrix according to the backwater temperature sequence; and calculating a correlation coefficient between each row in the water supply temperature sequence and the backwater temperature time lag matrix, and taking the delay time corresponding to the correlation coefficient with the maximum value as the temperature delay time of the working point. Through the mode, the water supply temperature delay time can be accurately predicted by the aid of the method, so that heating power of a heating system is adjusted normally.
Description
Technical Field
The application relates to the technical field of regulation and control in the town heat supply industry, in particular to a method and a device for predicting delay time of water supply temperature and electronic equipment.
Background
The urban centralized heating system mainly comprises five parts, namely a heat source, a primary pipe network, a heating power station, a secondary pipe network and a heat user. The urban central heating system is a system which is large and complex in composition, and coupling and association of different degrees are formed among heat sources, pipe sections, heating power stations, heat users and the like.
The regulation and control modes of the heating power station are mainly divided into a quality regulation mode, a quantity regulation mode, a quality and phase quality regulation quantity regulation mode and the like. The quality regulation is essentially temperature regulation, and the heat exchange quantity of primary water supply entering a heat exchange station is regulated by regulating the opening degree of a valve at the primary side of the heating power station, so that the aim of regulating the temperature of secondary water supply is fulfilled; the essence of the quantity regulation is flow regulation, and the flow is regulated by regulating the frequency of a water pump on the secondary side of the heating power station, so that the total heat conveyed by the secondary network is regulated.
The heating station obtains target control temperature of the heating station in the next day according to weather forecast and a heating station climate model, different heating systems in different cities have different selection of control targets, some heating stations take secondary water supply temperature as a control target, some secondary water return temperature as a control target, part heating stations take secondary water supply and return water average temperature as a control target, and part heating stations take primary water return temperature as a control target. Based on the target control temperature, the heating power station is often regulated and controlled by combining the manual experience or body feeling of a regulating and controlling person in the actual regulation and control of the heating power station.
The heating station has time delay from the regulation action to the temperature response of the end of a user, the delay time is different under different heating stations, different weather conditions, different working conditions and different secondary network structures, and the temperature delay time under different working conditions is difficult to acquire on line, so that the problems of improper regulation time (over-advanced or lag) and low user thermal comfort degree exist in the regulation of the heating station.
Disclosure of Invention
The application provides a method and a device for predicting water supply temperature delay time and electronic equipment, and aims to solve the problem that the temperature delay time cannot be accurately calculated in the prior art.
In order to solve the above technical problem, the present application provides a method for predicting a water supply temperature delay time, including: calculating a water supply temperature change value within a preset time width according to the preset time width; when the water supply temperature change value meets a preset threshold value, obtaining a working condition point of water supply temperature change; respectively acquiring a water supply temperature sequence and a return water temperature sequence from the beginning of the working point to the end of the time width according to the working point and the time width of the water supply temperature change; constructing a backwater temperature time lag matrix according to the backwater temperature sequence; and calculating a correlation coefficient between each row in the water supply temperature sequence and the backwater temperature time lag matrix, and taking the delay time corresponding to the correlation coefficient with the maximum value as the temperature delay time of the working point.
In order to solve the above technical problem, the present application provides a water supply temperature delay time prediction apparatus, including: the water supply temperature change module is used for calculating a water supply temperature change value within a preset time width; the working point module is used for obtaining a working point of water supply temperature change when the water supply temperature change value meets a preset threshold value; the temperature sequence module is used for respectively acquiring a water supply temperature sequence and a return water temperature sequence from the beginning of the working point to the end of the time width according to the working point and the time width of the water supply temperature change; the hysteresis matrix module is used for constructing a backwater temperature time hysteresis matrix according to the backwater temperature sequence; and the temperature delay time module is used for calculating a correlation coefficient between each row in the water supply temperature sequence and the backwater temperature time delay matrix and taking the delay time corresponding to the correlation coefficient with the maximum value as the temperature delay time of the working point.
In order to solve the above technical problem, the present application provides an electronic device, which includes a memory and a processor, wherein the memory is connected to the processor, the memory stores a computer program, and the computer program is executed by the processor to implement the above method for predicting the water supply temperature delay time
In order to solve the above technical problem, the present application provides a computer-readable storage medium storing a computer program, which when executed implements the above method for predicting the water supply temperature delay time.
The application provides a method and a device for predicting delay time of water supply temperature and electronic equipment, wherein response delay of the water supply temperature is calculated through operating points with obviously changed water supply temperature, specifically, the operating points with changed water supply temperature are detected by adopting sliding time width and mean value calculation, and the temperature delay time of a secondary network is determined by analyzing the correlation between the water supply temperature and return water temperature within the time width of the operating points. Through the mode, the water supply temperature delay time can be accurately predicted by the aid of the method, so that heating power of a heating system is adjusted normally.
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic view of an embodiment of supply and return water temperature curves for a secondary network at a typical working day;
FIG. 2 is a schematic flow chart illustrating an embodiment of a method for predicting a delay time of a water supply temperature according to the present application;
FIG. 3 is a schematic diagram of an embodiment of temperature delay time calculation and correlation coefficient calculation results according to the present application;
FIG. 4 is a schematic diagram of a topology of an embodiment of a BP neural network;
FIG. 5 is a schematic flow chart diagram of one embodiment of a neural network algorithm;
FIG. 6 is a diagram of data analysis for an embodiment of true and predicted values;
FIG. 7 is a schematic structural diagram of an embodiment of the water supply temperature delay time prediction apparatus according to the present invention;
FIG. 8 is a schematic structural diagram of an embodiment of an electronic device of the present application;
FIG. 9 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present application, the following describes in detail the method, apparatus and electronic device for predicting the delay time of the water supply temperature provided by the present application with reference to the accompanying drawings and the detailed description.
When the central heating system transports hot water, temperature response time delay exists due to transmission distance and heat transfer working conditions. For the secondary network, room temperature acquisition equipment rarely exists at the end user side currently, so the typical temperature delay is reflected in the change of secondary temperature supply and secondary temperature return.
The temperature response delay time of the secondary network side of the central heating system is relatively rarely researched at present, most of researches on the temperature response delay time come from the building side, the temperature response delay time and the influence factors thereof are researched by a simulation or experiment method, and the thermal delay of subsystems or materials in the building is analyzed through the simulation and experiment. There is also a small percentage of research in building thermal response time identification through observation and analysis of historical data.
The method based on mechanism modeling or experiment can accurately analyze the delay time of the thermodynamic system and the influence factors thereof, but requires higher expert knowledge and time cost; the delay time of a single thermodynamic system can be rapidly acquired by a method of manually and empirically analyzing and observing historical data. However, these two methods are mainly suitable for temperature delay time identification of offline scenes. However, in an actual heating system, the number of secondary networks and heating power stations is large, the secondary networks and the heating power stations are distributed in all corners of a city, the types, structures, materials, population and years of buildings connected under the heating power stations are different, the secondary networks can be continuously changed and adjusted along with the development of the city, the delay time of the heating system is calculated and predicted on line by a set of accurate and reliable methods, and the result has great guidance value for heating regulation and control personnel. Therefore, a method for rapidly identifying the temperature delay time of the secondary network on line is needed.
Referring to fig. 1, fig. 1 is a schematic diagram of an embodiment of supply and return water temperature curves of a secondary grid at a typical working day. It can be seen that there is a time lag in the response of the secondary return water temperature to the change in supply water when the secondary supply water temperature rises or falls in a stepwise manner. This time lag is called the bar temperature delay time of the district heating system secondary network, and the complete definition of the secondary network temperature delay time is: and when the working condition of the heating station changes, the time difference when the secondary water supply temperature and the secondary water return temperature reach the maximum point or the new stable point respectively.
Regarding the identification method of the delay time, experts propose a method based on manual observation for identification, which can perform off-line identification of the temperature delay time of a small number of specific objects, but is difficult to perform on-line rapid identification of the temperature delay time of a plurality of secondary networks. The delay time may be different due to the change of the number of hot users of the structure of the secondary network and the external working conditions.
Based on this, the present application provides a method for predicting water supply temperature delay time, please refer to fig. 2, fig. 2 is a schematic flow chart of an embodiment of the method for predicting water supply temperature delay time of the present application, in this embodiment, the method for predicting water supply temperature delay time may include steps S110 to S150, and each step is as follows:
s110: and calculating the water supply temperature change value within the preset time width.
Setting a time width delta t, and equally dividing the time width delta t into a front time width and a rear time width delta t which are equally spacedi1And Δ ti2The following are:
wherein, t'iRepresenting the ith acquisition point of the day.
Will be time-wide by Δ tGradually sliding forward on the time axis, respectively calculating delta ti1、Δti2Mean value of water supply temperature T over a wide time interval1And T2The following are:
calculating T1And T2Absolute value of the difference of (1) | T1-T2And will absolute value | T1-T2L is a supply water temperature change within the time width Δ t.
S120: and when the water supply temperature change value meets a preset threshold value, obtaining a working condition point of the water supply temperature change.
When absolute value | T1-T2Determining corresponding time t 'when | is greater than preset threshold value delta'iFor the operating point t of the supply water temperature variationi(ii) a When absolute value | T1-T2When | is less than or equal to the preset threshold value delta, the forward sliding time width delta t is continued until a working condition point t of water supply temperature change is obtainediOr the time width at slides to the cutoff time.
Wherein the preset threshold δ is a temperature change threshold set according to expert knowledge.
S130: and respectively acquiring a water supply temperature sequence and a water return temperature sequence from the start of the working point to the end of the time width according to the working point and the time width of the water supply temperature change.
According to operating point tiAnd the time width delta t, respectively obtaining the working condition points tiWater supply temperature sequence TG from start to end of time width DeltatiAnd backwater temperature sequence T' hi；
Wherein,
TGi＝[Ti，Ti+1，...，Ti+Δt]
T′hi＝[T′i，T′i+1，...，T′i+Δt]
Ti、T′irespectively represent tiThe water supply temperature and the water return temperature at the moment.
S140: and constructing a backwater temperature time lag matrix according to the backwater temperature sequence.
The sequence of return water temperatures is advanced stepwise, and a new sequence is obtained each time. Specifically, a maximum moving step number k is set, and a new sequence T' h is obtained through a forward return water temperature sequencei+1，T′hi+2，...，T′hi+kConstructing a backwater temperature time lag matrix Q according to the backwater temperature time lag matrix Q;
wherein,
it should be noted that the maximum number of hysteresis steps k, the preset temperature change threshold δ, and the time width Δ t need to be set according to expert knowledge or manual experience.
S150: and calculating a correlation coefficient between each row in the water supply temperature sequence and the backwater temperature time lag matrix, and taking the delay time corresponding to the correlation coefficient with the maximum value as the temperature delay time of the working point.
Calculating a water supply temperature sequence TGiAnd a correlation coefficient r between each column in the backwater temperature time lag matrix Q; wherein the correlation coefficient r is used for measuring the linear correlation between two variables, and the range is from-1 to 1;
the closer the correlation coefficient r is to 1, the more positive the correlation, the closer to-1, the more negative the correlation, and the closer to 0, the less significant the correlation.
The correlation coefficient r is calculated as follows:
wherein Z is0Represents the variable ZiAverage value of (1), F0Represents the variable FiAverage value of (d); ziIs a water supply temperature sequence TGiThe relevant variables of (a); fiAre the relevant variables of the backwater temperature time lag matrix Q.
The embodiment provides a method for predicting the delay time of the water supply temperature, the response delay of the water supply temperature is calculated through the operating point with obviously changed water supply temperature, the operating point with changed water supply temperature is detected by specifically adopting the calculation of the width and the mean value of the sliding time, and the temperature delay time of a secondary network is determined by analyzing the correlation between the water supply temperature and the return water temperature within the time width of the operating point. Through the mode, the water supply temperature delay time can be accurately predicted according to the embodiment, so that the heating power regulation of the heating system is normal.
To better illustrate the solution of the present embodiment, the following examples,
referring to fig. 3, fig. 3 is a schematic diagram of an embodiment of temperature delay time calculation and correlation coefficient calculation results according to the present application.
As can be seen from FIG. 3, the first point (0, 0.4) represents the original supply water temperature sequence TG at the same timeiAnd backwater sequence T' hiThe correlation coefficient of (a) was 0.4. Because there is delay between supply water temperature and return water temperature, the change trends of both have a bit error, therefore the correlation coefficient calculated is lower. The second point (1, 0.59) represents a correlation coefficient of 0.59 with the supply water temperature sequence after the return water temperature sequence has moved forward one time step. The correlation coefficient reaches a maximum of 0.98 when moving to step 7, indicating that at this time the time delay between the water return temperature sequence and the water supply temperature has been substantially offset by the number of forward steps, so that the secondary grid temperature delay time step in this case is 7 steps. When the time step for the backwater temperature to advance exceeds the delay of the temperature step, the correlation coefficient of the backwater temperature is further reduced. The step size for the collection point is one value every 1 minute, and the temperature delay time is 7 minutes.
Further, the current temperature delay time can be evaluated under the current heating working conditions (heating temperature, pressure, flow, weather and the like) of the heating power station according to historical data.
The method is an on-line rapid calculation method for the temperature delay time based on the historical data of the temperature of the heating return water of the time sequence. And a prediction method of temperature delay time is provided according to the secondary network operation data (flow, temperature, heat and pressure), the external weather data (air temperature, air pressure, humidity and wind speed) and the secondary network attribute data (heat supply area and user building type) of the central heat supply system. The method comprises the following specific steps:
(1) data collection:
in recent years, automatic measuring devices such as heat meters, pressure meters, flow meters and the like are gradually installed on heat stations, and data can be acquired. Weather data of cities needs to be collected, and besides, related attribute data of the secondary networks, such as heat supply areas of the secondary networks, types of heat stations and the like, are obtained from heat companies.
The characteristic data are as follows:
(2) data preprocessing:
the data preprocessing comprises missing value filling, abnormal value processing and data normalization processing.
For numerical data, the following equation can be followed:
where x represents sample data, xmaxAnd xminRepresenting the maximum and minimum values of the sample data, respectively.
(3) And (3) correlation analysis:
in order to search for features that affect the temperature delay time of the secondary network, a correlation analysis is first performed on all the original features. And displaying the correlation data of each parameter. Where H is the temperature delay time calculated as described above.
It can be found that the correlation coefficient of the heating area and the delay time is the highest (r is 0.66), which indicates that the heating area and the temperature delay time of the secondary network are relatively related. From the energy balance point of view, the delay characteristic of the secondary network comprises two parts: flow delay and heat exchange delay. The larger the heat supply area is, the longer the length of the water supply pipeline of the secondary network is. The longer the pipe, the longer the thermal delay caused by the hot water flow process. In terms of heat exchange dimension, factors such as temperature, pressure, mass flow rate and the like can change the heat exchange coefficient between the hot water and the indoor space, so that the delay time can be influenced. The correlation coefficient of the delay time with other characteristics is relatively small compared to the heating area, such as the correlation coefficient of the primary side instantaneous flow (s, 1m) and the delay time is only 0.33. Meanwhile, the weather data and the delay time do not show a direct correlation, and the maximum correlation coefficient is only 0.1. Therefore, the characteristic parameters with large temperature delay time correlation are selected for research, and the characteristics comprise: heat supply area, heat station type, primary water supply (steam) pressure, primary water return (drainage) pressure, primary hot water (steam) flow, secondary water supply temperature, secondary water return pressure and secondary hot water flow.
(4) BP neural network prediction:
the BP neural network is a multilayer feedforward neural network and is mainly characterized by signal forward transmission and error backward propagation. In forward pass, the input signal is processed layer by layer from the input layer through the hidden layer to the output layer. The neuronal state of each layer only affects the neuronal state of the next layer. If the expected output cannot be obtained by the output layer, the backward propagation is carried out, and the network weight and the threshold are adjusted according to the prediction error, so that the predicted output of the BP neural network continuously approaches the expected output. Referring to fig. 4-5, fig. 4 is a schematic diagram of a topology of an embodiment of a BP neural network; FIG. 5 is a flow diagram of an embodiment of a neural network algorithm.
In FIG. 4, X1，X2...，XnIs the input value of the BP neural network, Y1，Y2...，YnIs a predictor of BP neural network, WijAnd WjkIs the weight of BP neural network. As can be seen from fig. 1, the BP neural network can be regarded as a nonlinear function, and the network input value and the predicted value are respectively an independent variable and a dependent variable of the function. When the number of input nodes is n and the number of output nodes is mThe BP neural network expresses a functional mapping from n independent variables to m dependent variables.
The BP neural network is divided into 4 parts of BP neural network structure determination, BP neural network training, BP neural network confirmation and BP neural network prediction. The BP neural network structure is determined according to the number of input and output parameters; the BP neural network training is mainly to the setting of training parameter, the setting item mainly includes the transfer function of hidden layer and output layer, network training function, learning function, iteration times, convergence accuracy and learning rate, etc.; the BP neural network confirms that the prediction result is evaluated and the training parameters are adjusted; the BP neural network prediction is mainly performed according to real-time data.
Due to the above-described dependency description, the input parameters are: the system comprises a heat supply area, a heat station type, primary water supply (steam) pressure, primary water return (drainage) pressure, primary hot water (steam) flow, secondary water supply temperature, secondary water return pressure and secondary hot water flow, and the predicted value is temperature delay time.
And (4) testing results:
the heating number of a heating period of a certain heating station in 6 months is obtained, the time step length is 5 minutes, 200 training data sets and 50 test data sets of variable working condition data are selected, and the result is shown in figure 6.
Based on the calculated temperature delay time, the embodiment can also perform feature analysis and feature engineering by using the correlation, and predict the temperature delay time by using the BP neural network, so that the heating power regulation of the heating system is normal.
Based on the foregoing method for predicting water supply temperature delay time, the present application further provides a device for predicting water supply temperature delay time, please refer to fig. 7, fig. 7 is a schematic structural diagram of an embodiment of the device for predicting water supply temperature delay time of the present application, and in this embodiment, the device 100 for predicting water supply temperature delay time may specifically include a water supply temperature variation module 110, a working point module 120, a temperature sequence module 130, a hysteresis matrix module 140, and a temperature delay time module 150.
And a water supply temperature change module 110 for calculating a water supply temperature change value within a preset time width.
And the operating point module 120 is configured to obtain an operating point of the water supply temperature change when the water supply temperature change value meets a preset threshold.
And the temperature sequence module 130 is configured to obtain a water supply temperature sequence and a water return temperature sequence from the start of the operating point to the end of the time width according to the operating point and the time width of the water supply temperature change.
And the hysteresis matrix module 140 is used for constructing a backwater temperature time hysteresis matrix according to the backwater temperature sequence.
And the temperature delay time module 150 is configured to calculate a correlation coefficient between each column of the supply water temperature sequence and the backwater temperature delay matrix, and use a delay time corresponding to the correlation coefficient with the largest value as the temperature delay time of the operating point.
Based on the above method for predicting the delay time of the water supply temperature, the present application further provides an electronic device, as shown in fig. 8, and fig. 8 is a schematic structural diagram of an embodiment of the electronic device of the present application. The electronic device 200 may comprise a memory 21 and a processor 22, the memory 21 being connected to the processor 22, the memory 21 having stored thereon a computer program, which when executed by the processor 22, implements the method of any of the above embodiments. The steps and principles thereof have been described in detail in the above method and will not be described in detail herein.
In the present embodiment, the processor 22 may also be referred to as a Central Processing Unit (CPU). The processor 22 may be an integrated circuit chip having signal processing capabilities. The processor 22 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Based on the method for predicting the delay time of the water supply temperature, the application also provides a computer readable storage medium. Referring to fig. 9, fig. 9 is a schematic structural diagram of an embodiment of a computer-readable storage medium according to the present application. The computer-readable storage medium 300 has stored thereon a computer program 31, which computer program 31, when being executed by a processor, carries out the method of any of the above embodiments. The steps and principles thereof have been described in detail in the above method and will not be described in detail herein.
Further, the computer-readable storage medium 300 may be various media that can store program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic tape, or an optical disk.
It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. In addition, for convenience of description, only a part of structures related to the present application, not all of the structures, are shown in the drawings. The step numbers used herein are also for convenience of description only and are not intended as limitations on the order in which the steps are performed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first", "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The above embodiments are merely examples and are not intended to limit the scope of the present disclosure, and all modifications, equivalents, and flow charts using the contents of the specification and drawings of the present disclosure or those directly or indirectly applied to other related technical fields are intended to be included in the scope of the present disclosure.
Claims (10)
1. A method for predicting a delay time of a water supply temperature, comprising:
calculating a water supply temperature change value in a preset time width according to the preset time width;
when the water supply temperature change value meets a preset threshold value, obtaining a working condition point of water supply temperature change;
respectively acquiring a water supply temperature sequence and a return water temperature sequence from the start of the working point to the end of the time width according to the working point and the time width of the water supply temperature change;
constructing a backwater temperature time lag matrix according to the backwater temperature sequence;
and calculating a correlation coefficient between the water supply temperature sequence and each row in the backwater temperature time lag matrix, and taking the delay time corresponding to the correlation coefficient with the maximum value as the temperature delay time of the working condition point.
2. The method for predicting the delay time of water supply temperature according to claim 1, wherein the calculating the variation value of water supply temperature within a preset time width according to the time width comprises:
setting a time width delta t, and equally dividing the time width delta t into a front time width and a rear time width delta t with equal intervalsi1And Δ ti2；
Gradually sliding the time width delta t forward on a time axis, and respectively calculating delta ti1、Δti2Mean value of water supply temperature T over a wide time interval1And T2；
Calculating T1And T2Absolute value of the difference of (1) | T1-T2And the absolute value | T1-T2L asThe supply water temperature within the time width Δ t varies.
3. The method for predicting the delay time of the water supply temperature according to claim 2, wherein the obtaining the operating point of the water supply temperature change when the water supply temperature change value meets a preset threshold value comprises:
when the absolute value | T1-T2When | is greater than the preset threshold value, determining the corresponding moment as the working condition point t of the water supply temperature changei；
When the absolute value | T1-T2And when the | is less than or equal to the preset threshold, continuously sliding the time width delta t forwards until a working condition point of the water supply temperature change is obtained or the time width delta t slides to the cut-off time.
4. The method for predicting the delay time of the water supply temperature according to claim 3, wherein the step of respectively acquiring the water supply temperature sequence and the water return temperature sequence from the start of the operating point to the end of the time width according to the operating point and the time width of the water supply temperature change comprises the steps of:
according to operating point tiAnd the time width delta t is used for respectively acquiring a water supply temperature sequence TG from the start of the working condition point to the end of the time widthiAnd backwater temperature sequence T' hi；
Wherein,
TGi＝[Ti，Ti+1，...，Ti+Δt]
T′hi＝[T′i，T′i+1，...，T′i+Δt]
Ti、T′irespectively represent tiThe water supply temperature and the water return temperature at the moment.
5. The method for predicting water supply temperature delay time according to claim 4, wherein the constructing a backwater temperature time lag matrix according to the backwater temperature sequence comprises:
setting the maximum moving step number k, and obtaining a new sequence T' h by advancing the backwater temperature sequencei+1，T′hi+2，...，T′hi+kConstructing the backwater temperature time lag matrix Q;
wherein,
6. the method for predicting a water supply temperature delay time according to claim 5, wherein the calculating a correlation coefficient between the water supply temperature sequence and each column of the backwater temperature time lag matrix comprises:
calculating the water supply temperature sequence TGiAnd a correlation coefficient r between each column in the backwater temperature time lag matrix Q; wherein the correlation coefficient r is a measure of the linear correlation between two variables, and ranges from-1 to 1.
7. The method for predicting a delay time of a water supply temperature according to claim 6, wherein the correlation coefficient r is calculated as follows:
wherein Z is0Represents the variable ZiAverage value of (1), F0Represents the variable FiAverage value of (d); ziIs the water supply temperature sequence TGiThe relevant variables of (a); fiIs the relevant variable of the backwater temperature time lag matrix Q.
8. A water supply temperature delay time prediction device characterized by comprising:
the water supply temperature change module is used for calculating a water supply temperature change value in a preset time width according to the preset time width;
the working point module is used for obtaining a working point of the water supply temperature change when the water supply temperature change value meets a preset threshold value;
the temperature sequence module is used for respectively acquiring a water supply temperature sequence and a water return temperature sequence from the start of the working point to the end of the time width according to the working point of the water supply temperature change and the time width;
the hysteresis matrix module is used for constructing a backwater temperature time hysteresis matrix according to the backwater temperature sequence;
and the temperature delay time module is used for calculating a correlation coefficient between the water supply temperature sequence and each column in the backwater temperature time lag matrix and taking the delay time corresponding to the correlation coefficient with the maximum value as the temperature delay time of the working condition point.
9. An electronic device comprising a memory and a processor, the memory being connected to the processor, the memory storing a computer program which, when executed by the processor, implements the water supply temperature delay time prediction method according to any one of claims 1 to 7.
10. A computer-readable storage medium characterized in that a computer program is stored, which when executed implements the water supply temperature delay time prediction method according to any one of claims 1 to 7.
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