Secondary network heat supply temperature control method and device based on negative pressure water mixing technology

Abstract

The invention discloses a secondary network heat supply temperature control method and device based on a negative pressure water mixing technology, and relates to the technical field of secondary network heat supply control. The secondary network heat supply temperature control method and device based on the negative pressure water mixing technology comprises the steps that S1, operation data, structure steady state data and thermal environment data in a current heat supply task are collected, and a standardized heat supply working condition data set is constructed after preprocessing; s2, local hydraulic fluctuation characteristics are evaluated, and the water mixing proportion, the water supplementing flow and bypass path configuration are dynamically adjusted; s3, jointly analyzing the temperature difference of supply water and return water, the heat deviation and the valve state, and driving water mixing adjustment and path optimization; s4, evaluating adjustment intensity and heat response deviation, and realizing abnormal disturbance response and adjustment strategy correction; and S5, recording negative pressure, uneven heat and cold and response data for subsequent diagnosis and steady state identification. The problems that a traditional water mixing control means is difficult to effectively inhibit pressure fluctuation of the water return end, local negative pressure is easily formed, and local energy waste and uneven heat distribution are caused are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of secondary network heating control, and in particular to a secondary network heating temperature control method and device based on negative pressure water mixing technology. Background Art

[0002] With the continued growth of urban thermal loads and the increasing demand for intelligent heating, heating systems are gradually evolving from extensive operations to digital and refined management. In this context, multi-parameter collaborative control technology, based on the fusion of physical models and real-time data, has become a key support for achieving intelligent temperature control and dynamic pressure balance. Among them, negative pressure mixing technology, by regulating the proportion of partial return and replenishment water, can achieve local pressure backfill and thermal balance without disturbing the main circulation. This is an important and practical control strategy for the current multi-point distributed heating network regulation.

[0003] For example, patent application CN109976419B proposes an automatic control system and method for regional cooling and heating steam desuperheating and pressure reduction. The method includes: calculating regional cooling / heating load demand; calculating the number of steam-consuming equipment that needs to be operated, determining the increase or decrease in steam-consuming equipment, and screening equipment; comprehensively calculating the steam pressure control target value, temperature control target value, and system pressure / temperature alarm limit; determining whether the current steam inlet pressure / temperature is lower than the system pressure / temperature low alarm limit, and if so, reporting an alarm to a higher-level computer; when the pressure at the steam network inlet exceeds the system pressure, automatically adjusting the steam pressure regulating valve opening to perform pressure reduction control using a PID algorithm; when the temperature at the steam network inlet exceeds the system temperature limit, automatically controlling the desuperheating valve opening and the operating frequency of the desuperheating pump to perform desuperheating control using a PID algorithm; and starting the equipment when the temperature after desuperheating and pressure reduction reaches the required level. This invention can improve work efficiency, reduce operating costs, and save energy.

[0004] For example, an embodiment of the invention patent with publication number CN104823119B describes a thermostat and related methods using model predictive control. The method of controlling a thermostat using model predictive control may include determining a parameterized model. The parameterized model can be used to predict ambient temperature values of an enclosed space. A set of radiant heating system control strategies can be selected for evaluation to determine an optimal control strategy from the set of control strategies. In order to determine the optimal control strategy, a prediction algorithm can be executed, in which each control strategy is applied to the parameterized model to predict the ambient temperature trajectory and each ambient temperature trajectory is processed in view of a predetermined evaluation function. Processing the ambient temperature trajectory in this manner may include minimizing the cost value associated with the ambient temperature trajectory. The radiant heating system can then be controlled according to the selected optimal control strategy.

[0005] However, existing heating system regulation methods often rely on static setpoint control or simplified model-driven closed-loop regulation, primarily relying on temperature feedback. These methods lack dynamic identification and coordinated compensation mechanisms for key factors such as return water pressure fluctuations, mixed water response lag, and changes in terminal heat load. Especially in large-scale, zoned, and multi-node operation scenarios, traditional control methods suffer from response lags and insufficient regulation accuracy in areas prone to negative pressure, making it difficult to quickly respond to and proactively intervene in complex disturbance conditions.

[0006] In response to the above problems, there is an urgent need for a secondary network heating temperature control method and device based on negative pressure water mixing technology. Summary of the Invention

[0007] Technical problems solved In response to the shortcomings of the existing technology, the present invention provides a secondary network heating temperature control method and device based on negative pressure water mixing technology, which solves the problem that traditional water mixing control means are difficult to effectively suppress pressure fluctuations at the return water end, and are prone to forming local negative pressure, resulting in local energy waste and uneven heat distribution.

[0008] Technical Solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a secondary network heating temperature control method and device based on negative pressure water mixing technology, including S1, collecting operation data, structural steady-state data and thermal environment data in the current heating task, and pre-processing the collected operation data, structural steady-state data and thermal environment data to construct a standardized heating condition data set; S2, based on the standardized heating condition data set, performing a multi-factor coupling evaluation on the local hydraulic fluctuation characteristics of the current secondary network area, and dynamically adjusting the mixing ratio, water replenishment flow and bypass path configuration based on the evaluation results; S3, based on the standardized heating condition data set, A multi-dimensional joint analysis is conducted on the supply and return water temperature difference, heat supply and demand deviation, and valve response status, and the mixed water ratio correction, make-up water flow control, and regulation path optimization are driven in real time based on the analysis results. S4 uses the local hydraulic fluctuation characteristic analysis results and the multi-dimensional joint analysis results of the supply and return water temperature difference, heat supply and demand deviation, and valve response status as input to conduct a comprehensive evaluation of the current mixed water regulation intensity and heat response deviation, and complete the rapid response to various abnormal disturbances and closed-loop correction of the regulation strategy based on the evaluation results. S5 records the entire process data of the local negative pressure, uneven cold and heat, and response results, and completes subsequent diagnosis, regulation strategy optimization, and steady-state identification.

[0009] Furthermore, the operation data, structural steady-state data and thermal environment data in the current heating task are collected, and the collected operation data, structural steady-state data and thermal environment data are standardized and normalized to construct a standardized heating condition data set. The specific steps are: collecting operation data, the operation data include: supply water temperature, return water temperature, return water pressure value, return water flow rate value, pipeline pressure, mixing valve opening and supply and return water temperature difference, and recording the instantaneous measurement value, return water flow average, historical fluctuation average and return water pressure average of each data; collecting structural steady-state data, the structural steady-state data include: pressure fluctuation frequency of each key node, historical maximum fluctuation amplitude and continuous pressure drop duration; collecting thermal environment data, the thermal environment data include: instantaneous heat load at the end of the heat user, heat curve deviation value, each The number of mixing water nodes that need to be adjusted within the cycle, the return water heat fluctuation amplitude and the supply and return water temperature difference, and the response reference value of the heat load deviation value and the average heat load value are recorded at the same time; the total number of sampling cycles and the monitoring time intervals of all collected parameters, the data continuity integrity rate, the instantaneous sampling error amplitude and the sampling anomaly ratio are recorded; through a unified high-precision clock synchronization mechanism, the collected operation data, structural steady-state data and thermal environment data are time-aligned, unit converted and field format standardized, and missing fields and error anomalies are cleaned; the standardized data are normalized to eliminate scale errors and interference factors caused by terminal hardware differences and sensor accuracy deviations; the operation data, structural steady-state data and thermal environment data that have completed standardization and normalization are uniformly stored to construct a standardized heating condition data set.

[0010] Furthermore, the specific steps of performing a multi-factor coupling evaluation on the local hydraulic fluctuation characteristics of the current secondary network area based on the standardized heating condition data set are as follows: extracting the return water pressure value at each sampling moment from the standardized heating condition data set, calculating the difference in the return water pressure values between each adjacent moment in turn and dividing it by the monitoring time interval, taking the absolute value of the ratio of the return water pressure value difference to the monitoring time interval to obtain the pressure change rate per unit time; taking the average of the pressure change rate per unit time at each sampling moment and multiplying it with the ratio of the supply and return water temperature difference and the return water pressure average value at the current moment to obtain the thermal disturbance effect value; extracting the current return water heat fluctuation amplitude, data continuity integrity rate and sampling anomaly ratio from the standardized heating condition data set, multiplying the return water heat fluctuation amplitude with the data continuity integrity rate and then dividing it by the sampling anomaly ratio to obtain the evaluation deviation value; subtracting the thermal disturbance effect value from the mean return water flow and then subtracting the evaluation deviation value to obtain the local pressure stability value of the current return water section.

[0011] Furthermore, the described steps of dynamically adjusting the mixing ratio, water replenishment flow and bypass path configuration based on the evaluation results to complete the backwater pressure backfill and hydraulic disturbance suppression are as follows: real-time comparison of the local pressure stability value and the stability level threshold, the stability level threshold is divided into a first stability threshold and a second stability threshold: when the local pressure stability value is greater than or equal to the first stability threshold, it is determined to be a pressure stable section, the current mixing water regulation coefficient, valve opening and flow path are maintained unchanged, and the original heat supply balance regulation strategy is directly executed without the introduction of an additional compensation mechanism; when the local pressure stability value is less than the first stability threshold and greater than or equal to the second stability threshold, it is determined to be a mild fluctuation section, the pressure disturbance warning state is entered, and the mixing water regulation parameter is enabled The dynamic fine-tuning mode of the temperature, pressure and flow rate is shortened, and a disturbance buffer correction factor is introduced in the process of mixing water ratio calculation and path selection to weaken the impact of sudden data on the adjustment results and enhance the stability of the temperature control strategy. When the local pressure stability value is less than the second stability threshold, it is determined to be a local negative pressure high-risk section. The execution of the conventional temperature control strategy based on the current acquisition parameters is immediately suspended, the emergency control response mechanism is started, the pressure recovery path is called, and a temporary alternative control instruction for the current cycle is constructed. At the same time, the current operating state is marked as a potential unstable condition and included in the risk monitoring list. The change process of the disturbance index, control feedback characteristics and adjustment strategy response path within this cycle are fully recorded.

[0012] Furthermore, the specific steps of conducting a multi-dimensional joint analysis of the current supply and return water temperature difference, heat supply and demand deviation and valve response status based on the standardized heating condition data set are as follows: extracting the supply and return water temperature difference, return water flow rate value and mixing water valve opening of each mixing water node from the standardized heating condition data set, multiplying the supply and return water temperature difference, return water flow rate value and mixing water valve opening to obtain the mixing water response intensity value of the mixing water node in the current cycle; extracting the real-time return water pressure value of each node at the current moment, performing difference calculation with the average value of the return water pressure in the current sliding time window and Take the absolute value and multiply it by the return water pressure adjustment factor to obtain the return water state deviation value; extract the heat load deviation value of each mixing water node at the current moment and divide it by the average heat load value in the same area, multiply the ratio of the heat load deviation value to the average heat load value by the heat load balance adjustment factor to obtain the heat supply offset intensity value; divide the mixing water response intensity by the sum of the corresponding return water state deviation value and the heating offset intensity value to obtain the regulation efficiency value of the mixing water node, accumulate the regulation efficiency values of all mixing water nodes to obtain the dynamic mixing water regulation value of the current cycle.

[0013] Furthermore, the specific steps of driving the correction of the mixing ratio, the control of the water supply flow rate and the optimization of the regulation path in real time based on the analysis results are as follows: comparing the current dynamic mixing adjustment value with the regulation response threshold in real time, the regulation response threshold includes a first regulation threshold and a second regulation threshold: when the dynamic mixing adjustment value is less than or equal to the second regulation threshold, it is determined to be a low regulation intensity section, and the existing mixing ratio, water supply flow rate and bypass path configuration are maintained unchanged, and the mixing parameter state of the cycle is marked as a stable sample, the mixing calculation frequency is synchronously reduced, and the controller execution resources are released for other high-priority module processing; when the dynamic mixing adjustment value is greater than the second regulation threshold and less than or equal to the second regulation threshold, When the first adjustment threshold is reached, it is determined to be a medium-intensity adjustment section, and the adjustment sensitive area monitoring mode is entered, the frequency of mixing parameter updates is increased, the temperature difference and pressure sampling window span are shortened, and the adjustment coefficient change trend analysis is introduced; when the dynamic mixing adjustment value is greater than the first adjustment threshold, it is determined to be a high-risk adjustment section, and the mixing disturbance buffer mechanism is immediately switched to the mixing adjustment algorithm. The parameter structure of the mixing adjustment algorithm is dynamically adjusted, and an adjustment smoothing factor is introduced to suppress hydraulic backflow caused by excessive adjustment, and all current parameter adaptive adjustment tasks are suspended. The mixing control closed loop is executed, the current adjustment state is temporarily frozen, and the data feature mark of the high-intensity adjustment period is stored as an abnormal adjustment sample.

[0014] Furthermore, the multi-dimensional joint analysis results of the local hydraulic fluctuation characteristics analysis results and the supply and return water temperature difference, heat supply and demand deviation and valve response status are used as input to comprehensively evaluate the current water mixing regulation intensity and heat response deviation. The specific steps are: obtaining the local pressure stability value and the dynamic water mixing regulation value, multiplying the local pressure stability value, the dynamic water mixing regulation value and the heat usage curve deviation value, and further multiplying the product of the local pressure stability value, the dynamic water mixing regulation value and the heat usage curve deviation value by the absolute value of the difference between the pressure fluctuation frequency and the historical maximum fluctuation amplitude to obtain the response intervention intensity value; dividing the disturbance feedback adjustment factor by the average return water flow in the current sliding window, and adding one to obtain the dynamic adjustment value; dividing the response intervention intensity value by the dynamic adjustment value, and taking the logarithm of the result of dividing the response intervention intensity value by the dynamic adjustment value to obtain the control execution response value at the current moment.

[0015] Furthermore, the specific steps of completing the rapid response to each abnormal disturbance and closed-loop correction of the adjustment strategy based on the evaluation results are: real-time comparison of the control execution response value and the adjustment intensity evaluation threshold, the evaluation threshold includes a first adjustment threshold and a second adjustment threshold: when the control execution response value is less than or equal to the second adjustment threshold, it is determined to be a section with low control demand, the current mixing ratio, water replenishment flow and bypass path configuration are maintained unchanged, only the basic level temperature control correction is performed, and the current cycle control state is marked as a low disturbance sample; when the control execution response value is greater than the second adjustment threshold and less than or equal to the first adjustment threshold, it is determined to be a section with moderate adjustment intensity, and enters a stable state. The dynamic intervention enhancement mode is activated to shorten the control feedback cycle, and fine-tune the mixing water adjustment coefficient and valve opening configuration, and activate the light-load bypass mechanism in advance to reduce the interference of local pressure fluctuations on the overall hydraulic cycle; when the control execution response value is greater than the first adjustment threshold, it is determined to be a high-response pressure section, and immediately switch to the emergency adjustment mode, enhance the execution amplitude of the adjustment valve, improve the speed response rate of the variable frequency pump, and forcibly activate the main bypass channel to guide the flow diversion to quickly backfill the return water pressure and prevent the formation of negative pressure. At the same time, the adaptive adjustment of conventional parameters is suspended, and the isolation control logic is executed. The thermal disturbance path, pressure change curve and response hysteresis characteristics of the current section are archived and recorded.

[0016] Furthermore, the full-process data recording of the local negative pressure, uneven cold and heat, and response results is used to complete subsequent diagnosis, control strategy optimization, and steady-state identification. The specific steps are: after completing the control execution and feedback response, enter the abnormal marking and control log archiving process, and record the negative pressure fluctuations, mixed water adjustment actions, and response results in the current cycle in the entire process to achieve refined tracking of the operating status and collection management of historical samples; based on the typical characteristics of sudden drop in return water pressure, continuous over-limit deviation of temperature control, and forced activation of bypass paths, control cycles with abnormal risks are automatically marked, and the corresponding trigger time, abnormality type, and judgment basis are recorded; then the key control parameters in the cycle are fully recorded; at the same time, combined with the return water pressure recovery curve, terminal heat compensation results, and control response delay, the effect of each adjustment is quantitatively evaluated to mark whether the adjustment has achieved the steady-state goal.

[0017] A second aspect of the present invention provides a secondary network heating temperature control device based on negative pressure water mixing technology, comprising: a temperature control data acquisition terminal for collecting, in real time, information on supply water temperature, return water temperature, terminal heat load, and other operating parameters during the heating process, and simultaneously collecting dynamic adjustment status of the water mixing node and heat absorption feedback information from the user terminal and transmitting them to a central processing module; The central processing module is used to pre-process the received multi-source heating operation data, build a unified standardized heating condition data set, and push the processing results to the regulation analysis and control judgment unit; The regulation analysis and control judgment unit is used to comprehensively evaluate the multi-dimensional operating characteristics based on the standardized heating condition data set, construct dynamic water mixing regulation parameters, and evaluate the regulation effect and trend evolution direction in combination with the historical steady-state operation baseline, and transmit the comprehensive evaluation results to the execution control and feedback regulation unit; the execution control and feedback regulation unit is used to generate compensation operations based on the regulation analysis results, and collect the pressure backfill curve and heat recovery status during the execution process in real time, feedback the regulation results, and report the key disturbance parameters and control records to the log archiving and early warning generation unit; the log archiving and early warning generation unit is used to record the pressure disturbance events, regulation execution behaviors and feedback results in each cycle, and generate a structured operation log.

[0018] Beneficial effects

[0019] The present invention has the following beneficial effects: (1) The secondary network heating temperature control method and device based on negative pressure water mixing technology integrates the collection and standardized processing of multi-source operating data such as water supply temperature, return water pressure, heat load and valve status, constructs a unified standardized working condition data set, and then realizes dynamic modeling and collaborative analysis of multi-node working conditions of the secondary network, effectively solving the problems of isolated parameter collection and insufficient characterization of operating status in existing technologies.

[0020] (2) The secondary network heating temperature control method and device based on negative pressure water mixing technology, by integrating the local pressure stability value and the water mixing adjustment coefficient, jointly evaluates the disturbance intensity, pressure deviation and heat load response, constructs the control execution response value and generates the adjustment strategy in a hierarchical manner, thereby enhancing the system's ability to respond quickly to sudden negative pressure and unbalanced heating conditions, and effectively solving the problems of lagging adjustment mechanism and lack of hydraulic linkage in the existing technology.

[0021] (3) The secondary network heating temperature control method and device based on negative pressure water mixing technology realizes intelligent perception and regulation feedback of the entire heating disturbance process by establishing a closed-loop control mechanism covering thermal fluctuation identification, dynamic correction of adjustment parameters and switching of bypass activation strategies, thereby improving the system's operational stability and adjustment adaptability to complex heating conditions, and effectively solving the problems of single control logic and imperfect feedback mechanism in existing technologies.

[0022] (4) The secondary network heating temperature control method and device based on negative pressure water mixing technology, by integrating the four functional modules of data acquisition, processing and analysis, adjustment execution and exception archiving, builds a closed-loop execution link for the entire process of heating system control, significantly improves the coordination and intelligence level of the heating system in data utilization, strategy optimization and fault response, and effectively solves the problems of module dispersion and broken control chain in existing technologies.

[0023] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the secondary network heating temperature control method based on negative pressure water mixing technology of the present invention; Figure 2 This is a structural diagram of the secondary network heating temperature control device based on the negative pressure water mixing technology of the present invention; Figure 3 A line graph of the control execution response value involved in the present invention; DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] See also Figure 1-Figure 3 , an embodiment of the present invention provides a technical solution: a secondary network heating temperature control method and device based on negative pressure water mixing technology, including S1, collecting operation data, structural steady-state data and thermal environment data in the current heating task, and pre-processing the collected operation data, structural steady-state data and thermal environment data to construct a standardized heating condition data set; S2, based on the standardized heating condition data set, performing a multi-factor coupling evaluation on the local hydraulic fluctuation characteristics of the current secondary network area, and dynamically adjusting the mixing ratio, make-up water flow and bypass path configuration based on the evaluation results; S3, based on the standardized heating condition data set, performing a multi-dimensional joint analysis of the current supply and return water temperature difference, heat supply and demand deviation and valve response state, and driving the mixing ratio correction, make-up water flow control and regulation path optimization in real time based on the analysis results; S4, using the local hydraulic fluctuation characteristic analysis results and the multi-dimensional joint analysis results of the supply and return water temperature difference, heat supply and demand deviation and valve response state as input, comprehensively evaluating the current mixing water regulation intensity and heat response deviation, and completing a rapid response to each abnormal disturbance and a closed-loop correction of the regulation strategy based on the evaluation results; Specifically, the operating data, structural steady-state data, and thermal environment data of the current heating task are collected, and the collected operating data, structural steady-state data, and thermal environment data are standardized and normalized to construct a standardized heating condition data set. The specific steps are as follows: During the operational data collection phase, multiple monitoring terminals deployed in water supply and return pipelines, mixing nodes, and at user terminals collect key operational data in real time, including supply water temperature, return water temperature, return water pressure, return water flow rate, pipeline pressure, mixing valve opening, and supply and return water temperature difference. This data comprehensively covers temperature transmission status, hydraulic regulation status, and heat energy distribution. Simultaneously, the system records the instantaneous measurement value of each data item, the average return water flow rate within the sliding time window, the historical fluctuation average, and the average return water pressure of the current cycle, providing multi-scale indicator support for subsequent trend analysis and intervention decisions. During the structural steady-state data collection phase, high-frequency pressure sensors deployed at key nodes in the pipe network collect the pressure fluctuation frequency, historical maximum fluctuation amplitude, and continuous pressure drop duration of each node to identify hydraulic instability factors and potential negative pressure development trends. During the thermal environment data collection phase, heat monitoring modules deployed at user terminals and distributed mixing water control points are used to obtain the user's instantaneous heat load, heat curve deviation, the number of mixing water nodes that need to be adjusted per cycle, return water heat fluctuation amplitude, and supply and return water temperature difference. The response reference indicators of heat load deviation and average heat load value are also recorded. Record the total number of samples collected during the entire cycle, and annotate all acquisition parameters with the corresponding monitoring time interval, data continuity and integrity rate, instantaneous sampling error amplitude, and sampling anomaly ratio, providing an evaluation basis for subsequent data validity assessment and preprocessing strategy selection; Through a uniformly deployed high-precision clock synchronization mechanism, the collected operational data, structural steady-state data, and thermal environment data are time-aligned, physical unit conversion and field naming are standardized, and missing items and abnormal measurement results are automatically cleaned, ensuring data consistency and compatibility across different terminals. After completing the structural cleaning, normalization processing is performed on all standardized data to eliminate dimensional inconsistencies and numerical deviations caused by differences in monitoring equipment type, sensor accuracy, and communication cycle; Finally, the standardized and normalized operating data, structural steady-state data and thermal environment data are stored in a unified manner to construct a standardized heating condition data set with complete structure, consistent values and suitable for multi-model control calculations, providing a unified data basis for subsequent local pressure stability analysis, mixing water adjustment parameter generation and control strategy execution.

[0027] In this implementation, multiple monitoring terminals are deployed at key locations to comprehensively collect operating parameters, structural steady-state information, and thermal environmental conditions. The raw data is then standardized and normalized to construct a unified, standardized heating condition dataset. This primarily facilitates the collection and processing of heterogeneous data from multiple sources, improving its temporal consistency, format uniformity, and dimensional comparability. This provides high-quality, structured, and accessible core data support for subsequent local pressure identification, dynamic adjustment calculations, and control strategy generation, ensuring the fundamental capabilities for accurate identification, intelligent decision-making, and closed-loop control under complex operating conditions.

[0028] Specifically, based on the standardized heating condition data set, a multi-factor coupling evaluation is performed on the local hydraulic fluctuation characteristics of the current secondary network area. The specific steps are: extract the return water pressure value at each sampling moment from the standardized heating condition data set, calculate the difference in the return water pressure values between each adjacent moment in turn and divide it by the monitoring time interval, and take the absolute value of the ratio of the return water pressure value difference to the monitoring time interval to obtain the pressure change rate per unit time; take the average of the pressure change rate per unit time at each sampling moment and multiply it with the ratio of the supply and return water temperature difference and the return water pressure average value at the current moment to obtain the thermal disturbance effect value; extract the current return water heat fluctuation amplitude, data continuity integrity rate and sampling anomaly ratio from the standardized heating condition data set, multiply the return water heat fluctuation amplitude by the data continuity integrity rate and then divide it by the sampling anomaly ratio to obtain the evaluation deviation value; subtract the thermal disturbance effect value from the mean return water flow and then subtract the evaluation deviation value to obtain the local pressure stability value of the current return water section.

[0029] The calculation formula of the local pressure stability value is: ; Where, Indicates the number of sampling periods in the current sliding time window; Indicates the current moment; Indicates the monitoring time interval; It represents the average backwater pressure within the current sliding time window, which is used to characterize the basic hydraulic state of the target area and serves as a static benchmark for local pressure assessment; Indicates the backwater pressure value at the time of sampling i, which is used to construct the pressure drop sequence, participate in the calculation of the pressure drop rate per unit time, and reflect the local change trend during the pressure change process; Indicates the historical load average, which is used to describe the load benchmark level within a short period during the drilling process and reflect the steady-state operation trend; The temperature difference between the supply and return water at the i-th sampling moment is used to reflect the instantaneous imbalance of heat energy transmission. The thermal difference may often indicate an imbalance in mixed water regulation or a sudden load change. Indicates the mean value of the return water flow in the current sliding window, which is used to normalize the pressure drop and thermal disturbance intensity, and reflects the reference level of hydraulic transport capacity; Indicates the current return water heat fluctuation amplitude, which is obtained by multiplying the return water flow and temperature. It is used to reflect the instantaneous instability of the user's terminal load; Indicates the data continuity integrity rate within the current sampling period, which is used to measure the sensor operation stability and sampling coverage quality; Indicates the sampling anomaly ratio within the current time window. It is used to measure the degree of distortion of the current data and has an important impact on the reliability of the stable value.

[0030] This implementation comprehensively assesses the hydraulic stability of the current backwater area based on a multi-dimensional approach encompassing factors such as the rate of change of backwater pressure within a sliding time window, the intensity of pressure fluctuations, the amplitude of thermal disturbances, and data acquisition quality. This formula can sensitively capture local pressure drop trends, identify potential negative pressure formation signals, and quantify the impact of disturbances on operational stability. Its core function is to provide a dynamic, quantifiable stability indicator that guides the triggering of mixing and regulation strategies, the determination of regulation intensity, and the rapid identification of abnormal risks, thereby improving responsiveness and control accuracy to complex hydraulic disturbances.

[0031] Specifically, the mixing ratio, water replenishment flow rate, and bypass path configuration are dynamically adjusted based on the evaluation results to complete the backwater pressure backfill and hydraulic disturbance suppression. The specific steps are: real-time comparison of the local pressure stability value and the stability level threshold. The stability level threshold includes the first stability threshold and the second stability threshold: When the local pressure stability value is greater than or equal to the first stability threshold, the current return water section is determined to be in a pressure stable state, indicating that the return water pressure fluctuation is controllable and the hydraulic disturbance is small. At this time, the mixing water adjustment coefficient, valve opening, and flow path configuration executed in the current cycle are maintained unchanged. No additional compensation or parameter correction operations are required, and the original heat balance adjustment strategy can be directly continued to ensure the continuity of the main loop and the stability of the control strategy. When the local pressure stability value is lower than the first stability threshold but higher than or equal to the second stability threshold, the section is judged as a mild fluctuation section and enters the pressure disturbance warning state, indicating that there is a local disturbance trend but has not yet reached a critical risk. The dynamic fine-tuning mode of the mixing water regulation parameters is automatically activated, and the amplitude of the regulation instructions is compressed and the output interval is optimized. At the same time, the sliding mean window length of the temperature, pressure and flow is shortened to improve the response sensitivity to short-period disturbances. In addition, a disturbance buffer correction factor is introduced in the mixing water ratio calculation and path selection process to dynamically correct the regulation target, reduce the direct impact of data mutations on the control strategy, and enhance the stability and robustness of the temperature control logic. When the local pressure stability value is lower than the second stability threshold, it is determined that the current area is in a high-risk section of local negative pressure. The conventional temperature control strategy based on the current acquisition parameters is immediately suspended, and the emergency control response mechanism is activated. The bypass path and boost command are activated, and the historical pressure recovery path template is called to construct a temporary alternative control command for this cycle for rapid intervention. At the same time, the operating state is marked as a potential unstable condition and included in the risk monitoring list. The change process of the disturbance index, the control execution feedback characteristics and the adjustment strategy response path in the current cycle are also recorded simultaneously to provide complete data support for subsequent abnormal attribution analysis and strategy optimization.

[0032] In this implementation, the hydraulic disturbance level of the current backwater section is dynamically determined by comparing the local pressure stability value with the stability level threshold in real time, and the corresponding control strategy path is selected accordingly. This mechanism not only maintains the efficient execution of the existing control scheme when the pressure is stable, but also triggers fine-tuning compensation in the event of slight fluctuations, enhancing adaptability to short-period disturbances; in high-risk situations, it quickly switches to emergency control mode to achieve timely intervention in potential negative pressure instability. Overall, this step significantly improves the recognition accuracy, control sensitivity, and response timeliness of local hydraulic disturbances, providing a key hierarchical decision-making basis for ensuring continuous and stable operation.

[0033] Specifically, based on the standardized heating condition data set, a multi-dimensional joint analysis of the current supply and return water temperature difference, heat supply and demand deviation and valve response status is carried out. The specific steps are as follows: extract the supply and return water temperature difference, return water flow rate value and mixing water valve opening of each mixing water node from the standardized heating condition data set, multiply the supply and return water temperature difference, return water flow rate value and mixing water valve opening to obtain the mixing water response intensity value of the mixing water node in the current cycle; extract the real-time return water pressure value of each node at the current moment, calculate the difference between it and the average value of the return water pressure in the current sliding time window, and take the absolute value. For the value, multiply the absolute value by the return water pressure adjustment factor to obtain the return water state deviation value; extract the heat load deviation value of each mixing water node at the current moment and divide it by the average heat load value in the same area, multiply the ratio of the heat load deviation value to the average heat load value by the heat load balance adjustment factor to obtain the heat supply offset intensity value; divide the mixing water response intensity by the sum of the corresponding return water state deviation value and the heating offset intensity value to obtain the regulation efficiency value of the mixing water node, and accumulate the regulation efficiency values of all mixing water nodes to obtain the dynamic mixing water regulation value of the current cycle.

[0034] The calculation formula of dynamic mixed water adjustment value is: ; Where, Indicates the number of mixing nodes that need to be adjusted in the current cycle; Indicates the supply and return water temperature difference of the j-th mixing node in the current period. It is used to measure the instantaneous imbalance of heat transfer in the area and is the core indicator for determining the degree of temperature control deviation. Indicates the return water velocity value of the j-th mixing node in the current period, which is used to reflect the actual conduction efficiency of the thermodynamic cycle of this node. The higher the flow rate, the stronger the hydraulic response. Indicates the opening of the mixing valve at the j-th mixing node in the current period, which is used to evaluate the response amplitude of the mixing actuator and determine the mixing regulation intensity in combination with the temperature difference and flow rate; Indicates the real-time return water pressure value of the j-th mixing water node in the current period. It is used to determine whether there is pressure anomaly or insufficient return water in the area. It is an important criterion for pressure balance during the regulation process. Indicates the average backwater pressure of each mixing node in the recent sliding time window. It is used as a comparison benchmark for the deviation of the current backwater pressure, reflecting the operating baseline under non-disturbance conditions, making it easier to identify abnormal fluctuations or potential negative pressure trends. Indicates the heat load deviation value of the j-th mixing water node in the current period, which is used to reflect the difference between the actual heat supply of the node and the user demand. It is an important basis for judging the degree of heat supply adaptation; Indicates the average heat load value in the same area, which is used to normalize the current heat load deviation and eliminate the risk of misjudgment caused by long-term background differences; It represents the backwater pressure adjustment factor, with a value range of 0.5 to 2. It is derived from the statistical results of the pressure drop recovery efficiency in the historical high-frequency pressure fluctuation section. Specifically, it is determined by extracting the pressure recovery curve after the local pressure drop from multiple stable operation cycles, calculating the weighted average of the recovery slope and the stabilization time, and comparing it with the benchmark recovery rate in combination with the instantaneous pressure drop trend of the current mixing node. When it is detected that a node frequently recovers slowly or the backwater fluctuates repeatedly, the factor is appropriately increased to improve the regulation intensity of the backwater anomaly and enhance the pressure difference correction capability. If the backwater pressure stability of the node is high and the regulation response is fast in the historical operation, the backwater pressure adjustment factor is set to a lower value to reduce excessive regulation of the stable node and improve the adaptability and energy efficiency of the overall regulation strategy. It represents the heat load balance adjustment factor, with a value range of 1 to 5. It is derived from the long-term comparative analysis results of the daily load response curves and heat energy allocation errors of different types of users in the heating area. Specifically, it analyzes the characteristic values of the load deviation duration, deviation amplitude, and adjustment hysteresis rate of each user end in a typical heating cycle, and dynamically assigns weights based on the heat load deviation level in the current cycle. When a node is identified to have long-term high-frequency fluctuations, unstable heat demand response, or a hysteresis in the adjustment strategy response, the factor value is increased to enhance the ability to correct the abnormal heat load at that node. If the degree of heat load deviation at the node is low and the user heat demand curve changes smoothly, the heat load balance adjustment factor is assigned a smaller value to reduce its interference with the mixed water adjustment value, ensuring the rationality of the regulated heat energy distribution and the speed of adjustment convergence.

[0035] In this implementation plan, the intensity of the regulation demand for multiple mixing nodes in the current cycle is comprehensively evaluated, and the mixing regulation value is dynamically calculated to drive the key control behaviors of mixing ratio correction, water replenishment flow control, and bypass path selection. The formula constructs the numerator of the regulation response intensity by integrating the multi-dimensional operating parameters of the supply and return water temperature difference, return water flow rate, valve opening, real-time return water pressure deviation, and thermal load imbalance, reflecting the temperature control demand and hydraulic disturbance response capability. At the same time, the return water pressure regulation factor and thermal load balance regulation factor are introduced to normalize and correct the regulation results, suppress the excessive amplification of the regulation output by abnormal data fluctuations, and improve the regulation accuracy and stability under complex load change conditions. The dynamic mixing regulation value calculated by this formula can be used as the basic input of the core control instruction to achieve adaptive thermal balance control under multiple regions and multiple disturbance sources, and enhance the ability to respond quickly to hydraulic imbalance and negative pressure risks.

[0036] Specifically, based on the analysis results, the specific steps of driving the mixing ratio correction, water replenishment flow control and regulation path optimization in real time are as follows: real-time comparison of the current dynamic mixing water adjustment value with the adjustment response threshold, which includes the first adjustment threshold and the second adjustment threshold: When the dynamic water mixing regulation value is less than or equal to the second regulation threshold, it is determined to be in a low regulation intensity section, indicating that the current overall thermal load is well balanced, the return water pressure and terminal response are stable, and no regulatory intervention is required. The existing water mixing ratio, water replenishment flow and bypass path configuration are maintained unchanged to avoid redundant resource calls. At the same time, the water mixing regulation state of this cycle is marked as a stable sample and included in the historical steady-state template for subsequent trend baseline updates. The water mixing calculation frequency is simultaneously reduced and the controller execution cycle is extended to release control resources for other high-priority real-time scheduling modules, thereby improving overall operational efficiency. When the dynamic water mixing adjustment value is greater than the second adjustment threshold and less than or equal to the first adjustment threshold, it is determined to be in the medium adjustment intensity section and enters the adjustment sensitive zone monitoring mode, indicating that there is a mild thermal fluctuation or local supply and demand imbalance. The frequency of mixing parameter updates will be actively increased, and the sliding mean window span of temperature difference and pressure will be shortened to enhance the ability to identify short-term disturbances. A mechanism for analyzing the change trend of the adjustment coefficient will be introduced to comprehensively analyze the directionality, change rate and fluctuation amplitude of the adjustment value to prepare for possible upgrades to the adjustment strategy. When the dynamic mixing water regulation value is greater than the first regulation threshold, it is determined to be a high-risk section of regulation intensity, and the mixing water disturbance buffer mechanism is immediately switched to, the parameter structure of the mixing water regulation algorithm is reconstructed, and the regulation smoothing factor and regulation response delay processing module are introduced to effectively suppress the risks of hydraulic backflow and pressure difference jump caused by drastic changes in the mixing water coefficient in a short period of time; at the same time, all current parameter adaptive adjustment tasks are suspended, the mixing water control state is temporarily frozen, and the closed-loop regulation mode is entered, retaining only the core execution path to ensure operational safety, and the thermal deviation characteristics, control command output trajectory and feedback response results involved in this high-intensity regulation cycle are completely stored as abnormal regulation samples, providing key data support for subsequent control optimization and strategy learning.

[0037] In this implementation scheme, by comparing the current dynamic water mixing adjustment value with the preset multi-level response threshold in real time, the operating status under different adjustment intensity intervals is identified, thereby dynamically triggering differentiated adjustment strategies and resource allocation mechanisms. By dividing the adjustment value into three sections: low intensity, medium intensity and high risk, the adjustment can be kept silent when the heating load is balanced, active fine-tuning can be performed at the beginning of the fluctuation, and the buffer control mode can be quickly switched when the disturbance is severe, effectively avoiding unnecessary frequent adjustments and resource waste, while improving the response sensitivity and intervention accuracy to sudden thermal anomalies. This step not only ensures the stability and robustness of the control behavior, but also realizes the real-time coordination of the adjustment strategy and the operating load. It is a key scheduling link for realizing intelligent temperature control, stable heating and optimal allocation of adjustment resources.

[0038] Specifically, the multi-dimensional joint analysis results of the local hydraulic fluctuation characteristics and the supply and return water temperature difference, heat supply and demand deviation and valve response status are used as input to comprehensively evaluate the current water mixing regulation intensity and heat response deviation. The specific steps are: obtain the local pressure stability value and the dynamic water mixing regulation value, multiply the local pressure stability value, the dynamic water mixing regulation value and the heat usage curve deviation value, and further multiply the product of the local pressure stability value, the dynamic water mixing regulation value and the heat usage curve deviation value by the absolute value of the difference between the pressure fluctuation frequency and the historical maximum fluctuation amplitude to obtain the response intervention intensity value; divide the disturbance feedback regulation factor by the mean return water flow in the current sliding window, and add one to obtain the dynamic regulation value; divide the response intervention intensity value by the dynamic regulation value, and take the logarithm of the result of dividing the response intervention intensity value by the dynamic regulation value to obtain the control execution response value at the current moment.

[0039] The calculation formula of the control execution response value is: ; Where, It represents the local pressure stability value obtained by comprehensive evaluation of the current period, integrating the pressure drop rate, disturbance intensity and pressure fluctuation trend, and is used to dynamically characterize the hydraulic fluctuation state; It represents the dynamic mixed water adjustment value calculated based on multiple factors, reflecting the overall adjustment demand intensity in terms of current heat supply matching, temperature difference control and valve response; Indicates the pressure fluctuation frequency in the current cycle. It is used to measure the number of significant changes in backwater pressure per unit time and is a key indicator for characterizing the activity of instantaneous disturbances. Indicates the historical maximum fluctuation amplitude, which is used to reflect the maximum instability range under extreme disturbance conditions and is an important reference benchmark for assessing whether the current degree of fluctuation is abnormal; Indicates the current thermal curve deviation value, reflecting the immediate deviation degree of temperature control dimension; Indicates the mean value of the return water flow in the current sliding window, which is used to normalize the pressure drop and thermal disturbance intensity, and reflects the reference level of hydraulic transport capacity; The disturbance feedback adjustment factor, ranging from 1 to 3, is derived from the statistical characteristics of the response amplitudes to various types of disturbances during stable operation cycles. The calculation method extracts a sequence of key operating parameters in a highly stable state from multiple typical heating tasks, analyzes their instantaneous fluctuation frequency and recovery slope after adjustment, and constructs a signal-to-noise ratio index system for different types of disturbance parameters. Combined with the fluctuation amplitude of the acquisition field in the current cycle and the sampling noise level, the abnormal sensitivity under standard deviation conditions is dynamically calculated to determine the value of the disturbance feedback adjustment factor. When a parameter is identified to frequently experience high-amplitude abnormal fluctuations across multiple cycles, exhibiting obvious non-steady-state disturbance characteristics, the value of this factor is increased to enhance the ability to suppress abnormal disturbances and prevent them from being amplified in the mixed water control and affecting the overall strategy. Conversely, if the current operating parameter has been stable in historical operation and the disturbance density is low, the value of this factor is lowered to avoid excessive regulation of the stability index, thereby ensuring that the output of the regulation formula maintains a reasonable balance between sensitivity and stability.

[0040] In this embodiment, the local pressure stability value of node 1 is set to 1.132, the dynamic mixing water regulation value is 1.404, the pressure fluctuation frequency is 16.20, the historical maximum fluctuation amplitude is 5.16, the thermal curve deviation value is 0.079, the average return water flow is 37.11, and the disturbance feedback regulation factor is 1.83; the local pressure stability value of node 2 is set to 0.960, the dynamic mixing water regulation value is 1.236, the pressure fluctuation frequency is 22.13, the historical maximum fluctuation amplitude is 8.18, the thermal curve deviation value is 0.066, the average return water flow is 38.44, and the disturbance feedback regulation factor is 1.44; the local pressure stability value of node 3 is set to 0.902, the dynamic mixing water regulation value is 1.032, the pressure fluctuation frequency is The local pressure stability value of node 4 is set to 1.145, the dynamic mixing water regulation value is 0.844, the pressure fluctuation frequency is 20.61, the historical maximum fluctuation amplitude is 7.54, the thermal curve deviation value is 0.131, the average backwater flow is 26.65, and the disturbance feedback regulation factor is 1.68; the local pressure stability value of node 5 is set to 1.112, the dynamic mixing water regulation value is 1.018, the pressure fluctuation frequency is 22.71, the historical maximum fluctuation amplitude is 9.54, the thermal curve deviation value is 0.113, the average backwater flow is 28.42, and the disturbance feedback regulation factor is 1.68. The feedback regulation factor is 2.89; the local pressure stability value of node 6 is set to 1.119, the dynamic mixing water regulation value is 1.028, the pressure fluctuation frequency is 19.94, the historical maximum fluctuation amplitude is 6.25, the thermal curve deviation value is 0.137, the average return water flow is 31.41, and the disturbance feedback regulation factor is 1.65; the local pressure stability value of node 7 is set to 1.131, the dynamic mixing water regulation value is 1.311, the pressure fluctuation frequency is 20.23, the historical maximum fluctuation amplitude is 7.05, the thermal curve deviation value is 0.130, the average return water flow is 37.27, and the disturbance feedback regulation factor is 2.04; the local pressure stability value of node 8 is set to 0.922, the dynamic mixing water regulation value is 1.246, and the pressure The fluctuation frequency is 19.28, the historical maximum fluctuation amplitude is 8.78, the thermal curve deviation is 0.069, the average return flow rate is 37.91, and the disturbance feedback adjustment factor is 2.41. The local pressure stability value at node 9 is set to 1.008, the dynamic mixing adjustment value is 1.421, the pressure fluctuation frequency is 15.25, the historical maximum fluctuation amplitude is 6.14, the thermal curve deviation is 0.139, the average return flow rate is 25.10, and the disturbance feedback adjustment factor is 1.73. The local pressure stability value at node 10 is set to 0.935, the dynamic mixing adjustment value is 1.131, the pressure fluctuation frequency is 16.08, the historical maximum fluctuation amplitude is 5.38, the thermal curve deviation is 0.104, and the average return flow rate is 32.66, and the disturbance feedback adjustment factor is 2.94.

[0041] Table 1 Control execution response value data table

[0042] like Figure 3 As shown in Table 1 and Figure 3 It can be seen that the control execution response value of control node 7 is the highest, indicating that its local pressure stability and mixing water regulation intensity deviate significantly, and the disturbance feedback regulation factor is relatively high. This comprehensively reflects that this node is most sensitive to hydraulic disturbances and heat offsets in the current heating process, and it is necessary to prioritize the implementation of dynamic mixing water compensation and flow regulation strategies to ensure the rapid response of local hydraulics and temperature control accuracy; relatively speaking, the control execution response value of control node 8 is negative, indicating that its mixing water regulation fluctuations and historical anomaly accumulation are relatively strong in the current cycle, and there is a lag trend in feedback. It is not suitable to continue parameter optimization and adjustment for the time being, and it should enter the regulation buffer mode to avoid further aggravation of disturbances by the regulation strategy; the control execution response value distribution diagram intuitively shows the differences in the working condition adaptability and regulation sensitivity of each node. The node with a higher value is more suitable as a priority regulation object, which can be used to drive the mixing water device for rapid response and closed-loop control, thereby improving the overall steady-state maintenance capability and abnormal recovery efficiency.

[0043] Specifically, the specific steps for completing the rapid response to each abnormal disturbance and closed-loop correction of the regulation strategy based on the evaluation results are as follows: real-time comparison of the control execution response value with the regulation intensity evaluation threshold, which includes the first regulation threshold and the second regulation threshold: When the control execution response value is less than or equal to the second adjustment threshold, it is determined to be a low control demand section, indicating that the current state is stable and the impact of disturbances is limited. The current mixing ratio, make-up water flow, and bypass path configuration remain unchanged, and only basic temperature control correction strategies are implemented, such as periodic fine-tuning of the supply and return water temperature difference coefficient. At the same time, the current cycle control state is marked as a low-disturbance sample for subsequent construction of a control steady-state baseline, supporting long-term trend identification and scheduling prediction model correction; When the control execution response value is greater than the second regulation threshold and less than or equal to the first regulation threshold, it is determined to be in the moderate regulation intensity section, and obvious hydraulic disturbances or thermal load deviations begin to appear. It is necessary to enter the steady-state intervention enhancement mode, shorten the control feedback cycle, and fine-tune the mixing water regulation coefficient and valve opening configuration. At the same time, the light load bypass mechanism is activated in advance to reduce the interference of local pressure fluctuations on the overall hydraulic cycle. At this stage, the trend tracking mechanism will also be activated to dynamically analyze the curvature of the regulation response change to assess whether there is a trend of continued strengthening and provide early warning when necessary. When the control execution response value is greater than the first adjustment threshold, it is judged to be a high-response pressure section, indicating that it is on the verge of severe disturbance or instability. It is necessary to immediately switch to the emergency adjustment mode, increase the execution amplitude of the regulating valve, improve the speed response rate of the variable frequency pump, and forcibly activate the main bypass channel to guide the flow diversion to quickly backfill the return water pressure and prevent the formation of negative pressure; at the same time, suspend the adaptive adjustment of conventional parameters, execute the isolation control logic, archive the thermal disturbance path, pressure change curve and response lag characteristics of the current section, and simultaneously issue a high-priority processing request to start the key working condition analysis process to support subsequent precise tracing and adjustment strategy optimization.

[0044] In this implementation plan, by comparing the current control execution response value with the set regulation intensity assessment threshold in real time, the current hydraulic disturbance level and temperature control regulation demand state are identified, and the mixing water ratio, make-up water flow, valve opening, bypass path configuration and other key control parameters are adaptively adjusted accordingly, so as to achieve accurate response to fluctuations in heating conditions of low, medium and high intensities, improve the ability to quickly identify complex thermal disturbances, regulation stability and energy efficiency control accuracy, and at the same time archive key control features for subsequent abnormality identification and strategy optimization, providing basic support for the construction of closed-loop temperature control and multi-level linkage response mechanism.

[0045] Specifically, the entire process of data recording of local negative pressure, uneven hot and cold conditions, and response results is used to complete subsequent diagnosis, control strategy optimization, and steady-state identification. The specific steps are as follows: After completing control execution and feedback response, the abnormality marking and control log archiving process is automatically entered to start the full-process tracking and data retention of key control events in the current heating cycle. Specifically, it includes continuous recording of the negative pressure fluctuation amplitude detected in this cycle, the dynamic change process of the mixed water adjustment parameters, and the execution sequence of the control response action to construct a complete operating condition evolution trajectory; if a significant drop in return water pressure is detected, or the terminal temperature control deviation continues to exceed the safety threshold, the cycle will be automatically marked as an abnormal control cycle, an abnormal flag will be generated, and the corresponding abnormal trigger time point, abnormal type category, and judgment logic source will be archived.

[0046] Subsequently, key control parameters involved in the abnormal period are recorded item by item to ensure traceability and integrity. Furthermore, core indicators, including the dynamic recovery process of return water pressure, the compensation trend of terminal heat load, and the delay time of control response actions, are combined to quantitatively evaluate the regulation effect, determining whether each control operation successfully achieved the temperature control steady-state target. All evaluation results, control paths, and response characteristics are uniformly archived in the control log database for subsequent regulation strategy optimization, control behavior tracing, and abnormal pattern learning, thereby enhancing overall intelligent decision-making and fault response capabilities.

[0047] In this implementation plan, abnormal behaviors, key disturbance events, and control feedback characteristics during the temperature control process are tracked and structured throughout the entire process to build a traceable and quantifiable control behavior archive, thereby providing high-quality data support for subsequent control strategy optimization, abnormality reproduction analysis, and intelligent diagnostic modeling, and improving stability, safety, and intelligent response capabilities under complex working conditions.

[0048] The second aspect of the present invention provides a secondary network heating temperature control device based on negative pressure water mixing technology, including: a temperature control data acquisition terminal, which is used to be deployed at key pipe network nodes and user terminals during the heating process, and to collect real-time operating parameters including supply water temperature, return water temperature, terminal heat load, pipeline pressure, flow rate, valve opening, mixing ratio and supply and return water temperature difference, automatically synchronize sampling timestamps and complete data encryption transmission; at the same time, the dynamic adjustment status of the water mixing node and the heat absorption feedback information of the user terminal are linked together, including heat deviation value, adjustment frequency and heat load change before and after adjustment, through wired or wireless industrial communication The protocol is uploaded to the central processing module; the central processing module is used to perform field standardization, physical unit unification, dimension normalization and missing data repair processing on the received multi-source heating operation data, build a unified standardized heating condition data set, and based on the sliding window and batch recognition mechanism, continuously extract key disturbance indicators, pressure fluctuation frequency and temperature control deviation characteristics, and push the processing results to the regulation analysis and control judgment unit to achieve efficient data fusion and structural consistency management; the regulation analysis and control judgment unit is used to comprehensively evaluate the current hydraulic stability and thermal matching degree based on the standardized heating condition data set Based on the dynamic trend evolution status, dynamic water mixing adjustment parameters including water replenishment flow, water mixing ratio correction value and bypass path adjustment factor are constructed. At the same time, combined with the historical steady-state operation baseline, the effectiveness of the current cycle adjustment strategy and the trend evolution direction are evaluated, and whether it enters the disturbance transition zone or the high response pressure section is identified. The comprehensive evaluation results are transmitted to the execution control and feedback adjustment unit; the execution control and feedback adjustment unit is used to generate temperature control compensation operations based on the adjustment analysis results, including operating instructions for adjusting valve opening, starting the variable frequency pump to increase pressure, and switching the bypass channel, driving the water mixing device to achieve temperature balance and pressure backfill control; During the execution process, the pressure backfill curve, terminal heat recovery status and control response delay are collected synchronously to feedback the adjustment results, and the key disturbance parameters, abnormal judgment results and control records in this cycle are reported to the log archiving and early warning generation unit; the log archiving and early warning generation unit is used to record the pressure disturbance events, adjustment execution behavior, feedback response characteristics and abnormal marking information in each cycle, and generate a structured operation log, including trigger conditions, abnormality level, control path and parameter change trajectory, to provide a traceable data foundation and risk portrait for subsequent control strategy optimization, steady-state model training and reconstruction of high-risk working conditions.

[0049] In this implementation, the temperature control data acquisition terminal is used to collect key operating parameters during the heating process, including supply water temperature, return water temperature, terminal heat load, mixing valve status, flow rate, and pressure, as well as the regulation behavior of the mixing nodes and heat feedback from the user end points. Intelligent sensor terminals deployed at key locations enable high-frequency, high-precision data acquisition, providing complete, continuous, and reliable data support for subsequent operational status analysis and control strategy formulation. The central processing module is responsible for field standardization, time alignment, unit conversion, data cleansing, and normalization of the operational data uploaded by each acquisition terminal, constructing a standardized heating operating condition dataset with a unified format, temporal continuity, and physical consistency. As the data processing core, this module is responsible for removing sampling outliers, correcting missing data, and outputting structured data to provide high-quality input for the regulation analysis module. The regulation analysis and control judgment unit is responsible for comprehensively evaluating the multi-dimensional operational characteristics of temperature deviation, pressure fluctuation, and heat load imbalance based on the standardized operating condition dataset, and calculating dynamic mixing control parameters, including the target make-up flow rate, mixing ratio correction factor, and bypass channel adjustment recommendations. The system also compares historical steady-state operating data to determine whether the system is currently in the stable zone, transition zone, or high-risk zone, generating control instructions that are then fed into the control execution module. The execution control and feedback regulation unit converts the control instructions generated by the regulation analysis module into specific execution actions, including adjusting the opening of the mixing valve, adjusting the speed of the variable-frequency pump, activating or deactivating the bypass path, and driving the heating system to achieve dynamic temperature balance and pressure backfill control. During control execution, response data from the regulation process (such as pressure recovery curves and heat recovery efficiency) is collected in real time, and the regulation effect and response characteristics are evaluated to enable closed-loop feedback and generate a basis for strategy optimization. The log archiving and alert generation unit is responsible for maintaining a structured archive of control execution records, abnormal state triggering events, and feedback responses within each sampling period, including regulation instructions, activation delays, disturbance characteristics, and thermal response. High-risk periods can be flagged based on the persistence of abnormal indicators, the number of regulation failures, or the degree of thermal deviation, and an alert report can be generated for long-term steady-state modeling, abnormal behavior tracking, and control strategy training.

[0050] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0051] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A secondary network heating temperature control method based on negative pressure water mixing technology, characterized in that ,include: S1, collects the operating data, structural steady-state data and thermal environment data of the current heating task, and pre-processes the collected operating data, structural steady-state data and thermal environment data to construct a standardized heating condition data set; S2, based on a standardized heating condition dataset, conducts a multi-factor coupling assessment of the local hydraulic fluctuation characteristics of the current secondary network area, and dynamically adjusts the mixing ratio, make-up flow, and bypass path configuration based on the assessment results; S3, based on a standardized heating condition dataset, conducts a multi-dimensional joint analysis of the current supply and return water temperature difference, heat supply and demand deviation, and valve response status. Based on the analysis results, it drives real-time correction of the mixing ratio, water replenishment flow control, and regulation path optimization. S4, using the results of the local hydraulic fluctuation characteristics analysis and the multi-dimensional joint analysis results of the supply and return water temperature difference, heat supply and demand deviation, and valve response status as input, comprehensively evaluates the current mixed water regulation intensity and heat response deviation. Based on the evaluation results, it completes the rapid response to various abnormal disturbances and completes the closed-loop correction of the regulation strategy; S5 records the entire process of local negative pressure, uneven hot and cold temperatures, and response results to complete subsequent diagnosis, control strategy optimization, and steady-state identification.

2. The secondary network heating temperature control method based on negative pressure water mixing technology according to claim 1 is characterized in that: The specific steps of collecting the operating data, structural steady-state data and thermal environment data in the current heating task, and standardizing and normalizing the collected operating data, structural steady-state data and thermal environment data to construct a standardized heating condition data set are as follows: Collect operating data, including: supply water temperature, return water temperature, return water pressure, return water flow rate, pipeline pressure, mixing valve opening, and supply and return water temperature difference. At the same time, record the instantaneous measurement value, return water flow average, historical fluctuation average, and return water pressure average of each data item; Collect structural steady-state data, including: pressure fluctuation frequency, historical maximum fluctuation amplitude, and continuous pressure drop duration of each key node; Collect thermal environment data, including: instantaneous heat load at the heat user end, heat curve deviation, number of mixing water nodes that need to be adjusted in each cycle, return water heat fluctuation amplitude and supply and return water temperature difference, and record the response reference value of heat load deviation and average heat load value; Record the total number of sampling cycles and the monitoring time intervals of all acquisition parameters, data continuity and integrity rate, instantaneous sampling error amplitude and sampling anomaly ratio; Through a unified high-precision clock synchronization mechanism, the collected operational data, structural steady-state data, and thermal environment data are time-aligned, unit-converted, and field format standardized to remove missing fields and abnormal errors. The standardized data is then normalized to eliminate scale errors and interference factors caused by differences in terminal hardware and sensor accuracy deviations. The standardized and normalized operating data, structural steady-state data and thermal environment data are stored in a unified manner to construct a standardized heating condition data set.

3. The secondary network heating temperature control method based on negative pressure water mixing technology according to claim 1 is characterized in that: The specific steps of performing a multi-factor coupling evaluation of the local hydraulic fluctuation characteristics of the current secondary network area based on the standardized heating condition data set are as follows: Extract the return water pressure value at each sampling moment from the standardized heating condition data set, calculate the difference between the return water pressure values at each adjacent moment and divide it by the monitoring time interval, and take the absolute value of the ratio of the return water pressure difference to the monitoring time interval to obtain the pressure change rate per unit time; The average value of the pressure change rate per unit time at each sampling moment is taken and multiplied by the ratio of the supply and return water temperature difference and the average value of the return water pressure at the current moment to obtain the thermal disturbance effect value; The current return water heat fluctuation amplitude, data continuity completeness rate, and sampling anomaly ratio are extracted from the standardized heating condition data set. The return water heat fluctuation amplitude is multiplied by the data continuity completeness rate and then divided by the sampling anomaly ratio to obtain the evaluation deviation value. The local pressure stability value of the current backwater section is obtained by subtracting the thermal disturbance effect value from the mean backwater flow and then subtracting the evaluation deviation value.

4. The secondary network heating temperature control method based on negative pressure water mixing technology according to claim 1 is characterized in that: The specific steps for dynamically adjusting the mixing ratio, water replenishment flow and bypass path configuration based on the evaluation results to complete the backwater pressure backfill and hydraulic disturbance suppression are as follows: Compare the local pressure stability value with the stability level threshold in real time. The stability level threshold is divided into the first stability threshold and the second stability threshold: When the local pressure stability value is greater than or equal to the first stability threshold, it is determined to be a pressure stable section, and the current mixing water adjustment coefficient, valve opening and flow path are maintained unchanged. The original heat supply balance adjustment strategy is directly implemented without introducing an additional compensation mechanism; When the local pressure stability value is less than the first stability threshold and greater than or equal to the second stability threshold, it is determined to be a mild fluctuation section and enters the pressure disturbance warning state. The dynamic fine-tuning mode of the mixing water adjustment parameters is activated, the sliding mean window length of the temperature, pressure and flow is shortened, and a disturbance buffer correction factor is introduced in the mixing water ratio calculation and path selection process to reduce the impact of sudden data on the adjustment results and enhance the stability of the temperature control strategy. When the local pressure stability value is less than the second stability threshold, it is determined to be a local negative pressure high-risk section. The execution of the conventional temperature control strategy based on the current acquisition parameters is immediately suspended, the emergency control response mechanism is started, the pressure recovery path is called, and a temporary alternative control instruction is constructed for the current cycle. At the same time, the current operating state is marked as a potential unstable condition and included in the risk monitoring list. The disturbance indicator change process, control feedback characteristics and adjustment strategy response path within this cycle are fully recorded.

5. The secondary network heating temperature control method based on negative pressure water mixing technology according to claim 1 is characterized in that: The specific steps of performing a multi-dimensional joint analysis of the current supply and return water temperature difference, heat supply and demand deviation, and valve response status based on the standardized heating condition data set are as follows: The supply and return water temperature difference, return water flow rate, and mixing valve opening of each mixing water node are extracted from the standardized heating condition data set. The supply and return water temperature difference, return water flow rate, and mixing valve opening are multiplied together to obtain the mixing water response intensity value of the mixing water node in the current cycle. Extract the real-time backwater pressure value of each node at the current moment, calculate the difference between it and the average backwater pressure in the current sliding time window, and take the absolute value. Multiply the absolute value by the backwater pressure adjustment factor to obtain the backwater state deviation value; Extract the heat load deviation value of each mixing water node at the current moment and divide it by the average heat load value in the same area. Multiply the ratio of the heat load deviation value to the average heat load value by the heat load balance adjustment factor to obtain the heat supply offset intensity value. The mixing water response intensity is divided by the sum of the corresponding return water state deviation value and the heating offset intensity value to obtain the regulation efficiency value of the mixing water node. The regulation efficiency values of all mixing water nodes are accumulated to obtain the dynamic mixing water regulation value of the current cycle.

6. The secondary network heating temperature control method based on negative pressure water mixing technology according to claim 1 is characterized in that: The specific steps of driving the water mixing ratio correction, water replenishment flow control and regulation path optimization in real time based on the analysis results are as follows: The current dynamic mixed water adjustment value is compared with the adjustment response threshold in real time. The adjustment response threshold includes the first adjustment threshold and the second adjustment threshold: When the dynamic water mixing adjustment value is less than or equal to the second adjustment threshold, it is determined to be a low adjustment intensity section, and the existing water mixing ratio, water replenishment flow and bypass path configuration are maintained unchanged. At the same time, the water mixing parameter state of this cycle is marked as a stable sample, and the water mixing calculation frequency is simultaneously reduced, and the controller execution resources are released for other high-priority module processing; When the dynamic water mixing adjustment value is greater than the second adjustment threshold and less than or equal to the first adjustment threshold, it is determined to be in the medium adjustment intensity section and enters the adjustment sensitive zone monitoring mode. The frequency of water mixing parameter updates is increased, the temperature difference and pressure sampling window span are shortened, and the adjustment coefficient change trend analysis is introduced. When the dynamic water mixing regulation value is greater than the first regulation threshold, it is determined to be a high-risk section of regulation intensity, and the water mixing disturbance buffer mechanism is immediately switched to. The parameter structure of the water mixing regulation algorithm is dynamically adjusted, and an adjustment smoothing factor is introduced to suppress hydraulic backflow caused by excessive regulation. All current parameter adaptive adjustment tasks are suspended, and a closed loop of water mixing control is executed to temporarily freeze the current regulation state. At the same time, the data feature mark of the high-intensity regulation period is stored as an abnormal regulation sample.

7. The secondary network heating temperature control method based on negative pressure water mixing technology according to claim 1 is characterized in that: The specific steps for comprehensively evaluating the current mixed water regulation intensity and heat response deviation using the local hydraulic fluctuation characteristic analysis results and the multi-dimensional joint analysis results of the supply and return water temperature difference, heat supply and demand deviation, and valve response status as input are as follows: Obtain the local pressure stability value and the dynamic water mixing adjustment value, multiply the local pressure stability value, the dynamic water mixing adjustment value, and the heat curve deviation value, and further multiply the product of the local pressure stability value, the dynamic water mixing adjustment value, and the heat curve deviation value by the absolute value of the difference between the pressure fluctuation frequency and the historical maximum fluctuation amplitude to obtain the response intervention intensity value; Divide the disturbance feedback adjustment factor by the mean return water flow rate in the current sliding window, and add 1 to obtain the dynamic adjustment value; The response intervention intensity value is divided by the dynamic adjustment value, and the logarithm of the result of dividing the response intervention intensity value by the dynamic adjustment value is taken to obtain the control execution response value at the current moment.

8. The secondary network heating temperature control method based on negative pressure water mixing technology according to claim 1 is characterized in that: The specific steps for completing the rapid response to each abnormal disturbance and closed-loop correction of the adjustment strategy based on the evaluation results are as follows: The control execution response value is compared with the adjustment strength evaluation threshold in real time, wherein the evaluation threshold includes a first adjustment threshold and a second adjustment threshold: When the control execution response value is less than or equal to the second adjustment threshold, it is determined to be a section with low control demand. The current mixing ratio, make-up water flow rate and bypass path configuration are maintained unchanged. Only the basic level temperature control correction is performed. At the same time, the current cycle control state is marked as a low disturbance sample. When the control execution response value is greater than the second regulation threshold and less than or equal to the first regulation threshold, it is determined to be in the moderate regulation intensity section and enters the steady-state intervention enhancement mode, shortening the control feedback cycle, and fine-tuning the mixed water regulation coefficient and valve opening configuration. The light load bypass mechanism is activated in advance to reduce the interference of local pressure fluctuations on the overall hydraulic cycle; When the control execution response value is greater than the first adjustment threshold, it is determined to be a high-response pressure section, and the system immediately switches to the emergency adjustment mode, increases the execution amplitude of the regulating valve, improves the speed response rate of the variable frequency pump, and forcibly activates the main bypass channel to guide the flow diversion to quickly backfill the return water pressure and prevent the formation of negative pressure. At the same time, the adaptive adjustment of conventional parameters is suspended, and the isolation control logic is executed. The thermal disturbance path, pressure change curve and response hysteresis characteristics of the current section are archived and recorded.

9. The secondary network heating temperature control method based on negative pressure water mixing technology according to claim 1 is characterized in that: The specific steps of recording the entire process of local negative pressure, uneven hot and cold, and response results for subsequent diagnosis, control strategy optimization, and steady-state identification are as follows: After completing control execution and feedback response, the system enters the abnormal marking and control log archiving process. By recording the negative pressure fluctuations, mixed water adjustment actions and response results in the current cycle, it achieves refined tracking of the operating status and collection management of historical samples. Based on the typical characteristics of sudden drop in return water pressure, continuous over-limit deviation of temperature control, and forced activation of bypass path, control cycles with abnormal risks are automatically marked, and the corresponding trigger time, abnormality type and judgment basis are recorded; then, the key control parameters within the cycle are comprehensively recorded; at the same time, combined with the return water pressure recovery curve, terminal heat compensation results and control response delay, the effect of each adjustment is quantitatively evaluated, and it is noted whether the adjustment has achieved the steady-state target.

10. The secondary network heating temperature control device based on negative pressure water mixing technology is characterized by: include: The temperature control data acquisition terminal is used to collect real-time data including supply water temperature, return water temperature, terminal heat load and other operating parameters during the heating process. At the same time, it collects the dynamic adjustment status of the mixing water node and the heat absorption feedback information of the user terminal and transmits it to the central processing module; The central processing module is used to pre-process the received multi-source heating operation data, build a unified standardized heating condition data set, and push the processing results to the regulation analysis and control judgment unit; The regulation analysis and control judgment unit is used to comprehensively evaluate multi-dimensional operating characteristics based on the standardized heating condition data set, construct dynamic water mixing regulation parameters, and simultaneously evaluate the regulation effect and trend evolution direction in combination with the historical steady-state operation baseline, and transmit the comprehensive evaluation results to the execution control and feedback regulation unit; The execution control and feedback regulation unit is used to generate compensation operations based on the regulation analysis results, collect the pressure backfill curve and heat recovery status in real time during the execution process, provide feedback on the regulation results, and report key disturbance parameters and control records to the log archiving and early warning generation unit; The log archiving and early warning generation unit is used to record pressure disturbance events, adjustment execution behaviors and feedback results in each cycle, and generate a structured operation log.

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