A pump station unattended automatic control system and method based on PLC control and energy-saving algorithm

Abstract

The application relates to the technical field of intelligent control, and discloses a pump station unattended automatic control system and method based on PLC control and an energy-saving algorithm, which comprises a dynamic power distribution module, an energy consumption optimization module, a coordination control module, a safety monitoring module, a start-stop control module and a parameter optimization module, wherein: dynamic power distribution is performed on a host computer to obtain the running number of the host computer and a load distribution ratio; energy consumption optimization is performed on an auxiliary machine to obtain a power set value of the auxiliary machine; a coordination control signal is output to a frequency converter to generate an adjustment instruction; the running state of the pump station after the execution of the adjustment instruction is subjected to multi-parameter safety monitoring to obtain a safety state index; start-stop control is performed on the host computer of the pump station to confirm the running state of the host computer; and based on the running state of the host computer, a control parameter optimization instruction is generated to complete closed-loop correction of the pump station; and the application can improve the efficiency of the pump station unattended automatic control system based on the PLC control and the energy-saving algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, and particularly relates to a pump station unattended automatic control system and method based on PLC control and energy-saving algorithm. BACKGROUND

[0002] In the field of pump station automation control, the traditional control method generally uses relay logic circuit combined with manual experience for operation management. This method has obvious technical limitations: the control system has slow response speed and cannot realize accurate flow and pressure regulation; there is a lack of collaborative control strategy between pump groups, resulting in low operation efficiency; the equipment is frequently started and stopped, and there is a large impact in the switching process, which seriously affects the service life of the equipment. At the same time, due to the lack of effective monitoring and protection mechanism, the system often cannot respond in time when a fault occurs, increasing the risk of equipment damage.

[0003] The existing automatic pump station control system mostly uses a simple PID control algorithm. This control method has obvious deficiencies when facing complex and variable working conditions. The system cannot dynamically adjust the operating parameters according to the actual load demand, resulting in low energy utilization; it lacks intelligent diagnosis function, and abnormal states of equipment are difficult to be discovered and handled in time; at the same time, since a complete energy efficiency evaluation system has not been established, the running personnel cannot accurately grasp the energy consumption status of the system, and cannot implement effective energy-saving optimization measures, which restricts the further improvement of the operation efficiency of the pump station. SUMMARY

[0004] The present application provides a pump station unattended automatic control system and method based on PLC control and energy-saving algorithm to solve the problems raised in the background.

[0005] To achieve the above purpose, the present application provides a pump station unattended automatic control system based on PLC control and energy-saving algorithm, characterized in that the system comprises a dynamic power distribution module, an energy consumption optimization module, a coordinated control module, a safety monitoring module, a start-stop control module and a parameter optimization module, wherein:

[0006] The dynamic power distribution module is used for dynamically distributing power to the main machine based on real-time water consumption data of the pump station, to obtain the number of running machines and the load distribution ratio of the main machine;

[0007] The energy consumption optimization module is used for optimizing the energy consumption of the auxiliary machine based on medium temperature data and total power data of the main machine, to obtain the power set value of the auxiliary machine;

[0008] The coordinated control module is used for outputting a coordinated control signal to the frequency converter based on the number of running machines of the main machine, the load distribution ratio and the power set value, to generate an adjustment instruction of the frequency converter;

[0009] The safety monitoring module is configured to perform multi-parameter safety monitoring on the running state of the pump station after the adjustment instruction is executed, and obtain a safety state index of the pump station.

[0010] The start-stop control module is configured to perform start-stop control on the host of the pump station based on the safety state index, and confirm the running state of the host.

[0011] The parameter optimization module is configured to generate a control parameter optimization instruction of the pump station based on the running state of the host, so as to complete closed-loop correction of the pump station.

[0012] In a preferred embodiment, when the dynamic power distribution module performs dynamic power distribution on the host based on real-time water consumption data of the pump station, to obtain the running number and load distribution ratio of the host, the dynamic power distribution module is specifically configured to:

[0013] Based on the real-time water consumption data of the pump station, interval determination is performed on the water consumption of the user end, and the flow interval of the water consumption of the user end is confirmed.

[0014] Based on the flow interval, running configuration decision is performed on the host, and the running number of the host is confirmed.

[0015] Based on the running number and the real-time water consumption data, load balancing allocation is performed on the host, and the load distribution ratio of the host is obtained.

[0016] In a preferred embodiment, when the energy consumption optimization module performs energy consumption optimization on the auxiliary machine based on medium temperature data and total power data of the host, to obtain the power setting value of the auxiliary machine, the energy consumption optimization module is specifically configured to:

[0017] Based on the medium temperature data and the total power data of the host, working condition parameter fusion is performed on the auxiliary machine, and the thermal load parameter of the auxiliary machine is obtained.

[0018] Based on the thermal load parameter, power demand fitting is performed on the cooling water pump, and the initial power setting value of the cooling water pump is obtained.

[0019] Running safety boundary determination is performed on the initial power setting value, and a verification result of the power setting value is obtained.

[0020] Based on the verification result, the power setting value of the auxiliary machine is confirmed.

[0021] In a preferred embodiment, when the energy consumption optimization module performs power demand fitting on the cooling water pump based on the thermal load parameter, to obtain the initial power setting value of the cooling water pump, the energy consumption optimization module is specifically configured to:

[0022] Based on historical operation data, the power parameters of the cooling water pump are associated analyzed to obtain the correlation of the power parameters;

[0023] Based on the correlation, the linear relationship coefficient of the power parameters and the heat load parameters is confirmed;

[0024] Based on the linear relationship coefficient and the heat load parameters, the initial power setting value of the cooling water pump is calculated, and the calculation formula of the initial power setting value is as follows:

[0025] ;

[0026] In the formula, is the initial power setting value of the cooling water pump, is the real-time total power of the main machine, is the medium temperature data.

[0027] In a preferred embodiment, when the coordination control module executes the coordination control signal output to the frequency converter based on the number of running main machines, the load distribution ratio and the power setting value, and generates the adjustment instruction of the frequency converter, it is specifically used for:

[0028] Based on the number of running main machines and the load distribution ratio, the control strategy of the main machine is integrated to obtain the coordination control parameters of the main machine;

[0029] Based on the power setting value, the frequency parameter setting of the auxiliary machine is performed to obtain the coordination control parameters of the auxiliary machine;

[0030] Based on the coordination control parameters of the main machine and the coordination control parameters of the auxiliary machine, the instruction coordination integration of the frequency converters of the main machine and the auxiliary machine is performed to generate the coordination control signal of the frequency converter;

[0031] The coordination control signal is packaged to obtain the adjustment instruction of the frequency converter.

[0032] In a preferred embodiment, when the safety monitoring module performs multi-parameter safety monitoring on the pump station running state after the execution of the adjustment instruction to obtain the safety state index of the pump station, it is specifically used for:

[0033] Multi-source data synchronous collection is performed on the pump station running state after the execution of the adjustment instruction to obtain the pressure data and the flow data of the pump station running state;

[0034] Based on the pressure data and the flow data, the running quality of the pump station running state is evaluated to obtain the state comprehensive parameters of the pump station running state;

[0035] Based on the state comprehensive parameter, a safety situation assessment is performed on the pump station operation state to obtain a safety state index of the pump station.

[0036] In a preferred embodiment, when performing the safety situation assessment on the pump station operation state based on the state comprehensive parameter to obtain the safety state index of the pump station, the safety monitoring module is specifically configured to:

[0037] The state comprehensive parameter is compared with a preset safety threshold interval item by item to obtain an abnormality determination result of the pump station operation state.

[0038] Based on the abnormality determination result, a safety risk coefficient of the pump station operation state is calculated, wherein the safety risk coefficient calculation formula is as follows:

[0039] ;

[0040] In the formula, is the safety risk coefficient, is the number of the state comprehensive parameter, is a weight factor of an i-th monitoring parameter in the state comprehensive parameter, is a real-time value of the i-th monitoring parameter in the state comprehensive parameter, is a middle value of a safety threshold interval corresponding to the i-th monitoring parameter in the state comprehensive parameter, is a safety threshold interval width corresponding to the i-th monitoring parameter in the state comprehensive parameter, is an absolute value, is a summation operation. The safety risk coefficient is mapped to a predefined risk level to determine a risk level of the pump station operation state. According to the risk level, a safety state index of the pump station is generated.

[0041] In a preferred embodiment, when performing the start-stop control of the host of the pump station based on the safety state index and confirming the operation state of the host, the start-stop control module is specifically configured to:

[0042] Based on the safety state index, a risk assessment is performed on the pump station operation state to obtain an operation risk assessment result of the pump station.

[0043] Based on the operation risk assessment result, a start-stop strategy decision is made on the host to obtain a start-stop control instruction of the host.

[0044]

[0045] ​​​​

[0046] input the start-stop control instruction to the host computer, and confirm the running state of the host computer.

[0047] In a preferred embodiment, when the parameter optimization module executes the control parameter optimization instruction of the pump station based on the running state of the host computer to complete the closed-loop correction of the pump station, it is specifically used for:

[0048] monitoring the running time and start-stop frequency of the host computer to obtain the running load data of the host computer;

[0049] based on the preset balanced running interval, matching and determining the running load data to obtain the balance degree result of the running state of the host computer;

[0050] generating a control parameter correction instruction based on the balance degree result;

[0051] issuing the control parameter correction instruction to the control execution interface of the host computer to adjust the running state of the pump station;

[0052] re-monitoring the running load data of the host computer after adjustment to verify the improvement of the balance degree of the host computer and complete the closed-loop correction.

[0053] In order to solve the above problems, the application also provides a pump station unattended automatic control method based on PLC control and energy-saving algorithm, which comprises:

[0054] S1, based on the real-time water data of the pump station, the dynamic power distribution of the host computer is performed to obtain the running number and load distribution ratio of the host computer;

[0055] S2, based on the medium temperature data and the total power data of the host computer, the energy consumption optimization of the auxiliary machine is performed to obtain the power setting value of the auxiliary machine;

[0056] S3, based on the running number of the host computer, the load distribution ratio and the power setting value, a coordination control signal is output to the frequency converter to generate the adjustment instruction of the frequency converter;

[0057] S4, the safety state index of the pump station is obtained by performing multi-parameter safety monitoring on the running state of the pump station after the adjustment instruction;

[0058] S5, based on the safety state index, the start-stop control of the host computer of the pump station is performed to confirm the running state of the host computer;

[0059] S6, based on the running state of the host computer, the control parameter optimization instruction of the pump station is generated to complete the closed-loop correction of the pump station.

[0060] Compared with the prior art, the present application has the following beneficial effects:

[0061] 1. The present application accurately determines the flow interval by the dynamic power distribution module according to real-time water consumption data, scientifically decides the number of host machines running and realizes load balancing allocation, so that the host machine operation fits the actual demand; the energy consumption optimization module fuses medium temperature and host machine total power data, and accurately sets auxiliary machine power through working condition parameter fusion, power demand fitting and safety boundary verification; the coordinated control module integrates host and auxiliary machine control parameters, generates collaborative regulation instructions, and realizes efficient cooperation of host and auxiliary machines. Multi-module synergy, greatly improving energy utilization efficiency, reducing the overall operation energy consumption of the pumping station, and realizing fine optimization of energy efficiency.

[0062] 2. The present application synchronously collects key parameters such as pressure and flow through multi-source data, and generates accurate safety state indicators through running quality evaluation and safety situation analysis combined with risk quantitative calculation; the start-stop control module carries out risk assessment according to the safety indicators, formulates differentiated start-stop strategies, and ensures the safety and controllability of host machine start-stop; the parameter optimization module monitors the host machine running load data in real time, generates correction instructions based on balance degree analysis, continuously optimizes control parameters through closed-loop verification, ensures balanced operation of the host machine, reduces equipment impact, improves operation stability and control accuracy, effectively prolongs the service life of the equipment, and provides solid and reliable technical support for unattended mode of the pumping station. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 The system architecture diagram of the unattended automatic control system of the pumping station based on PLC control and energy-saving algorithm provided by an embodiment of the present application is shown in the figure.

[0064] Figure 2 The flowchart of the unattended automatic control method of the pumping station based on PLC control and energy-saving algorithm provided by an embodiment of the present application is shown in the figure.

[0065] The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments belong to part of the embodiments of the present application, not all the embodiments. 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.

[0067] The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the description of the application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that "or" as used herein is

[0068] Depending on the context, the word "if" or "if" can be interpreted to mean "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if determined" or "if detecting (a stated condition or event)" can be interpreted to mean "when determined" or "in response to determining" or "when detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)," depending on the context.

[0069] In addition, the sequence of steps in each of the method embodiments described below is merely an example and is not strictly limited.

[0070] In fact, the server equipment deployed by the pump station unattended automation control system based on PLC control and energy saving algorithm can be composed of one or more devices. The pump station unattended automation control system based on PLC control and energy saving algorithm can be implemented as a business instance, a virtual machine, a hardware device. For example, the pump station unattended automation control system based on PLC control and energy saving algorithm can be implemented as a business instance deployed on one or more devices in the cloud node. In short, the pump station unattended automation control system based on PLC control and energy saving algorithm can be understood as a software deployed on a cloud node, used to provide a pump station unattended automation control system based on PLC control and energy saving algorithm for each user end. Alternatively, the pump station unattended automation control system based on PLC control and energy saving algorithm can also be implemented as a virtual machine deployed in one or more devices in the cloud node. The virtual machine has application software installed for managing each user end. Alternatively, the pump station unattended automation control system based on PLC control and energy saving algorithm can also be implemented as a server composed of a plurality of same or different types of hardware devices, and one or more hardware devices are set to provide a pump station unattended automation control system based on PLC control and energy saving algorithm for each user end.

[0071] In an implementation form, the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm and the user end are mutually adaptive. That is, the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm is installed as an application on a cloud service platform, and the user end is a client that establishes a communication connection with the application. Or the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm is realized as a website, and the user end is realized as a webpage. Or the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm is realized as a cloud service platform, and the user end is realized as an applet in an instant messaging application.

[0072] As shown in Figure 1 FIG. 1 is a system architecture diagram of the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm according to an embodiment of the present application.

[0073] The unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm 100 can be set in a cloud server. In an implementation form, the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm 100 can be one or more service devices, or can be installed as an application on a cloud (for example, a server of a mobile service operator, a server cluster, etc.), or can be developed as a website. According to the functions realized, the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm 100 can include a dynamic power distribution module 101, an energy consumption optimization module 102, a coordinated control module 103, a safety monitoring module 104, a start-stop control module 105, and a parameter optimization module 106. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, and are stored in the memory of the electronic device.

[0074] In an embodiment of the present application, each of the modules in the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm can be independently realized and called by other modules. Here, calling can be understood as that a module can be connected to multiple modules of another type and provide corresponding services for the connected multiple modules. In the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm provided by the embodiment of the present application, the application range of the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm architecture can be adjusted by adding modules and directly calling without modifying program codes, cluster horizontal expansion is realized, so as to achieve the purpose of quickly and flexibly expanding the unattended automatic control system of the pump station based on the PLC control and the energy saving algorithm. In actual application, the modules can be set in the same device or different devices, or can be set in a virtual device, such as a service instance in a cloud server.

[0075] The following will combine specific embodiments, respectively, for each component and specific work flow of a pump station unattended automation control system based on PLC control and energy-saving algorithm:

[0076] The dynamic power distribution module 101 is configured to perform dynamic power distribution on the host based on real-time water consumption data of the pump station, to obtain the number of running hosts and the load distribution ratio of the host;

[0077] In the embodiment of the application, when the dynamic power distribution module performs dynamic power distribution on the host based on real-time water consumption data of the pump station, to obtain the number of running hosts and the load distribution ratio of the host, it is specifically configured to:

[0078] Based on the real-time water consumption data of the pump station, the user end water consumption is interval determined, and the flow interval of the user end water consumption is confirmed;

[0079] Based on the flow interval, the host is configured to run and decide, and the number of running hosts is confirmed;

[0080] Based on the number of running hosts and the real-time water consumption data, the host is load balanced and deployed, and the load distribution ratio of the host is obtained.

[0081] Specifically, the interval determination of the user end water consumption based on the real-time water consumption data of the pump station is realized through multi-source data fusion analysis. The system collects flow data in real time through the electromagnetic flowmeter installed at the outlet pipe of the pump station, and the sampling frequency reaches 100Hz, ensuring the accuracy of the data. The sliding time window technology is used to calculate the weighted average flow with a period of 5 minutes. According to the actual water consumption Qreal, the system presets three flow running intervals: when Qreal≤120m³ / h, it is a low load interval; when 120m³ / h

[0082] Further, the running configuration decision of the host based on the flow interval is realized through PLC control logic. In the low load interval, the system starts 1 100kW host, and the load rate is controlled between 70% and 90%, to avoid the efficiency decline caused by low load operation; in the medium load interval, the system starts 2 160kW hosts, and adopts balanced load distribution strategy; in the high load interval, the system starts 3 hosts, including 2 160kW hosts and 1 100kW host. In the decision process, the PLC monitors the running state of each host in real time, to ensure the smooth switching of the equipment and avoid damage to the equipment caused by frequent start and stop.

[0083] Further, the load balancing allocation of the host based on the number of running stations and real-time water consumption data is completed through the PLC iterative calculation algorithm. The system calculates the total demand power according to the real-time water flow and the required head. For the case of 2 hosts running, a load distribution ratio of 5:5 is adopted; for the case of 3 hosts running, a load distribution ratio of 4:4:2 is adopted. Through no more than 5 iterations of calculation, the PLC ensures that the deviation of the actual load of each host from the target load is controlled within 5%, effectively avoiding overloading or idling of the equipment.

[0084] Further, the system establishes a real-time load monitoring and adjustment mechanism. Through the power sensor installed on the host, the actual load of each host is monitored in real time. When the detected load deviation exceeds the allowed range, the PLC automatically adjusts the frequency output of the frequency converter to accurately control the host running state. At the same time, the system also monitors the change of the pipe network pressure, and dynamically fine-tunes the load distribution through the fuzzy PID algorithm to ensure the stability of the water supply pressure. The system performs load balancing evaluation every 30 seconds, and immediately starts the recalculation and adjustment program when it finds that the load distribution is unbalanced.

[0085] Further, the system also has the function of equipment operation optimization. By recording the cumulative running time of each host, the frequency of equipment use is automatically balanced, and the service life of the equipment is prolonged. During load distribution, the host with higher running efficiency is preferentially selected to bear the main load, ensuring that the system runs in the high-efficiency interval. At the same time, the system automatically adjusts the operation strategy according to the peak and valley periods of the power grid, appropriately optimizes the load distribution during the peak period of electricity price, and further reduces the operation cost.

[0086] In summary, this dynamic power distribution module realizes the optimal control of pump station host operation through accurate flow interval determination, scientific operation configuration decision and precise load balancing allocation. The system uses PLC core control and iterative calculation algorithm to ensure the accuracy and stability of load distribution, effectively improves the operation efficiency of the pump station, and significantly reduces the energy consumption.

[0087] In summary, the innovation of this module lies in the organic combination of real-time monitoring, intelligent decision-making and precise control, forming a complete closed-loop control system. Through multi-level optimization strategy and self-adaptive adjustment mechanism, the system can minimize energy consumption while ensuring stable water supply, providing reliable technical support for unattended automatic control of pump stations.

[0088] The energy consumption optimization module 102 is configured to perform energy consumption optimization on the auxiliary machine based on the medium temperature data and the total power data of the host, to obtain a power set value of the auxiliary machine.

[0089] In the embodiment of the present application, when the energy consumption optimization module performs energy consumption optimization on the auxiliary machine based on the medium temperature data and the total power data of the host machine to obtain the power setting value of the auxiliary machine, it is specifically used for:

[0090] Based on the medium temperature data and the total power data of the host machine, the working condition parameters of the auxiliary machine are fused to obtain the thermal load parameter of the auxiliary machine;

[0091] Based on the thermal load parameter, the power demand of the cooling water pump is fitted to obtain the initial power setting value of the cooling water pump;

[0092] The initial power setting value is subjected to running safety boundary judgment to obtain a verification result of the power setting value;

[0093] Based on the verification result, the power setting value of the auxiliary machine is confirmed.

[0094] Specifically, the working condition parameter fusion of the auxiliary machine based on the medium temperature data and the total power data of the host machine is realized by a multi-sensor data weighted fusion method. The system collects the inlet temperature and outlet temperature data of the cooling medium in real time, and simultaneously monitors the total power consumption data of the host machine. The weighted average algorithm is used to combine the temperature difference and power data to calculate the thermal load parameter reflecting the heat exchange demand of the system. This parameter comprehensively reflects the thermal management load required by the auxiliary machine under the current working condition, and provides accurate input for subsequent power optimization.

[0095] Further, the power demand fitting of the cooling water pump based on the thermal load parameter is completed by a regression analysis driven by historical data. The system calls the stored historical operation data to establish the mapping relationship between the thermal load parameter and the power of the cooling water pump, and determines the power value corresponding to the current thermal load by a curve fitting method to generate the initial power setting value of the cooling water pump. This process ensures a high matching between the power setting value and the actual thermal demand, and avoids the efficiency loss caused by excessive or insufficient power.

[0096] Further, the running safety boundary judgment of the initial power setting value is realized by a pre-set threshold comparison and a dynamic adjustment mechanism. The system compares the initial power setting value with the safe running range of the cooling water pump, including the minimum power limit and the maximum power limit, and simultaneously checks whether the power change rate is within the allowed fluctuation range. If the initial value exceeds the safety boundary, the system automatically corrects it to the nearest safe value, and generates a verification result containing the pass status and adjustment record, to ensure that the setting value meets the equipment protection requirements.

[0097] Further, the power setting value of the auxiliary machine is confirmed based on the checking result, which is completed by executing feedback closed-loop control. The system sends the checked power setting value to the frequency converter of the cooling water pump, monitors the actual running power of the water pump in real time, and compares it with the setting value. If a deviation is detected, dynamic correction is performed through a proportional-integral-derivative control algorithm, and finally a stable and reliable auxiliary machine power setting value is output, ensuring that the auxiliary machine operates in an optimal energy efficiency state.

[0098] In summary, this energy consumption optimization module realizes fine regulation and control of auxiliary machine power through multi-source data fusion, intelligent power fitting, strict safety verification and closed-loop execution confirmation. Under the premise of ensuring equipment safety, this method significantly improves the energy efficiency of auxiliary machines and provides key technical support for the overall energy-saving goal of the pumping station.

[0099] In summary, the innovation of this module lies in the organic combination of real-time monitoring, model prediction and safety protection, forming a complete energy consumption optimization closed loop. Through adaptive adjustment and continuous optimization, the system can accurately respond to changes in working conditions, meet the demand for thermal management while minimizing energy consumption, and effectively improve the economy and reliability of unattended operation of the pumping station.

[0100] The coordination control module 103 is configured to output a coordination control signal to the frequency converter based on the number of running main machines, the load distribution ratio and the power setting value, and generate an adjustment instruction of the frequency converter;

[0101] In the embodiment of the present application, when the coordination control module outputs a coordination control signal to the frequency converter based on the number of running main machines, the load distribution ratio and the power setting value, and generates an adjustment instruction of the frequency converter, it is specifically configured to:

[0102] Based on the number of running main machines and the load distribution ratio, the control strategy of the main machine is integrated to obtain the coordination control parameter of the main machine;

[0103] Based on the power setting value, the frequency parameter of the auxiliary machine is set to obtain the coordination control parameter of the auxiliary machine;

[0104] Based on the coordination control parameter of the main machine and the coordination control parameter of the auxiliary machine, the instruction of the frequency converter of the main machine and the auxiliary machine is integrated to generate the coordination control signal of the frequency converter;

[0105] The coordination control signal is packaged to obtain the adjustment instruction of the frequency converter.

[0106] Specifically, the control strategy integration of the host machine is achieved by a PLC control algorithm based on the number of running host machines and the load distribution ratio. The system determines the number of host machines to be controlled according to the number of running host machines, and calculates the target power value of each host machine in combination with the load distribution ratio. For the case of single host machine operation, the system directly adjusts the frequency converter output according to the load distribution ratio; for the case of multiple host machines operating in parallel, the system adopts a master-slave control strategy, designates one host machine as the master controller, and the remaining host machines follow the frequency instruction of the master controller, and adjusts the operating frequency of each host machine in real time through a PID algorithm to ensure the accuracy of load distribution.

[0107] Further, the frequency parameter setting of the auxiliary machine based on the power set value is completed by a frequency conversion algorithm. During parameter setting, the system considers the startup characteristics, acceleration time and overload capacity of the auxiliary machine, sets a reasonable frequency change slope to avoid equipment impact caused by sudden frequency change. The system also uses a feedforward control algorithm to dynamically compensate the frequency instruction according to the medium temperature variation trend, improving the timeliness of control.

[0108] Further, the instruction coordination integration of the frequency converters of the host machine and the auxiliary machine based on the coordination control parameters of the host machine and the coordination control parameters of the auxiliary machine is achieved by a timing optimization algorithm. The system establishes an instruction priority mechanism, and the start-stop control of the host machine has the highest priority, and the adjustment instruction of the auxiliary machine is executed after the host machine state is stable. In the timing of sending instructions, the system adopts an interleaved sending strategy to avoid communication congestion caused by multiple frequency converters responding at the same time. At the same time, the system monitors the feedback state of each frequency converter in real time, and ensures the synchronization and accuracy of instruction execution through closed-loop verification to generate coordinated control signals.

[0109] Further, the instruction encapsulation of the coordination control signal is completed by a standard communication protocol. The system encapsulates the control signal according to the Modbus RTU protocol format, including device address, function code, data field and check code. For the frequency instruction, the system uses a 32-bit floating-point number format to ensure control accuracy while considering transmission efficiency. Before sending the instruction, the system performs format verification and collision detection on the encapsulated data packet to ensure the integrity and uniqueness of the instruction. The final generated adjustment instruction is sent to each frequency converter through the RS485 communication bus for execution.

[0110] In summary, this coordination control module realizes the perfect cooperation of host machine and auxiliary machine operation through multi-level control strategy integration and intelligent instruction coordination mechanism. The accurate calculation of control parameters ensures that the equipment operates in the optimal working condition, the timing optimization of instructions ensures the timeliness of system response, and the standardized instruction encapsulation ensures the reliability of control.

[0111] In summary, the innovation of this module lies in integrating dispersed control commands into a unified coordinated control system. Through intelligent decision-making mechanisms and standardized communication protocols, it achieves efficient collaborative operation of pump station equipment. This coordinated control approach not only improves system operating efficiency but also significantly enhances system stability and reliability, providing crucial technical support for unattended automated control of pump stations.

[0112] The safety monitoring module 104 is used to perform multi-parameter safety monitoring on the pump station's operating status after the adjustment command is executed, and to obtain the pump station's safety status indicators.

[0113] In this embodiment of the invention, when the safety monitoring module performs multi-parameter safety monitoring of the pump station's operating status after the adjustment command is executed, and obtains the safety status indicators of the pump station, it is specifically used for:

[0114] After the adjustment command is executed, the pump station's operating status is simultaneously collected from multiple sources to obtain the pressure data and flow data of the pump station's operating status.

[0115] Based on the pressure and flow data, the operating status of the pumping station is evaluated to obtain comprehensive parameters of the pumping station's operating status.

[0116] Based on the aforementioned comprehensive status parameters, a safety status assessment is conducted on the operating status of the pumping station to obtain the safety status index of the pumping station.

[0117] When the safety monitoring module performs a safety situation assessment of the pump station's operating status based on the comprehensive status parameters to obtain the pump station's safety status indicators, it is specifically used for:

[0118] The status parameters are compared item by item with the preset safety threshold range to obtain the abnormality judgment result of the pump station operation status.

[0119] Based on the anomaly determination results, the safety risk coefficient of the pump station's operating status is calculated, wherein the formula for calculating the safety risk coefficient is as follows:

[0120] ;

[0121] In the formula, The aforementioned safety risk coefficient, The number of the state synthesis parameters. The first of the state synthesis parameters The weighting factors of each monitoring parameter The first of the state synthesis parameters Real-time values ​​of each monitoring parameter The first of the state synthesis parameters The midpoint of the safety threshold range corresponding to each monitoring parameter The first of the state synthesis parameters The width of the safety threshold range corresponding to each monitoring parameter To take the absolute value, For summation operations;

[0122] The safety risk coefficient is mapped to a predefined risk level to determine the risk level of the pump station's operating status.

[0123] Based on the risk level, a safety status index for the pumping station is generated.

[0124] The start-stop control module 105 is used to control the start-stop of the main unit of the pumping station based on the safety status indicators and to confirm the operating status of the main unit.

[0125] In this embodiment of the invention, when the start-stop control module performs start-stop control of the main unit of the pumping station based on the safety status indicators and confirms the operating status of the main unit, it is specifically used for:

[0126] Based on the aforementioned safety status indicators, a risk assessment is conducted on the operating status of the pumping station to obtain the operational risk assessment results of the pumping station.

[0127] Based on the operational risk assessment results, start-up and shutdown policy decisions are made for the host, and start-up and shutdown control commands for the host are obtained.

[0128] The start / stop control command is input to the host, and the operating status of the host is confirmed.

[0129] Specifically, the synchronous acquisition of multi-source data on the pump station's operating status after the execution of adjustment commands is achieved through a distributed intelligent sensor network. This system deploys various types of sensors at key process nodes in the pump station, including main pipeline pressure sensors, branch pressure sensors, main pipe flow meters, and zone flow meters. The pressure sensors employ high-precision piezoresistive sensing elements and are equipped with temperature compensation to ensure a measurement accuracy of 0.5 class within the 0-2.5MPa range. The flow sensors, based on the principle of electromagnetic induction and with built-in signal conditioning circuitry, achieve an accuracy of 0.5 class within the 0-500m³ / h measurement range. All sensors synchronously acquire data at a sampling frequency of 10 times per second through a precise time synchronization mechanism and transmit the data in real time to the central processing unit via industrial Ethernet to construct a complete dataset of the pump station's operating status.

[0130] Further, the operation quality assessment of the pump station based on pressure data and flow data is achieved by establishing a multi-dimensional evaluation index system. The system includes three levels of evaluation dimensions: the basic operation layer assesses pressure stability and flow matching degree, the energy efficiency layer assesses system operation efficiency, and the reliability layer assesses equipment operation state. At the basic operation layer, the system calculates the pressure fluctuation coefficient and the flow deviation rate, where the pressure fluctuation coefficient is obtained by statistical analysis of the pressure data of the last 100 sampling points, and the flow deviation rate is calculated by comparing the difference between the actual flow and the set flow. At the energy efficiency layer, the system calculates the pump group efficiency, the pipe network efficiency and the system comprehensive efficiency in real time. At the reliability layer, the system monitors the deviation degree of the equipment operation parameters from the rated value. Through the fuzzy comprehensive evaluation algorithm, after normalizing each dimension index, the weighted fusion is carried out according to the preset weight coefficient, and finally the state comprehensive parameter in the range of 0-1 is obtained, which accurately reflects the overall operation quality of the pump station.

[0131] Specifically, the safety situation assessment of the pump station operation state based on the state comprehensive parameter is achieved by establishing a dynamic risk assessment model. The model considers real-time operation state, historical operation data and equipment characteristics, and adopts the analytic hierarchy process to construct the evaluation framework. First, the system compares the state comprehensive parameter with the preset safety threshold interval in real time, and the safety threshold interval is dynamically adjusted according to the equipment characteristics, operation environment and process requirements. Second, the system analyzes the parameter change trend, and predicts the state development trend through time series analysis algorithm. At the same time, the system combines the equipment operation history record, including maintenance record, fault record and performance degradation data, to assess the health state of the equipment. Finally, the expert system reasoning mechanism is adopted to integrate the evaluation results, and an accurate safety state index is generated, which provides comprehensive and reliable basis for subsequent control decision.

[0132] Further, the state comprehensive parameter is compared with the preset safety threshold interval item by item through intelligent pattern recognition algorithm. The system establishes a three-level threshold system for each key monitoring parameter: normal operation interval, warning interval and dangerous interval. The normal operation interval is set based on the best operation state of the equipment, the warning interval considers the allowable fluctuation range of the equipment, and the dangerous interval takes the safety limit of the equipment as the boundary. The system uses sliding window technology to monitor the parameter change in real time, and identifies parameter abnormalities through anomaly detection algorithm. At the same time, the system establishes a parameter correlation analysis model to analyze the coupling relationship between multiple parameters, effectively distinguishes real abnormalities and false alarms, and significantly improves the accuracy and reliability of abnormality judgment.

[0133] Further, the calculation of the safety risk coefficient of the pump station operation state based on the abnormality determination result is realized by constructing a risk quantification model. The model uses a multi-factor weighted evaluation method, considering four dimensions of the weight of abnormal parameters, abnormal duration, abnormal severity, and abnormal development trend. Each monitoring parameter is given different weight coefficients according to its importance, and important parameters such as main pipeline pressure have higher weights. The system calculates the single risk index by analyzing the amplitude and duration of the parameter deviation from the normal value. At the same time, the system monitors the development trend of the abnormality, and gives higher risk weight to the continuously deteriorating abnormality. Finally, the safety risk coefficient in the range of 0-100 is obtained by weighted summation, which accurately quantifies the safety condition of the pump station operation.

[0134] Further, the mapping of the safety risk coefficient to the predefined risk level is realized by establishing a hierarchical evaluation system. The system sets four risk levels: safe, attention, warning, and danger. Each risk level corresponds to a clear device state description and disposal requirement. The system not only classifies the levels according to the absolute value of the risk coefficient, but also considers the risk change rate, and implements more strict level determination for the rapidly rising risk coefficient. At the same time, the system establishes a level confirmation mechanism, which requires the risk level state to be valid after a certain time, avoiding misjudgment caused by instantaneous fluctuations, and ensuring the accuracy and stability of risk assessment.

[0135] Further, the risk assessment of the pump station operation state based on the safety state index is realized by constructing a comprehensive evaluation model. The model integrates real-time monitoring data, device operation history, environmental parameters, and maintenance records, etc. multi-source information, and uses data fusion technology for comprehensive analysis. The system first analyzes the trend of the safety risk coefficient, and predicts the short-term risk development trend by the exponential smoothing method. Secondly, the system evaluates the severity and urgency of the risk, the severity considers the possible consequences of the risk, and the urgency evaluates the time requirement of risk disposal. At the same time, the system analyzes the propagation characteristics of the risk, and evaluates the influence of single device risk on the overall operation of the system. Finally, a comprehensive risk assessment result is generated, including risk level, development trend, influence range, and disposal priority.

[0136] Further, the start-stop strategy decision for the host based on the operation risk assessment result is realized by establishing an intelligent decision system. The system adopts a decision mechanism combining rule-based reasoning and case-based reasoning. At the rule-based reasoning level, the system matches the pre-set control rule base according to the risk assessment result, and the rule base contains standard disposal processes corresponding to different risk levels. At the case-based reasoning level, the system retrieves similar historical cases and learns from successful disposal experience. During the decision process, the system considers multiple factors such as device safety, process requirements, operation economy, and power grid state, and generates the optimal start-stop strategy through a multi-objective optimization algorithm. For different risk levels, the system implements differentiated control strategies: the safe level maintains the current operation, the attention level implements parameter optimization, the early warning level starts preventive adjustment, and the dangerous level executes protective shutdown.

[0137] Further, the input of the start-stop control instruction to the host and the confirmation of the operation state of the host are realized by establishing a closed-loop control mechanism. The system adopts a three-layer control architecture: instruction sending layer, execution monitoring layer, and effect verification layer. At the instruction sending layer, the system transmits the control instruction to the host controller through a secure communication protocol and implements instruction verification and encryption measures. At the execution monitoring layer, the system monitors the response state of the host in real time, collects key parameters such as operating current, speed, and vibration, and verifies the instruction execution effect. At the effect verification layer, the system evaluates the actual effectiveness of the control measures, quantifies the control effect by comparing the operating parameters before and after control, and establishes an abnormal handling mechanism. When the instruction execution deviation is detected, the system automatically starts the backup control scheme to ensure safe and reliable operation of the system.

[0138] Further, the system also establishes a perfect safety protection mechanism. This includes data security protection, using encryption transmission and storage technology to ensure monitoring data security; operation safety protection, implementing multiple authentication and permission management; device safety protection, setting multiple hardware protection loops. The system regularly performs safety audits, records all operation logs and safety events, and provides complete data support for accident analysis. At the same time, the system has a self-diagnosis function, which can automatically detect sensor faults, communication interruptions, and other abnormal conditions, and start the corresponding emergency disposal program.

[0139] In summary, this safety monitoring and start-stop control system builds a comprehensive pump station safety protection system through advanced data acquisition technology, intelligent analysis algorithm, and reliable control strategy. From accurate data acquisition to deep state assessment, to precise control execution, each link adopts the optimal technical solution to ensure the safety and reliability of the pump station operation to the highest standard.

[0140] In general, the innovation of the system is to perfectly combine modern sensing technology, intelligent analysis algorithm and advanced control theory, and establish an intelligent safety protection system with self-learning and self-adaptive ability. Through the synergistic effect of real-time monitoring, intelligent early warning and precise control, the system can actively identify risks, quickly respond to abnormalities and effectively prevent accidents, providing full technical support for the unattended safe operation of the pump station, and significantly improving the intelligent level and management efficiency of the pump station operation.

[0141] The parameter optimization module 106 is configured to generate a control parameter optimization instruction of the pump station based on the running state of the host machine, so as to complete closed-loop correction of the pump station.

[0142] In the embodiment of the present application, when the parameter optimization module is used to generate a control parameter optimization instruction of the pump station based on the running state of the host machine, so as to complete closed-loop correction of the pump station, it is specifically used for:

[0143] Monitoring the running time and start-stop frequency of the host machine to obtain running load data of the host machine;

[0144] Based on the preset balanced running interval, the running load data is matched and determined to obtain a balanced degree result of the host machine running state;

[0145] Based on the balanced degree result, a control parameter correction instruction is generated;

[0146] The control parameter correction instruction is sent to the control execution interface of the host machine to adjust the running state of the pump station;

[0147] The running load data of the host machine after adjustment is re-monitored to verify the improvement of the host machine running balanced degree, and the closed-loop correction is completed.

[0148] Specifically, the running time and start-stop frequency of the host machine are monitored through a data acquisition system. The system records the running time, start-stop frequency and load change curve of each host machine in real time through PLC, and establishes a complete running file. A sliding time window algorithm is used to calculate the cumulative running time and start-stop frequency of each host machine in 24 hours, and the load rate change trend is combined to generate accurate running load data. The system also monitors the running parameters such as current, voltage and temperature of the host machine to ensure the comprehensiveness and reliability of the load data.

[0149] Further, the matching determination of the operation load data based on the preset balanced operation interval is completed through an intelligent analysis algorithm. The system sets the ideal operation interval of each host, including the single continuous operation time range, the upper limit of daily start-stop times, and the load rate fluctuation range. By comparing the real-time operation data with the preset interval, the operation balance degree index of each host is calculated. The fuzzy comprehensive evaluation method is adopted to comprehensively consider the uniformity of operation time distribution, the rationality of start-stop frequency, and the balance of load distribution, and a quantitative balance degree result is generated to accurately reflect the coordination degree of host operation.

[0150] Specifically, the control parameter correction instruction generated based on the balance degree result is realized through an optimization decision mechanism. According to the balance degree analysis result, the system identifies the host with unbalanced operation and its deviation degree. For the host with too long operation time, the system automatically reduces its operation priority; for the host with too frequent start-stop, the system appropriately prolongs its single operation cycle; for the case of unbalanced load distribution, the load distribution ratio is recalculated. Through the parameter self-tuning algorithm, the control parameter correction instruction containing operation time sequence adjustment, load redistribution, etc. is generated to ensure that the system operation tends to be balanced.

[0151] Further, the control parameter correction instruction is issued to the control execution interface of the host through a standard communication protocol. The system uses Modbus TCP / IP protocol to encapsulate the correction instruction as a standardized data packet, which is transmitted to each host controller through industrial Ethernet. The instruction contains specific frequency setting value, start-stop time plan, and load distribution ratio, etc. During the instruction issuing process, the system implements a double verification mechanism to ensure the accuracy and integrity of the instruction, and records the instruction execution log for subsequent tracking analysis.

[0152] Specifically, the re-monitoring of the operation load data of the adjusted host is realized through a closed-loop feedback mechanism. After the parameter adjustment is executed, the system immediately starts the monitoring program to track the changes of the operation state of each host. By comparing the operation data before and after the adjustment, the improvement of the balance degree index is analyzed. The trend prediction algorithm is adopted to evaluate the sustained effect of parameter adjustment, ensuring the stable and good operation state of the system. At the same time, an effect evaluation model is established to quantify the actual effectiveness of parameter optimization, providing data support for subsequent optimization.

[0153] Further, the system also establishes a parameter optimization self-learning mechanism. By continuously collecting operation data and optimization effect, the setting value of the balanced operation interval is constantly corrected, and the control parameter adjustment strategy is optimized. The machine learning algorithm is adopted to analyze historical optimization cases, establish the mapping relationship between parameter adjustment and operation effect, improve the accuracy and efficiency of optimization decision, and realize the continuous self-improvement of the system.

[0154] In general, the parameter optimization module builds a complete closed-loop optimization system through real-time monitoring, intelligent analysis and accurate adjustment. The accurate monitoring of the operation load provides a data basis for optimization decision, the balance degree determination ensures the pertinence of optimization, and the closed-loop verification mechanism guarantees the reliability of the optimization effect.

[0155] In general, the innovation of the module lies in the organic combination of operation monitoring, intelligent diagnosis and parameter adjustment, forming a self-adaptive optimization mechanism. Through continuous parameter correction and effect verification, the system can automatically maintain the optimal operation state, significantly improving the balance and economy of the pump station operation, and providing important technical support for realizing long-term stable operation.

[0156] Referring to Figure 2 The flowchart of the pump station unattended automation control method based on PLC control and energy-saving algorithm provided by the embodiment of the application is shown. In the embodiment, the pump station unattended automation control method based on PLC control and energy-saving algorithm comprises:

[0157] S1, based on the real-time water consumption data of the pump station, performing dynamic power distribution on the host computer to obtain the number of running hosts and the load distribution ratio of the host computer;

[0158] S2, based on the medium temperature data and the total power data of the host computer, performing energy consumption optimization on the auxiliary machine to obtain the power set value of the auxiliary machine;

[0159] S3, based on the number of running hosts, the load distribution ratio and the power set value, outputting a coordinated control signal to the frequency converter to generate an adjustment instruction of the frequency converter;

[0160] S4, performing multi-parameter safety monitoring on the running state of the pump station after the adjustment instruction is executed to obtain the safety state index of the pump station;

[0161] S5, based on the safety state index, performing start-stop control on the host computer of the pump station to confirm the running state of the host computer;

[0162] S6, based on the running state of the host computer, generating a control parameter optimization instruction of the pump station to complete the closed-loop correction of the pump station.

[0163] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0164] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. The artificial intelligence is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by a digital computer, perceiving an environment, acquiring knowledge and using the knowledge to obtain optimal results.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An unattended automated control system for pumping stations based on PLC control and energy-saving algorithms, characterized in that, The system includes a dynamic power allocation module, an energy consumption optimization module, a coordination control module, a safety monitoring module, a start / stop control module, and a parameter optimization module, wherein: The dynamic power allocation module is used to dynamically allocate power to the main unit based on the real-time water usage data of the pumping station, so as to obtain the number of operating main units and the load allocation ratio. The energy consumption optimization module is used to optimize the energy consumption of the auxiliary machine based on the medium temperature data and the total power data of the host machine, to obtain the power setpoint of the auxiliary machine, including: Based on the medium temperature data and the total power data of the main unit, the operating parameters of the auxiliary unit are fused to obtain the heat load parameters of the auxiliary unit; Based on historical operating data, a correlation analysis was performed on the power parameters of the cooling water pump to obtain the correlation relationship of the power parameters; Based on the aforementioned correlation, the linear relationship coefficient between the power parameter and the heat load parameter is confirmed. Based on the linear relationship coefficient and the heat load parameters, the initial power setpoint of the cooling water pump is calculated, wherein the calculation formula for the initial power setpoint is as follows: ； In the formula, This is the initial power setting value for the cooling water pump. This represents the real-time total power of the host computer. The temperature data of the medium; The initial power setting value is subjected to an operational safety boundary determination to obtain the verification result of the initial power setting value; Based on the verification results, the power setting value of the auxiliary machine is confirmed; The coordination control module is used to output coordination control signals to the frequency converter based on the number of operating hosts, the load distribution ratio, and the power setting value, and to generate adjustment commands for the frequency converter. The safety monitoring module is used to perform multi-parameter safety monitoring on the pump station's operating status after the adjustment command is executed, and to obtain the safety status indicators of the pump station. The start-stop control module is used to control the start-stop of the main unit of the pumping station based on the safety status indicators and to confirm the operating status of the main unit. The parameter optimization module is used to generate control parameter optimization instructions for the pumping station based on the operating status of the host, so as to complete the closed-loop correction of the pumping station.

2. The unattended automated control system for pumping stations based on PLC control and energy-saving algorithms as described in claim 1, characterized in that, When the dynamic power allocation module performs dynamic power allocation to the main unit based on real-time water usage data from the pump station to obtain the number of operating main units and the load allocation ratio, it is specifically used for: Based on the real-time water usage data of the pumping station, the water usage at the user end is determined by range, and the flow range of the water usage at the user end is confirmed. Based on the traffic range, a runtime configuration decision is made for the host to determine the number of hosts running. Based on the number of operating units and the real-time water usage data, the host is load balanced and allocated to obtain the load distribution ratio of the host.

3. The unattended automated control system for pumping stations based on PLC control and energy-saving algorithms as described in claim 1, characterized in that, When the coordination control module executes the output of coordination control signals to the frequency converter and generates adjustment commands for the frequency converter based on the number of operating units of the host, the load distribution ratio, and the power setpoint, it is specifically used for: Based on the number of operating hosts and the load distribution ratio, control strategies for the hosts are integrated to obtain the coordinated control parameters of the hosts. Based on the power setpoint, the frequency conversion parameters of the auxiliary machine are tuned to obtain the coordinated control parameters of the auxiliary machine; Based on the coordination control parameters of the host machine and the coordination control parameters of the auxiliary machine, the inverters of the host machine and the auxiliary machine are integrated with commands to generate the coordination control signal of the inverter. The coordination control signal is encapsulated into instructions to obtain the frequency converter's adjustment instructions.

4. The unattended automated control system for pumping stations based on PLC control and energy-saving algorithms as described in claim 1, characterized in that, When the safety monitoring module performs multi-parameter safety monitoring of the pump station's operating status after the execution of the adjustment command to obtain the pump station's safety status indicators, it is specifically used for: After the adjustment command is executed, the pump station's operating status is simultaneously collected from multiple sources to obtain the pressure data and flow data of the pump station's operating status. Based on the pressure and flow data, the operating status of the pumping station is evaluated to obtain comprehensive parameters of the pumping station's operating status. Based on the aforementioned comprehensive status parameters, a safety status assessment is conducted on the operating status of the pumping station to obtain the safety status index of the pumping station.

5. The unattended automated control system for pumping stations based on PLC control and energy-saving algorithms as described in claim 4, characterized in that, When the safety monitoring module performs a safety situation assessment of the pump station's operating status based on the comprehensive status parameters to obtain the pump station's safety status indicators, it is specifically used for: The status parameters are compared item by item with the preset safety threshold range to obtain the abnormality judgment result of the pump station operation status. Based on the anomaly determination results, the safety risk coefficient of the pump station's operating status is calculated, wherein the formula for calculating the safety risk coefficient is as follows: ； In the formula, The aforementioned safety risk coefficient, The number of the state synthesis parameters. The first of the state synthesis parameters The weighting factors of each monitoring parameter The first of the state synthesis parameters Real-time values ​​of each monitoring parameter The first of the state synthesis parameters The midpoint of the safety threshold range corresponding to each monitoring parameter The first of the state synthesis parameters The width of the safety threshold range corresponding to each monitoring parameter To take the absolute value, For summation operations; The safety risk coefficient is mapped to a predefined risk level to determine the risk level of the pump station's operating status. Based on the risk level, a safety status index for the pumping station is generated.

6. The unattended automated control system for pumping stations based on PLC control and energy-saving algorithms as described in claim 1, characterized in that, When the start-stop control module performs start-stop control of the main unit of the pumping station based on the safety status indicators and confirms the operating status of the main unit, it specifically performs the following functions: Based on the aforementioned safety status indicators, a risk assessment is conducted on the operating status of the pumping station to obtain the operational risk assessment results of the pumping station. Based on the operational risk assessment results, start-up and shutdown policy decisions are made for the host, and start-up and shutdown control commands for the host are obtained. The start / stop control command is input to the host, and the operating status of the host is confirmed.

7. The unattended automated control system for pumping stations based on PLC control and energy-saving algorithms as described in claim 1, characterized in that, When the parameter optimization module executes commands to generate control parameter optimization instructions for the pumping station based on the operating status of the host, in order to complete the closed-loop correction of the pumping station, it is specifically used for: Monitor the runtime and start / stop frequency of the host to obtain the host's operating load data; Based on a preset balanced operating range, the operating load data is matched and judged to obtain the balance result of the host operating status. Based on the balance result, a control parameter correction instruction is generated; The control parameter correction command is sent to the control execution interface of the host to adjust the operating status of the pumping station; The operating load data of the host after the adjustment is re-monitored to verify the improvement in the host's operating balance and complete the closed-loop correction.

8. A method for unattended automated control of pumping stations based on PLC control and energy-saving algorithms, characterized in that, The method includes: S1. Based on the real-time water usage data of the pump station, perform dynamic power allocation on the main unit to obtain the number of operating main units and the load allocation ratio. S2. Based on the medium temperature data and the total power data of the main unit, optimize the energy consumption of the auxiliary unit to obtain the power setting value of the auxiliary unit, including: Based on the medium temperature data and the total power data of the main unit, the operating parameters of the auxiliary unit are fused to obtain the heat load parameters of the auxiliary unit; Based on historical operating data, a correlation analysis was performed on the power parameters of the cooling water pump to obtain the correlation relationship of the power parameters; Based on the aforementioned correlation, the linear relationship coefficient between the power parameter and the heat load parameter is confirmed. Based on the linear relationship coefficient and the heat load parameters, the initial power setpoint of the cooling water pump is calculated, wherein the calculation formula for the initial power setpoint is as follows: ； In the formula, This is the initial power setting value for the cooling water pump. This represents the real-time total power of the host computer. The temperature data of the medium; The initial power setting value is subjected to an operational safety boundary determination to obtain the verification result of the initial power setting value; Based on the verification results, the power setting value of the auxiliary machine is confirmed; S3. Based on the number of operating units of the host, the load distribution ratio, and the power setting value, output a coordination control signal to the frequency converter to generate the adjustment command of the frequency converter; S4. Perform multi-parameter safety monitoring on the pump station's operating status after the adjustment command is executed to obtain the pump station's safety status indicators. S5. Based on the safety status indicators, start and stop the main unit of the pump station to confirm the operating status of the main unit; S6. Based on the operating status of the host, generate control parameter optimization instructions for the pumping station to complete the closed-loop correction of the pumping station.

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