Unattended intelligent control method and system for heat exchange station

Abstract

The invention discloses an unattended intelligent control method and system for a heat exchange station, and relates to the technical field of heat energy automatic control, and the method comprises the steps: collecting multi-source environment parameter data, constructing an environment parameter sequence set, constructing a heat load prediction model, and outputting a predicted heat load value through extracting a change relation between local temperature trend characteristics and a time sequence. Carrying out residual comparison on an actual thermal load value and the predicted thermal load value, constructing a residual fluctuation measurement sequence, carrying out multi-dimensional state judgment in combination with a preset threshold range, outputting an operation state identifier, carrying out linkage adjustment on a water pump frequency parameter and a water mixing valve opening parameter, calling an optimization control strategy set, and carrying out optimization control on the water pump frequency parameter and the water mixing valve opening parameter. And generating an updated water pump adjusting instruction and an updated valve adjusting instruction. The operation reliability and the energy utilization efficiency of the heat exchange station are improved, unattended intelligent control is achieved, and the technical problems that in a traditional control method, prediction precision is low, abnormal recognition is not accurate, and control parameter coordination is poor are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control of thermal energy, in particular to a heat exchange station unattended intelligent control method and system. BACKGROUND

[0002] The current operation and management of heat exchange stations generally have high dependence on manpower, slow response to regulation and control, and low energy utilization efficiency. In the traditional mode, the operating personnel need to monitor the inlet and outlet temperatures and pressures of the plate heat exchanger and the water replenishment state in the system in real time. If a sudden situation such as abnormal pressure or water level occurs, a delayed response may cause a decrease in heating efficiency or even equipment failure. In addition, due to the inability to achieve unattended operation, the operation cost of the heat exchange station in some areas is high, the artificial inspection intensity is large, the equipment state data is difficult to upload and remotely control in time, and it is unable to meet the urgent needs of efficient, energy-saving and safe intelligent heating systems. Therefore, how to realize the automatic and intelligent control of the heat exchange station to support unattended operation, and to realize real-time acquisition of key operation parameters, remote data interaction and state feedback, has become a key problem that needs to be solved at present.

[0003] CN103438503A discloses an intelligent control method and system for unattended operation of a heat exchange station. The output end of the programmable controller PLC of the intelligent control system is connected with two frequency converters to form a programmable controller PLC. The input end of the programmable controller PLC is connected with an outdoor temperature compensator, temperature and pressure sensors of the inlet and outlet of the high-temperature water on the primary side of the plate heat exchanger, temperature and pressure sensors of the inlet and outlet of the return water on the secondary side of the plate heat exchanger, and a liquid level sensor in the water replenishment tank. The corresponding output end is connected with an electric regulating valve on the high-temperature water on the primary side of the plate heat exchanger, an electromagnetic valve connected with the tap water pipeline, and an electromagnetic valve connected with the pipeline. The output end of the first frequency converter is connected with a circulating pump, and the output end of the second frequency converter is connected with a water replenishment pump. The system can realize automatic constant temperature and pressure heating, automatic water replenishment, abnormal alarm and power failure restart functions, has certain intelligent characteristics, and can send real-time field data to the control terminal for remote access and monitoring, thereby improving the management efficiency and operation reliability. Although the above-mentioned technology can realize a certain degree of automatic control and remote data transmission, the system structure is still mainly based on fixed logic PLC control, lacks dynamic adjustment and intelligent optimization capability, and when facing external environmental changes (such as sudden load change and sudden temperature difference fluctuation), the regulation and control response is lagged, and the adaptive operation strategy cannot be realized. At the same time, this scheme does not involve higher-level intelligent control methods such as edge computing, fault prediction analysis or multi-source data fusion, and still needs to rely on management personnel for strategy configuration and manual intervention in actual operation, and it is difficult to realize the true sense of "intelligent unattended operation", especially in large-scale central heating systems, it is difficult to meet the technical requirements of low energy consumption, high response and less maintenance. SUMMARY

[0004] In view of the problems of low energy efficiency, strong artificial dependence, insufficient remote monitoring and intelligent adjustment ability of the existing existing heat exchange station in the operation process, the present application is proposed.

[0005] Therefore, the problem to be solved by the present application is how to realize real-time acquisition and intelligent linkage control of key operating parameters of the heat exchange station, support remote management and fault adaptive processing, and thus realize an efficient and safe unattended operation mode.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application embodiment provides a heat exchange station unattended intelligent control method, which comprises, Acquire multi-source environmental parameter data and construct an environmental parameter sequence set; Based on the environmental parameter sequence set, a heat load prediction model is constructed, the local temperature trend characteristics and time series change relationship are extracted, and the predicted heat load value is output; Compare the actual heat load value with the predicted heat load value, construct a residual fluctuation measure sequence, and combine the preset threshold range for multi-dimensional state discrimination, and output the running state identifier; Based on the running state identifier, the water pump frequency parameter and the water mixing valve opening parameter are linked and adjusted, and an optimized control strategy set is called to generate updated water pump adjustment instructions and valve adjustment instructions.

[0007] As a preferred scheme of the heat exchange station unattended intelligent control method of the present application, wherein: based on the running state identifier, the water pump frequency parameter and the water mixing valve opening parameter are linked and adjusted, and an optimized control strategy set is called to generate updated water pump adjustment instructions and valve adjustment instructions, comprising: Read the current water pump frequency parameter and water mixing valve opening parameter from the heat exchange station control system, and select the corresponding optimized control strategy set according to the running state identifier, wherein the optimized control strategy set includes normal condition strategy and abnormal condition strategy; When the running state identifier is normal, the water pump frequency adjustment interval and the water mixing valve opening adjustment interval are divided according to the normal condition strategy, and the current parameters are substituted into the normal condition strategy to calculate the target adjustment amount; When the running state identifier is abnormal, the abnormal condition strategy is started, the adjustment direction is determined according to the fluctuation trend of the residual sequence, and the range of the water pump frequency adjustment interval and the water mixing valve opening adjustment interval is expanded; Generate water pump adjustment instructions and valve adjustment instructions according to the target adjustment amount, wherein the water pump adjustment instructions include a target frequency value; the valve adjustment instructions include a target opening value; The water pump adjustment instruction is sent to a frequency converter, the valve adjustment instruction is sent to an actuator, and the adjustment result is recorded in a historical database.

[0008] As a preferred scheme of the heat exchange station unattended intelligent control method, the method for obtaining the running state identifier is, An actual heat load value is calculated according to a primary supply-return water temperature difference, a secondary supply-return water temperature difference and a heat exchange station flow at a current time, and a difference between the actual heat load value and the predicted heat load value is recorded as a residual value; The residual values are arranged in time sequence to form a residual sequence, and a mean value, a standard deviation and a coefficient of variation of the residual sequence are calculated to obtain residual statistical characteristic values; A first threshold interval, a second threshold interval and a third threshold interval are established according to normal running data calibrated in advance; The residual statistical characteristic values are compared with the first threshold interval, the second threshold interval and the third threshold interval to mark abnormal points; The number and distribution of abnormal points within a preset time are counted, and when the number of abnormal points exceeds a preset number threshold or continuous abnormal points occur, the current heat exchange station running state is marked as an abnormal state.

[0009] As a preferred scheme of the heat exchange station unattended intelligent control method, the comparison of the residual statistical characteristic values with the first threshold interval, the second threshold interval and the third threshold interval comprises: If the mean value of the residual sequence is located in the first threshold interval and the standard deviation is less than the lower limit of the second threshold interval, it is determined as a regulation-insensitive abnormal point; If the mean value of the residual sequence exceeds the upper limit of the first threshold interval and the coefficient of variation exceeds the upper limit of the third threshold interval, it is determined as an over-regulation abnormal point; If the mean value of the residual sequence is located in the first threshold interval, but the standard deviation exceeds the upper limit of the second threshold interval and the coefficient of variation is located in the third threshold interval, it is determined as a steady-state fluctuation abnormal point; If the change rate of the mean value of the residual sequence exceeds a preset change rate and the coefficient of variation is lower than the lower limit of the third threshold interval, it is determined as a sudden change abnormal point.

[0010] As a preferred scheme of the heat exchange station unattended intelligent control method, the method for obtaining the predicted heat load value comprises: The sequence set of environmental parameters is divided into a plurality of training sample subsets according to a preset time window; Difference operation is performed on the primary supply and return water temperature and the secondary supply and return water temperature in the training sample subset to obtain a primary temperature difference and a secondary temperature difference, and the primary temperature difference and the secondary temperature difference and the outdoor temperature at the corresponding moment are combined to form a temperature feature vector; The temperature feature vector is associated and paired with the water pump frequency and the heat network pressure at the corresponding moment to construct an input feature matrix, and the historical heat load data at the corresponding moment is taken as an output label; The input feature matrix and the output label are trained by using a support vector regression algorithm to obtain kernel function parameters and slack variables of a heat load prediction model; A local temperature trend correction factor is introduced, and the preliminary prediction value is weighted and corrected according to the outdoor temperature change rate to generate a predicted heat load value.

[0011] As a preferred scheme of the heat exchange station unattended intelligent control method, the generation method of the heat load prediction model comprises, The input feature matrix is normalized to map the environment parameter sequence set to the interval [0, 1] to obtain a standardized feature matrix; A support vector regression model is constructed by using a radial basis kernel function, a kernel function is defined, and a heat load prediction model is formed; Based on the standardized feature matrix and the output label, an ε-insensitive loss function is used to establish a regression objective function and a constraint condition; The regression objective function is solved by using a sequential minimal optimization algorithm to obtain an optimal kernel width parameter and a penalty factor, and the optimal hyperplane parameter of the heat load prediction model is determined.

[0012] As a preferred scheme of the heat exchange station unattended intelligent control method, the environment parameter sequence set comprises a primary supply and return water temperature, a secondary supply and return water temperature, a water pump frequency, an outdoor temperature, a heat network pressure and historical heat load data.

[0013] In a second aspect, an embodiment of the present application provides a heat exchange station unattended intelligent control system, which comprises: An environment parameter acquisition and sequence construction module is configured to acquire multi-source environment parameter data and construct an environment parameter sequence set; A heat load prediction model construction module is configured to construct a heat load prediction model based on the environment parameter sequence set, extract local temperature trend features and time sequence change relations, and output a predicted heat load value; A residual error analysis and state discrimination module is configured to compare an actual heat load value with the predicted heat load value, construct a residual error fluctuation degree sequence, perform multi-dimensional state discrimination in combination with a preset threshold range, and output an operation state identifier; An optimized control strategy execution module performs linkage adjustment on the water pump frequency parameter and the water mixing valve opening degree parameter based on the running state identification, and calls an optimized control strategy set to generate updated water pump adjustment instructions and valve adjustment instructions.

[0014] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory and a processor, and the memory stores a computer program, wherein the computer program instructions are executed by the processor to implement the steps of the heat exchange station unattended intelligent control method according to the first aspect of the present application.

[0015] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program instructions are executed by the processor to implement the steps of the heat exchange station unattended intelligent control method according to the first aspect of the present application.

[0016] The present application has the following beneficial effects: by collecting multi-source environmental parameter data and constructing an environmental parameter sequence set, the comprehensive perception ability of the system for the running environment of the heat exchange station is ensured; by using the heat load prediction model constructed by the support vector regression algorithm and the local temperature trend correction factor, the accurate prediction of the heat load change is realized; based on the residual analysis of the actual heat load and the predicted heat load and the multi-dimensional state discrimination mechanism, various abnormal states such as non-sensitive adjustment, over-adjustment, steady-state fluctuation and mutation can be accurately identified, and the fault diagnosis capability is improved; through the linkage adjustment of the water pump frequency parameter and the water mixing valve opening degree parameter and the optimized control strategy set for different running states, the efficient and stable operation of the heat exchange station is realized; the present application not only significantly improves the operation reliability and energy utilization efficiency of the heat exchange station, but also realizes the real unattended intelligent control, solves the technical problems of low prediction accuracy, inaccurate abnormal identification and poor control parameter coordination in the traditional control method, and provides an effective solution for the intelligent upgrading of the heating system. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings. Among them: Figure 1 The flowchart of the heat exchange station unattended intelligent control method of embodiment 1.

[0018] Figure 2 The system structure diagram of the heat exchange station unattended intelligent control system of embodiment 2. DETAILED DESCRIPTION

[0019] In order to make the above objectives, features and advantages of the present application more obvious and comprehensible, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0020] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application, however, can be practiced in a variety of ways other than those specifically described herein without departing from the scope of the present application, and it is understood that variations can be made in view of the above detailed description of the application or practiced except in its broadest forms within the scope of the claims.

[0021] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent of or selected from other embodiments.

[0022] Embodiment 1 Reference Figure 1 For the first embodiment of the present application, the embodiment provides an unattended intelligent control method for a heat exchange station, comprising: S1: Collecting multi-source environmental parameter data and constructing an environmental parameter sequence set.

[0023] S1.1: Collecting primary water supply temperature and primary return water temperature through a first temperature sensor arranged in the primary pipe network of the heat exchange station.

[0024] S1.2: At the same time, collecting secondary water supply temperature and secondary return water temperature through a second temperature sensor arranged in the secondary pipe network of the heat exchange station.

[0025] S1.3: Collecting heat network pressure of the primary pipe network through a first pressure sensor arranged in the heat exchange station and collecting water pump frequency data through a rotation speed sensor arranged in the water pump.

[0026] S1.4: Collecting outdoor temperature through a meteorological sensor arranged outside the heat exchange station and reading historical heat load data from a database.

[0027] S1.5: Arranging the primary water supply temperature, the primary return water temperature, the secondary water supply temperature, the secondary return water temperature, the heat network pressure, the water pump frequency data, the outdoor temperature and the historical heat load data in time stamp order to form an environmental parameter sequence set.

[0028] S1.6: Storing the environmental parameter sequence set in a data buffer area and eliminating abnormal data in the environmental parameter sequence set to obtain a corrected environmental parameter sequence set.

[0029] It should be noted that the environmental parameter sequence set includes primary supply and return water temperature, secondary supply and return water temperature, water pump frequency, outdoor temperature, heating network pressure and historical heat load data.

[0030] S2: Based on the set of environmental parameter sequences, a heat load prediction model is constructed. By extracting the relationship between local temperature trend characteristics and time series changes, the predicted heat load value is output.

[0031] S2.1: Divide the environmental parameter sequence set into multiple training sample subsets according to a preset time window.

[0032] S2.2: Perform difference calculation on the primary supply and return water temperatures and the secondary supply and return water temperatures in the training sample subset to obtain the primary temperature difference and the secondary temperature difference, and combine the primary temperature difference and the secondary temperature difference with the outdoor temperature at the corresponding time to form a temperature feature vector.

[0033] S2.3: Associate and pair the temperature feature vector with the water pump frequency and heating network pressure at the corresponding time to construct the input feature matrix, and use the historical heat load data at the corresponding time as the output label.

[0034] S2.4: Use the support vector regression algorithm to train the input feature matrix and output labels to obtain the kernel function parameters and slack variables of the heat load prediction model.

[0035] Specifically include: S2.4.1: Normalize the input feature matrix and map the set of environmental parameter sequences to the interval [0,1] to obtain a standardized feature matrix; S2.4.2: Use radial basis kernel function to build support vector regression model, define kernel function, and form heat load prediction model; Preferably, the specific formula of the heat load prediction model is as follows: ; in, To predict the heat load value, and is the Lagrange multiplier, is the temperature trend correction factor, b is the bias term, is the feature vector of the i-th training sample, is the current input feature vector, is the number of support vectors, is the local temperature trend correction factor, is an improved radial basis kernel function, and the specific formula is as follows: ; Among them, σ is the kernel function width parameter, is the jth characteristic component of the i-th sample, is the jth feature component of the current input, is the dynamic weight of the jth feature, and the specific formula is as follows: ; wherein, is the dynamic weight of the jth feature, is a feature weight adjustment parameter, and is 0.5, is the standard deviation of the jth feature, and m is the feature dimension.

[0036] It should be noted that, the value range of is [0, ], wherein is the rated heat load of the heat exchange station; the value range of is [0, 1], which represents the feature weight.

[0037] S2.4.3: Based on the standardized feature matrix and the output label, a regression objective function and a constraint condition are established using an epsilon-insensitive loss function; S2.4.4: The regression objective function is solved by a sequential minimal optimization algorithm to obtain the optimal kernel width parameter and the penalty factor, and to determine the optimal hyperplane parameter of the heat load prediction model.

[0038] S2.5: A local temperature trend correction factor is introduced, and the preliminary prediction value is weighted and corrected according to the outdoor temperature change rate to generate a predicted heat load value.

[0039] Preferably, the specific formula of the local temperature trend correction factor is as follows: ; wherein, is the outdoor temperature, is a reference temperature value, is a temperature change rate influence coefficient, and is 3600s, is a primary supply and return water temperature difference, is a secondary supply and return water temperature difference.

[0040] In an optional embodiment, the latest collected environmental parameter sequence and the historical data are reorganized according to a preset proportion, and the time sequence correlation weight of the input feature matrix is dynamically adjusted; the adjusted input feature matrix is input into the heat load prediction model, the high-dimensional space similarity is calculated through a radial basis kernel function, and the optimal hyperplane parameter is combined to output a preliminary prediction value.

[0041] S3: The actual heat load value and the predicted heat load value are compared for residual error, a residual fluctuation degree sequence is constructed, and multi-dimensional state discrimination is performed in combination with a preset threshold range to output a running state identifier.

[0042] S3.1: Calculate the actual heat load value according to the primary water temperature difference, the secondary water temperature difference and the heat exchange station flow at the current time, and record the difference between the actual heat load value and the predicted heat load value as a residual value.

[0043] Preferably, the specific formula of the residual value is as follows: ; wherein, is the residual value at time t, is the actual heat load value at time t, is the predicted heat load value at time t, is the reference heat load value, taking 20% of the rated heat load, is the flow fluctuation correction factor.

[0044] S3.2: Arrange the residual values in time sequence to form a residual sequence, and calculate the mean, standard deviation and coefficient of variation of the residual sequence to obtain residual statistical characteristic values.

[0045] S3.3: Establish a first threshold interval, a second threshold interval and a third threshold interval according to the normal operation data labeled in advance.

[0046] It should be noted that the first threshold interval corresponds to the mean range of the residual sequence, the second threshold interval corresponds to the standard deviation range, and the third threshold interval corresponds to the coefficient of variation range.

[0047] For example, the threshold standard established according to the system historical operation data is as follows: the first threshold interval (mean range) is set to [-20kW, 20kW], which is used to evaluate the overall level of prediction deviation; the second threshold interval (standard deviation range) is [5kW, 15kW], which is used to measure the fluctuation amplitude of data; the third threshold interval (coefficient of variation range) is [0.1, 0.3], which is used to judge the stability of fluctuation.

[0048] S3.4: Compare the residual statistical characteristic values with the first threshold interval, the second threshold interval and the third threshold interval, and mark the abnormal points.

[0049] In an optional embodiment, if the mean of the residual sequence is located in the first threshold interval and the standard deviation is less than the lower limit of the second threshold interval, it is determined as a regulation-insensitive abnormal point; if the mean of the residual sequence exceeds the upper limit of the first threshold interval and the coefficient of variation exceeds the upper limit of the third threshold interval, it is determined as an over-regulation abnormal point; if the mean of the residual sequence is located in the first threshold interval, but the standard deviation exceeds the upper limit of the second threshold interval and the coefficient of variation is located in the third threshold interval, it is determined as a steady-state fluctuation abnormal point; if the change rate of the mean of the residual sequence exceeds the preset change rate and the coefficient of variation is lower than the lower limit of the third threshold interval, it is determined as a sudden change abnormal point.

[0050] Specifically, in the heat exchange station operation monitoring process, the statistical characteristic analysis of the residual sequence is the key link to determine the system state; with 1 hour as the sampling period, the residual values (unit: kW) of the continuous 6 sampling points are: [-15, -12, -10, 5, 8, 20] in turn. The following statistical characteristics can be obtained by calculation: the arithmetic mean of the residual sequence is -0.67 kW, which reflects that the predicted value is slightly higher than the actual value as a whole in this period; the standard deviation calculation result is 13.2 kW, indicating that the data dispersion degree is large; the coefficient of variation is about 0.197 after standardization, which reflects the relative fluctuation level of the data.

[0051] For example, if the residual mean of a certain period is -5 kW (within the first threshold interval), and the standard deviation is 4 kW (lower than the lower limit of the second threshold 5 kW), it is determined to be a regulation insensitive abnormal point, indicating that the system response is slow; if the residual mean suddenly increases to 25 kW (exceeding the upper limit of the first threshold 20 kW), and the coefficient of variation reaches 0.35 (exceeding the upper limit of the third threshold 0.3), it is determined to be an over-regulation abnormal point, indicating that the control instruction overshoots and the regulation amplitude needs to be suppressed; if the residual mean remains at 10 kW (within the first threshold interval), but the standard deviation rises to 18 kW (exceeding the upper limit of the second threshold 15 kW), and the coefficient of variation is 0.2 (within the third threshold interval), it is determined to be a steady-state fluctuation abnormal point, indicating that the system has periodic fluctuations. If the residual mean of the adjacent two sampling points jumps from -10 kW to 15 kW (change rate 25 kW / h, exceeding the preset change rate 20 kW / h), and the coefficient of variation drops to 0.08 (lower than the lower limit of the third threshold 0.1), it is determined to be a mutation abnormal point, indicating a sudden disturbance (such as valve failure).

[0052] Actual application scenario: On a certain day, the residual sequence mean is 18 kW (normal), the standard deviation is 4 kW (normal), but the coefficient of variation suddenly drops to 0.05 (abnormally low), so combined with the change rate exceeding the limit, the system is marked as a mutation abnormal point, triggering an emergency check to see if the water pump is jammed.

[0053] S3.5: Count and distribute the number of abnormal points within a preset time, and when the number of abnormal points exceeds a preset number threshold or consecutive abnormal points appear, mark the current heat exchange station operation state as abnormal.

[0054] In an optional implementation, when one of the following situations occurs within 24 hours, the operation state is identified as an abnormal state: regulation insensitive abnormal points occur in 3 consecutive sampling periods; over-regulation abnormal points occur in 2 consecutive sampling periods; the cumulative number of steady-state fluctuation abnormal points within any 4 hours exceeds 35% of the total number of sampling points; after the occurrence of regulation divergence abnormal points, the residual mean of the next sampling period does not return to the first threshold interval; after the occurrence of mutation abnormal points, the coefficient of variation of the next 2 sampling periods continuously drops below the lower limit of the third threshold interval.

[0055] In an optional embodiment, when the following conditions are met within 24 hours, the running state identifier is set to the normal state: the residual sequence mean is always within the first threshold interval; the standard deviation fluctuation range does not exceed 80% of the second threshold interval; the maximum value of the coefficient of variation does not exceed 90% of the upper limit of the third threshold interval; the single duration of any type of abnormal point does not exceed 2 sampling periods; and the proportion of the total number of abnormal points within 24 hours to the total number of sampling points is less than 20%.

[0056] S4: Based on the running state identifier, the water pump frequency parameter and the water mixing valve opening parameter are adjusted in linkage, and an updated water pump adjustment instruction and a valve adjustment instruction are generated by calling an optimized control strategy set.

[0057] S4.1: The current water pump frequency parameter and water mixing valve opening parameter are read from the heat exchange station control system, and a corresponding optimized control strategy set is selected according to the running state identifier, wherein the optimized control strategy set includes a normal condition strategy and an abnormal condition strategy.

[0058] S4.2: When the running state identifier is the normal state, the water pump frequency adjustment interval and the water mixing valve opening adjustment interval are divided according to the normal condition strategy, and the current parameters are substituted into the normal condition strategy to calculate the target adjustment amount.

[0059] Preferably, the optimized control strategy set is pre-established according to historical operation data, typical condition response law and energy consumption constraint conditions, and is divided into a normal condition strategy and an abnormal condition strategy.

[0060] Specifically, the normal condition strategy mainly includes a steady state control strategy, a smooth transition strategy and an energy efficiency optimization strategy; the steady state control strategy: limits the water pump frequency adjustment interval to 40%~80% of the rated frequency, controls the water mixing valve opening adjustment interval to 20%~80%, limits the single adjustment step (frequency ≤ 2 Hz, opening ≤ 5%), and sets the minimum response time to be not less than 300 seconds; the smooth transition strategy: adopts a gradient adjustment mode, the coordinated change ratio of the water pump frequency and the valve opening is maintained at 2:1, and a dead zone is set when the residual is less than 5% to avoid adjustment; the energy efficiency optimization strategy: prioritizes adjusting the water mixing valve opening under the premise of meeting the heating requirement, maintains the minimum feasible water pump frequency, and avoids frequent small adjustments to achieve stable operation and energy saving of the system.

[0061] Further, the abnormal condition strategy includes a fast response strategy, an abnormality handling strategy and a safety protection strategy; the fast response strategy: expands the water pump frequency adjustment interval to 0~100%, and the water mixing valve opening adjustment interval to 0~100%; the abnormality handling strategy: when the running state identifier is the abnormal state, the water pump frequency adjustment interval is set to 0~100%, the water mixing valve opening adjustment interval is set to 0~100%, and the single duration of any type of abnormal point is limited to not more than 2 sampling periods; the safety protection strategy: when the running state identifier is the abnormal state, the water pump frequency adjustment interval is set to 0~100%, the water mixing valve opening adjustment interval is set to 0~100%, and the proportion of the total number of abnormal points within 24 hours to the total number of sampling points is limited to be less than 20%. , the water mixing valve opening degree regulation interval is adjusted to [10%, 90%], the single regulation step is increased (frequency is less than or equal to 5 Hz, and opening degree is less than or equal to 10%), the response time is shortened to not less than 120 seconds, the abnormal processing strategy is that, when the regulation is not sensitive, the regulation step is increased and the response time is shortened, when the regulation is over-regulated, reverse compensation regulation is adopted and the regulation step is reduced, when the steady fluctuation is increased, the regulation dead zone is increased and the response time is prolonged, and when the mutation abnormality is started, the emergency regulation mode is started, and the safety protection strategy is that, the upper and lower limit hard constraints are set, the anti-oscillation protection mechanism is introduced, the regulation divergence judgment and rollback mechanism is established, and the safe and reliable operation of the system under abnormal conditions is ensured.

[0062] S4.3: When the running state identifier is an abnormal state, the abnormal condition strategy is started, the regulation direction is determined according to the fluctuation trend of the residual sequence, and the water pump frequency regulation interval and the water mixing valve opening degree regulation interval are expanded.

[0063] S4.4: The water pump regulation instruction and the valve regulation instruction are generated according to the target regulation quantity, wherein the water pump regulation instruction includes a target frequency value; and the valve regulation instruction includes a target opening value.

[0064] Specifically, it includes: S4.4.1: The target regulation quantity is input into the regulation quantity conversion formula to calculate the water pump target frequency regulation value and the valve target opening regulation value. Preferably, the specific formula of the water pump target frequency regulation value and the valve target opening regulation value is as follows:

[0065] ; Wherein, the water pump target frequency regulation value is f, the current reference frequency value is f0, the frequency regulation coefficient is k f, and the value is 0.1-0.5, the heat load deviation amplification coefficient is k h, the heat load residual value is h, the temperature change rate influence coefficient is k t, the outdoor temperature is T, the valve target opening regulation value is u, the current reference opening value is u0, the opening degree regulation coefficient is k u, and the value is 0.1-0.3, the opening degree regulation sensitivity coefficient is k u.

[0066] S4.4.2: The current water pump frequency and valve opening value are read, the water pump target frequency regulation value is superimposed with the current water pump frequency value to obtain the water pump target frequency value, and the valve target opening regulation value is superimposed with the current valve opening value to obtain the valve target opening value. In an optional embodiment, a frequency limit condition is applied to the water pump target frequency value: if the water pump target frequency value is less than the minimum frequency value, the water pump target frequency value is set to the minimum frequency value; if the water pump target frequency value is greater than the maximum frequency value, the water pump target frequency value is set to the maximum frequency value.

[0067] In an optional embodiment, an opening limit condition is applied to the valve target opening value: if the valve target opening value is less than the minimum opening value, the valve target opening value is set to the minimum opening value; if the valve target opening value is greater than the maximum opening value, the valve target opening value is set to the maximum opening value.

[0068] S4.4.5: generating a water pump adjustment instruction according to the water pump target frequency value, wherein the water pump adjustment instruction comprises a device type identifier, the water pump target frequency value, an adjustment timestamp, and an adjustment direction identifier; S4.4.6: generating a valve adjustment instruction according to the valve target opening value, wherein the valve adjustment instruction comprises a device type identifier, the valve target opening value, an adjustment timestamp, and an adjustment direction identifier.

[0069] S4.5: sending the water pump adjustment instruction to the frequency converter, sending the valve adjustment instruction to the actuator, and recording the adjustment result in the historical database.

[0070] In summary, the present application ensures the comprehensive sensing ability of the system to the operation environment of the heat exchange station by collecting multi-source environmental parameter data and constructing an environmental parameter sequence set; realizes accurate prediction of heat load changes through a heat load prediction model constructed by a support vector regression algorithm and a local temperature trend correction factor; can accurately identify various abnormal states such as non-sensitive adjustment, over-adjustment, steady-state fluctuation, and mutation based on residual analysis and multi-dimensional state discrimination mechanism of actual heat load and predicted heat load, thereby improving the fault diagnosis capability; realizes efficient and stable operation of the heat exchange station through the linkage adjustment of the water pump frequency parameter and the water mixing valve opening parameter and the optimization control strategy set for different operation states; the present application not only significantly improves the operation reliability and energy utilization efficiency of the heat exchange station, but also realizes truly unattended intelligent control, solves the technical problems of low prediction accuracy, inaccurate abnormal identification, and poor control parameter coordination in the traditional control method, and provides an effective solution for the intelligent upgrading of the heating system.

[0071] Embodiment 2 With reference to Figure 2 For the second embodiment of the present application, the embodiment provides an unattended intelligent control system for a heat exchange station, comprising: an environmental parameter acquisition and sequence construction module configured to acquire multi-source environmental parameter data and construct an environmental parameter sequence set; The heat load prediction model construction module constructs a heat load prediction model based on the set of environmental parameter sequences, extracts a local temperature trend feature and a time sequence change relationship, and outputs a predicted heat load value. The residual error analysis and state discrimination module compares an actual heat load value and a predicted heat load value, constructs a residual error fluctuation degree sequence, discriminates a multi-dimensional state in combination with a preset threshold range, and outputs a running state identifier. The optimized control strategy execution module adjusts a water pump frequency parameter and a water mixing valve opening degree parameter in linkage based on the running state identifier, and calls an optimized control strategy set to generate updated water pump adjustment instructions and valve adjustment instructions.

[0072] It should be noted that the technical scheme of the heat exchange station unattended intelligent control system and the technical scheme of the heat exchange station unattended intelligent control method described above belong to the same concept. The details of the technical scheme of the heat exchange station unattended intelligent control system in this embodiment are not described in detail, and can be referred to the description of the technical scheme of the heat exchange station unattended intelligent control system.

[0073] The above-mentioned unit modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to call and execute the operations corresponding to the above-mentioned modules by the processor.

[0074] The embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, operator network, NFC (near field communication) or other technologies. The computer program is executed by the processor to implement a multi-task edge computing resource scheduling method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. It can also be an external keyboard, touchpad or mouse, etc.

[0075] The embodiment also provides a computer readable storage medium having a computer program stored thereon, which is executed by the processor to implement the method proposed in the above-mentioned embodiment.

[0076] The storage medium proposed in the embodiment belongs to the same inventive concept as the method proposed in the above embodiment, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary universal hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, etc., including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of the embodiments of the present application.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

[0079] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages.

[0080] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions described in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 an apparatus with a means for performing the function specified by one or more processes and / or blocks

[0081] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 one or more processes and / or blocks Figure 1 one or more processes and / or blocks

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 one or more processes and / or blocks Figure 1 one or more processes and / or blocks

[0083] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those of skill in the art once they have the benefit of the present disclosure without departing from the spirit and scope of the application. Accordingly, the appended claims are intended to encompass within their scope all such variations and modifications as are within the scope of the application.

[0084] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. An unattended intelligent control method for a heat exchange station, characterized in that: Comprising, Collecting multi-source environmental parameter data, and constructing an environmental parameter sequence set; Based on the environmental parameter sequence set, a heat load prediction model is constructed, and by extracting the local temperature trend characteristics and the time series change relationship, the predicted heat load value is output; Residual comparison is performed between the actual heat load value and the predicted heat load value to construct a residual fluctuation degree sequence, and multi-dimensional state discrimination is performed in combination with a pre-set threshold range to output a running state identifier; Based on the running state identifier, the water pump frequency parameter and the water mixing valve opening parameter are linked and adjusted, and an optimized control strategy set is called to generate updated water pump adjustment instructions and valve adjustment instructions.

2. The unattended intelligent control method of the heat exchange station according to claim 1, characterized in that: Based on the running state identifier, the water pump frequency parameter and the water mixing valve opening parameter are linked and adjusted, and an optimized control strategy set is called to generate updated water pump adjustment instructions and valve adjustment instructions, comprising: Read the current water pump frequency parameter and water mixing valve opening parameter from the heat exchange station control system, and select the corresponding optimized control strategy set according to the running state identifier, wherein the optimized control strategy set includes normal working condition strategy and abnormal working condition strategy; When the running state identifier is normal, the water pump frequency adjustment interval and the water mixing valve opening adjustment interval are divided according to the normal working condition strategy, and the current parameters are substituted into the normal working condition strategy to calculate the target adjustment amount; When the running state identifier is abnormal, the abnormal working condition strategy is started, the adjustment direction is determined according to the fluctuation trend of the residual sequence, and the range of the water pump frequency adjustment interval and the water mixing valve opening adjustment interval is expanded; According to the target adjustment amount, water pump adjustment instructions and valve adjustment instructions are generated, wherein the water pump adjustment instructions include a target frequency value; the valve adjustment instructions include a target opening value; The water pump adjustment instructions are sent to the frequency converter, and the valve adjustment instructions are sent to the actuator, and the adjustment results are recorded in the historical database.

3. The unattended intelligent control method of the heat exchange station according to claim 2, characterized in that: The method for obtaining the running state identifier is, Calculate the actual heat load value according to the primary supply and return water temperature difference, the secondary supply and return water temperature difference, and the heat exchange station flow at the current time, and record the difference between the actual heat load value and the predicted heat load value as a residual value; Arrange the residual value in time sequence to form a residual sequence, calculate the mean, standard deviation and coefficient of variation of the residual sequence to obtain residual statistical characteristic values; Establish a first threshold interval, a second threshold interval and a third threshold interval according to pre-labeled normal operation data; Compare the residual statistical characteristic values with the first threshold interval, the second threshold interval and the third threshold interval to mark abnormal points; When the number of abnormal points exceeds the pre-set number threshold or continuous abnormal points occur, the current heat exchange station running state is marked as abnormal.

4. The unattended intelligent control method of heat exchange station according to claim 3, characterized in that: The comparison of the residual statistical characteristic values with the first threshold interval, the second threshold interval and the third threshold interval comprises: If the mean of the residual sequence is located in the first threshold interval and the standard deviation is less than the lower limit of the second threshold interval, it is determined as a regulation-insensitive abnormal point; If the residual sequence mean exceeds the upper limit of the first threshold interval and the coefficient of variation exceeds the upper limit of the third threshold interval, it is determined as an over-regulation abnormal point; If the residual sequence mean is within the first threshold interval, but the standard deviation exceeds the upper limit of the second threshold interval and the coefficient of variation is within the third threshold interval, it is determined as a steady-state fluctuation abnormal point; If the rate of change of the residual sequence mean exceeds the preset rate of change and the coefficient of variation is below the lower limit of the third threshold interval, it is determined as a mutation abnormal point.

5. The unattended intelligent control method of heat exchange station according to claim 3, characterized in that: The method for obtaining the predicted heat load value is, The environmental parameter sequence set is divided into a plurality of training sample subsets according to a preset time window; The primary and secondary water supply and return water temperatures in the training sample subsets are subjected to difference operation to obtain primary and secondary temperature differences, and the primary and secondary temperature differences and the outdoor temperature at the corresponding time are combined to form a temperature feature vector; The temperature feature vector is associated and paired with the water pump frequency and heat network pressure at the corresponding time to construct an input feature matrix, and the historical heat load data at the corresponding time is taken as an output label; A support vector regression algorithm is used to train the input feature matrix and the output label to obtain kernel function parameters and slack variables of the heat load prediction model; A local temperature trend correction factor is introduced, the preliminary predicted value is weighted and corrected according to the outdoor temperature rate of change, and a predicted heat load value is generated.

6. The unattended intelligent control method of heat exchange station according to claim 5, characterized in that: The method for constructing the heat load prediction model is, The input feature matrix is subjected to normalization processing, and the environmental parameter sequence set is mapped to the [0, 1] interval to obtain a standardized feature matrix; A support vector regression model is constructed using a radial basis kernel function, a kernel function is defined, and a heat load prediction model is formed; Based on the standardized feature matrix and the output label, an ε-insensitive loss function is used to establish a regression objective function and a constraint condition; The regression objective function is solved by a sequential minimal optimization algorithm to obtain optimal kernel width parameters and a penalty factor, and optimal hyperplane parameters of the heat load prediction model are determined.

7. The unattended intelligent control method of heat exchange station according to claim 5, characterized in that: The environmental parameter sequence set includes primary and secondary water supply and return water temperatures, water pump frequency, outdoor temperature, heat network pressure, and historical heat load data.

8. An unattended intelligent control system for a heat exchange station, based on the unattended intelligent control method for a heat exchange station according to any one of claims 1 to 7, characterized in that: It comprises, An environmental parameter acquisition and sequence construction module is configured to acquire multi-source environmental parameter data and construct an environmental parameter sequence set; A heat load prediction model construction module is configured to construct a heat load prediction model based on the environmental parameter sequence set, extract local temperature trend features and time sequence change relationships, and output a predicted heat load value; A residual analysis and state discrimination module is configured to compare an actual heat load value with the predicted heat load value, construct a residual fluctuation metric sequence, and perform multi-dimensional state discrimination in combination with a preset threshold range to output an operating state identifier; An optimized control strategy execution module is configured to adjust water pump frequency parameters and water mixing valve opening degree parameters in linkage based on the operating state identifier, and call an optimized control strategy set to generate updated water pump adjustment instructions and valve adjustment instructions. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to implement the steps of the heat exchange station unattended intelligent control method according to any one of claims 1-7. The processor executes the computer program to implement the steps of the heat exchange station unattended intelligent control method according to any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by a processor to realize the steps of the heat exchange station unattended intelligent control method according to any one of claims 1-7.

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