A digital twin mechanism model optimization method and system

Abstract

The present invention discloses a digital twin mechanism model optimization method and system, which specifically relates to the field of digital twin technology, including S1, model establishment area determination, determining the data within the target project water supply area as the target model establishment area, and dividing it into sub-areas according to an equal time division method, and numbering each sub-area within the target model establishment area as 1, 2,..., n in sequence; S2, data acquisition, real-time acquisition of water conservancy professional information and project water supply data in the model establishment area, and transmitting the project water supply data to the data processing step, and transmitting the water conservancy professional information to the model construction step; the present invention adopts an advanced digital twin mechanism model, has excellent real-time performance, can easily realize real-time monitoring and dynamic adjustment of physical entities, and has advanced data processing technology, can efficiently process massive, heterogeneous data.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and more specifically, to a digital twin mechanism model optimization method and system. Background Art

[0002] With the continuous development of information technology, digital twin technology has been widely used in various fields. The digital twin mechanism model is the core component of the digital twin system. Its accuracy and reliability directly affect the performance of the digital twin system. Therefore, the development of a new digital twin mechanism model optimization method and system plays an important role in the field of digital twin technology.

[0003] Traditional digital twin model building methods include data collection, data processing, and model construction; data collection collects various types of data from real-world systems, including sensor data, historical data, and real-time data; data processing integrates, processes, and fuses the collected data to form the basic data of the digital twin; model construction will be based on the processed data and use advanced digital technology to build a digital twin mechanism model.

[0004] However, in actual use, it still has some shortcomings, such as inaccurate data. The traditional digital twin mechanism model system lacks real-time performance, making it difficult to achieve real-time monitoring and dynamic adjustment of physical entities, and the data processing technology is relatively backward, making it difficult to effectively process massive and heterogeneous data; the data is not comprehensive, and the traditional system mainly relies on a single data source, resulting in limited data coverage; it lacks the ability to collect data from multiple angles and levels, and cannot fully reflect the true state and behavior of physical objects; the traditional system cannot optimize data results, lacks comprehensive data insights and decision support functions, and cannot provide decision makers with accurate and timely information.

[0005] Therefore, there is an urgent need to provide a digital twin mechanism model optimization method and system to solve the problems of inaccurate data, incomplete data and weak decision-making ability of the existing digital twin mechanism model system. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a digital twin mechanism model optimization method and system, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a digital twin mechanism model optimization method, comprising:

[0008] S1. Determine the model establishment area. Determine the data within the target project water supply area as the target model establishment area, divide it into sub-areas according to the equal time division method, and number each sub-area within the target model establishment area as 1, 2, ..., n in sequence;

[0009] S2. Data acquisition: Real-time acquisition of water conservancy information and engineering water supply data within the model establishment area, and delivery of engineering water supply data to the data processing step, and delivery of water conservancy information to the model construction step;

[0010] S3, data processing, processing the project water supply data obtained in S2 to obtain the pump group data influence coefficient, the water pipeline data influence coefficient and the motor data influence coefficient, and transmitting them to the comprehensive data analysis step;

[0011] S4: Model construction. Based on the water conservancy professional information and historical water conservancy professional information obtained in S2, a digital twin mechanism model of the pump group project water supply process is established to simulate the relevant characteristics of the pump group, water pipeline, and motor in the project water supply process, forming an integrated digital twin mechanism model, obtaining the project water supply simulation data, and transmitting it to the model data processing step;

[0012] S5, model data processing, processing the engineering water supply simulation data obtained in S4 to obtain the influence coefficient of the pump group simulation data, the influence coefficient of the water pipeline simulation data, and the influence coefficient of the motor simulation data, and transmitting them to the comprehensive data analysis step;

[0013] S6. Comprehensive data analysis: import the pump group data influence coefficient value, water pipeline data influence coefficient value, motor data influence coefficient value, pump group simulation data influence coefficient value, water pipeline simulation data influence coefficient value, and motor simulation data influence coefficient value obtained in S3 and S5 into the model construction rationality index mathematical model to obtain the model construction rationality index value, and transmit it to the model evaluation and judgment step;

[0014] S7, model evaluation and judgment, comparing the model construction rationality index obtained in S6 with the preset model construction rationality index, calculating the difference between the model construction rationality index and the preset model construction rationality index, when the difference is less than the preset difference, delivering the judgment result and the digital twin mechanism model data to the human-computer interaction step, when the difference is greater than the preset difference, delivering the digital twin mechanism model data as historical water conservancy professional information to the model construction step;

[0015] S8, human-computer interaction, transmits the judgment results obtained in S7 and the digital twin mechanism model data to the user information terminal.

[0016] Preferably, the engineering water supply data pump group data includes influencing parameters, water pipeline data influencing parameters and motor data influencing parameters;

[0017] The pump group data influencing parameters include the density of the delivered liquid, denoted as ; Liquid flow rate, denoted as Q; Water pump head, denoted as h; Water pump input power, denoted as P; Parameters affecting water pipeline data include liquid flow rate, denoted as Q, water pressure in water pipeline, denoted as p, water pipeline flow velocity, denoted as v, and water pipeline leakage, denoted as L; Parameters affecting motor data include motor current, denoted as I, motor voltage, denoted as U, motor speed, denoted as V, and motor torque, denoted as t.

[0018] Preferably, the data processing step includes a pump group data influence coefficient calculation step, a water pipeline data influence coefficient calculation step, and a motor data influence coefficient calculation step;

[0019] The pump group data influence coefficient calculation step is used to import the pump group data influence parameters into the pump group data influence coefficient mathematical model to obtain the pump group data influence coefficient value; the water pipeline data influence coefficient calculation step is used to import the water pipeline data influence parameters into the water pipeline data influence coefficient mathematical model to obtain the water pipeline data influence coefficient value; the motor data influence coefficient calculation step is used to import the motor data influence parameters into the motor data influence coefficient mathematical model to obtain the motor data influence coefficient value.

[0020] Preferably, the mathematical model of the pump group data influence coefficient is specifically:

[0021] ,

[0022] The mathematical model of the water pipeline data influence coefficient is as follows:

[0023] ,

[0024] The mathematical model of the motor data influence coefficient is as follows:

[0025] ,

[0026] Where g represents the acceleration due to gravity, Q i represents the liquid flow rate during the i-th time period, represents the density of the transported liquid in the i-th time period, h i represents the pump head in the i-th time period, P i represents the pump input power in the i-th time period, v i represents the flow rate of the water pipeline in the i-th time period, v max Indicates the maximum allowable flow rate of the water pipeline, L i represents the leakage of the water pipeline in the i-th time period; p i The water pressure in the water pipeline during the i-th time period, pmax Indicates the maximum allowable water pressure in the water pipeline, p min Indicates the minimum allowable water pressure in the water pipeline. Indicates the rated water pressure of the water pipeline, U i Represents the motor voltage in the i-th time period, I i Represents the motor current in the i-th time period, V i Indicates the motor speed in the i-th time period, t i Represents the motor torque in the i-th time period.

[0027] Preferably, the engineering water supply simulation data includes influencing parameters of pump group simulation data, influencing parameters of water pipeline simulation data and influencing parameters of motor simulation data;

[0028] The pump group data influencing parameters include the simulated density of the transported liquid, denoted as ; The simulated flow rate of the transported liquid is recorded as Q s ; The simulated head of the water pump is recorded as h s ; The simulated input power of the pump is denoted as P s ; The parameters affecting the water pipeline data include the simulated flow rate of the transported liquid, denoted as Q s , simulated water pressure in the water pipeline, denoted as p s , simulated flow velocity of the water pipeline, denoted as v s , simulated leakage of water pipeline, denoted as L s ; The parameters affecting the motor data include the motor simulation current, denoted as I s , motor analog voltage, denoted as U s , the motor simulation speed, denoted as V s , the motor simulated torque, denoted as t s .

[0029] Preferably, the model data processing includes the steps of calculating the influence coefficient of the pump group simulation data, the water pipeline simulation data and the motor simulation data;

[0030] The pump group simulation data influence coefficient calculation step is used to import the pump group simulation data influence parameters into the pump group simulation data influence coefficient mathematical model to obtain the pump group simulation data influence coefficient value; the water pipeline simulation data influence coefficient calculation step is used to import the water pipeline simulation data influence parameters into the water pipeline simulation data influence coefficient mathematical model to obtain the water pipeline simulation data influence coefficient value; the motor simulation data influence coefficient calculation step is used to import the motor simulation data influence parameters into the motor simulation data influence coefficient mathematical model to obtain the motor simulation data influence coefficient value.

[0031] Preferably, the mathematical model of the influence coefficient of the pump group simulation data is specifically:

[0032] ,

[0033] The mathematical model of the water pipeline data influence coefficient is as follows:

[0034] ,

[0035] The mathematical model of the motor data influence coefficient is as follows:

[0036] ,

[0037] Where g represents the acceleration due to gravity, v max Indicates the maximum allowable flow rate of the water pipeline, p max Indicates the maximum allowable water pressure in the water pipeline, p min Indicates the minimum allowable water pressure in the water pipeline. Indicates the rated water pressure of the water pipeline.

[0038] Preferably, the model construction rationality index mathematical model is specifically:

[0039] ,

[0040] in represents the engineering water supply data evaluation index, Represents the evaluation index of engineering water supply simulation data.

[0041] Preferably, the engineering water supply data evaluation index is specifically:

[0042] ,

[0043] The specific evaluation index of engineering water supply simulation data is as follows:

[0044] .

[0045] Preferably, a digital twin mechanism model optimization system includes:

[0046] Model establishment area determination module: used to determine the data within the target project water supply area as the target model establishment area, and divide it into sub-areas according to the equal time division method, and number each sub-area within the target model establishment area as 1, 2, ..., n in sequence;

[0047] Data acquisition module: used to obtain water conservancy professional information and engineering water supply data in the model building area in real time, and transmit the engineering water supply data to the data processing module, and transmit the water conservancy professional information to the model building module;

[0048] Data processing module: used to process the engineering water supply data obtained by the data acquisition module to obtain the pump group data influence coefficient, the water pipeline data influence coefficient and the motor data influence coefficient, and transmit them to the comprehensive data analysis module;

[0049] Model building module: This module is used to establish a digital twin mechanism model of the pump group project water supply process based on the water conservancy professional information and historical water conservancy professional information obtained by the data acquisition module. It simulates the relevant characteristics of the pump group, water pipeline, and motor during the project water supply process, forms an integrated digital twin mechanism model, obtains the project water supply simulation data, and transmits it to the model data processing module.

[0050] Model data processing module: used to process the engineering water supply simulation data obtained by the model construction module to obtain the influence coefficient of the pump group simulation data, the influence coefficient of the water pipeline simulation data and the influence coefficient of the motor simulation data, and transmit them to the comprehensive data analysis module;

[0051] Comprehensive data analysis module: used to import the pump group data influence coefficient value, water pipeline data influence coefficient value, motor data influence coefficient value, pump group simulation data influence coefficient value, water pipeline simulation data influence coefficient value and motor simulation data influence coefficient value obtained in the data processing module and the model data processing module into the model construction rationality index mathematical model to obtain the model construction rationality index value, and transmit it to the model evaluation and judgment module;

[0052] Model evaluation and judgment module: used to compare the model construction rationality index obtained by the comprehensive data analysis module with the preset model construction rationality index, calculate the difference between the model construction rationality index and the preset model construction rationality index, and when the difference is less than the preset difference, transmit the judgment result and the digital twin mechanism model data to the human-computer interaction module; when the difference is greater than the preset difference, transmit the digital twin mechanism model data as historical water conservancy professional information to the model construction module;

[0053] Human-computer interaction module: used to transmit the judgment results obtained by the model evaluation and judgment module and the digital twin mechanism model data to the user information end.

[0054] Technical effects and advantages of the present invention:

[0055] The present invention adopts an advanced digital twin mechanism model with excellent real-time performance, which can easily realize real-time monitoring and dynamic adjustment of physical entities. In addition, the advanced data processing technology can efficiently process massive and heterogeneous data.

[0056] This invention integrates multiple data sources and builds a digital twin mechanism model from multiple perspectives, including pump groups, water pipelines, and motors, ensuring extensive data coverage. It has the ability to collect data from multiple angles and levels, fully and accurately reflecting the real state and behavior of physical objects.

[0057] The present invention has a data feedback function, can optimize data, provides comprehensive data insight and decision support functions, and can provide decision makers with accurate and timely information. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0059] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] As attached Figure 1 A digital twin mechanism model optimization method shown includes: S1, model establishment area determination, S2, data acquisition, S3, data processing, S4, model construction, S5, model data processing, S6, comprehensive data analysis, S7, model evaluation and judgment, and S8, human-computer interaction.

[0062] S1. Determine the model establishment area. Determine the data within the target project water supply area as the target model establishment area, and divide it into sub-areas according to the equal time division method. The sub-areas within the target model establishment area are numbered 1, 2, ..., n in sequence.

[0063] S2. Data collection: real-time acquisition of water conservancy professional information and engineering water supply data in the model establishment area, and delivery of engineering water supply data to the data processing step, and delivery of water conservancy professional information to the model construction step.

[0064] In this embodiment, it should be specifically explained that the engineering water supply data pump group data includes influencing parameters, water pipeline data influencing parameters and motor data influencing parameters.

[0065] In this embodiment, it should be specifically noted that the pump group data influencing parameters include the density of the transported liquid, which is denoted as ; Liquid flow rate, denoted as Q; Water pump head, denoted as h; Water pump input power, denoted as P; Parameters affecting water pipeline data include liquid flow rate, denoted as Q, water pressure in water pipeline, denoted as p, water pipeline flow velocity, denoted as v, and water pipeline leakage, denoted as L; Parameters affecting motor data include motor current, denoted as I, motor voltage, denoted as U, motor speed, denoted as V, and motor torque, denoted as t.

[0066] S3, data processing, processes the engineering water supply data obtained in S2 to obtain the pump group data influence coefficient, the water pipeline data influence coefficient and the motor data influence coefficient, and transmits them to the comprehensive data analysis step.

[0067] In this embodiment, it should be specifically explained that the data processing step includes a pump group data influence coefficient calculation step, a water pipeline data influence coefficient calculation step, and a motor data influence coefficient calculation step.

[0068] In this embodiment, it is specifically necessary to explain that the pump group data influence coefficient calculation step is used to import the pump group data influence parameters into the pump group data influence coefficient mathematical model to obtain the pump group data influence coefficient value; the water pipeline data influence coefficient calculation step is used to import the water pipeline data influence parameters into the water pipeline data influence coefficient mathematical model to obtain the water pipeline data influence coefficient value; the motor data influence coefficient calculation step is used to import the motor data influence parameters into the motor data influence coefficient mathematical model to obtain the motor data influence coefficient value.

[0069] In this embodiment, it should be specifically noted that the mathematical model of the pump group data influence coefficient is specifically:

[0070] ,

[0071] The mathematical model of the water pipeline data influence coefficient is as follows:

[0072] ,

[0073] The mathematical model of the motor data influence coefficient is as follows:

[0074] ,

[0075] Where g represents the acceleration due to gravity, Q i represents the liquid flow rate during the i-th time period, represents the density of the transported liquid in the i-th time period, h i represents the pump head in the i-th time period, P i represents the pump input power in the i-th time period, v i represents the flow rate of the water pipeline in the i-th time period, v max Indicates the maximum allowable flow rate of the water pipeline, Li represents the leakage of the water pipeline in the i-th time period; p i The water pressure in the water pipeline during the i-th time period, p max Indicates the maximum allowable water pressure in the water pipeline, p min Indicates the minimum allowable water pressure in the water pipeline. Indicates the rated water pressure of the water pipeline, U i Represents the motor voltage in the i-th time period, I i Represents the motor current in the i-th time period, V i Indicates the motor speed in the i-th time period, t i Represents the motor torque in the i-th time period.

[0076] S4. Model construction: Based on the water conservancy professional information and historical water conservancy professional information obtained in S2, a digital twin mechanism model of the pump group project water supply process is established to simulate the relevant characteristics of the pump group, water pipeline and motor in the project water supply process, forming an integrated digital twin mechanism model, obtaining the project water supply simulation data, and transmitting it to the model data processing step.

[0077] In this embodiment, it should be specifically explained that the engineering water supply simulation data includes influencing parameters of pump group simulation data, influencing parameters of water pipeline simulation data and influencing parameters of motor simulation data.

[0078] In this embodiment, it should be specifically noted that the pump group data influencing parameters include the simulated density of the transported liquid, denoted as ; The simulated flow rate of the transported liquid is recorded as Q s ; The simulated head of the water pump is recorded as h s ; The simulated input power of the pump is denoted as P s ; The parameters affecting the water pipeline data include the simulated flow rate of the transported liquid, denoted as Q s , simulated water pressure in the water pipeline, denoted as p s , simulated flow velocity of the water pipeline, denoted as v s , simulated leakage of water pipeline, denoted as L s ; The parameters affecting the motor data include the motor simulation current, denoted as I s , motor analog voltage, denoted as U s , the motor simulation speed, denoted as V s , the motor simulated torque, denoted as t s .

[0079] S5, model data processing, processes the engineering water supply simulation data obtained in S4 to obtain the influence coefficient of the pump group simulation data, the influence coefficient of the water pipeline simulation data and the influence coefficient of the motor simulation data, and transmits them to the comprehensive data analysis step.

[0080] In this embodiment, it should be specifically explained that the model data processing includes the steps of calculating the influence coefficient of the pump group simulation data, the water pipeline simulation data and the motor simulation data.

[0081] In this embodiment, it is specifically necessary to explain that the pump group simulation data influence coefficient calculation step is used to import the pump group simulation data influence parameters into the pump group simulation data influence coefficient mathematical model to obtain the pump group simulation data influence coefficient value; the water pipeline simulation data influence coefficient calculation step is used to import the water pipeline simulation data influence parameters into the water pipeline simulation data influence coefficient mathematical model to obtain the water pipeline simulation data influence coefficient value; the motor simulation data influence coefficient calculation step is used to import the motor simulation data influence parameters into the motor simulation data influence coefficient mathematical model to obtain the motor simulation data influence coefficient value.

[0082] In this embodiment, it should be specifically noted that the mathematical model of the influence coefficient of the pump group simulation data is specifically:

[0083] ,

[0084] The mathematical model of the influence coefficient of water pipeline simulation data is as follows:

[0085] ,

[0086] The mathematical model of the motor data simulation influence coefficient is as follows:

[0087] ,

[0088] Where g represents the acceleration due to gravity, v max Indicates the maximum allowable flow rate of the water pipeline, p max Indicates the maximum allowable water pressure in the water pipeline, p min Indicates the minimum allowable water pressure in the water pipeline. Indicates the rated water pressure of the water pipeline.

[0089] S6. Comprehensive data analysis: import the pump group data influence coefficient value, water pipeline data influence coefficient value, motor data influence coefficient value, pump group simulation data influence coefficient value, water pipeline simulation data influence coefficient value and motor simulation data influence coefficient value obtained in S3 and S5 into the model construction rationality index mathematical model to obtain the model construction rationality index value, and transmit it to the model evaluation and judgment step.

[0090] In this embodiment, it should be specifically explained that the mathematical model of the model construction rationality index is specifically:

[0091] ,

[0092] in

[0093] ,

[0094] ,

[0095] in represents the engineering water supply data evaluation index, Represents the evaluation index of engineering water supply simulation data.

[0096] S7, model evaluation and judgment, compare the model construction rationality index obtained in S6 with the preset model construction rationality index, calculate the difference between the model construction rationality index and the preset model construction rationality index, when the difference value is less than the preset difference value, the judgment result and the digital twin mechanism model data are sent to the human-computer interaction step, when the difference value is greater than the preset difference value, the digital twin mechanism model data is sent to the model construction step as historical water conservancy professional information.

[0097] In this embodiment, it should be specifically explained that the preset model construction rationality index is the mean of the historical model construction rationality values; the preset difference value is the minimum value of the difference between the historical model construction rationality index and the preset model construction rationality index.

[0098] S8, human-computer interaction, transmits the judgment results obtained in S7 and the digital twin mechanism model data to the user information terminal.

[0099] As attached Figure 2 The figure shows an overall system diagram of a digital twin mechanism model optimization system. The system specifically includes: a model establishment area determination module, a data acquisition module, a data processing module, a model construction module, a model data processing module, a comprehensive data analysis module, a model evaluation and judgment module, and a human-computer interaction module.

[0100] The model establishment area determination module is used to determine the data within the target project water supply area as the target model establishment area, and divide it into sub-areas according to the equal time division method, and number each sub-area within the target model establishment area as 1, 2, ..., n in sequence;

[0101] The data acquisition module is used to obtain water conservancy professional information and engineering water supply data in the model establishment area in real time, and transmit the engineering water supply data to the data processing module and transmit the water conservancy professional information to the model construction module.

[0102] The data processing module is used to process the engineering water supply data obtained by the data acquisition module to obtain the pump group data influence coefficient, the water pipeline data influence coefficient and the motor data influence coefficient, and transmit them to the comprehensive data analysis module.

[0103] The model construction module is used to establish a digital twin mechanism model of the pump group project water supply process based on the water conservancy professional information and historical water conservancy professional information obtained by the data acquisition module, simulate the relevant characteristics of the pump group, water pipeline and motor in the project water supply process, form an integrated digital twin mechanism model, obtain the project water supply simulation data, and transmit it to the model data processing module.

[0104] The model data processing module is used to process the engineering water supply simulation data obtained by the model construction module to obtain the influence coefficient of the pump group simulation data, the influence coefficient of the water pipeline simulation data and the influence coefficient of the motor simulation data, and transmit them to the comprehensive data analysis module.

[0105] The comprehensive data analysis module is used to import the pump group data influence coefficient value, water pipeline data influence coefficient value, motor data influence coefficient value, pump group simulation data influence coefficient value, water pipeline simulation data influence coefficient value and motor simulation data influence coefficient value obtained in the data processing module and the model data processing module into the model construction rationality index mathematical model to obtain the model construction rationality index value and transmit it to the model evaluation and judgment module.

[0106] The model evaluation and judgment module is used to compare the model construction rationality index obtained by the comprehensive data analysis module with the preset model construction rationality index, and calculate the difference between the model construction rationality index and the preset model construction rationality index. When the difference value is less than the preset difference value, the judgment result and the digital twin mechanism model data are transmitted to the human-computer interaction module. When the difference value is greater than the preset difference value, the digital twin mechanism model data is transmitted to the model construction module as historical water conservancy professional information.

[0107] The human-computer interaction module is used to transmit the judgment results obtained by the model evaluation and judgment module and the digital twin mechanism model data to the user information end.

[0108] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict.

[0109] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A digital twin mechanism model optimization method, characterized in that: include: S1. Determine the model establishment area. Determine the data within the target project water supply area as the target model establishment area, divide it into sub-areas according to the equal time division method, and number each sub-area within the target model establishment area as 1, 2, ..., n in sequence; S2. Data acquisition: Real-time acquisition of water conservancy information and engineering water supply data within the model establishment area, and delivery of engineering water supply data to the data processing step, and delivery of water conservancy information to the model construction step; The engineering water supply data pump group data includes influencing parameters, water pipeline data influencing parameters and motor data influencing parameters; The pump group data influencing parameters include the density of the delivered liquid, denoted as ; Liquid flow rate, denoted as Q; Pump head, denoted as h; Pump input power, denoted as P; Parameters affecting water pipeline data include liquid flow rate, denoted as Q, water pressure in water pipeline, denoted as p, flow velocity in water pipeline, denoted as v, and leakage in water pipeline, denoted as L; Parameters affecting motor data include motor current, denoted as I, motor voltage, denoted as U, motor speed, denoted as V, and motor torque, denoted as t; S3, data processing, processing the project water supply data obtained in S2 to obtain the pump group data influence coefficient, the water pipeline data influence coefficient and the motor data influence coefficient, and transmitting them to the comprehensive data analysis step; The data processing step includes a pump group data influence coefficient calculation step, a water pipeline data influence coefficient calculation step, and a motor data influence coefficient calculation step; The pump group data influence coefficient calculation step is used to import the pump group data influence parameter into the pump group data influence coefficient mathematical model to obtain the pump group data influence coefficient value; the water pipeline data influence coefficient calculation step is used to import the water pipeline data influence parameter into the water pipeline data influence coefficient mathematical model to obtain the water pipeline data influence coefficient value; the motor data influence coefficient calculation step is used to import the motor data influence parameter into the motor data influence coefficient mathematical model to obtain the motor data influence coefficient value; The mathematical model of the pump group data influence coefficient is specifically: ， The mathematical model of the water pipeline data influence coefficient is as follows: ， The mathematical model of the motor data influence coefficient is as follows: ， Where g represents the acceleration due to gravity, Q i represents the liquid flow rate during the i-th time period, represents the density of the transported liquid in the i-th time period, h i represents the pump head in the i-th time period, P i represents the pump input power in the i-th time period, v i represents the flow rate of the water pipeline in the i-th time period, v max Indicates the maximum allowable flow rate of the water pipeline, L i represents the leakage of the water pipeline in the i-th time period; p i The water pressure in the water pipeline during the i-th time period, p max Indicates the maximum allowable water pressure in the water pipeline, p min Indicates the minimum allowable water pressure in the water pipeline. Indicates the rated water pressure of the water pipeline, U i Represents the motor voltage in the i-th time period, I i Represents the motor current in the i-th time period, V i Indicates the motor speed in the i-th time period, t i represents the motor torque in the i-th time period; S4: Model construction. Based on the water conservancy professional information and historical water conservancy professional information obtained in S2, a digital twin mechanism model of the pump group project water supply process is established to simulate the relevant characteristics of the pump group, water pipeline, and motor in the project water supply process, forming an integrated digital twin mechanism model, obtaining the project water supply simulation data, and transmitting it to the model data processing step; S5, model data processing, processing the engineering water supply simulation data obtained in S4 to obtain the influence coefficient of the pump group simulation data, the influence coefficient of the water pipeline simulation data, and the influence coefficient of the motor simulation data, and transmitting them to the comprehensive data analysis step; S6. Comprehensive data analysis: import the pump group data influence coefficient value, water pipeline data influence coefficient value, motor data influence coefficient value, pump group simulation data influence coefficient value, water pipeline simulation data influence coefficient value, and motor simulation data influence coefficient value obtained in S3 and S5 into the model construction rationality index mathematical model to obtain the model construction rationality index value, and transmit it to the model evaluation and judgment step; S7, model evaluation and judgment, comparing the model construction rationality index obtained in S6 with the preset model construction rationality index, calculating the difference between the model construction rationality index and the preset model construction rationality index, when the difference is less than the preset difference, delivering the judgment result and the digital twin mechanism model data to the human-computer interaction step, when the difference is greater than the preset difference, delivering the digital twin mechanism model data as historical water conservancy professional information to the model construction step; S8, human-computer interaction, transmits the judgment results obtained in S7 and the digital twin mechanism model data to the user information terminal.

2. A digital twin mechanism model optimization method according to claim 1, characterized in that: The engineering water supply simulation data includes the influencing parameters of the pump group simulation data, the influencing parameters of the water pipeline simulation data and the influencing parameters of the motor simulation data; The pump group data influencing parameters include the simulated density of the transported liquid, denoted as ; The simulated flow rate of the transported liquid is denoted as Q s ; The simulated head of the water pump is recorded as h s ; The simulated input power of the pump is denoted as P s ; The parameters affecting the water pipeline data include the simulated flow rate of the transported liquid, denoted as Q s , simulated water pressure in the water pipeline, denoted as p s , simulated flow velocity of the water pipeline, denoted as v s , simulated leakage of water pipeline, denoted as L s ; The parameters affecting the motor data include the motor simulation current, denoted as I s , motor analog voltage, denoted as U s , the motor simulation speed, denoted as V s , the motor simulated torque, denoted as t s。 3. The digital twin mechanism model optimization method according to claim 1, characterized in that: The model data processing includes the steps of calculating the influence coefficient of the pump group simulation data, the water pipeline simulation data and the motor simulation data; The pump group simulation data influence coefficient calculation step is used to import the pump group simulation data influence parameters into the pump group simulation data influence coefficient mathematical model to obtain the pump group simulation data influence coefficient value; the water pipeline simulation data influence coefficient calculation step is used to import the water pipeline simulation data influence parameters into the water pipeline simulation data influence coefficient mathematical model to obtain the water pipeline simulation data influence coefficient value; the motor simulation data influence coefficient calculation step is used to import the motor simulation data influence parameters into the motor simulation data influence coefficient mathematical model to obtain the motor simulation data influence coefficient value.

4. The digital twin mechanism model optimization method according to claim 3, characterized in that: The mathematical model of the influence coefficient of the pump group simulation data is specifically: ， The mathematical model of the influence coefficient of water pipeline simulation data is as follows: ， The mathematical model of the influence coefficient of motor simulation data is as follows: ， Where g represents the acceleration due to gravity, v max Indicates the maximum allowable flow rate of the water pipeline, p max Indicates the maximum allowable water pressure in the water pipeline, p min Indicates the minimum allowable water pressure in the water pipeline. Indicates the rated water pressure of the water pipeline.

5. The digital twin mechanism model optimization method according to claim 1, characterized in that: The mathematical model of the model construction rationality index is specifically: ， in represents the engineering water supply data evaluation index, Represents the evaluation index of engineering water supply simulation data.

6. The digital twin mechanism model optimization method according to claim 5, characterized in that: The engineering water supply data evaluation index is specifically: ， The specific evaluation index of engineering water supply simulation data is as follows: 。 7. A digital twin mechanism model optimization system for implementing the digital twin mechanism model optimization method of any one of claims 1 to 6, characterized in that: include: Model establishment area determination module: used to determine the data within the target project water supply area as the target model establishment area, and divide it into sub-areas according to the equal time division method, and number each sub-area within the target model establishment area as 1, 2, ..., n in sequence; Data acquisition module: used to obtain water conservancy professional information and engineering water supply data in the model building area in real time, and transmit the engineering water supply data to the data processing module, and transmit the water conservancy professional information to the model building module; Data processing module: used to process the engineering water supply data obtained by the data acquisition module to obtain the pump group data influence coefficient, the water pipeline data influence coefficient and the motor data influence coefficient, and transmit them to the comprehensive data analysis module; Model building module: This module is used to establish a digital twin mechanism model of the pump group project water supply process based on the water conservancy professional information and historical water conservancy professional information obtained by the data acquisition module. It simulates the relevant characteristics of the pump group, water pipeline, and motor during the project water supply process, forms an integrated digital twin mechanism model, obtains the project water supply simulation data, and transmits it to the model data processing module. Model data processing module: used to process the engineering water supply simulation data obtained by the model construction module to obtain the influence coefficient of the pump group simulation data, the influence coefficient of the water pipeline simulation data and the influence coefficient of the motor simulation data, and transmit them to the comprehensive data analysis module; Comprehensive data analysis module: used to import the pump group data influence coefficient value, water pipeline data influence coefficient value, motor data influence coefficient value, pump group simulation data influence coefficient value, water pipeline simulation data influence coefficient value and motor simulation data influence coefficient value obtained in the data processing module and the model data processing module into the model construction rationality index mathematical model to obtain the model construction rationality index value, and transmit it to the model evaluation and judgment module; Model evaluation and judgment module: used to compare the model construction rationality index obtained by the comprehensive data analysis module with the preset model construction rationality index, calculate the difference between the model construction rationality index and the preset model construction rationality index, and when the difference is less than the preset difference, transmit the judgment result and the digital twin mechanism model data to the human-computer interaction module; when the difference is greater than the preset difference, transmit the digital twin mechanism model data as historical water conservancy professional information to the model construction module; Human-computer interaction module: used to transmit the judgment results obtained by the model evaluation and judgment module and the digital twin mechanism model data to the user information end.

Images