A heat station circulating pump control method and system

Abstract

The application provides a heat station circulating pump control method and system, and belongs to the technical field of heat stations, and specifically comprises the following steps: determining the use failure probability of the circulating pump by using a prediction model based on a PSO-ELM neural network algorithm through the operation data of the circulating pump; determining the pipe network operation failure probability of the circulating pump by using a prediction model based on a GRU algorithm through the operation data of the primary network and the secondary network of the heat station, and determining the failure probability of the circulating pump according to the pipe network operation failure probability and the use problem probability of the circulating pump; determining the adjustment frequency and the adjustment range of the circulating pump according to the failure probability, the operation power of the circulating pump and the pipe network heat loss of the secondary network of the heat station; and controlling the circulating pump according to the adjustment frequency and the adjustment range of the circulating pump, so that the safety and reliability of the control of the circulating pump of the heat station are ensured.

Description

Technical Field

[0001] This invention belongs to the field of circulating pump control technology, and particularly relates to a circulating pump control method and system for a heating station. Background Technology

[0002] As the intermediate link between the primary and secondary networks of a heating system, the safety and reliability of the heating station's operation are crucial to the overall heating system's reliability. Similarly, the circulation pump, as a key device for flow regulation in the secondary network, is also critical to the overall safety of the heating system. Therefore, achieving status sensing and control of the circulation pump has become an urgent technical problem to be solved.

[0003] To achieve status perception and control of circulating pumps, existing technologies often rely on monitoring the flow rate of the secondary network and the supply and return water temperatures of the primary network to perceive and control the pump's operating status. Specifically, the invention patent "Method, Device, Terminal and Storage Medium for Controlling Circulating Pumps in Heating Stations" uses real-time monitoring data, historical monitoring data, and fault judgment conditions from the primary network to determine the probability of pump failure, thereby controlling the pump's operating status. However, it fails to consider combining the pump's failure probability with the adjustment needs of the secondary network to determine the pump's adjustment frequency and amplitude. When there is a high probability of failure, frequent adjustments may increase the likelihood of pump failure. Therefore, without combining the pump's failure probability with the adjustment needs of the secondary network to determine the adjustment frequency and amplitude, the safety and reliability of the circulating pump's operation cannot be guaranteed.

[0004] To address the aforementioned technical problems, this invention provides a method and system for controlling a circulating pump in a heating station. Summary of the Invention

[0005] According to one aspect of the present invention, a method for controlling a circulating pump in a heating station is provided.

[0006] A method for controlling a circulating pump in a heating station, characterized in that it specifically includes:

[0007] S11 acquires historical heating data of the heating area of ​​the heating station, and combines the predicted meteorological data of the heating area with the heating capacity of the heating station to determine the change in heating load of the heating station within a set time in the future. If the change in heating load is greater than a preset value, proceed to the next step.

[0008] S12 acquires the operating data of the circulating pump of the heating station, determines the cumulative number of adjustments and the number of adjustments at different adjustment ranges based on the operating data of the circulating pump, and, in conjunction with the working time of the circulating pump under abnormal operating conditions, uses a prediction model based on the PSO-ELM neural network algorithm to determine the probability of failure of the circulating pump.

[0009] S13 acquires the operating data of the primary and secondary networks of the heating station, and uses a prediction model based on the GRU algorithm to determine the probability of network operation failure of the circulating pump based on the operating data of the primary and secondary networks of the heating station. Based on the probability of network operation failure of the circulating pump and the probability of usage problems, the failure probability of the circulating pump is determined. If the failure probability of the circulating pump does not meet the requirements, proceed to the next step.

[0010] S14 determines the number of adjustments and adjustment range of the circulating pump based on the failure probability and operating power of the circulating pump and the heat loss of the secondary network of the heating station, and controls the circulating pump based on the number of adjustments and adjustment range of the circulating pump.

[0011] A further technical solution is that the historical heating data of the heating area includes, but is not limited to, the heating data of the heating area during the same period of the most recent week and the heating data of the most recent day.

[0012] A further technical solution is that the predicted meteorological data includes, but is not limited to, solar thermal radiation, ambient temperature, and wind speed.

[0013] A further technical solution is that the method for determining the variation in heating load is as follows:

[0014] S21 uses the current time and historical heating data of the heating area to obtain the historical heating data of the heating station for the most recent week within a set future time, and uses the historical heating data of the heating station for the most recent week within a set future time to determine the future demand baseline load of the heating station.

[0015] S22 determines the deviation between the predicted meteorological data of the heating area and the meteorological data of the most recent week within a set future time period based on the predicted meteorological data of the heating area, and determines the historical similar days and historical deviation days of the most recent week based on the deviation, and determines whether the future demand baseline load of the heating station meets the demand based on the number of historical deviation days and historical similar days. If yes, the change in the heating load of the heating station within a set future time period is determined by the future demand baseline load of the heating station and the heating capacity of the heating station. If no, proceed to the next step.

[0016] S23 determines the correction amount for the future demand load of the heating station by the number and deviation of the historical deviation days and the number and deviation of the historical similar days in the heating area;

[0017] S24 determines the future demand load of the heating station by using the correction amount of the future demand load of the heating station, the future baseline demand load, and the predicted weather data of the heating area, and determines the change in the heating load of the heating station within a set future time by using the future demand load and the heating capacity of the heating station.

[0018] A further technical solution is that the preset value is determined based on the heating area of ​​the heating zone of the heating station, the number of heating users, and the heat output of the heating station. The larger the heating area of ​​the heating zone of the heating station, the more heating users, and the more heat output of the heating station, the larger the preset value.

[0019] A further technical solution involves determining the failure probability of the circulating pump as follows:

[0020] S31 determines the service life of the circulating pump based on the operating data of the circulating pump, and determines whether it is necessary to determine the failure probability of the circulating pump based on the service life of the circulating pump. If not, the failure probability of the circulating pump is determined to be 0. If not, proceed to the next step.

[0021] S32 determines the overload operating time of the circulating pump based on its operating data, and uses this overload operating time as the operating time under abnormal conditions. It then combines this overload operating time with the operating power of the circulating pump during this abnormal operating time to determine the historical operating status value of the circulating pump under abnormal conditions. Based on the historical overload operating status value of the circulating pump, it determines whether further evaluation of the probability of failure of the circulating pump is needed. If yes, proceed to step S34; otherwise, proceed to step S33.

[0022] S33 determines the cumulative number of adjustments to the circulating pump based on its operating data, and determines the historical adjustment status value of the circulating pump by combining the number of adjustments at different adjustment ranges. Based on the historical adjustment status value of the circulating pump, it is determined whether further evaluation of the probability of failure of the circulating pump is needed. If so, the probability of failure of the circulating pump is determined by the historical overload operation status value of the circulating pump. If not, proceed to step S34.

[0023] S34 uses the historical overload operating status value, historical adjustment status value, and operating years of the circulating pump to determine the failure probability of the circulating pump by employing a prediction model based on the PSO-ELM neural network algorithm.

[0024] A further technical solution involves determining whether it is necessary to determine the probability of failure of the circulating pump based on its service life, specifically including:

[0025] When the service life of the circulating pump exceeds the set service life, it is determined that the probability of failure of the circulating pump needs to be determined.

[0026] When the service life of the circulating pump is not greater than the set service life, the probability of failure of the circulating pump is determined by the service life of the circulating pump and the working time under abnormal conditions.

[0027] A further technical solution involves determining the probability of pipeline network operation failure of the circulating pump as follows:

[0028] S41 determines the return water pressure and temperature of the primary network using the operating data of the primary network of the heating station, and determines the supply water pressure and temperature of the secondary network using the operating data of the secondary network of the heating station. The deviation between the return water pressure of the secondary network and the return water pressure of the primary network is used to determine whether the circulating pump has any suspected abnormalities. If yes, proceed to step S43; otherwise, proceed to step S42.

[0029] S42 determines whether the circulating pump has any suspected abnormality based on the deviation between the water supply temperature of the secondary network and the return water temperature of the primary network. If yes, proceed to step S43; otherwise, determine that the probability of the circulating pump malfunction is zero.

[0030] S43 obtains the heating area of ​​the heating station and the length of the heating pipeline network, and combines the deviation between the return water pressure of the secondary network and the return water pressure of the primary network, and the deviation between the supply water temperature of the secondary network and the return water temperature of the primary network, and uses a prediction model based on the GRU algorithm to determine the probability of pipeline operation failure of the circulating pump.

[0031] A further technical solution involves determining the failure probability of the circulating pump as follows:

[0032] Determine whether the probability of pipeline network operation failure and the probability of usage problem of the circulating pump are both greater than the preset failure probability threshold. If so, determine the failure probability of the circulating pump by the average of the probability of pipeline network operation failure and the probability of usage problem. If not, proceed to the next step.

[0033] Determine whether the probability of pipeline network operation failure or usage problem of the circulating pump is greater than a preset failure probability threshold. If so, determine the failure probability of the circulating pump by the maximum value of the probability of pipeline network operation failure and the probability of usage problem. If not, proceed to the next step.

[0034] The failure probability of the circulating pump is determined by the minimum value of the probability of pipeline network failure and the probability of usage problems.

[0035] A further technical solution involves determining the number of adjustments and the adjustment range of the circulating pump as follows:

[0036] The heating load adjustment amount of the heating station is determined based on the heat loss of the secondary network and the change in heating load.

[0037] The maximum operating load of the circulating pump and the maximum number of adjustments within a set time are determined based on the failure probability of the circulating pump in the heating station.

[0038] The operating reliability function of the circulating pump is determined by taking the maximum operating load of the circulating pump, the maximum number of adjustments within a set time, and the adjustment amount of the heating load of the heating station as constraints, the operating power of the circulating pump as a constraint, the operating power of the circulating pump and the number of adjustments as constraints, and the number of adjustments of the circulating pump and the adjustment range of different number of adjustments as the target of maximizing the operating reliability function.

[0039] On the other hand, this application provides a heating station circulating pump control system, which employs the above-mentioned heating station circulating pump control method, specifically including:

[0040] The load variation determination module, the fault probability determination module, and the circulating pump control module, wherein the fault probability determination module includes using the fault probability determination module and operating the fault probability determination module.

[0041] The load variation determination module is responsible for acquiring historical heating data of the heating area of ​​the heating station, and combining the predicted meteorological data of the heating area with the heating capacity of the heating station to determine the amount of heating load variation of the heating station within a set time in the future.

[0042] The failure probability determination module is responsible for acquiring the operating data of the circulating pump of the heating station, determining the cumulative number of adjustments and the number of adjustments at different adjustment ranges based on the operating data of the circulating pump, and combining the working time of the circulating pump under abnormal operating conditions, using a prediction model based on the PSO-ELM neural network algorithm to determine the failure probability of the circulating pump.

[0043] The operation failure probability determination module is responsible for acquiring the operation data of the primary and secondary networks of the heating station, and using a prediction model based on the GRU algorithm to determine the network operation failure probability of the circulating pump based on the network operation failure probability of the circulating pump and the failure probability of the circulating pump based on the network operation failure probability of the circulating pump and the probability of usage problems.

[0044] The circulating pump control module is responsible for determining the number of adjustments and the adjustment range of the circulating pump based on the failure probability and operating power of the circulating pump and the heat loss of the secondary network of the heating station, and controlling the circulating pump according to the number of adjustments and the adjustment range.

[0045] On the other hand, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed in a computer, causes the computer to execute the above-described method for controlling a circulating pump in a thermal power station.

[0046] On the other hand, this application provides a computer program product, characterized in that the computer program product stores instructions, which, when executed by a computer, cause the computer to implement the above-described method for controlling a circulating pump in a thermal power station.

[0047] The beneficial effects of this invention are as follows:

[0048] By determining the variation in heating load at the heating station within a set future timeframe, the probability of circulating pump failure can be determined based on the variation in heating load, thus avoiding the problem of excessive system processing pressure caused by frequent failure probability assessments.

[0049] By combining the operating data of the circulating pump, the failure probability of the circulating pump is determined, thereby realizing the determination of failure probability from the historical operating data of the circulating pump and ensuring the comprehensiveness of the failure probability judgment.

[0050] By combining the operating data of the primary and secondary networks of the heating station, the probability of pipeline operation failure of the circulating pump is determined, thereby realizing the determination of the probability of operation failure of the circulating pump from the real-time operating data of the pipeline network, and realizing the determination of the failure probability from another perspective.

[0051] By combining the failure probability and operating conditions of the circulating pump, the number of adjustments and the adjustment range of the circulating pump can be determined, which ensures both the safety and reliability of the circulating pump adjustment and the heating demand of the heating station. Attached Figure Description

[0052] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0053] Figure 1 This is a flowchart of a heat station circulating pump control method according to Embodiment 1. Detailed Implementation

[0054] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0055] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc. Example

[0056] To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a method for controlling a circulating pump in a heating station is provided, characterized by specifically including:

[0057] S11 acquires historical heating data of the heating area of ​​the heating station, and combines the predicted meteorological data of the heating area with the heating capacity of the heating station to determine the change in heating load of the heating station within a set time in the future. If the change in heating load is greater than a preset value, proceed to the next step.

[0058] S12 acquires the operating data of the circulating pump of the heating station, determines the cumulative number of adjustments and the number of adjustments at different adjustment ranges based on the operating data of the circulating pump, and, in conjunction with the working time of the circulating pump under abnormal operating conditions, uses a prediction model based on the PSO-ELM neural network algorithm to determine the probability of failure of the circulating pump.

[0059] S13 acquires the operating data of the primary and secondary networks of the heating station, and uses a prediction model based on the GRU algorithm to determine the probability of network operation failure of the circulating pump based on the operating data of the primary and secondary networks of the heating station. Based on the probability of network operation failure of the circulating pump and the probability of usage problems, the failure probability of the circulating pump is determined. If the failure probability of the circulating pump does not meet the requirements, proceed to the next step.

[0060] S14 determines the number of adjustments and adjustment range of the circulating pump based on the failure probability and operating power of the circulating pump and the heat loss of the secondary network of the heating station, and controls the circulating pump based on the number of adjustments and adjustment range of the circulating pump.

[0061] A further technical solution is that the historical heating data of the heating area includes, but is not limited to, the heating data of the heating area during the same period of the most recent week and the heating data of the most recent day.

[0062] A further technical solution is that the predicted meteorological data includes, but is not limited to, solar thermal radiation, ambient temperature, and wind speed.

[0063] A further technical solution is that the method for determining the variation in heating load is as follows:

[0064] S21 uses the current time and historical heating data of the heating area to obtain the historical heating data of the heating station for the most recent week within a set future time, and uses the historical heating data of the heating station for the most recent week within a set future time to determine the future demand baseline load of the heating station.

[0065] S22 determines the deviation between the predicted meteorological data of the heating area and the meteorological data of the most recent week within a set future time period based on the predicted meteorological data of the heating area, and determines the historical similar days and historical deviation days of the most recent week based on the deviation, and determines whether the future demand baseline load of the heating station meets the demand based on the number of historical deviation days and historical similar days. If yes, the change in the heating load of the heating station within a set future time period is determined by the future demand baseline load of the heating station and the heating capacity of the heating station. If no, proceed to the next step.

[0066] S23 determines the correction amount for the future demand load of the heating station by the number and deviation of the historical deviation days and the number and deviation of the historical similar days in the heating area;

[0067] S24 determines the future demand load of the heating station by using the correction amount of the future demand load of the heating station, the future baseline demand load, and the predicted weather data of the heating area, and determines the change in the heating load of the heating station within a set future time by using the future demand load and the heating capacity of the heating station.

[0068] A further technical solution is that the preset value is determined based on the heating area of ​​the heating zone of the heating station, the number of heating users, and the heat output of the heating station. The larger the heating area of ​​the heating zone of the heating station, the more heating users, and the more heat output of the heating station, the larger the preset value.

[0069] A further technical solution involves determining the failure probability of the circulating pump as follows:

[0070] S31 determines the service life of the circulating pump based on the operating data of the circulating pump, and determines whether it is necessary to determine the failure probability of the circulating pump based on the service life of the circulating pump. If not, the failure probability of the circulating pump is determined to be 0. If not, proceed to the next step.

[0071] S32 determines the overload operating time of the circulating pump based on its operating data, and uses this overload operating time as the operating time under abnormal conditions. It then combines this overload operating time with the operating power of the circulating pump during this abnormal operating time to determine the historical operating status value of the circulating pump under abnormal conditions. Based on the historical overload operating status value of the circulating pump, it determines whether further evaluation of the probability of failure of the circulating pump is needed. If yes, proceed to step S34; otherwise, proceed to step S33.

[0072] S33 determines the cumulative number of adjustments to the circulating pump based on its operating data, and determines the historical adjustment status value of the circulating pump by combining the number of adjustments at different adjustment ranges. Based on the historical adjustment status value of the circulating pump, it is determined whether further evaluation of the probability of failure of the circulating pump is needed. If so, the probability of failure of the circulating pump is determined by the historical overload operation status value of the circulating pump. If not, proceed to step S34.

[0073] S34 uses the historical overload operating status value, historical adjustment status value, and operating years of the circulating pump to determine the failure probability of the circulating pump by employing a prediction model based on the PSO-ELM neural network algorithm.

[0074] A further technical solution involves determining whether it is necessary to determine the probability of failure of the circulating pump based on its service life, specifically including:

[0075] When the service life of the circulating pump exceeds the set service life, it is determined that the probability of failure of the circulating pump needs to be determined.

[0076] When the service life of the circulating pump is not greater than the set service life, the probability of failure of the circulating pump is determined by the service life of the circulating pump and the working time under abnormal conditions.

[0077] A further technical solution involves determining the probability of pipeline network operation failure of the circulating pump as follows:

[0078] S41 determines the return water pressure and temperature of the primary network using the operating data of the primary network of the heating station, and determines the supply water pressure and temperature of the secondary network using the operating data of the secondary network of the heating station. The deviation between the return water pressure of the secondary network and the return water pressure of the primary network is used to determine whether the circulating pump has any suspected abnormalities. If yes, proceed to step S43; otherwise, proceed to step S42.

[0079] S42 determines whether the circulating pump has any suspected abnormality based on the deviation between the water supply temperature of the secondary network and the return water temperature of the primary network. If yes, proceed to step S43; otherwise, determine that the probability of the circulating pump malfunction is zero.

[0080] S43 obtains the heating area of ​​the heating station and the length of the heating pipeline network, and combines the deviation between the return water pressure of the secondary network and the return water pressure of the primary network, and the deviation between the supply water temperature of the secondary network and the return water temperature of the primary network, and uses a prediction model based on the GRU algorithm to determine the probability of pipeline operation failure of the circulating pump.

[0081] A further technical solution involves determining the failure probability of the circulating pump as follows:

[0082] Determine whether the probability of pipeline network operation failure and the probability of usage problem of the circulating pump are both greater than the preset failure probability threshold. If so, determine the failure probability of the circulating pump by the average of the probability of pipeline network operation failure and the probability of usage problem. If not, proceed to the next step.

[0083] Determine whether the probability of pipeline network operation failure or usage problem of the circulating pump is greater than a preset failure probability threshold. If so, determine the failure probability of the circulating pump by the maximum value of the probability of pipeline network operation failure and the probability of usage problem. If not, proceed to the next step.

[0084] The failure probability of the circulating pump is determined by the minimum value of the probability of pipeline network failure and the probability of usage problems.

[0085] A further technical solution involves determining the number of adjustments and the adjustment range of the circulating pump as follows:

[0086] The heating load adjustment amount of the heating station is determined based on the heat loss of the secondary network and the change in heating load.

[0087] The maximum operating load of the circulating pump and the maximum number of adjustments within a set time are determined based on the failure probability of the circulating pump in the heating station.

[0088] The operating reliability function of the circulating pump is determined by taking the maximum operating load of the circulating pump, the maximum number of adjustments within a set time, and the adjustment amount of the heating load of the heating station as constraints, the operating power of the circulating pump as a constraint, the operating power of the circulating pump and the number of adjustments as constraints, and the number of adjustments of the circulating pump and the adjustment range of different number of adjustments as the target of maximizing the operating reliability function.

[0089] By determining the variation in heating load at the heating station within a set future timeframe, the probability of circulating pump failure can be determined based on the variation in heating load, thus avoiding the problem of excessive system processing pressure caused by frequent failure probability assessments.

[0090] By combining the operating data of the circulating pump, the failure probability of the circulating pump is determined, thereby realizing the determination of failure probability from the historical operating data of the circulating pump and ensuring the comprehensiveness of the failure probability judgment.

[0091] By combining the operating data of the primary and secondary networks of the heating station, the probability of pipeline operation failure of the circulating pump is determined, thereby realizing the determination of the probability of operation failure of the circulating pump from the real-time operating data of the pipeline network, and realizing the determination of the failure probability from another perspective.

[0092] By combining the failure probability and operating conditions of the circulating pump, the number of adjustments and the adjustment range of the circulating pump can be determined, which ensures both the safety and reliability of the circulating pump adjustment and the heating demand of the heating station. Example

[0093] On the other hand, this application provides a heating station circulating pump control system, which employs the above-mentioned heating station circulating pump control method, specifically including:

[0094] The load variation determination module, the fault probability determination module, and the circulating pump control module, wherein the fault probability determination module includes using the fault probability determination module and operating the fault probability determination module.

[0095] The load variation determination module is responsible for acquiring historical heating data of the heating area of ​​the heating station, and combining the predicted meteorological data of the heating area with the heating capacity of the heating station to determine the amount of heating load variation of the heating station within a set time in the future.

[0096] The failure probability determination module is responsible for acquiring the operating data of the circulating pump of the heating station, determining the cumulative number of adjustments and the number of adjustments at different adjustment ranges based on the operating data of the circulating pump, and combining the working time of the circulating pump under abnormal operating conditions, using a prediction model based on the PSO-ELM neural network algorithm to determine the failure probability of the circulating pump.

[0097] The operation failure probability determination module is responsible for acquiring the operation data of the primary and secondary networks of the heating station, and using a prediction model based on the GRU algorithm to determine the network operation failure probability of the circulating pump based on the network operation failure probability of the circulating pump and the failure probability of the circulating pump based on the network operation failure probability of the circulating pump and the probability of usage problems.

[0098] The circulating pump control module is responsible for determining the number of adjustments and the adjustment range of the circulating pump based on the failure probability and operating power of the circulating pump and the heat loss of the secondary network of the heating station, and controlling the circulating pump according to the number of adjustments and the adjustment range. Example

[0099] This application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed in a computer, it causes the computer to execute the above-described method for controlling a circulating pump in a thermal power station. Example

[0100] This application provides a computer program product, characterized in that the computer program product stores instructions, which, when executed by a computer, cause the computer to implement the above-described method for controlling a circulating pump in a thermal power station.

[0101] In this embodiment of the invention, the term "multiple" refers to two or more, unless otherwise explicitly defined. The terms "install," "connect," and "fix" should be interpreted broadly. For example, "connect" can mean a fixed connection, a detachable connection, or an integral connection. Those skilled in the art can understand the specific meaning of the above terms in this embodiment of the invention based on the specific circumstances.

[0102] In the description of the embodiments of the present invention, it should be understood that the terms "upper" and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or unit referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of the present invention.

[0103] In the description of this specification, the terms "an embodiment," "a preferred embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0104] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. For those skilled in the art, the embodiments of the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present invention should be included within the protection scope of the embodiments of the present invention.

Claims

1. A method for controlling a circulating pump in a heating station, characterized in that, Specifically, it includes: S11 acquires historical heating data of the heating area of ​​the heating station, and combines the predicted meteorological data of the heating area with the heating capacity of the heating station to determine the change in heating load of the heating station within a set time in the future. If the change in heating load is greater than a preset value, proceed to the next step. S12 acquires the operating data of the circulating pump of the heating station, determines the cumulative number of adjustments and the number of adjustments at different adjustment ranges based on the operating data of the circulating pump, and, in conjunction with the working time of the circulating pump under abnormal operating conditions, uses a prediction model based on the PSO-ELM neural network algorithm to determine the probability of failure of the circulating pump. S13 acquires the operating data of the primary and secondary networks of the heating station, and uses a prediction model based on the GRU algorithm to determine the probability of network operation failure of the circulating pump based on the operating data of the primary and secondary networks of the heating station. Based on the probability of network operation failure of the circulating pump and the probability of usage problems, the failure probability of the circulating pump is determined. If the failure probability of the circulating pump does not meet the requirements, proceed to the next step. S14 determines the number of adjustments and adjustment range of the circulating pump based on the failure probability and operating power of the circulating pump and the heat loss of the secondary network of the heating station, and controls the circulating pump based on the number of adjustments and adjustment range of the circulating pump.

2. The method for controlling a circulating pump in a heating station as described in claim 1, characterized in that, The historical heating data for the heating area includes heating data for the same period in the most recent week and heating data for the most recent day.

3. The method for controlling a circulating pump in a heating station as described in claim 1, characterized in that, The predicted meteorological data includes solar thermal radiation, ambient temperature, and wind speed.

4. The method for controlling a circulating pump in a heating station as described in claim 1, characterized in that, The method for determining the variation in heating load is as follows: S21 determines the historical heating data of the heating station for the most recent week within a set future time period based on the current time and the historical heating data of the heating area, and determines the future demand baseline load of the heating station based on the historical heating data of the heating station for the most recent week within a set future time period. S22 determines the deviation between the predicted meteorological data of the heating area and the meteorological data of the most recent week within a set future time period based on the predicted meteorological data of the heating area, and determines the historical similar days and historical deviation days of the most recent week based on the deviation, and determines whether the future demand baseline load of the heating station meets the demand based on the number of historical deviation days and historical similar days. If yes, the change in the heating load of the heating station within a set future time period is determined by the future demand baseline load of the heating station and the heating capacity of the heating station. If no, proceed to the next step. S23 determines the correction amount for the future demand load of the heating station by the number and deviation of the historical deviation days and the number and deviation of the historical similar days in the heating area; S24 determines the future demand load of the heating station by using the correction amount of the future demand load of the heating station, the future baseline demand load, and the predicted weather data of the heating area, and determines the change in the heating load of the heating station within a set future time by using the future demand load and the heating capacity of the heating station.

5. The method for controlling a circulating pump in a heating station as described in claim 4, characterized in that, The preset value is determined based on the heating area of ​​the heating station's heating region, the number of heating users, and the heating capacity of the heating station. The larger the heating area of ​​the heating station's heating region, the more heating users, and the more heating capacity the heating station provides, the larger the preset value will be.

6. The method for controlling a circulating pump in a heating station as described in claim 1, characterized in that, The method for determining the failure probability of the circulating pump is as follows: S31 determines the service life of the circulating pump based on the operating data of the circulating pump, and determines whether it is necessary to determine the failure probability of the circulating pump based on the service life of the circulating pump. If not, the failure probability of the circulating pump is determined to be 0. If yes, proceed to the next step. S32 determines the overload operating time of the circulating pump based on its operating data, and uses this overload operating time as the operating time under abnormal conditions. It then combines this overload operating time with the operating power of the circulating pump during this abnormal operating time to determine the historical operating status value of the circulating pump under abnormal conditions. Based on the historical overload operating status value of the circulating pump, it determines whether further evaluation of the probability of failure of the circulating pump is needed. If yes, proceed to step S34; otherwise, proceed to step S33. S33 determines the cumulative number of adjustments to the circulating pump based on its operating data, and determines the historical adjustment status value of the circulating pump by combining the number of adjustments at different adjustment ranges. Based on the historical adjustment status value of the circulating pump, it is determined whether further evaluation of the probability of failure of the circulating pump is needed. If so, the probability of failure of the circulating pump is determined by the historical overload operation status value of the circulating pump. If not, proceed to step S34. S34 uses the historical overload operating status value, historical adjustment status value, and operating years of the circulating pump to determine the failure probability of the circulating pump by employing a prediction model based on the PSO-ELM neural network algorithm.

7. The method for controlling a circulating pump in a heating station as described in claim 6, characterized in that, Determining whether the probability of failure of the circulating pump needs to be determined based on its service life includes: When the service life of the circulating pump exceeds the set service life, it is determined that the probability of failure of the circulating pump needs to be determined. When the service life of the circulating pump is not greater than the set service life, the probability of failure of the circulating pump is determined by the service life of the circulating pump and the working time under abnormal conditions.

8. The method for controlling a circulating pump in a heating station as described in claim 1, characterized in that, The method for determining the probability of pipeline network failure of the circulating pump is as follows: S41 determines the return water pressure and temperature of the primary network using the operating data of the primary network of the heating station, and determines the supply water pressure and temperature of the secondary network using the operating data of the secondary network of the heating station. The deviation between the return water pressure of the secondary network and the return water pressure of the primary network is used to determine whether the circulating pump has any suspected abnormalities. If yes, proceed to step S43; otherwise, proceed to step S42. S42 determines whether the circulating pump has any suspected abnormality based on the deviation between the water supply temperature of the secondary network and the return water temperature of the primary network. If yes, proceed to step S43; otherwise, determine that the probability of the circulating pump malfunction is zero. S43 obtains the heating area of ​​the heating station and the length of the heating pipeline network, and combines the deviation between the return water pressure of the secondary network and the return water pressure of the primary network, and the deviation between the supply water temperature of the secondary network and the return water temperature of the primary network, and uses a prediction model based on the GRU algorithm to determine the probability of pipeline operation failure of the circulating pump.

9. The method for controlling a circulating pump in a heating station as described in claim 1, characterized in that, The method for determining the failure probability of the circulating pump is as follows: Determine whether the probability of pipeline network operation failure and the probability of usage problem of the circulating pump are both greater than the preset failure probability threshold. If so, determine the failure probability of the circulating pump by the average of the probability of pipeline network operation failure and the probability of usage problem. If not, proceed to the next step. Determine whether the probability of pipeline network operation failure or usage problem of the circulating pump is greater than a preset failure probability threshold. If so, determine the failure probability of the circulating pump by the maximum value of the probability of pipeline network operation failure and the probability of usage problem. If not, proceed to the next step. The failure probability of the circulating pump is determined by the minimum value of the probability of pipeline network failure and the probability of usage problems.

10. The method for controlling a circulating pump in a heating station as described in claim 1, characterized in that, The method for determining the number of adjustments and the adjustment range of the circulating pump is as follows: The heating load adjustment amount of the heating station is determined based on the heat loss of the secondary network and the change in heating load. The maximum operating load of the circulating pump and the maximum number of adjustments within a set time are determined based on the failure probability of the circulating pump in the heating station. The operating reliability function of the circulating pump is determined by taking the maximum operating load of the circulating pump, the maximum number of adjustments within a set time, and the adjustment amount of the heating load of the heating station as constraints, the operating power of the circulating pump as a constraint, the operating power of the circulating pump and the number of adjustments as constraints, and the number of adjustments of the circulating pump and the adjustment range of different number of adjustments as the target of maximizing the operating reliability function.

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