A full parameter feedback type intelligent thermal room layered cooperative control system and method
By employing a multidimensional heterogeneous parameter sensing and hierarchical collaborative calculation method, the problem of dimensional interference and control deadlock in thermal chambers was solved, achieving precise control and extending equipment life, thereby improving the stability and economy of the heating system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SUZHOU BOMO THERMAL PROD CO LTD
- Filing Date
- 2026-03-25
- Publication Date
- 2026-06-05
AI Technical Summary
Existing full-parameter intelligent control systems are prone to dimensional interference and control deadlock in thermal chambers, especially when faced with multi-dimensional heterogeneous parameters, which makes it difficult to achieve precise control, leading to increased equipment wear and deterioration of heating quality.
By employing a multidimensional heterogeneous parameter sensing module, a hierarchical collaborative computing module, a dynamic decoupling execution module, and a soft measurement self-healing monitoring module, and through decoupling calculation and dynamic decoupling execution, a unified dimensional evaluation of thermodynamics and hydraulics and flexible clamping regulation are achieved. Combined with virtual energy storage scheduling, the problems of hierarchical target mutual exclusion and feedback storms are solved.
It achieves smooth dynamic hydraulic balance across the entire network, extends equipment life, reduces energy consumption, improves system robustness, and maintains stable operation even when some sensors fail.
Smart Images
Figure CN122149018A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automated control technology for centralized heating networks, specifically to a full-parameter feedback type intelligent thermal cell hierarchical collaborative control system and method. Background Technology
[0002] Refined regulation of heating networks has become a core element in improving heating efficiency. As a crucial heat exchange and hydraulic distribution node connecting the primary network and secondary user terminals, the operating status of heating cells directly determines the heating quality for end users and the overall energy consumption level of the network. In recent years, with the popularization of IoT and edge computing technologies, heating cells have gradually upgraded from traditional manual experience-based regulation or single climate compensation control to an intelligent control mode integrating multi-sensor data such as temperature, pressure, flow rate, and valve opening. Furthermore, a hierarchical control concept of cloud-based global control and edge cell control has been initially explored in the system architecture.
[0003] However, in the complex actual heating conditions, the existing full-parameter intelligent control and hierarchical architecture are gradually revealing deep-seated technical bottlenecks that are difficult to overcome using conventional decoupling methods. First, the dimensional interference and control deadlock problems caused by full-parameter feedback are becoming increasingly prominent. Thermodynamic systems have extremely strong physical coupling characteristics. Hydraulic responses (such as pipeline pressure and flow rate) typically change on the order of seconds or milliseconds, while thermal responses (such as secondary network supply water temperature and indoor ambient temperature) have thermal inertia lasting for several hours. Existing systems often place these heterogeneous parameters with completely different time scales and physical dimensions into the same control closed loop or a conventional cascade PID controller for processing. When contradictory commands such as "low terminal temperature requires opening the valve" and "excessive primary side return water pressure difference requires closing the valve" occur, the controller, lacking a unified evaluation dimension, is prone to falling into a deadlock state of "mutual struggle." This not only leads to frequent oscillations of the regulating valve and aggravates mechanical wear of the equipment, but also renders full-parameter sensing meaningless in guiding precise control.
[0004] When faced with sudden changes in heat load, such as a sharp drop in temperature, the local controllers of the lower-level heating cells may drastically adjust the primary network valves to seize available pressure head in order to quickly meet the heating targets of their respective areas, thus causing hydraulic imbalances in adjacent cells. Meanwhile, the upper-level collaborative algorithm, in order to maintain the hydraulic pressure differential balance of the global network and energy conservation of the main pumping station, will continuously issue intervention commands to limit flow. This cross-level scheduling, lacking constraints from underlying physical mechanisms and dynamic game theory, is prone to causing severe "feedback storms" when multiple nodes are simultaneously adjusting. This means that the lower-level control actions are constantly delayed, rejected, or forcibly corrected by upper-level commands, causing the entire control system to fall into optimization oscillations, resulting in not only a surge in energy consumption but also a sharp deterioration in heating quality. Summary of the Invention
[0005] The purpose of this invention is to provide a fully parameter feedback-type intelligent thermal chamber hierarchical collaborative control system and method to solve the problems mentioned in the background art.
[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a multi-parameter feedback-type intelligent thermal chamber hierarchical collaborative control system and method, comprising a multi-dimensional heterogeneous parameter sensing module, a hierarchical collaborative calculation module, a dynamic decoupling execution module, and a soft-sensor self-healing monitoring module. The multi-dimensional heterogeneous parameter sensing module is used to collect transient hydraulic data of the primary pipe network, thermal hysteresis data of the secondary pipe network, and operating status data of the underlying equipment of the thermal chamber from all directions. The hierarchical collaborative calculation module is communicatively connected to the multi-dimensional heterogeneous parameter sensing module and is used to perform decoupling calculations on the collected heterogeneous parameters in terms of time scale and physical dimensions and generate global collaborative adjustment commands. The dynamic decoupling execution module is connected to the hierarchical collaborative calculation module and is used to perform anti-oscillation clamping adjustment on the regulating valve of the thermal chamber according to the collaborative adjustment commands. The soft-sensor self-healing monitoring module is used to perform back-calculation compensation calculations through data redundancy when the multi-dimensional heterogeneous parameter sensing module partially fails.
[0007] According to the above technical solution, the multidimensional heterogeneous parameter sensing module includes a hydraulic transient sensing unit, a thermal hysteresis sensing unit, and a valve control status sensing unit. The hydraulic transient sensing unit is used to collect millisecond-level changes in the primary pipeline supply and return water pressure difference and flow rate data. The thermal hysteresis sensing unit is used to collect hourly-level changes in the secondary pipeline supply water temperature, return water temperature, and indoor temperature data of associated buildings. The valve control status sensing unit is used to collect the valve opening degree and action rate of the primary pipeline electric regulating valve in real time.
[0008] According to the above technical solution, the hierarchical collaborative computing module includes a heterogeneous parameter dimensionless unit, a hot water decoupling game unit, and a virtual energy storage scheduling unit. The heterogeneous parameter dimensionless unit is used to map thermal and hydraulic parameters of different time scales and physical units into evaluation indices with unified dimensions. The hot water decoupling game unit is used to handle hierarchical target mutual exclusion caused by sudden changes in heat load and calculate and output the optimal adjustment threshold to avoid feedback storms. The virtual energy storage scheduling unit is used to dynamically deduct or compensate for the heat distribution of local small rooms based on the building thermal inertia assessment results.
[0009] According to the above technical solution, the dynamic decoupling execution module includes a flexible clamping adjustment unit and a micro water hammer suppression unit. The flexible clamping adjustment unit is used to limit the instantaneous maximum stroke of the regulating valve to prevent control deadlock, and the micro water hammer suppression unit is used to perform buffer motion control on the valve when the pipeline pressure changes suddenly.
[0010] According to the above technical solution, the soft measurement self-healing monitoring module has a built-in multi-dimensional parameter coupling derivation matrix. When the hydraulic transient sensing unit experiences hardware failure or data loss, it can reverse-engineer the currently missing virtual flow data by utilizing the mapping relationship between valve opening and pipeline pressure difference under historical normal operating conditions.
[0011] A fully parameter feedback-based intelligent thermal cell hierarchical collaborative control method includes the following steps: Step S1: The operating data of the thermal chamber is collected at high frequency through the multidimensional heterogeneous parameter sensing module to form a multidimensional data stream containing hydraulic transient parameters, thermal hysteresis parameters and valve control state parameters; Step S2: In response to the input of the multidimensional data stream, the hierarchical collaborative computing module activates the dimensionless unit for heterogeneous parameters to calculate the urgency index of the current thermal chamber's thermo-hydraulic coupling. ; Step S3: The hot water decoupling game unit calculates... The value is used to determine whether there is dimensional interference between the local heat demand of the current chamber and the global hydraulic balance, and to generate dynamic penalty function weights. Step S4: When the total heat source of the pipeline network is insufficient or the heating load is at its peak in the morning or evening, the virtual energy storage scheduling unit calculates the thermal inertia decay coefficient of the associated buildings and performs "peak shaving and valley filling" scheduling of global heat. Step S5: The dynamic decoupling execution module performs tiered flexible clamping adjustment of the electric regulating valve according to the above-mentioned coordination strategy.
[0012] According to the above technical solution, the urgency index for thermo-hydraulic coupling is calculated in step S2. The method further includes: Step S21: Obtain the current indoor temperature collected by the thermal hysteresis sensing unit. With target temperature temperature difference And the actual pressure difference of the primary side pipeline network collected by the hydraulic transient sensing unit. With safety differential pressure threshold The ratio parameter; Step S22: Real-time retrieval of the regulating valve's action rate collected by the valve control status sensing unit. The urgency index was calculated using dimensionless unit analysis with heterogeneous parameters. The calculation formula is as follows: in, This is the weighting coefficient for heat demand. For hydraulic constraint weighting coefficients, Preset base values for the system and , and All are non-linear amplification factors. To prevent oscillation penalty coefficient; where, when temperature deviation The larger the index The non-linear, sharp increase indicates an urgent demand for heating; however, when the differential pressure in the pipe network approaches or exceeds the safety threshold, the hydraulic constraint term will increase significantly; simultaneously, if the current operating rate of the regulating valve... Too fast, The term will be directly reduced as a dynamic penalty function. This value, thus fundamentally eliminating the violent opening behavior of the regulating valve caused by excessively low temperature at a single point at a purely mathematical level, achieves a fundamental decoupling of thermal and hydraulic forces.
[0013] According to the above technical solution, the method for performing graded flexible clamping adjustment of the electric regulating valve based on the cooperative strategy in step S5 further includes: The urgency index calculated based on step S2 The absolute value is dynamically adjustable in three levels, among which: when At this time, the system determines that it is in the normal thermal offset range and executes the daily flexible fine-tuning mode, with the regulating valve operating at an extremely low rate. Follow up with compensation; when When the system determines that it has encountered a sudden change in external weather or a sudden increase in load, it enters a dynamic decoupling compensation mode and introduces feedforward control to smoothly increase the valve opening. when When the system determines that the underlying demand and the global hydraulic system are extremely incompatible, it triggers the micro-water hammer suppression unit to enter the clamping protection mode, forcibly locking the maximum opening increment of the regulating valve regardless of the temperature deviation at the underlying level. And implement a stepped gradual opening strategy until... Falling back to The following, among which All are system preset thresholds.
[0014] According to the above technical solution, the calculation method for calculating the thermal inertia attenuation coefficient of the associated building and performing scheduling in step S4 includes: Step S41: During the observation period when the primary network heating flow is stopped or reduced. Inside, the temperature drop curve of the building's indoor temperature was continuously collected; Step S42: Calculate the building's thermal inertia coefficient ,in This represents the indoor temperature drop during the observation period. The average indoor temperature. Outdoor temperature and Environmental baseline parameters; Step S43: When The value is less than the system's preset inertia threshold. This indicates that the building has excellent thermal insulation performance but cools down extremely slowly. Under conditions of insufficient overall heat source in the pipeline network, the virtual energy storage dispatch unit proactively issues a "deduction" command, intercepting the pre-allocated flow of the heating chamber. Compensation is applied to other high heat loss areas, among which This is the energy storage deprivation factor. The original plan was to supply heat by utilizing the thermal inertia of the physical building itself as a virtual energy storage pool for the pipeline network, thus achieving peak shaving and valley filling without increasing hardware costs.
[0015] According to the above technical solution, when the soft-sensor self-healing monitoring module detects a fault signal from a primary pipeline flow meter, it immediately truncates the abnormal flow data and extracts the pressure difference at the current moment. and absolute valve opening Call the embedded adaptive flow derivation formula The calculated virtual traffic value replaces the missing physical feedback value to maintain the operation of the hierarchical collaborative computing module. This is the inherent flow characteristic function of this type of control valve. These are time-varying compensation coefficients generated through self-learning based on previous historical normal data.
[0016] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention breaks through the dilemma of "dimensional interference" and "control deadlock" that traditional thermal chambers are prone to when facing multidimensional heterogeneous parameters. Through a self-built dimensionless evaluation matrix and dynamic game algorithm, this invention can deeply decouple the second-level hydraulic transients from the hour-level thermal inertia, completely eliminating the "feedback storm" that is easily caused in the hierarchical architecture, and achieving smooth and accurate dynamic hydraulic balance across the entire network.
[0017] The more prominent advantage lies in: 1. This invention can also transform the most difficult-to-handle "building thermal inertia" in the heating system into a "virtual energy storage pool" for the pipeline network. Without adding physical energy storage equipment, peak shaving and valley filling with great economic value can be achieved through algorithm-based peak-shifting and valley filling.
[0018] 2. This invention eliminates frequent and ineffective oscillations of the control valve at the source by designing a smooth clamping algorithm based on a global perspective. This not only significantly extends the mechanical life of expensive valve components but also effectively suppresses the "micro-water hammer" phenomenon in the secondary pipeline network, reducing the risk of pipe bursts.
[0019] 3. The soft measurement fault-tolerant mechanism built on the deep coupling algorithm enables the system to still have strong data self-healing ability under extreme conditions of partial sensor failure, which greatly improves the operational robustness of the unattended thermal chamber. Attached Figure Description
[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a system framework diagram of the present invention; Figure 2 This is a flowchart illustrating the method. Detailed Implementation
[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0022] Please see Figure 1 , Figure 1 The diagram illustrates the framework of the hierarchical collaborative control system for a fully parameter feedback intelligent thermal chamber disclosed in this embodiment, which includes: a multidimensional heterogeneous parameter sensing module, a hierarchical collaborative calculation module, a dynamic decoupling execution module, and a soft measurement self-healing monitoring module.
[0023] Conventional centralized heating networks typically employ simple PID controllers or climate compensators for regulating individual heating cells at the terminal. While this approach is logically simple, it often leads to problems when faced with sudden weather changes or significant fluctuations in localized heat loads. The system tends to blindly adjust valves based solely on the single indicator of "indoor temperature." Because hydraulic response is extremely fast (on the order of seconds), while changes in building room temperature are extremely slow (with hourly thermal inertia), this mismatch in time scales causes frequent opening and closing of regulating valves. This not only easily leads to overall hydraulic imbalance in the network but also shortens valve lifespan.
[0024] To address the control deadlock caused by conventional methods and the dimensional interference problem brought about by full-parameter feedback, this solution proposes a full-parameter feedback intelligent thermal cell hierarchical collaborative control system, which includes a multi-dimensional heterogeneous parameter sensing module configured to collect all-round hydraulic transient data of the primary pipeline network, thermal hysteresis data of the secondary pipeline network, and operating status data of the underlying equipment of the thermal cell.
[0025] Specifically, the multidimensional heterogeneous parameter sensing module includes: a hydraulic transient sensing unit, configured to collect millisecond-level changes in the primary pipeline supply and return water pressure difference and flow rate data; a thermal hysteresis sensing unit, configured to collect hourly-level changes in the secondary pipeline supply water temperature, return water temperature, and indoor temperature data of associated buildings; and a valve control status sensing unit, configured to collect real-time data on the valve opening degree and action rate of the primary pipeline electric regulating valve.
[0026] However, simply acquiring the massive amount of multidimensional heterogeneous parameters and directly feeding them into a traditional controller can easily lead to conflicting control commands. For example, if the indoor temperature is too low and a valve needs to be opened wider, but the pipeline pressure differential has exceeded the limit and a valve needs to be closed, the system will malfunction. To address this issue, this invention introduces a hierarchical collaborative computing module, configured to decouple the time scale and physical dimensions of the acquired heterogeneous parameters and generate global collaborative adjustment commands.
[0027] In terms of the module's logical architecture, the hierarchical collaborative computing module includes a heterogeneous parameter dimensionless unit, a hot water decoupling game unit, and a virtual energy storage scheduling unit. The heterogeneous parameter dimensionless unit serves as a data foundation, mapping thermal and hydraulic parameters of different time scales and physical units to a unified-dimensional evaluation index. The hot water decoupling game unit acts as the core decision-making hub, handling hierarchical target mutual exclusion caused by sudden changes in heat load and calculating the optimal adjustment threshold to avoid feedback storms. The virtual energy storage scheduling unit dynamically deducts or compensates for heat distribution in local cells based on the building's thermal inertia assessment results.
[0028] Please see Figure 2 Based on the above system architecture, the overall operation logic of the full-parameter feedback intelligent thermal cell hierarchical collaborative control method of the present invention includes the following specific steps: Step S1: The operating data of the thermal chamber is collected at high frequency through the multidimensional heterogeneous parameter sensing module to form a multidimensional data stream containing hydraulic transient parameters, thermal hysteresis parameters and valve control state parameters; Step S2: In response to the input of the multidimensional data stream, the dimensionless unit of heterogeneous parameters is activated, and the urgency index of thermo-hydraulic coupling in the current thermal cell is calculated. ; Step S3: The hot water decoupling game unit calculates... The value is used to determine whether there is dimensional interference between the local heat demand of the current chamber and the global hydraulic balance, and to generate dynamic penalty function weights. Step S4: When the total heat source of the pipeline network is insufficient or the heating load is at its peak in the morning or evening, the virtual energy storage scheduling unit calculates the thermal inertia decay coefficient of the associated buildings and performs "peak shaving and valley filling" scheduling of global heat. Step S5: The dynamic decoupling execution module performs tiered flexible clamping adjustment of the electric regulating valve according to the above-mentioned coordination strategy.
[0029] In the calculation process of step S2 above, the heterogeneous parameter dimensionless unit first obtains the current indoor temperature collected by the thermal hysteresis sensing unit. With target temperature temperature difference And the actual pressure difference of the primary side pipeline network collected by the hydraulic transient sensing unit. With safety differential pressure threshold The ratio parameter; subsequently, the valve control status sensing unit collects the regulating valve action rate in real time. The urgency index is calculated using the formula. Its calculation formula is expressed as: in, This is the weighting coefficient for heat demand. For hydraulic constraint weighting coefficients, Preset base values for the system and and All are nonlinear amplification factors. In this scheme, The preferred value is 0.8. The technical rationale for this value is that an exponential decay rate of 0.8 can quickly increase the urgency of demand when a temperature deviation first appears. However, when the temperature difference is too large (such as an extreme temperature difference caused by opening doors and windows wide), the exponential term... It will quickly saturate and approach 1, thus preventing the system from endlessly increasing the valve opening demand due to extreme cases.
[0030] Crucially, in the formula The terms constitute the anti-oscillation dynamic penalty function of this invention. To prevent oscillation penalty coefficients. For example, with traditional methods, when encountering a sudden drop in room temperature, the PID controller will instantly output a very large opening command, causing the regulating valve to open violently and triggering hydraulic oscillations; while this invention, using the above formula, calculates the instantaneous operating rate of the regulating valve. When the speed is too fast, the penalty will increase dramatically, thus directly offsetting and reducing it at the mathematical formula level. The total value forces the regulating valve to "slow down." This soft logic damping achieves a fundamental decoupling of thermal and hydraulic forces.
[0031] The urgency index for accurately measuring holographic working conditions was obtained. Subsequently, this solution is configured with a dynamically decoupled execution module, used to perform tiered flexible clamping regulation of the electric control valve according to a collaborative strategy. At the underlying execution architecture, the dynamically decoupled execution module includes a flexible clamping regulation unit and a micro-water hammer suppression unit. The flexible clamping regulation unit limits the instantaneous maximum stroke of the control valve to prevent control deadlock, while the micro-water hammer suppression unit performs buffered motion control on the valve during sudden changes in pipeline pressure to ensure the physical safety of the pipeline network.
[0032] Specifically, the dynamic decoupling execution module according to The absolute value, in conjunction with the above two sets of units, is dynamically adjusted in three levels: when When the system determines that it is within the normal thermal offset range, it executes the daily flexible fine-tuning mode, and the flexible clamping adjustment unit controls the regulating valve at an extremely low rate. Follow up with compensation; when When the system determines that it has encountered a sudden change in external weather or a sudden increase in load, it enters a dynamic decoupling compensation mode and introduces feedforward control to smoothly increase the valve opening. when When the system determines that the underlying demand and the global hydraulic system are extremely incompatible, it directly triggers the micro-water hammer suppression unit to enter the clamping protection mode, forcibly locking the maximum opening increment of the regulating valve regardless of the temperature deviation at the underlying level. And implement a stepped gradual opening strategy until... Falling back to The following is an explanation of how this invention, through hierarchical threshold control, completely eliminates the traditional linear approach, implicitly extending the mechanical life of the equipment and effectively preventing micro-water hammer in the secondary pipeline network.
[0033] Furthermore, to further tap the system's potential and break the technical bias that conventional heating must be "allocated on demand," this solution innovatively introduces a virtual energy storage dispatch unit. During peak hours when the total heat source in the pipeline network is insufficient, the virtual energy storage dispatch unit executes the following logic: First, during the observation period of stopping or reducing the primary network heating flow. Inside, the temperature drop curves of the building's interior are continuously collected; then, the building's thermal inertia coefficient is calculated using the following formula: Among them This represents the indoor temperature drop during the observation period. The average indoor temperature. Outdoor temperature and For environmental baseline parameters. In this formula, when... The smaller the value, the better the performance in the same harsh, cold outdoor environment ( Under extreme conditions, the cooling rate of the building It is still extremely small, meaning that the building's walls have extremely strong heat insulation and heat storage capacity.
[0034] when The value is less than the system's preset inertia threshold. At that time, the virtual energy storage dispatch unit proactively issued a "deduction" command, intercepting the pre-allocated flow of the heating chamber. Compensation is applied to other high heat loss areas, among which This is the energy deprivation coefficient. For example, traditional pipe networks often face the dilemma of insufficient overall heating during morning and evening peak hours; while this solution, through the above calculations, transforms high-insulation buildings into physical "virtual energy storage pools" for the pipe network, achieving cross-regional heat lending and peak shaving without adding any hardware heat storage tanks.
[0035] Finally, addressing the pain point of sensor aging and damage in harsh industrial environments, this system is equipped with a soft-sensor self-healing monitoring module. In traditional thermal chambers, if the flow meter fails or signal loss occurs, the entire control logic will be paralyzed, triggering a shutdown alarm. Therefore, the soft-sensor self-healing monitoring module in this solution pre-constructs a multi-dimensional parameter coupling derivation matrix. This module is triggered and activated only when the hydraulic transient sensing unit experiences a hardware failure or data loss. It then uses the mapping relationship between valve opening and pipeline pressure differential under historical normal operating conditions to reverse-engineer the currently missing virtual flow data and take over system control.
[0036] The soft-sensor self-healing monitoring module in this solution immediately cuts off abnormal flow data and extracts the pressure difference at the current moment when it detects a fault signal from a primary pipeline flow meter. and absolute valve opening The built-in adaptive traffic derivation formula is used to calculate the virtual traffic value: in This is the inherent flow characteristic function of this type of control valve. These are time-varying compensation coefficients generated through self-learning based on prior historical normal data. The formula is derived by reversing this process. By directly replacing the missing physical feedback values, the hierarchical collaborative computing module maintains stable operation, providing maintenance personnel with valuable buffer time and significantly improving the system robustness of the unattended thermal chamber.
[0037] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0038] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0039] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0040] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.
Claims
1. A full-parameter feedback type intelligent thermal chamber hierarchical collaborative control system, characterized in that, The system includes a multidimensional heterogeneous parameter sensing module, a hierarchical collaborative calculation module, a dynamic decoupling execution module, and a soft-sensor self-healing monitoring module. The multidimensional heterogeneous parameter sensing module is used to collect transient hydraulic data of the primary pipe network, thermal hysteresis data of the secondary pipe network, and operating status data of the underlying equipment of the thermal chamber from all directions. The hierarchical collaborative calculation module is communicatively connected to the multidimensional heterogeneous parameter sensing module and is used to perform decoupling calculations on the time scale and physical dimensions of the collected heterogeneous parameters and generate global collaborative adjustment commands. The dynamic decoupling execution module is connected to the hierarchical collaborative calculation module and is used to perform anti-oscillation clamping adjustment on the regulating valve of the thermal chamber according to the collaborative adjustment commands. The soft-sensor self-healing monitoring module is used to perform back-calculation compensation calculations through data redundancy when the multidimensional heterogeneous parameter sensing module partially fails.
2. The full-parameter feedback type intelligent thermal chamber hierarchical collaborative control system according to claim 1, characterized in that: The multidimensional heterogeneous parameter sensing module includes a hydraulic transient sensing unit, a thermal hysteresis sensing unit, and a valve control status sensing unit. The hydraulic transient sensing unit is used to collect millisecond-level changes in the primary pipeline supply and return water pressure difference and flow rate data. The thermal hysteresis sensing unit is used to collect hourly-level changes in the secondary pipeline supply water temperature, return water temperature, and indoor temperature data of related buildings. The valve control status sensing unit is used to collect the valve opening degree and action rate of the primary pipeline electric regulating valve in real time.
3. The full-parameter feedback type intelligent thermal chamber hierarchical collaborative control system according to claim 1, characterized in that: The hierarchical collaborative computing module includes a heterogeneous parameter dimensionless unit, a hot water decoupling game unit, and a virtual energy storage scheduling unit. The heterogeneous parameter dimensionless unit is used to map thermal and hydraulic parameters with different time scales and physical units into evaluation indices with unified dimensions. The hot water decoupling game unit is used to handle hierarchical target mutual exclusion caused by sudden changes in heat load and calculate and output the optimal adjustment threshold to avoid feedback storms. The virtual energy storage scheduling unit is used to dynamically deduct or compensate for the heat distribution of local small rooms based on the building thermal inertia assessment results.
4. The full-parameter feedback type intelligent thermal chamber hierarchical collaborative control system according to claim 1, characterized in that: The dynamic decoupling execution module includes a flexible clamping adjustment unit and a micro water hammer suppression unit. The flexible clamping adjustment unit is used to limit the instantaneous maximum stroke of the control valve to prevent control deadlock, and the micro water hammer suppression unit is used to perform buffer motion control on the valve when the pipeline pressure changes suddenly.
5. The full-parameter feedback type intelligent thermal chamber hierarchical collaborative control system according to claim 2, characterized in that: The soft-sensor self-healing monitoring module has a built-in multi-dimensional parameter coupling derivation matrix. When the hydraulic transient sensing unit experiences a hardware failure or data loss, it can reverse-engineer the currently missing virtual flow data by utilizing the mapping relationship between valve opening and pipeline pressure difference under historical normal operating conditions.
6. A fully parameter feedback-type intelligent thermal chamber hierarchical collaborative control method, applicable to the system according to any one of claims 1 to 5, characterized in that: The method includes the following steps: Step S1: The operating data of the thermal chamber is collected at high frequency through the multidimensional heterogeneous parameter sensing module to form a multidimensional data stream containing hydraulic transient parameters, thermal hysteresis parameters and valve control state parameters; Step S2: In response to the input of the multidimensional data stream, the hierarchical collaborative computing module activates the dimensionless unit for heterogeneous parameters to calculate the urgency index of the current thermal-hydraulic coupling in the thermal chamber. ; Step S3: The hot water decoupling game unit calculates... The value is used to determine whether there is dimensional interference between the local heat demand of the current chamber and the global hydraulic balance, and to generate dynamic penalty function weights. Step S4: When the total heat source of the pipeline network is insufficient or the heating load is at its peak in the morning or evening, the virtual energy storage scheduling unit calculates the thermal inertia decay coefficient of the associated buildings and performs "peak shaving and valley filling" scheduling of global heat. Step S5: The dynamic decoupling execution module performs tiered flexible clamping adjustment of the electric regulating valve according to the above-mentioned collaborative strategy.
7. The method for hierarchical collaborative control of a fully parameter feedback intelligent thermal cell according to claim 6, characterized in that: In step S2, the urgency index of thermo-hydraulic coupling is calculated. The method further includes: Step S21: Obtain the current indoor temperature collected by the thermal hysteresis sensing unit. With target temperature temperature difference And the actual pressure difference of the primary side pipeline network collected by the hydraulic transient sensing unit. With safety differential pressure threshold The ratio parameter; Step S22: Real-time retrieval of the regulating valve's action rate collected by the valve control status sensing unit. The urgency index was calculated using dimensionless unit analysis with heterogeneous parameters. The calculation formula is as follows: in, This is the weighting coefficient for heat demand. For hydraulic constraint weighting coefficients, Preset base values for the system and , and All are non-linear amplification factors. To prevent oscillation penalty coefficient; where, when temperature deviation The larger the index The non-linear, sharp increase indicates an urgent demand for heating; however, when the differential pressure in the pipe network approaches or exceeds the safety threshold, the hydraulic constraint term will increase significantly; simultaneously, if the current operating rate of the regulating valve... Too fast, The term will be directly reduced as a dynamic penalty function. This value, thus fundamentally eliminating the violent opening behavior of the regulating valve caused by excessively low temperature at a single point at a purely mathematical level, achieves a fundamental decoupling of thermal and hydraulic forces.
8. The method for hierarchical collaborative control of a fully parameter feedback intelligent thermal cell according to claim 7, characterized in that: The method for performing graded flexible clamping adjustment of the electric regulating valve according to the cooperative strategy in step S5 further includes: The urgency index calculated based on step S2 The absolute value is dynamically adjustable in three levels, among which: when At this time, the system determines that it is in the normal thermal offset range and executes the daily flexible fine-tuning mode, with the regulating valve operating at an extremely low rate. Follow up with compensation; when When the system determines that it has encountered a sudden change in external weather or a sudden increase in load, it enters a dynamic decoupling compensation mode and introduces feedforward control to smoothly increase the valve opening. when When the system determines that the underlying demand and the global hydraulic system are extremely incompatible, it triggers the micro-water hammer suppression unit to enter the clamping protection mode, forcibly locking the maximum opening increment of the regulating valve regardless of the temperature deviation at the underlying level. And implement a stepped gradual opening strategy until... Falling back to The following, among which All are system preset thresholds.
9. The method for hierarchical collaborative control of a fully parameter feedback intelligent thermal cell according to claim 6, characterized in that: The calculation method for calculating the thermal inertia attenuation coefficient of the associated building and scheduling in step S4 includes: Step S41: During the observation period when the primary network heating flow is stopped or reduced. Inside, the temperature drop curve of the building's indoor temperature was continuously collected; Step S42: Calculate the building's thermal inertia coefficient ,in This represents the indoor temperature drop during the observation period. The indoor average temperature Outdoor temperature and Environmental baseline parameters; Step S43: When The value is less than the system's preset inertia threshold. This indicates that the building has excellent thermal insulation performance but cools down extremely slowly. Under conditions of insufficient overall heat source in the pipeline network, the virtual energy storage dispatch unit proactively issues a "deduction" command, intercepting the pre-allocated flow of the heating chamber. Compensation is applied to other high heat loss areas, among which This is the energy storage deprivation factor. The original plan was to supply heat by utilizing the thermal inertia of the physical building itself as a virtual energy storage pool for the pipeline network, thus achieving peak shaving and valley filling without increasing hardware costs.
10. The full-parameter feedback type intelligent thermal chamber hierarchical collaborative control system according to claim 1, characterized in that: When the soft-sensor self-healing monitoring module detects a fault signal from a primary pipeline flow meter, it immediately truncates the abnormal flow data and extracts the pressure difference at the current moment. and absolute valve opening Call the embedded adaptive flow derivation formula The calculated virtual traffic value replaces the missing physical feedback value to maintain the operation of the hierarchical collaborative computing module, in which... This is the inherent flow characteristic function of this type of control valve. These are time-varying compensation coefficients generated through self-learning based on previous historical normal data.