Heat exchange station circulating pump control system based on tail end pressure difference

Abstract

The invention discloses a heat exchange station circulating pump control system based on tail end pressure difference, and relates to the technical field of heat supply system automation control. Comprising a tail end pressure difference monitoring module which is arranged on a water supply and return pipeline of a building unit at the tail end of the heat supply pipe network and used for collecting pressure signals of a water supply pipe and a water return pipe in real time; the data transmission module is used for uploading the pressure signal to the cloud platform; the data processing and control module comprises a cloud platform and a heat exchange station PLC, the cloud platform receives the pressure signal and obtains an actual pressure difference delta P, the actual pressure difference delta P is compared with a preset expected pressure difference delta P, and an adjusting instruction is generated; and the execution module comprises a frequency converter and a two-network circulating pump, and the PLC adjusts the output frequency of the frequency converter according to the adjusting instruction so as to change the rotating speed of the circulating pump, so that the actual pressure difference delta P approaches the expected pressure difference delta P. The real-time pressure difference of the pipe network tail end is remotely monitored, the rotating speed of the two-network circulating pump is dynamically adjusted, it is ensured that the heat supply pressure of a tail end user is stable, and meanwhile system energy consumption is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control of heat supply system, and particularly relates to a heat exchange station circulating pump control system based on terminal pressure difference. BACKGROUND

[0002] In a central heating system, the heat exchange station is a key hub connecting the primary network and the secondary network, and its operation efficiency and control accuracy directly affect the energy consumption of the entire heating network and the heating quality of users. Among them, the secondary network circulating pump is the core equipment for circulating hot water between the user end and the heat exchange station, and its speed regulation mode is the core factor determining the economy and stability of the heating system.

[0003] In the traditional heat exchange station control system, the speed regulation strategy of the secondary network circulating pump generally relies on the supply and return water pressure difference signals or temperature signals collected locally by the heat exchange station. Specifically, the control system obtains the supply and return water pressure difference through the pressure sensors installed at the outlet and return port of the heat exchange station, or collects the supply water temperature and return water temperature through the temperature sensor, which is used as a feedback signal to adjust the speed of the circulating pump through the frequency converter. However, this control mode based on local signals has many defects that are difficult to overcome in actual operation:

[0004] 1. The heating pipe network has the characteristics of long pipe length, many branches, and complex hydraulic conditions. The supply and return water pressure difference or temperature signal at the heat exchange station can only reflect the working condition of the surrounding area of the heat exchange station, and cannot truly reflect the actual pressure difference at the end of the pipe network, such as the farthest building or unit from the heat exchange station. Due to the pressure loss along the pipe network, and the real-time change of the heating demand of different users, there is often a large deviation between the local control signal and the actual working condition at the end. This deviation directly leads to insufficient flow, low indoor temperature, or sudden pressure rise causing pipe leakage at the far end, which seriously affects the stability of the heating quality.

[0005] 2. In the traditional control system, the circulating pump often runs in a fixed speed mode, or only uses simple threshold judgment for rough speed regulation. When the demand of the end user decreases, the circulating pump may still maintain a high speed, causing an unreasonable operating state of "large flow and small temperature difference", which leads to high energy consumption of the pump; and when the end demand suddenly increases, the flow may be insufficient due to untimely adjustment, which needs to be compensated by temporarily increasing the speed, further aggravating energy waste and significantly increasing the operating cost of the heating enterprise.

[0006] 3.The hydraulic condition of a heat supply pipe network is influenced by user heat behavior, outdoor environment temperature, pipe insulation condition and other factors, and is in dynamic change at all times.The traditional control system relies on manual inspection to obtain end condition information, or only adjusts based on local static data, resulting in significant lag in response to dynamic changes in the pipe network.For example, when a remote user experiences a sudden increase in load due to a valve opening, the local control system often needs several hours or even longer to indirectly perceive the change in return water temperature, during which time the end user has experienced a heat supply shortage, seriously affecting user experience.

[0007] In the prior art, a closed-loop control method based on the pressure difference of the supply and return water of a heat exchange station has appeared, that is, the output frequency of a frequency converter is automatically adjusted by real-time monitoring of the pressure difference of the supply and return water, but this type of technology is still limited to feedback of the local condition of the heat exchange station, and the actual pressure difference at the end of the pipe network is not included in the control logic.Because of the nonlinear relationship between the end pressure difference and the local pressure difference due to factors such as pipe resistance and flow distribution, adjusting only according to the local pressure difference cannot eliminate fluctuations in the end condition, and the above problems of insufficient control accuracy, high energy consumption and lagging response have not been fundamentally solved. SUMMARY

[0008] The purpose of the present application is to provide a heat exchange station circulating pump control system based on end pressure difference, which dynamically adjusts the circulating pump speed of the two networks by remotely monitoring the real-time pressure difference at the end of the pipe network, ensuring stable heating pressure for end users, while reducing system energy consumption.

[0009] To achieve the above purpose, the technical solution of the present application is as follows: a heat exchange station circulating pump control system based on end pressure difference, comprising:

[0010] An end pressure difference monitoring module is arranged on the supply and return water pipes of the last building unit of the heat supply pipe network, and is used to collect pressure signals of the water supply pipe and the return water pipe in real time;

[0011] A data transmission module is connected with the end pressure difference monitoring module, and is used to upload the pressure signals to a cloud platform;

[0012] A data processing and control module includes a cloud platform and a heat exchange station PLC controller, the cloud platform receives the pressure signals and obtains the actual pressure difference ΔP, compares the actual pressure difference ΔP with a preset expected pressure difference ΔP, generates an adjustment instruction, and sends it to the PLC controller;

[0013] An execution module is connected with the PLC controller, and includes a frequency converter and a two-network circulating pump, the PLC controller adjusts the output frequency of the frequency converter according to the adjustment instruction to change the speed of the circulating pump, so that the actual pressure difference ΔP approaches the expected pressure difference ΔP.

[0014] As a preferred scheme of the present application, the cloud platform is built-in with a fuzzy control algorithm and a PID control algorithm.

[0015] As a preferred scheme of the present application, the input variables of the fuzzy algorithm are error e and error change rate Δe, where e = expected pressure difference ΔP - actual pressure difference ΔP, and Δe = e(t) - e(t-1), and the output variable is frequency converter speed adjustment amount Δf.

[0016] As a preferred scheme of the present application, the fuzzy control algorithm is implemented as follows:

[0017] The input variables and output variables are converted into fuzzy language variables.

[0018] A fuzzy rule base is constructed to cover all combination scenarios of e and Δe, and the inference logic of input fuzzy variables to output fuzzy variables is defined in the form of a rule table.

[0019] Mamdani inference method is adopted, the membership degrees of input variables are combined with the fuzzy rule base, the "activation strength" is calculated rule by rule, and the outputs of all rules are aggregated by "min-max" composition method to form the final fuzzy output set.

[0020] The fuzzy output set is converted into accurate speed adjustment amount by the gravity center method.

[0021] As a preferred scheme of the present application, the fuzzy language variables of input variable error e include: negative big NB, negative medium NM, zero ZO, positive medium PM, and positive big PB.

[0022] The fuzzy language variables of input variable error change rate Δe include: fast drop FD, slow drop SD, no change NC, slow rise SR, and fast rise FR.

[0023] The fuzzy language variables of output variable Δf include: big drop BD, small drop SD, hold HOLD, small rise SA, and big rise BA.

[0024] As a preferred scheme of the present application, the gravity center method is used to obtain the gravity center position of the fuzzy region, and the speed adjustment amount is:

[0025]

[0026] wherein, u i is the membership degree of the i-th output fuzzy language variable, f i is the membership function center value of the i-th output fuzzy language variable, and n is the number of language variables in the output fuzzy set.

[0027] As a preferred scheme of the present application, a triangular or Gaussian membership function is designed for the input variable error e, which dynamically scales the width of the membership function according to the real-time pressure difference fluctuation amplitude.

[0028] As a preferred scheme of the present application, a reinforcement learning algorithm is introduced in the fuzzy control algorithm, and the rule weight is dynamically adjusted by means of historical data.

[0029] As a preferred scheme of the present application, when the error |e|> threshold value, the fuzzy control algorithm is adopted; when the error |e|≤ threshold value, the PID algorithm is adopted, and the error e = expected pressure difference ΔP - actual pressure difference ΔP.

[0030] As a preferred scheme of the present application, when the error |e| exceeds the alarm threshold value, the alarm mechanism is triggered.

[0031] The present application can achieve the following technical effects due to the use of the above technical scheme: compared with the prior art, the present application has the following beneficial effects by monitoring the end pressure difference of the heating pipe network in real time and dynamically adjusting the rotating speed of the heat exchange station two-network circulating pump based on the end pressure difference:

[0032] 1. Improve control accuracy: directly using the end pressure difference as the feedback signal, the actual heating condition of the remote user can be accurately reflected, the end pressure difference is kept stable, and the heating quality of the remote user is improved.

[0033] 2. Reduce energy consumption: by accurately adjusting the rotating speed of the circulating pump, the overrunning of the water pump is avoided, the system energy consumption is significantly reduced, and the operation cost is saved.

[0034] 3. Accelerate response speed: remote monitoring and dynamic closed-loop control are adopted, which can timely respond to the dynamic changes of the pipe network, improve the response speed by more than 40%, and reduce the occurrence of unstable heating.

[0035] 4. Enhance system adaptability: remote communication is realized through the data transmission module, which adapts to the wide-area deployment requirements of complex heating pipe networks; the combination of fuzzy control and PID algorithm makes the system have strong robustness and can handle sensor noise, system delay and other problems. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0037] Figure 1 Fig. 1 is a schematic diagram of a heat exchange station circulating pump control system based on end pressure difference;

[0038] Figure 2 The control logic diagram of the heat exchange station circulating pump control system based on the terminal pressure difference;

[0039] Figure 3 The installation schematic diagram of the terminal pressure sensor. DETAILED DESCRIPTION

[0040] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, but not all the embodiments. The description of the at least one exemplary embodiment is actually only illustrative, but not as any limitation on the present application and its application or use. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0042] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, there is a feature, step, operation, device, component and / or combination thereof.

[0043] Unless specifically stated otherwise, the relative arrangement of the components and steps illustrated in these embodiments, numerical expressions, and numerical values set forth in the specification are not meant to limit the scope of the present application. It should also be understood that the size of the various parts shown in the drawings is not necessarily to scale. Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered as part of the description of the application. In all examples shown and discussed herein, any specific value should be interpreted as merely illustrative and not as a limitation. Therefore, other examples of the exemplary embodiments can have different values. It should be noted that similar reference numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0044] It should be noted that unless specifically defined otherwise, the terms "mounting", "connected", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0045] Embodiment 1

[0046] Please refer to Figures 1-3 The embodiment provides a heat exchange station circulating pump control system based on end pressure difference, comprising:

[0047] An end pressure difference monitoring module is arranged on the supply and return water pipes of the last building unit of the heat supply pipe network, and is used to collect the pressure signals of the supply water pipe and the return water pipe in real time.

[0048] Specifically, a pressure sensor with a model of PTX-203 is arranged on the supply and return water pipes of the last building unit of the heat supply pipe network in a certain central heating community. The sensor has a range of 0-1.6 MPa and an accuracy of ±0.5%, and can accurately and timely collect the pressure signals of the supply water pipe and the return water pipe, thereby providing basic data for the system to obtain the actual end pressure difference.

[0049] A data transmission module is connected with the end pressure difference monitoring module, and is used to upload the pressure signals to a cloud platform.

[0050] Specifically, a transmission device integrated with a 4G module (preferably a chip with a model of EC25) can be selected to be connected with the pressure sensor of the end pressure difference monitoring module. The pressure signals collected by the sensor are periodically (5 minutes / time in this embodiment) uploaded to the cloud platform through the 4G network, so that the pressure data can be stably and efficiently transmitted, and the wide distribution of the heat supply pipe network and the real-time requirement of the data can be met.

[0051] A data processing and control module comprises a cloud platform and a heat exchange station PLC controller.

[0052] The cloud platform receives the pressure signals uploaded by the data transmission module, obtains the actual end pressure difference ΔP according to the formula ΔP=P 供 -P 回 , and compares the actual pressure difference ΔP with a preset expected pressure difference ΔP expected (0.15 MPa in this embodiment), and generates a speed regulation instruction by using a fuzzy control algorithm and a PID algorithm.

[0053] The heat exchange station PLC controller: preferably adopts a Siemens S7-200 smart PLC controller, receives the adjustment instructions issued by the cloud platform through the optical fiber network, serves as the control center of the execution module, accurately analyzes the instructions and drives the execution action.

[0054] The execution module is connected with the PLC controller and includes a frequency converter and a two-network circulating pump.

[0055] Specifically, after receiving the adjustment instructions, the PLC controller outputs a control signal to the frequency converter, adjusts the output frequency of the frequency converter, and then changes the rotating speed of the two-network circulating pump. In this embodiment, the pump speed is adjusted from 30 Hz to 35 Hz, the actual pressure difference ΔP is increased from 0.12 MPa to 0.15 MPa, and the actual pressure difference at the end approaches the expected pressure difference.

[0056] Embodiment 2

[0057] Please refer to Figures 1-3 The embodiment provides a control method based on the system in embodiment 1, which comprises the following steps:

[0058] Step 1. Before the system is put into operation, the expected pressure difference is preset on the cloud platform, for example, 0.15 MPa, and the communication protocol of the 4G network and the cloud platform is configured. The communication protocol includes setting the data upload interval, checking the communication frequency band, defining the data encryption rules, etc., to ensure stable and safe interaction between the sensor and the cloud platform.

[0059] Step 2. The pressure sensor of the end pressure difference monitoring module collects the end water supply and return water pressure at a cycle of 5 minutes / second. The collected pressure signal is packaged and coded by the 4G module, and then uploaded to the cloud platform through the 4G network to form a complete link of pressure data collection and transmission.

[0060] Step 3. Perform fuzzy-PID hybrid control logic judgment. When ∣e∣> threshold value, the fuzzy control algorithm is enabled to quickly eliminate large errors; when ∣e∣≤ threshold value, switch to PID algorithm fine tuning to improve steady-state control accuracy, and balance response speed and control stability.

[0061] Specifically, the fuzzy control algorithm is implemented as follows:

[0062] Fuzzification: Define the input variable error as e and error rate as De, where e = expected pressure difference AP - actual pressure difference AP, De = e(t) - e(t-1), and the output variable as the frequency converter speed adjustment Af. Set the fuzzy set language variable of e as {negative big (NB), negative medium (NM), zero (ZO), positive medium (PM), positive big (PB)}; the fuzzy set language variable of De as {fast drop (FD), slow drop (SD), no change (NC), slow rise (SR), fast rise (FR)}; and the fuzzy set language variable of Af as {big deceleration (BD), small deceleration (SD), hold (HOLD), small acceleration (SA), big acceleration (BA)}. Preferably, for the input variable error e, a triangular or Gaussian membership function is designed, which dynamically scales the width of the membership function according to the real-time pressure difference fluctuation amplitude (e.g., expanding the "PB / NB" range when the error is large).

[0063] Constructing a fuzzy rule base: Based on the experience of heating system experts and the characteristics of the pipe network, 25 fuzzy control rules covering all combinations of e and De are constructed. For example, if e = NB (AP is much lower than expected) and De = FD (error is rapidly decreasing), then Af = BA (big acceleration); if e = ZO (AP is close to the expected value) and De = NC (error is stable), then Af = HOLD (maintain the speed), and the inference logic of input fuzzy variables to output fuzzy variables is defined in the form of a rule table as follows.

[0064] Table 1 Fuzzy rule table

[0065] e\Δe FD SD NC SR FR NB BA BA SA HOLD SD NM BA SA HOLD SD BD ZO SA HOLD HOLD HOLD SD PM SD SD HOLD SA BA PB BD SD HOLD SA BA

[0066] Fuzzy inference: Using the Mamdani inference method, the membership degree of the input variable and the fuzzy rule base are combined to calculate the "activation strength" rule by rule, and all rule outputs are aggregated by the "min-max" composition method to form a fuzzy output set.

[0067] Defuzzification: Using the barycenter method, the fuzzy output is converted to an accurate speed adjustment Af according to the formula At the same time, a reinforcement learning algorithm (Q-Learning) is introduced to dynamically adjust the rule weights based on historical operation data to optimize the control response speed.

[0068] The cloud platform integrates the calculation results of the fuzzy control algorithm and the PID algorithm to generate the two-network circulating pump speed adjustment instruction, providing accurate control basis for the execution module.

[0069] Step 4. After receiving the adjustment instruction issued by the cloud platform, the heat exchange station PLC controller outputs a control signal to the frequency converter (preferably ABB ACS510) to adjust the output frequency of the frequency converter. In this embodiment, the frequency is adjusted to increase the circulating pump speed from 30 Hz to 35 Hz, which changes the water flow of the two-network circulating pump and the pressure distribution of the pipe network, so that the actual pressure difference ΔP at the end approaches the expected pressure difference ΔP, and the hydraulic working condition of the heating system is optimized.

[0070] It should be noted that during system operation, the cloud platform monitors the deviation of the actual pressure difference ΔP and the expected pressure difference ΔP in real time. If the deviation exceeds the threshold (the alarm threshold is |e| > 10% of the expected pressure difference ΔP): on the one hand, the alarm mechanism is triggered, and the operation and maintenance personnel are notified through sound and light alarms, platform message pushing, etc.; on the other hand, the correction process is automatically started, the fuzzy control algorithm and PID algorithm are called again, and a new adjustment instruction is generated to drive the execution module to adjust and ensure the stability of the end pressure difference.

[0071] In addition, during the initial debugging stage, in order to avoid the influence of the large fluctuation of the circulating pump speed on the stability of the pipe network, the output adjustment range is reduced (for example, Δf max = 5 Hz). Through simulation and field trial operation, the adjustment range is gradually relaxed, and the fuzzy control rule weight and PID algorithm parameters (proportional coefficient Kp, integral time Ti, and derivative time Td) are set in combination with the end pressure difference fluctuation curve, system energy consumption data, etc., until the system response speed, control accuracy, and stability are optimized.

[0072] After the system is formally put into operation, 24-hour and long-term operation data are continuously recorded, and the adjustment effects of fuzzy control and traditional PID control are compared:

[0073] Overshoot: The traditional PID control overshoot is about 15%-20%, and the fuzzy-PID hybrid control overshoot of the present application is controlled within 5%, effectively avoiding the hydraulic imbalance of the pipe network caused by the severe fluctuation of the end pressure difference.

[0074] Stabilization time: The traditional PID control needs 10-15 minutes from load change to stabilization of the end pressure difference, and the stabilization time of the control method of the present application is shortened to 3-5 minutes, improving the response speed of the system to dynamic working conditions.

[0075] Energy consumption comparison: Under the same heating load, the control method of the present application reduces the energy consumption of the two-network circulating pump by about 12%-18%, reduces the invalid energy consumption through precise speed regulation, and verifies that the energy-saving effect is significant.

[0076] Through the verification of this embodiment, the circulating pump control system of the heat exchange station based on the end pressure difference can effectively solve the problems of insufficient accuracy, response lag, and high energy consumption of the traditional control scheme, realize intelligent and refined regulation and control of the heating system, and provide a feasible technical path for energy saving and efficiency improvement and high-quality heating in the central heating industry.

[0077] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

[0078]

[0079] application.​

Claims

1. A terminal pressure differential based heat exchange station circulating pump control system, characterized by, The utility model relates to a kind of heat-supply network pressure difference monitoring system, comprising: End differential pressure monitoring module, be set in the heat-supply pipe network last end building unit's supply and return water pipe, for real-time acquisition supply water pipe and return water pipe pressure signal; Data transmission module, connected with the end differential pressure monitoring module, for uploading pressure signal to cloud platform; Data processing and control module, including cloud platform and heat exchange station PLC controller, the cloud platform receives pressure signal and obtains actual differential pressure ΔP, compares actual differential pressure ΔP with preset expected differential pressure ΔP, generates adjustment instruction, and issues to the PLC controller; Execution module, connected with the PLC controller, including frequency converter and two-network circulating pump, the PLC controller adjusts the output frequency of frequency converter according to adjustment instruction, to change the rotating speed of circulating pump, so that actual differential pressure ΔP approaches expected differential pressure ΔP.

2. The terminal pressure difference based heat exchange station circulating pump control system of claim 1, wherein, The cloud platform is built-in fuzzy control algorithm and PID control algorithm.

3. The control system for the terminal pressure difference based heat exchanger station circulating pump of claim 2, wherein, The input variable of the fuzzy algorithm is error e and error change rate Δe, wherein e=expected differential pressure ΔP-actual differential pressure ΔP, Δe=e (t) -e (t-1), and the output variable is frequency converter rotating speed adjustment amount Δf.

4. The control system for the circulating pump of the heat exchange station based on the differential pressure at the end according to claim 3, characterized in that, The fuzzy control algorithm implementation is as follows: Convert input variable and output variable into fuzzy language variable; Build fuzzy rule base, cover e and Δe whole combination scene, define input fuzzy variable to output fuzzy variable inference logic in the form of rule table; Using Mamdani inference method, combined with the membership degree of input variable and fuzzy rule base, calculate "activation intensity" rule by rule, and aggregate the output of all rules by "take small-take big" synthesis method to form the final fuzzy output set; Convert fuzzy output set into accurate rotating speed adjustment amount by barycenter method.

5. The control system for the terminal pressure difference based heat exchanger station circulating pump of claim 4, wherein, The fuzzy language variable of input variable error e includes: negative big NB, negative middle NM, zero ZO, positive middle PM and positive big PB; The fuzzy language variable of input variable error change rate Δe includes: fast drop FD, slow drop SD, invariable NC, slow rise SR and fast rise FR; The fuzzy language variable of output variable Δf includes: large deceleration BD, small deceleration SD, hold HOLD, small acceleration SA and large acceleration BA.

6. The control system for a terminal pressure differential based heat exchanger station circulating pump of claim 4, wherein, Get fuzzy area centroid position by barycenter method, and its rotating speed adjustment amount is: where u i is the membership degree of the i-th output fuzzy linguistic variable, f i is the membership function center value of the i-th output fuzzy linguistic variable; n is the number of linguistic variables in the output fuzzy set.

7. The control system for a terminal pressure differential based heat exchanger station circulating pump of claim 3, wherein, For input variable error e, design triangle or Gaussian membership function, which dynamically scales the width of membership function according to the real-time differential pressure fluctuation amplitude.

8. The control system for a terminal pressure differential based heat exchanger station circulating pump of claim 2, wherein, Introduce reinforcement learning algorithm in fuzzy control algorithm, and dynamically adjust rule weight with historical data.

9. The control system for a terminal pressure differential based heat exchanger station circulating pump of claim 2, wherein, When error |e|>threshold value, use fuzzy control algorithm;When error |e|≤threshold value, use PID algorithm, and its error e=expected differential pressure ΔP-actual differential pressure ΔP.

10. The control system for the circulating pump of the heat exchange station based on the differential pressure at the end according to claim 3 or 9, characterized in that, When error |e| exceeds alarm threshold, trigger alarm mechanism.

Images