Method, device and medium for determining actual operating characteristics of a variable frequency unit

Abstract

The present invention relates to a method, device and medium for determining the actual operating characteristics of a variable frequency unit. The variable frequency unit is applied to a water pump system. The method includes obtaining operating data of the variable frequency unit and historical frequency data of the water pump; based on the operating data, identifying multiple operating modes by a clustering algorithm to obtain corresponding operating data; for at least part of the multiple operating modes, converting the corresponding operating data into standard operating data under a standard frequency based on the historical frequency data; for the standard operating data, performing an optimization calculation on the operating characteristic curve of the variable frequency unit within a preset rule by using an optimization algorithm to calculate the actual operating characteristic curve of the variable frequency unit; and obtaining the actual operating characteristics of the variable frequency unit based on the actual operating characteristic curve. The present invention realizes online monitoring of the operating characteristics of the variable frequency unit when it is actually working, which is real and convenient, and plays an early warning role for the operating risk and degradation risk of the variable frequency unit.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical pumps, and in particular to a method, a device and a medium for determining actual operating characteristics of a variable frequency unit in a water pump system. Background Art

[0002] Water pump system, also known as pump group, is widely used in chemical, pharmaceutical, new energy, photovoltaic, semiconductor, environmental protection and other industries as liquid medium conveying equipment. In industrial engineering, there is a relatively complete foundation for the theoretical research on the working condition of pump group and the theoretical research on pipe loss. When the pump is running, the instantaneous flow rate, head, shaft power, efficiency and suction vacuum height are called the instantaneous working point of the pump, also known as the supply and demand balance point. This point is the intersection of the pump characteristic curve and the device characteristic curve.

[0003] Existing theories can calculate the operating characteristic curves of unit equipment in a water pump system, but they are rarely applied to online calculations under specific working conditions, and there is no actual operating characteristic curve evaluation of the unit equipment based on the production process. Therefore, it is impossible to determine whether the operating characteristics of the unit equipment have changed during use.

[0004] Moreover, in actual operation, variable frequency units have rich and complex control mechanisms and various operation control strategies, which bring about different degrees of operating condition differences and unit pipeline differences, making the evaluation of the operating characteristic curve of the variable frequency unit equipment more difficult. Summary of the invention

[0005] In response to the above problems, the present invention discloses a method, a computing device and a non-transitory computer-readable storage medium for determining the actual operating characteristics of a variable frequency unit, which calculates multiple operating modes of the variable frequency unit through a clustering algorithm, and calculates the actual operating characteristic curve based on the operating mode and the corresponding standard operating data, thereby obtaining the actual operating characteristics of the variable frequency unit to assist in the subsequent accurate identification and analysis of system operation risks.

[0006] According to a first aspect of the present disclosure, a method for determining actual operating characteristics of a variable frequency unit is provided, wherein the variable frequency unit is applied to a water pump system, and the method comprises obtaining operating data of the variable frequency unit and historical frequency data of the water pump; based on the operating data, identifying multiple operating modes of the variable frequency unit by a clustering algorithm to obtain operating data corresponding to each operating mode; for at least some of the multiple operating modes, converting the corresponding operating data into standard operating data at a standard frequency based on the historical frequency data; for the standard operating data, performing an optimization calculation on the operating characteristic curve of the variable frequency unit within a preset rule by using an optimization algorithm to calculate the actual operating characteristic curve of the variable frequency unit; and obtaining the actual operating characteristics of the variable frequency unit based on the actual operating characteristic curve.

[0007] According to a second aspect of the present disclosure, a computing device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of the first aspect of the present disclosure.

[0008] In a third aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method of the first aspect of the present disclosure.

[0009] In one embodiment, identifying multiple operating modes of a variable frequency unit through a clustering algorithm includes: selecting an operating time window based on historical data; obtaining a historical operating data sequence of the variable frequency unit; selecting operating condition feature data on the historical operating data sequence through the operating time window; performing clustering calculations on the operating condition feature data using a clustering algorithm to obtain multiple cluster categories; and filtering the multiple cluster categories to obtain operating modes corresponding to the filtered cluster categories and operating condition data corresponding to the operating mode.

[0010] In one embodiment, the operating condition characteristic data includes any one of the following items: instantaneous flow data of the frequency conversion unit, head data of the frequency conversion unit, pipe pressure data of the water pipe connected to the frequency conversion unit, number of water pumps turned on in the water pump system, and speed data and historical frequency data of the water pump.

[0011] In one embodiment, filtering multiple clustering categories includes obtaining instantaneous flow data and head data of the variable frequency unit from the operating condition data; performing clustering calculations on the instantaneous flow data and head data using a clustering algorithm to obtain clustering results; sorting multiple clustering categories in the clustering results to filter out filtering categories ranked in a predetermined order from the multiple clustering categories; and obtaining operating condition data and operating condition modes corresponding to the filtering categories.

[0012] In one embodiment, converting the corresponding operating condition data into standard operating condition data at a standard frequency includes obtaining actual frequency data of a water pump in the frequency conversion unit; determining whether the data in the actual frequency data is consistent; in response to the data in the actual frequency data being consistent, converting the operating condition data into standard operating condition data at a standard frequency according to the actual frequency data; in response to the actual frequency data including multiple inconsistent data, performing clustering calculations on the actual frequency to obtain fitting frequency data; and obtaining corresponding operating condition data according to the fitting frequency data, thereby using the corresponding operating condition data as standard operating condition data, wherein the actual frequency data is historical real-time frequency operating condition data.

[0013] In one embodiment, calculating the actual operating characteristic curve of the variable frequency unit includes setting the initial operating characteristic curve of the variable frequency unit to a univariate quadratic curve; formulating constraint conditions and rules; and under the constraint conditions and rules, using an optimization algorithm to perform fitting calculations on the initial operating characteristic curve to obtain the actual operating characteristic curve, wherein the constraint conditions and rules include that the function corresponding to the univariate quadratic curve is an upward convex function, and when the instantaneous flow of the variable frequency unit is zero, the initialization head is not less than a predetermined proportion of the original initialization head.

[0014] In one embodiment, the method also includes fitting a theoretical operating characteristic curve of the variable frequency unit under each operating mode based on the operating mode and the corresponding operating data; determining a hump condition of the actual operating characteristic curve, and comparing the deviation between the actual operating characteristic curve and the theoretical operating characteristic curve; and judging the degradation and operating risk of the variable frequency unit based on the determined hump condition and deviation.

[0015] In one embodiment, fitting the theoretical operating characteristic curve of the variable frequency unit includes calculating the corresponding theoretical curve parameters according to the speed ratio of the water pump when the number of water pumps turned on is one, thereby obtaining the theoretical operating characteristic curve under the standard frequency; and simulating the operating data of the variable frequency unit according to the speed ratio of each water pump and the connection method of the variable frequency unit when the number of water pumps turned on is not less than two, thereby fitting the theoretical operating characteristic curve according to the operating data.

[0016] By applying the disclosed method, the actual operating characteristics of the variable frequency unit can be monitored online. Furthermore, the online operating characteristic curve of the variable frequency unit can be calculated more accurately for the working mode under different operating conditions, and the relevant evaluation quantification can be uniformly performed, so that the performance evaluation characteristics can be more effectively and comprehensively performed based on the obtained operating characteristic curve. The monitoring and prediction of the operating risk and degradation of the variable frequency unit is realized, which provides effective decision support for the operation and maintenance personnel, and is conducive to improving the reliability and maintenance efficiency of the water pump system.

[0017] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements.

[0019] Figure 1 A flow chart of a method for determining actual operating characteristics of a variable frequency unit according to an embodiment of the present disclosure is shown.

[0020] Figure 2A A schematic diagram showing an actual operating characteristic curve and a theoretical operating characteristic curve obtained according to an embodiment of the present disclosure is shown.

[0021] Figure 2B A schematic diagram showing an actual operating characteristic curve and a theoretical operating characteristic curve obtained according to another embodiment of the present disclosure is shown.

[0022] Figure 3 A block diagram of an electronic device 300 for implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0024] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0025] As mentioned above, the prior art does not evaluate the actual operating characteristic curve based on the production process, and cannot determine whether the operating characteristic curve of the equipment changes during use. For example, the equipment degrades during actual use. At the same time, in actual operation, the rich and complex control mechanisms and various operation control strategies of the variable frequency unit make the actual working conditions and unit pipelines different to varying degrees, making it more difficult to evaluate the operating characteristic curve of the variable frequency unit equipment.

[0026] In order to solve the above problems, the present disclosure provides a method for determining the actual operating characteristics of the variable frequency unit, which can not only realize the calculation of the actual operating characteristic curve of the variable frequency unit, but also can perform real-time online operation characteristic curve calculation for the operating modes under different operating conditions, and uniformly perform relevant evaluation and quantification, so as to effectively and comprehensively evaluate the risks and degradation of the variable frequency unit equipment in actual operation. Figure 1A flow chart of a method for determining actual operating characteristics of a variable frequency unit according to an embodiment of the present disclosure is shown, wherein the variable frequency unit is applied to a water pump system. It should be understood that the method may also include additional blocks not shown and / or the blocks shown may be omitted, and the scope of the present disclosure is not limited in this respect.

[0027] like Figure 1 As shown in , the method for determining the actual operating characteristics of the variable frequency unit includes:

[0028] Step 101, obtaining the operating data of the frequency conversion unit and the historical frequency data of the water pump;

[0029] Step 102: Based on the operation data, identify multiple operating modes of the variable frequency unit by using a clustering algorithm to obtain operating data corresponding to each operating mode;

[0030] Step 103: for at least some of the operating modes among the multiple operating modes, convert the corresponding operating data into standard operating data under a standard frequency based on the historical frequency data;

[0031] Step 104: for the standard operating condition data, an optimization algorithm is used to perform optimization calculation on the operating characteristic curve of the variable frequency unit within a preset rule to calculate the actual operating characteristic curve of the variable frequency unit; and

[0032] Step 105: Based on the actual operation characteristic curve, obtain the actual operation characteristics of the variable frequency unit.

[0033] In step 102, it is considered that the variable frequency unit generally adapts to different working conditions by adjusting the number of pumps and the frequency of pumps. Different working conditions mainly come from the instantaneous flow and pipeline pressure itself. Therefore, the identification of the working condition mode mainly adopts the unsupervised clustering method, and the clustering calculation of the working condition mode is performed from the four business aspects of the unit instantaneous flow, pipeline pressure, the number of pumps started by the unit, and the frequency of pumps started by the unit, so as to realize the identification of the working condition mode.

[0034] Since there is no labeled data for this pattern recognition problem, non-label clustering is used for clustering. The specific steps are as follows.

[0035] A working condition time window is selected according to historical data; a historical operation data sequence of the variable frequency unit is obtained; working condition characteristic data is selected on the historical operation data sequence through the working condition time window; a clustering algorithm is used to perform clustering calculation on the working condition characteristic data to obtain multiple clustering categories, and multiple clustering categories are filtered to obtain working condition modes corresponding to the filtered clustering categories, and working condition data corresponding to the working condition modes.

[0036] Optionally, according to the frequency of data collection and the complexity of business scenarios, combined with expert experience and industry knowledge, the operating time window can be selected based on historical data; then appropriate historical operating characteristic data can be selected through the operating time window for data preparation; clustering algorithm is used for clustering calculation, and each type obtained by clustering calculation is recorded as an operating mode. Data fusion based on the identified operating mode can eliminate the calculation conflicts of operating characteristic curves that may be caused by differences in operating conditions at different frequencies.

[0037] It should be understood that the aforementioned historical characteristic data may include the instantaneous flow rate of the unit, the pipeline pressure, the number of pumps started by the unit, and the frequency of pump start-up. The operating condition characteristic data generally includes the instantaneous flow rate data of the variable frequency unit, the head data of the variable frequency unit, the pipe pressure data of the water pipe connected to the variable frequency unit, the number of pumps started in the water pump system, and any one of the speed data and historical frequency data of the water pump. In addition, DBSCAN can be used for algorithm clustering. Preferably, the adjustment of the hyperparameters can ensure that the clustered data accounts for 80% to 90% of all data.

[0038] Optionally, for each operating mode, the operating data may be preprocessed, which may be achieved by screening multiple clustering categories. Specifically, screening multiple clustering categories includes obtaining instantaneous flow data and head data of the variable frequency unit from the operating data; performing clustering calculations on the instantaneous flow data and head data using a clustering algorithm to obtain clustering results; sorting multiple clustering categories in the clustering results to screen out screening categories ranked in a predetermined order from the multiple clustering categories; and obtaining the operating data and operating mode corresponding to the screening category. Abnormal values ​​in the operating condition are eliminated.

[0039] Preferably, the aforementioned clustering algorithm is a DBSCAN algorithm. Preferably, when selecting a screening category ranked in a predetermined order from multiple clustering categories, the number of groups of the clustering categories can be sorted and screened, and the first few groups with a large proportion and a cumulative proportion of 80% are selected as basic data, and the remaining groups are regarded as outliers and are not subject to subsequent calculations.

[0040] In each simulated operating mode, there may be different pump frequency performances. For each single pump, the instantaneous flow of the pump is proportional to the pump speed ratio, and the pump head is proportional to the pump speed ratio. The calculation of the operating characteristic curve requires the specified speed condition. Therefore, according to the actual situation, according to the pump speed (often converted into the pump frequency), the operating condition data can be adjusted and integrated to prepare for the subsequent curve calculation.

[0041] Therefore, if only a single pump is turned on in the variable frequency unit or multiple pumps are turned on and the frequencies of these pumps are the same, the historical real-time frequency can be used to adjust the operating data (instantaneous flow data and head data) to the standard operating data under the standard frequency for subsequent calculations. If multiple pumps are turned on in the variable frequency unit, but the frequencies of these pumps are different, DBSCAN clustering is performed again according to the real-time frequency of each pump, and the top few groups with a large proportion and a cumulative proportion of 80% are selected as multiple groups of data for subsequent calculations.

[0042] Therefore, in step 103, converting the corresponding operating condition data into standard operating condition data under the standard frequency may include obtaining the actual frequency data of the water pump in the frequency conversion unit; judging whether the data in the actual frequency data are consistent; in response to the data in the actual frequency data being consistent, correcting the operating condition data to the standard operating condition data under the standard frequency according to the actual frequency data; in response to the actual frequency data including multiple inconsistent data, performing clustering calculation on the actual frequency to obtain fitting frequency data; and obtaining the corresponding operating condition data according to the fitting frequency data, thereby using the obtained corresponding operating condition data as the standard operating condition data. According to the frequency of the pump, correcting the operating condition data to the standard operating condition data under the standard frequency can avoid conflicts caused by the frequency in the calculation of the actual operating characteristic curve, and prepare for subsequent curve calculations. The aforementioned standard frequency may be the rated frequency of the water pump, wherein the actual frequency data is the historical real-time frequency operating condition data.

[0043] After clustering calculation and screening, the operating characteristic curves under multiple frequency groups and operating modes are calculated and evaluated, including the calculation of actual operating characteristic curves, the calculation of theoretical operating characteristic curves, the comparison, extraction and quantification of curve characteristics, etc., and finally these evaluation results are output as an overall evaluation result.

[0044] In step 104, calculating the actual operating characteristic curve of the variable frequency unit includes setting the initial operating characteristic curve of the variable frequency unit to a univariate quadratic curve; formulating constraint conditions and rules; and under the constraint conditions and rules, using an optimization algorithm to perform fitting calculations on the initial operating characteristic curve to obtain the actual operating characteristic curve, wherein the constraint conditions and rules include that the function corresponding to the univariate quadratic curve is an upward convex function, the curvature of the univariate quadratic curve is greater than the curvature of the theoretical curve, and when the instantaneous flow of the variable frequency unit is zero, the initialization head is not less than a predetermined proportion of the original initialization head.

[0045] The actual operation characteristic curve is calculated for a set of operating data under each operating mode. The calculation first determines that the function form of the curve is a quadratic function. Then, the curve parameters are added to constrain the curve to be an upward convex function, and the curvature of the curve is greater than the theoretical curve, that is, the actual operation characteristic curve cannot be better than the theoretical operation characteristic curve. When the instantaneous flow rate is 0, the initialization head cannot be lower than the predetermined proportion of the original initialization head. Finally, the optimization algorithm (optimization algorithm) is used to fit the actual operation characteristic curve. Preferably, the initialization head cannot be lower than the predetermined proportion of the original initialization head by 15%. Optionally, the optimization algorithm can be a linear programming method, a differential evolution method, a genetic algorithm, etc., which is not limited here.

[0046] In step 105, after calculating and obtaining the actual operating characteristic curve of the variable frequency unit, the method may also include fitting the theoretical operating characteristic curve of the variable frequency unit under each operating mode based on the operating mode and the corresponding operating data; determining the hump condition of the actual operating characteristic curve, and comparing the deviation between the actual operating characteristic curve and the theoretical operating characteristic curve; and judging the degradation and operating risk of the variable frequency unit based on the determined hump condition and deviation.

[0047] The number of theoretical operating characteristic curves is consistent with the number of actual operating characteristic curves. If the unit only starts one pump, the theoretical operating characteristic curve at a specific frequency level is obtained based on the pump speed ratio. If the unit starts two or more pumps, the operating data of each pump is simulated according to the speed ratio of each pump; the unit operating data is simulated according to the simulated operating data and the unit connection method; and the unit operating data is fitted with the unit theoretical operating characteristic curve data.

[0048] Therefore, in step 105, fitting the theoretical operating characteristic curve of the variable frequency unit includes calculating the corresponding theoretical curve parameters according to the speed ratio of the water pump when the number of water pumps turned on is one, thereby obtaining the theoretical operating characteristic curve under the standard frequency; and simulating the operating data of the variable frequency unit according to the speed ratio of each water pump and the connection method of the variable frequency unit when the number of water pumps turned on is not less than two, thereby fitting the theoretical operating characteristic curve according to the operating data.

[0049] In step 105, actual operation characteristic data is obtained based on the actual operation characteristic curve. Specifically, it may include following up the actual operation characteristic curve result, extracting important curve shape features, such as: the extreme value of the actual operation characteristic curve, the ratio of the curve maximum value to the curve initial value; comparing the actual operation characteristic curve with the theoretical operation characteristic curve, extracting comparative features, such as: the relative position of the curve, whether the curve has an intersection, etc.; and other features, such as: the operating condition interval where the historical operating condition point is located, etc.

[0050] Optionally, a knowledge base may be created based on business experience, and each operating characteristic curve may be inferred and evaluated according to the inference mechanism to define the performance type of the operating characteristic curve. The evaluation results of the operating characteristic curves under the multiple frequency groups and working modes calculated above are combined and summarized, and the types of operating characteristic curves showing signs of degradation are output as an overall evaluation result.

[0051] Optionally, based on the evaluation levels of variable frequency units and single pumps, the indicator network of the equipment, the characteristic calculation and quantitative analysis of the indicators, combined with expert experience and product manuals, a knowledge base can be created to build a rule model for root cause inference, put forward relevant suggestions, and assist in related preventive maintenance and water pump operation optimization.

[0052] For example, Figure 2A As shown, Q is the flow rate and H is the head. The extreme point Q_e of the actual operation characteristic curve is greater than 0, the extreme value h_max / h_0>1.02, and within the selected calculated operating time window, the proportion of historical flow values ​​less than the extreme point Q_e of the actual operation characteristic curve is greater than or equal to the threshold (for example, 60%). At this point, it can be seen that a hump appears on the actual operation characteristic curve, the hump is obvious, and the historical operating points are mainly located in front of the curve hump. This indicates that the equipment operation stability is extremely poor, and the same head will correspond to two flow rates. The inferred cause may be that the equipment is surging, and corresponding risk warnings and inferred cause reminders can be made.

[0053] In another possible outcome, Figure 2B As shown, Q is the flow rate and H is the head. The extreme point Q_e of the actual operation characteristic curve is greater than 0, and the extreme value h_max / h_0<=1.02; the deviation of the actual operation characteristic curve from the theoretical operation characteristic curve is greater than the threshold (for example, 8%); the actual operation characteristic curve and the theoretical operation characteristic curve have no intersection; the actual operation characteristic curve is lower than the theoretical operation characteristic curve. At this time, a hump appears on the actual operation characteristic curve, but the hump is not obvious, has a significant offset, and does not intersect with the theoretical operation characteristic curve, and is located below the theoretical operation characteristic curve. This indicates that the operating performance of the equipment has degraded and the overall pump head has decreased. The inferred causes include blockage of the impeller flow channel, internal leakage, or cavitation at the inlet, and corresponding risk warnings and inferred cause reminders can be made.

[0054] By applying the disclosed method, a more effective and comprehensive evaluation of performance characteristics can be achieved.

[0055] The actual operation characteristic curve evaluation method of the variable frequency unit provided by the present invention fills the gap in real-time online analysis of the operation characteristic curve. It can evaluate the calculation of the real-time operation characteristic curve based on the actual production process to determine whether the operation characteristic curve of the equipment changes during use.

[0056] Moreover, the pattern recognition method is introduced in the actual operation of the variable frequency unit. The complex operating state of the variable frequency unit in actual operation is split and identified, which effectively reduces the difficulty of evaluating the operating characteristic curve of the variable frequency unit equipment. The pattern recognition can explore the performance of the operating characteristic curve under different working modes, and provide relevant features for the overall performance evaluation of the equipment.

[0057] In addition, data fusion is performed based on the identified operating mode to eliminate the curve calculation conflicts that may be caused by differences in operating conditions at different frequencies. The actual operating characteristic curve evaluation is quantified, and the operating characteristic curves under multiple frequency groups and operating modes are calculated and evaluated, and these evaluation results are output as an overall evaluation result, which is conducive to maintenance personnel to find problems in time and conduct subsequent fault diagnosis.

[0058] The present disclosure also provides a computing device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform any step of the aforementioned method.

[0059] The present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any step of the aforementioned method.

[0060] Figure 3 The block diagram of the electronic device 300 for implementing the embodiment of the present disclosure is shown. The electronic device 300 can be used to implement Figure 1 As shown in the figure, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 302 or computer program instructions loaded from a storage unit 308 to a random access memory (RAM) 303. In RAM303, various programs and data required for the operation of the electronic device 300 can also be stored. The central processing unit (CPU) 301, ROM302 and RAM303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0061] A number of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0062] The central processing unit (CPU) 301 performs the various methods and processes described above, such as performing method 200. For example, in some embodiments, method 200 may be implemented as a computer software program, which is stored in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the central processing unit (CPU) 301, one or more operations of the method 200 described above may be performed. Alternatively, in other embodiments, the central processing unit (CPU) 301 may be configured to perform one or more actions of method 200 in any other appropriate manner (e.g., by means of firmware).

[0063] The present disclosure may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present disclosure.

[0064] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0065] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0066] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0067] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0068] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0069] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0070] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0071] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

[0072] The above are only optional embodiments of the present disclosure and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining the actual operating characteristics of a variable frequency unit, the variable frequency unit being applied to a water pump system, characterized in that: include: Obtaining the operating data of the variable frequency unit and the historical frequency data of the water pump; Based on the operation data, identifying multiple operating modes of the variable frequency unit by a clustering algorithm to obtain operating data corresponding to each operating mode; For at least some of the multiple operating modes, the corresponding operating data are converted into standard operating data at a standard frequency based on the historical frequency data, which includes obtaining actual frequency data of a water pump in the variable frequency unit, determining whether the data in the actual frequency data are consistent, and in response to the data in the actual frequency data being consistent, converting the operating data into standard operating data at a standard frequency according to the actual frequency data, and in response to the actual frequency data including a plurality of inconsistent data, performing clustering calculation on the actual frequency data to obtain fitting frequency data, and obtaining corresponding operating data according to the fitting frequency data, thereby using the corresponding operating data as standard operating data; For the standard operating condition data, an optimization algorithm is used to perform optimization calculation on the operating characteristic curve of the variable frequency unit within a preset rule to calculate the actual operating characteristic curve of the variable frequency unit; as well as Based on the actual operation characteristic curve, the actual operation characteristic of the variable frequency unit is obtained.

2. The method according to claim 1, characterized in that Calculating the multiple operating modes of the variable frequency unit by clustering algorithm includes: Select the working time window based on historical data; Acquire a historical operation data sequence of the variable frequency unit; Selecting operating condition characteristic data from the historical operation data sequence through the operating condition time window; Using a clustering algorithm to perform clustering calculation on the operating condition characteristic data to obtain a plurality of clustering categories; and The plurality of cluster categories are screened to obtain operating mode corresponding to the screened cluster category and operating data corresponding to the operating mode.

3. The method according to claim 2, characterized in that The operating condition characteristic data includes any one of the following items: The instantaneous flow data of the variable frequency unit, the head data of the variable frequency unit, the pipe pressure data of the water pipe connected to the variable frequency unit, the number of water pumps turned on in the water pump system, and the speed data and historical frequency data of the water pump.

4. The method according to claim 3, characterized in that Screening the multiple cluster categories includes: Acquire instantaneous flow data and head data of the variable frequency unit from the operating condition data; Using a clustering algorithm to perform clustering calculation on the instantaneous flow data and the head data to obtain a clustering result; sorting the multiple categories in the clustering result to filter out the filtered categories ranked in a predetermined order from the multiple categories; and The operating condition data and the operating condition mode corresponding to the screening category are obtained.

5. The method according to claim 4, characterized in that Calculating the actual operating characteristic curve of the variable frequency unit includes: Setting the initial operation characteristic curve of the variable frequency unit to a quadratic curve; Establishing constraint rules; and Under the constraint condition rules, the initial operation characteristic curve is fitted and calculated using an optimization algorithm to obtain the actual operation characteristic curve. The constraint condition rule includes that the function corresponding to the one-variable quadratic curve is an upward convex function, and when the instantaneous flow of the variable frequency unit is zero, the initialization head is not less than a predetermined proportion of the original initialization head.

6. The method according to claim 5, characterized in that The method further comprises: Based on the identification of the operating mode and the corresponding operating data, fitting the theoretical operating characteristic curve of the variable frequency unit under each operating mode; Determining a hump of the actual operating characteristic curve, and comparing a deviation between the actual operating characteristic curve and a theoretical operating characteristic curve; and The operation risk of the variable frequency unit is determined based on the determined hump condition and the deviation.

7. The method according to claim 6, characterized in that Fitting the theoretical operating characteristic curve of the variable frequency unit includes: When the number of water pumps turned on is one, the corresponding theoretical curve parameters are calculated according to the speed ratio of the water pumps, so as to obtain the theoretical operating characteristic curve under the standard frequency; and When the number of water pumps turned on is not less than two, the operating data of the variable frequency unit is simulated according to the speed ratio of each water pump and the connection mode of the variable frequency unit, so as to fit the theoretical operation characteristic curve according to the operating data.

8. A computing device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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