A method and system for temperature regulation of an outdoor heating system

Abstract

The present application relates to the technical field of automatic control and regulation system, and especially relates to a temperature regulation method and system for outdoor heating system. The method comprises the following steps: collecting system temperature measurement value, regulating valve opening, outdoor wind speed and medium flow and preprocessing; constructing data vector and first-order controlled autoregressive thermal model, and using recursive least square algorithm combined with Kalman gain and covariance matrix to update model parameter vector online; constructing adaptive attenuation factor based on variance of prediction residual in N sampling periods, and combining temperature deviation first-order difference to obtain anti-noise differential term; fusing and calculating control increment of proportional term, integral term, anti-noise differential term, wind speed feedforward compensation term based on wind speed change rate and flow feedforward compensation term based on flow change rate, and accumulating to control output at last moment to obtain new control instruction, which is output to electric regulating valve after limiting protection and mapping as current signal, so as to realize temperature regulation.

Description

Technical Field

[0001] This invention relates to the field of automatic control and regulation system technology, and in particular to a method and system for temperature regulation of outdoor heating systems. Background Technology

[0002] Outdoor heating systems (including outdoor gas heaters, electric radiant heaters, and outdoor heat pumps) are widely used in courtyards, terraces, outdoor dining areas, and temporary heating at construction sites. According to industry statistics, the global market for outdoor heating equipment continues to grow. Unlike indoor heating, outdoor heating systems operate in open, highly turbulent environments, facing physical disturbances such as sudden changes in wind speed, ambient temperature drift, and fluctuations in solar radiation. These disturbances make it difficult to stably control the temperature of the heated area near the preset value.

[0003] Currently, the incremental proportional-integral-derivative (PID) control algorithm is the most widely used underlying algorithm in the field of industrial heating temperature control. Due to its simple structure, high reliability, and a mature set of engineering tuning methods, it has remained the most widely used loop control algorithm for more than 70 years since its inception.

[0004] For example, in the patent "A Temperature Control Method for an Electric Heating Furnace Based on an Improved Fuzzy Smith-PID" with publication number "CN111983918A", this scheme obtains the actual temperature by a temperature sensor and compares it with a given value to obtain the deviation and the rate of change of deviation. Fuzzy inference is then used to obtain the tuning values ​​of the three PID control parameters. Simultaneously, an improved Smith predictor is added to the system for prediction compensation, and a nine-point control method is used to adjust the filtering time constant of the Smith predictor feedback channel. However, the physical disturbances in outdoor heating systems are continuously changing physical processes, resulting in continuous temperature deviations and the rate of change of deviation. When the physical disturbance causes the error to change continuously near the boundary of the region, the filtering time constant undergoes a step jump, leading to a sudden change in the control quantity. This results in a mismatch with the smoothness of the physical disturbance. Furthermore, the rule table of the nine-point control method is formulated offline and cannot be adaptively adjusted according to outdoor operating conditions.

[0005] For example, in the patent "A Real-Time Heating Load Control Method Based on Environmental Parameter Compensation" with publication number "CN107842908B", this scheme adopts a time-series-based heating load forecasting method to forecast the heating system load and uses environmental parameters as disturbances, setting up a feedforward compensation loop to eliminate the impact of environmental parameter changes on heating temperature. However, this feedforward compensation is based on fixed system model parameters. In outdoor heating scenarios, the dynamic characteristics of the controlled object change in real time with physical disturbances. When the actual object parameters deviate from the fixed model parameters of the feedforward compensator, the feedforward compensation amount does not match the actual demand, leading to a decrease in control quality. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for temperature regulation of outdoor heating systems, in order to solve the technical problem that the temperature regulation of outdoor heating systems cannot cope with physical disturbances, resulting in low temperature regulation efficiency.

[0007] In a first aspect, the present invention provides a method for temperature regulation of an outdoor heating system, employing the following technical solution: Collect system temperature measurements, regulating valve opening, outdoor wind speed, and heating medium flow rate of the heating system, and preprocess the collected data. A data vector is constructed based on the collected data, a first-order controlled autoregressive thermal model is constructed, and a model parameter vector to be identified is defined. The parameter vector of the model is updated using the recursive least squares algorithm combined with Kalman gain and parameter covariance matrix. based on The variance of the model prediction residuals within each sampling period is used to construct an adaptive decay factor, which is then combined with the first difference of the system temperature deviation at the current moment to obtain the noise-resistant differential term at the current moment. The proportional term, integral term, noise-resistant derivative term, wind speed feedforward compensation term based on the rate of change of outdoor wind speed, and flow feedforward compensation term based on the rate of change of medium flow rate in the PID algorithm are fused to calculate the control increment at the current moment. The control increment is accumulated to the control output at the previous moment to obtain a new control command. After being limited and protected and mapped into a current signal, the command is output to the electric regulating valve to achieve temperature regulation.

[0008] Furthermore, the data vector specifically comprises: the system temperature measurement value at the previous moment, the system regulating valve opening at the previous moment, the outdoor wind speed change rate at the current moment, and the system medium flow rate change rate at the current moment. Column vectors.

[0009] Furthermore, the parameter vector is specifically composed of the current estimated thermal inertia coefficient, the current estimated control drive coefficient, the current wind speed disturbance feedforward coefficient, and the current flow disturbance feedforward coefficient. Column vectors.

[0010] Furthermore, the first-order controlled autoregressive thermal model is specifically defined as follows: the measured system temperature at the current moment is equal to the sum of the predicted system temperature at the current moment and the prediction residual, wherein the predicted system temperature at the current moment is the product of the transpose of the system data vector at the current moment and the parameter vector at the previous moment.

[0011] Furthermore, the parameter update process is specifically as follows: The initial parameter vector is assigned the value of zero, and the initial covariance matrix is ​​assigned the value of a large numerical diagonal matrix, specifically the product of preset matrix coefficients and a 4th-order identity matrix. Within each data acquisition cycle, the following recursive calculation process is performed, including: First, the predicted residual of the system temperature at the current moment is calculated based on the model; Secondly, using the covariance matrix updated at the previous time step and the data vector at the current time step, the Kalman gain vector at the current time step is calculated; The Kalman gain is specifically: , for Kalman gain at time step for The data vector at each moment, for The transpose of the time-time data vector. for The covariance matrix of the parameter estimates at time t. Forgetting factor; Next, using the Kalman gain vector at the current moment and the predicted residual of the system temperature at the current moment, the parameter vector at the previous moment is corrected to obtain the updated parameter vector at the current moment. The updated parameter vector is specifically the product of the Kalman gain and the prediction residual at the current time, plus the parameter vector at the previous time. Finally, using the Kalman gain vector and the data vector at the current time, the covariance matrix of the parameter estimates is updated to obtain the updated covariance matrix at the current time. The updated covariance matrix is ​​then used as the input data for calculating the Kalman gain at the next time step, thus forming a closed-loop recursive iterative process. The updated covariance matrix at the current time can be: , for The covariance matrix updated at each time step. Forgetting factor, It is a 4th order identity matrix. for Kalman gain at time step for The transpose of the time-time data vector. for The parameter estimate covariance matrix at time t.

[0012] Furthermore, the noise reduction differential term acquisition process is as follows: calculate the deviation between the current system temperature target value and the system temperature measurement value and record it as the first deviation; perform a first-order difference calculation on the deviation and record it as the first difference; and take the past... The prediction residuals for each sampling period are calculated and their variances are calculated. Then, an attenuation sensitivity coefficient is set and multiplied by the variances, and the negative exponent is taken to obtain an exponential attenuation factor. Finally, the reference differential gain is multiplied by the first difference and the exponential attenuation factor to obtain the noise-resistant differential term at the current time.

[0013] Further, the control increment acquisition process is as follows: the product of the reference proportional gain and the first difference of the system temperature at the current moment is used as the proportional term; the product of the reference integral gain and the first deviation of the system temperature at the current moment is used as the integral term; the product of the outdoor wind speed change rate at the current moment and the inverse of the wind speed disturbance feedforward coefficient is used as the wind speed feedforward compensation term; the product of the system medium flow rate change rate at the current moment and the inverse of the flow rate disturbance feedforward coefficient is used as the flow rate feedforward compensation term; finally, the proportional term, integral term, wind speed feedforward compensation term, flow rate feedforward compensation term, and the noise reduction differential term are added together to obtain the control increment at the current moment.

[0014] Furthermore, the limiting protection process is as follows: if the new control command is greater than 100%, the final output command is 100%; if it is less than 0%, the final output command is 0%; if the new control command is within the range of 0% to 100%, the final output command is the new control command.

[0015] Furthermore, the mapping is a current signal, specifically: the final output command at the current moment is mapped proportionally to... The industry standard current signal.

[0016] Secondly, the present invention provides a temperature regulation system for an outdoor heating system, which adopts the following technical solution: An outdoor heating system temperature regulation system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned outdoor heating system temperature regulation method is implemented.

[0017] By adopting the above technical solution, the temperature regulation method of the outdoor heating system is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. Terminal devices can then be made based on the memory and processor for convenient use.

[0018] The present invention has the following technical effects: (1) Temperature sensors in outdoor heating systems inevitably suffer from measurement white noise caused by wind-induced vibration and electromagnetic interference. Traditional incremental PID controllers perform a second-order difference on the temperature measurement value, and the high-frequency noise is amplified by a factor of 4 and then multiplied by the differential gain, resulting in severe and unnecessary jitter in the control quantity. This invention uses the prediction residual of the first-order thermal regression model identified online by RLS to replace the direct difference of the temperature measurement value as the differential term input, thereby achieving a high-sensitivity response to physical disturbances and effective suppression of measurement noise. This solves the problem of actuator power jitter caused by outdoor temperature sensor noise and significantly extends the service life of electric regulating valves and variable frequency water pumps.

[0019] (2) This invention utilizes the natural sensitivity of the prediction residual to physical disturbances to achieve differential feedforward. When physical disturbances such as sudden gusts or cold air intrusion occur, the actual temperature will suddenly deviate from the smooth trajectory expected by the model. The residual responds immediately, and its difference acts in advance before the temperature deviates significantly from the target, thus achieving predictive compensation for physical disturbances. Furthermore, no low-pass filter is introduced, and there is no phase lag problem, which effectively improves the temperature control quality of outdoor heating systems under rapid physical disturbances such as sudden changes in wind speed and fluctuations in solar radiation.

[0020] (3) The thermal inertia time constant of the outdoor heating system changes in real time with operating parameters such as wind speed and flow rate. This invention updates the parameters of the first-order thermal regression model in real time through the RLS online identification algorithm, so that the model prediction value is always based on the latest model parameters, avoiding the decline in control quality caused by parameter mismatch in the fixed model scheme. Attached Figure Description

[0021] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts.

[0022] Figure 1 This is a flowchart of the method for temperature regulation in an outdoor heating system according to an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be understood that when the terms "first," "second," etc., are used in the claims, specification, and drawings of this invention, they are only used to distinguish different objects and not to describe a specific order. The terms "comprising" and "including" used in the specification and claims of this invention indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0025] This invention discloses a method for temperature regulation in an outdoor heating system, referring to... Figure 1 This includes steps S1-S4: S1: Data acquisition and preprocessing; In one embodiment, a temperature sensor is used to collect the actual temperature of the heating medium and record it as the system temperature measurement value. Then, the opening degree of the heating system regulating valve is obtained, and a three-cup anemometer is used to collect the outdoor wind speed and an electromagnetic flow meter is used to collect the flow rate of the heating system medium. The same data collection period is set for the collected data. After the data collection is completed, the amplitude is limited and outlier values ​​are removed to eliminate abnormal values ​​caused by communication interruption, electromagnetic pulse, etc.

[0026] Preferably, the data acquisition period is 5 seconds, but it can be adjusted according to the actual situation. This invention does not limit this.

[0027] S2: Construct a first-order controlled autoregressive thermal model and update the model parameter vector using the recursive least squares algorithm; In one embodiment, the system temperature measurement value at the previous moment, the system regulating valve opening at the previous moment, the outdoor wind speed change rate at the current moment, and the system medium flow rate change rate at the current moment are combined to form a system temperature measurement value at the previous moment, the system regulating valve opening rate at the previous moment, the outdoor wind speed change rate at the current moment, and the system medium flow rate change rate at the current moment. The column vectors are denoted as data vectors, and the estimated values ​​of the thermal inertia coefficient, control drive coefficient, wind speed disturbance feedforward coefficient, and flow disturbance feedforward coefficient at the current time are combined to form a data vector. The column vectors are denoted as parameter vectors, and a first-order controlled autoregressive thermal model is constructed. Specifically, the measured system temperature at the current moment is equal to the sum of the predicted system temperature at the current moment and the prediction residual. The predicted system temperature at the current moment and the prediction residual are calculated, the Kalman gain is calculated, and the parameter vector of the model is updated using the Kalman gain. The construction of the first-order controlled autoregressive thermal model can be as follows: , for The system temperature measurement value at that time. for The transpose of the time-time data vector. for The parameter vector at time step, for The predicted residual at time, for The predicted value of the system temperature at any given time; In the formula, The data vector at each time point is specifically as follows: , for The data vector at each moment, for The system temperature measurement value at that time. for The opening degree of the system regulating valve at any given time. for Constant outdoor wind speed variation rate. for The rate of change of system medium flow rate at any given time; In the formula, The parameter vector at time step is as follows: , for The parameter vector at time step, for The estimated value of the thermal inertia coefficient at time t. for The estimated values ​​of the control drive coefficients at time 1. for The wind speed disturbance feedforward coefficient at any given time. for The feedforward coefficient of the flow disturbance at any given time; It should be noted that the outdoor heating pipeline system can essentially be equivalent to a first-order thermal capacity and thermal resistance network. Its temperature change rate is proportional to the difference between the current temperature and the target temperature, which conforms to the basic principles of heat transfer. The first-order model can fully describe the main dynamic characteristics of the system. Furthermore, the predicted value generated based on the updated model parameters does not fluctuate with the high-frequency noise of the sensor. This allows the residual between the actual temperature measurement value and the predicted value to truly reflect the impact of external physical disturbances on the system. By replacing the direct difference of the temperature measurement value in the traditional differential term with this residual, both high sensitivity to physical disturbances and effective suppression of measurement noise can be achieved simultaneously. The parameter update process specifically involves: assigning the initial parameter vector to a zero vector, and assigning the initial covariance matrix to a large numerical diagonal matrix, wherein the large numerical diagonal matrix is ​​specifically the product of preset matrix coefficients and a 4th-order identity matrix. Within each data acquisition cycle, the following recursive calculation process is performed, specifically including: Preferably, the preset range of matrix coefficient values ​​is: In this embodiment, take ; First, the predicted residual of the system temperature at the current moment is calculated based on the model; Secondly, using the covariance matrix updated at the previous time step and the data vector at the current time step, the Kalman gain vector at the current time step is calculated; The Kalman gain is specifically: , for Kalman gain at time step for The data vector at each moment, for The transpose of the time-time data vector. for The covariance matrix of the parameter estimates at time t. The forgetting factor (0.98 in this paper); Next, using the Kalman gain vector at the current moment and the predicted residual of the system temperature at the current moment, the parameter vector at the previous moment is corrected to obtain the updated parameter vector at the current moment. The updated parameter vector is specifically the product of the Kalman gain and the prediction residual at the current time, plus the parameter vector at the previous time. Finally, using the Kalman gain vector and the data vector at the current time, the covariance matrix of the parameter estimates is updated to obtain the updated covariance matrix at the current time. The updated covariance matrix is ​​then used as the input data for calculating the Kalman gain at the next time step, thus forming a closed-loop recursive iterative process. The updated covariance matrix at the current time can be: , for The covariance matrix updated at each time step. Forgetting factor, It is a 4th order identity matrix. for Kalman gain at time step for The transpose of the time-time data vector. for The parameter estimate covariance matrix at time t.

[0028] S3: Construct an adaptive noise-resistant differential term based on residual energy assessment; In one embodiment, the deviation between the current system temperature target value and the system temperature measurement value is calculated and recorded as the first deviation; the first-order difference is calculated on the deviation and recorded as the first difference; and the past... The prediction residuals for each sampling period are calculated and their variances are calculated. Then, an attenuation sensitivity coefficient is set and multiplied by the variances, and the negative exponent is taken to obtain an exponential attenuation factor. Finally, the reference differential gain is multiplied by the first difference and the exponential attenuation factor to obtain the noise-resistant differential term at the current time.

[0029] The formula for calculating the noise resistance differential term can be: , for The noise-resistant differential term at time step, As the reference differential gain, This is the attenuation sensitivity coefficient. for Time in the past The variance of the predicted residuals within each sampling period. for The first difference in the system temperature at any given moment; Preferably, the attenuation sensitivity coefficient is 0.5, but it can be adjusted according to the actual situation. This invention does not limit this. Preferably, the range of the reference differential gain here is: In this embodiment, the value is taken as ; It should be noted that the range of values ​​is determined based on the dynamic characteristics of conventional outdoor solid thermal storage heating systems. In practical engineering applications, those skilled in the art can make adaptive adjustments according to the scale of the specific heating network, the heat capacity of the thermal storage body, and the response speed of the electric regulating valve. This invention does not impose an absolute limitation on this. It should be noted that the noise-resistant differential term constructed here replaces the differential input based on the second-order temperature difference in the traditional incremental PID by introducing an exponential decay mechanism based on the residual variance. This allows the differential gain to automatically decay according to the system noise level, thereby effectively suppressing control oscillations caused by high-frequency noise.

[0030] S4: Calculate the incremental PID control law that integrates dual feedforward and noise-resistant derivative; In one embodiment, the proportional term, integral term, noise-resistant derivative term, wind speed feedforward compensation term, and flow feedforward compensation term in the PID are fused to obtain the control increment at the current moment. This increment is then added to the control output at the previous moment to obtain the new control command at the current moment. The new control command is subjected to upper and lower limit protection to obtain the final output command at the current moment. The final output command at the current moment is then proportionally mapped to a 4~20mA industrial standard current signal. This industrial standard current signal is then output to the controller of the electric regulating valve of the outdoor heating system to achieve temperature regulation of the outdoor heating system.

[0031] The control increment at the current moment is specifically as follows: the product of the reference proportional gain and the first difference of the system temperature at the current moment is taken as the proportional term; the product of the reference integral gain and the first deviation of the system temperature at the current moment is taken as the integral term; the product of the outdoor wind speed change rate at the current moment and the inverse of the wind speed disturbance feedforward coefficient is taken as the wind speed feedforward compensation term; the product of the system medium flow rate change rate at the current moment and the inverse of the flow disturbance feedforward coefficient is taken as the flow rate feedforward compensation term; finally, the proportional term, integral term, wind speed feedforward compensation term, flow rate feedforward compensation term, and the noise resistance differential term are added together to obtain the control increment at the current moment. The formula for calculating the control increment at the current moment can be: , for Control increment at any time, As the reference proportional gain, for The first difference in system temperature at any given time. As the reference integral gain, for The first deviation of the system temperature at any given time. for The noise-resistant differential term at time step, for Constant outdoor wind speed variation rate. for The rate of change of the system medium flow rate at any given time for The wind speed disturbance feedforward coefficient at any given time. for The feedforward coefficient of the flow disturbance at time t. for The proportional term of time, for The integral term at time t, for Wind speed feedforward compensation term at any given time. for The flow feedforward compensation term at any given moment; Preferably, the range of the reference proportional gain here is: In this embodiment, the value is taken as The range of the reference integral gain is: In this embodiment, the value is taken as ; It should be noted that the range of values ​​is determined based on the dynamic characteristics of conventional outdoor solid thermal storage heating systems. In practical engineering applications, those skilled in the art can make adaptive adjustments according to the scale of the specific heating network, the heat capacity of the thermal storage body, and the response speed of the electric regulating valve. This invention does not impose an absolute limitation on this. The limiting protection process is as follows: if the new control command is greater than 100%, the final output command is 100% (i.e., the valve is fully open); if it is less than 0%, the final output command is 0% (i.e., the valve is fully closed); if the new control command is within the range of 0% to 100%, the final output command is the new control command. The formula for proportional mapping of the final output command can be: , for The final output is the current signal value mapped from the command. for The final output instruction is given at any given moment.

[0032] The above steps complete the temperature adjustment of the outdoor heating system.

[0033] This invention also discloses an outdoor heating system temperature regulation system, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the outdoor heating system temperature regulation method according to the invention.

[0034] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0035] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (DRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (DRAM), high-bandwidth memory, hybrid memory cube, etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device.

[0036] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

[0037] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for temperature regulation in an outdoor heating system, characterized in that, Including the following steps: Collect system temperature measurements, regulating valve opening, outdoor wind speed, and heating medium flow rate of the heating system, and preprocess the collected data. A data vector is constructed based on the collected data, a first-order controlled autoregressive thermal model is constructed, and a model parameter vector to be identified is defined. The parameter vector of the model is updated using the recursive least squares algorithm combined with Kalman gain and parameter covariance matrix. based on The variance of the model prediction residuals within each sampling period is used to construct an adaptive decay factor, which is then combined with the first difference of the system temperature deviation at the current moment to obtain the noise-resistant differential term at the current moment. The proportional term, integral term, noise-resistant derivative term, wind speed feedforward compensation term based on the rate of change of outdoor wind speed, and flow feedforward compensation term based on the rate of change of medium flow rate in the PID algorithm are fused to calculate the control increment at the current moment. The control increment is accumulated to the control output at the previous moment to obtain a new control command. After being limited and protected and mapped into a current signal, the command is output to the electric regulating valve to achieve temperature regulation.

2. The temperature regulation method for an outdoor heating system according to claim 1, characterized in that, The data vector is specifically composed of the system temperature measurement value at the previous moment, the system regulating valve opening at the previous moment, the outdoor wind speed change rate at the current moment, and the system medium flow rate change rate at the current moment. Column vectors.

3. The temperature regulation method for an outdoor heating system according to claim 1, characterized in that, The parameter vector is specifically composed of the current estimated thermal inertia coefficient, the current estimated control drive coefficient, the current wind speed disturbance feedforward coefficient, and the current flow disturbance feedforward coefficient. Column vectors.

4. The temperature regulation method for an outdoor heating system according to claim 1, characterized in that, The first-order controlled autoregressive thermal model is specifically defined as follows: the measured system temperature at the current moment is equal to the sum of the predicted system temperature at the current moment and the predicted residual, wherein the predicted system temperature at the current moment is the product of the transpose of the system data vector at the current moment and the parameter vector at the previous moment.

5. The temperature regulation method for an outdoor heating system according to claim 1, characterized in that, The parameter update process is as follows: The initial parameter vector is assigned the value of zero, and the initial covariance matrix is ​​assigned the value of a large numerical diagonal matrix, specifically the product of preset matrix coefficients and a 4th-order identity matrix. Within each data acquisition cycle, the following recursive calculation process is performed, including: First, the predicted residual of the system temperature at the current moment is calculated based on the model; Secondly, using the covariance matrix updated at the previous time step and the data vector at the current time step, the Kalman gain vector at the current time step is calculated; The Kalman gain is specifically: , for Kalman gain at time step for The data vector at each moment, for The transpose of the time-time data vector. for The covariance matrix of the parameter estimates at time t. Forgetting factor; Next, using the Kalman gain vector at the current moment and the predicted residual of the system temperature at the current moment, the parameter vector at the previous moment is corrected to obtain the updated parameter vector at the current moment. The updated parameter vector is specifically the product of the Kalman gain and the prediction residual at the current time, plus the parameter vector at the previous time. Finally, using the Kalman gain vector and the data vector at the current time, the covariance matrix of the parameter estimates is updated to obtain the updated covariance matrix at the current time. The updated covariance matrix is ​​then used as the input data for calculating the Kalman gain at the next time step, thus forming a closed-loop recursive iterative process. The updated covariance matrix at the current time can be: , for The covariance matrix updated at each time step. Forgetting factor, It is a 4th order identity matrix. for Kalman gain at time step for The transpose of the time-time data vector. for The parameter estimate covariance matrix at time t.

6. The temperature regulation method for an outdoor heating system according to claim 1, characterized in that, The noise reduction differential term acquisition process is as follows: calculate the deviation between the current system temperature target value and the system temperature measurement value and record it as the first deviation; perform a first-order difference calculation on the deviation and record it as the first difference; take the past... The prediction residuals for each sampling period are calculated and their variances are calculated. Then, an attenuation sensitivity coefficient is set and multiplied by the variances, and the negative exponent is taken to obtain an exponential attenuation factor. Finally, the reference differential gain is multiplied by the first difference and the exponential attenuation factor to obtain the noise-resistant differential term at the current time.

7. The temperature regulation method for an outdoor heating system according to claim 1, characterized in that, The control increment acquisition process is as follows: the product of the reference proportional gain and the first difference of the system temperature at the current moment is taken as the proportional term; the product of the reference integral gain and the first deviation of the system temperature at the current moment is taken as the integral term; the product of the outdoor wind speed change rate at the current moment and the inverse of the wind speed disturbance feedforward coefficient is taken as the wind speed feedforward compensation term; the product of the system medium flow rate change rate at the current moment and the inverse of the flow rate disturbance feedforward coefficient is taken as the flow rate feedforward compensation term; finally, the proportional term, integral term, wind speed feedforward compensation term, flow rate feedforward compensation term, and the noise resistance differential term are added together to obtain the control increment at the current moment.

8. The temperature regulation method for an outdoor heating system according to claim 1, characterized in that, The limiting protection process is as follows: if the new control command is greater than 100%, the final output command is 100%; if it is less than 0%, the final output command is 0%; if the new control command is within the range of 0% to 100%, the final output command is the new control command.

9. The temperature regulation method for an outdoor heating system according to claim 8, characterized in that, The mapping is a current signal, specifically: the final output command at the current moment is mapped proportionally to... Industry standard current signal.

10. A temperature control system for an outdoor heating system, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the temperature regulation method for an outdoor heating system according to any one of claims 1-9.

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