Digital twin energy simulation processing method, system and equipment based on data credibility and storage medium

Abstract

The invention relates to the technical field of energy system operation management, and particularly provides a digital twin energy simulation processing method, system and equipment based on data credibility and a storage medium, and the method comprises the steps: obtaining original energy data of similar equipment; calculating to obtain a credibility value corresponding to each piece of original energy data; performing credibility interpolation on the original energy data with the credibility value being 0 to obtain interpolation energy data, and recording the original energy data with the credibility value not being 0 and the interpolation energy data as effective operation data; based on the effective operation data and the credibility value, calculating a credibility weighting coefficient to obtain credibility weighted energy data; obtaining a station equivalent energy load based on the credibility weighted energy data and the credibility value; and updating the state variable of the pre-constructed energy digital twin model. By introducing a credibility weighting mechanism, the influence of abnormal data is reduced, and the stability and accuracy of energy simulation are improved.

Description

Technical Field

[0001] This invention belongs to the field of energy system operation and management technology, specifically relating to a digital twin energy simulation processing method, system, equipment, and storage medium based on data credibility. Background Technology

[0002] With the development of digital twins, smart energy, and integrated energy management technologies, analyzing, predicting, and simulating the operational status of energy systems based on multi-source energy data has become a crucial technical means for achieving safe operation and energy efficiency optimization in multi-energy facilities such as highway service areas, toll stations, and industrial parks. In these application scenarios, energy systems typically connect to multiple energy devices simultaneously, including grid power, photovoltaic power generation, energy storage devices, and charging / swapping facilities. These devices are diverse, operating conditions are complex, and load changes are frequent. To reduce operational risks and improve the scientific nature of energy dispatching, existing technologies typically collect operational data from various energy devices, construct corresponding models in virtual space, and simulate and analyze energy load change trends and dispatching effects, thereby providing decision-making references for the operation and management of energy systems.

[0003] In practical applications, due to unstable communication links, abnormal equipment status, or inconsistent sampling periods, energy data from multi-energy power plants inevitably suffers from missing, delayed, or abnormally fluctuating data. To address these issues, existing technologies typically employ data preprocessing to fill in the missing data. A common method is mean substitution, which uses the average value of similar equipment or adjacent time periods to interpolate missing data, ensuring the continuity of subsequent simulation and analysis. Furthermore, some techniques combine mean substitution with simple anomaly threshold judgment or smoothing to reduce the impact of individual abnormal data on simulation results. These methods are simple to implement and have low computational overhead, thus they are widely used in existing energy management and simulation systems.

[0004] However, the aforementioned mean-based substitution method still has significant drawbacks in the complex operating environment of multi-energy power plants. On the one hand, when both outliers and missing values ​​exist in the energy data, outliers are easily included in the mean calculation, causing the mean itself to shift. This further deviates the imputation results of missing data from the actual operating state, creating an error propagation effect. On the other hand, the mean substitution method implicitly assumes that all observation data have the same reliability, failing to distinguish the differences in data quality between different devices. This makes outliers have the same influence as normal data in simulation calculations, leading to highly sensitive simulation results to individual outlier measurement points. In scenarios with large fluctuations in energy load, these problems may lead to misjudgments of load levels and operational risks, thereby affecting the rationality and stability of energy dispatch decisions. Therefore, existing technologies struggle to simultaneously ensure the stability and accuracy of simulation results when faced with inconsistent energy data quality. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention provides a digital twin energy simulation processing method, system, device and storage medium based on data credibility to solve the above-mentioned technical problems.

[0006] In a first aspect, the present invention provides a digital twin energy simulation processing method based on data credibility, comprising: Obtain raw energy data for similar equipment; Based on the raw energy data and the preset confidence function, the confidence value corresponding to each piece of raw energy data is calculated. The original energy data with a confidence value of 0 is interpolated to obtain interpolated energy data. The original energy data with a confidence value of non-zero and the interpolated energy data are recorded as valid operating data. Based on valid operational data and credibility values, a credibility weighting coefficient is calculated, and the valid operational data is weighted based on the credibility weighting coefficient to obtain credibility-weighted energy data. Based on credibility-weighted energy data and credibility values, the operating status of similar energy equipment is fused with credibility weight to obtain the equivalent energy load of the site. Based on the site's equivalent energy load and credibility values, the state variables of the pre-built energy digital twin model are updated, so that energy data with different credibility levels have different degrees of impact on the state variable updates.

[0007] In one optional implementation, raw energy data of similar devices is obtained, including: Within the same sampling time, operational data is collected from each energy device in the multi-energy power station according to a preset time interval. The operational data includes power consumption data of charging devices, power generation data of generating devices, charging and discharging power data of energy storage devices, and power consumption data of station fixed loads.

[0008] In one optional implementation, based on the original energy data and a preset confidence function, the confidence value corresponding to each piece of original energy data is calculated, including: Determine if each data point in each original energy data set is missing. If not, calculate the reliability score for each original energy data set.

[0009]

[0010] If missing, the confidence level is 0.

[0011] In one optional implementation, the original energy data with a confidence value of 0 is subjected to confidence interpolation to obtain interpolated energy data, including: Interpolation calculations are performed on the raw energy data with a confidence level of 0:

[0012] in, This represents the interpolated energy data for the kth missing original energy data. This represents the set of data indexes that belong to the same type of energy equipment as the k-th original energy data. This represents the operating power value corresponding to the original energy data at index j. This represents the credibility value corresponding to the original energy data at index j.

[0013] In one optional implementation, a credibility weighting coefficient is calculated based on valid operational data and credibility values. The valid operational data is then weighted based on this credibility weighting coefficient to obtain credibility-weighted energy data, including:

[0014] in, This represents the credibility weighting coefficient for the i-th piece of original energy data. This represents the credibility value corresponding to the i-th piece of original energy data. This represents the valid operational data of the i-th original energy data, when... hour, ,when hour, , This represents the predicted operating value of the energy digital twin model for the i-th data point at the current moment. This represents the residual scaling parameter, used to characterize the allowable range of deviation. This represents the gating shape parameter, used to adjust the rate at which the weighted value decays as the deviation increases; i represents the energy data index. The valid operational data is weighted based on a credibility weighting coefficient to obtain credibility-weighted energy data:

[0015] in, This represents the credibility-weighted energy data for the i-th energy data point.

[0016] In one optional implementation, based on credibility-weighted energy data and credibility values, the operating status of similar energy devices is fused using credibility-weighted methods to obtain the site's equivalent energy load, including:

[0017] in, Indicates the equivalent energy load of the site. A collection of data indexes representing similar energy equipment.

[0018] In one optional implementation, the state variables of a pre-built energy digital twin model are updated based on the site's equivalent energy load and reliability values, such that energy data with different reliability levels have varying degrees of impact on the state variable updates, including: Read the state variables of the pre-built energy digital twin model at the current sampling time t. Read the equivalent energy load of the site corresponding to the current sampling time t. Read the set of reliable numerical values ​​of the equivalent energy load of the site. ; A set of reliable numerical values ​​based on the equivalent energy load of a site Calculate the site credibility coefficient :

[0019] in, This indicates that the calculation result will be truncated to... interval; Based on the equivalent energy load of the site at the current sampling time t State variables and site credibility coefficient Construction state update quantity :

[0020] in, This represents the preset state update step size coefficient; Based on state update amount For state variables Perform the update to obtain the updated state variables. : .

[0021] Secondly, the present invention provides a digital twin energy simulation processing system based on data credibility, comprising: The data acquisition module is used to acquire raw energy data from similar equipment. The credibility calculation module is used to calculate the credibility value corresponding to each piece of original energy data based on the original energy data and the preset credibility function. The credibility interpolation module is used to interpolate the credibility of the original energy data with a credibility value of 0 to obtain interpolated energy data. The original energy data with a credibility value of non-zero and the interpolated energy data are recorded as valid operating data. The credibility weighting module is used to calculate the credibility weighting coefficient based on the valid operating data and credibility value, and to perform weighting processing on the valid operating data based on the credibility weighting coefficient to obtain credibility weighted energy data; The site energy load calculation module is used to perform credibility-weighted fusion of the operating status of similar energy equipment based on credibility-weighted energy data and credibility values ​​to obtain the site's equivalent energy load. The state variable update module is used to update the state variables of a pre-built energy digital twin model based on the site's equivalent energy load and credibility values, so that energy data with different credibility levels have different degrees of impact on the state variable update.

[0022] Thirdly, a device is provided, comprising: A memory used to store digital twin energy simulation processing programs based on data credibility; A processor is configured to implement the steps of the data-trust-based digital twin energy simulation processing method provided in the first aspect when executing the data-trust-based digital twin energy simulation processing program.

[0023] Fourthly, a computer-readable storage medium is provided, on which a digital twin energy simulation processing program based on data credibility is stored. When the digital twin energy simulation processing program based on data credibility is executed by a processor, it implements the steps of the digital twin energy simulation processing method based on data credibility provided in the first aspect.

[0024] The beneficial effects of this invention are that the digital twin energy simulation processing method, system, device and storage medium based on data credibility provided by this invention, by not directly using a simple mean to replace the original energy data with a credibility value of 0 when interpolating, but combining the interpolation result with the corresponding credibility value, so that the influence of the interpolated data in subsequent calculations is constrained by credibility, thereby effectively suppressing the mean offset problem caused by abnormal or unreliable data participating in the interpolation calculation, and reducing the accumulation and amplification of errors in the data processing process.

[0025] This application uses credibility-weighted energy data to perform credibility-weighted fusion of the operating status of similar energy equipment to obtain the equivalent energy load of the site. This ensures that the contribution of different energy equipment operating data in load calculation matches their data credibility, avoiding excessive influence of individual abnormal measurement points or low-credibility data on the site load assessment results, thereby improving the ability of the site's equivalent energy load to reflect the actual operating status.

[0026] This application introduces the equivalent energy load and reliability values ​​of the site into the state variable update process of the energy digital twin model, so that energy data with different reliability have different weights in the model state update, thereby reducing the interference of low reliability data on the simulation results, improving the simulation stability and accuracy of the energy digital twin model under the condition of inconsistent energy data quality, and reducing the risk of energy dispatch decision-making due to load misjudgment.

[0027] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic flowchart of a method according to an embodiment of the present invention.

[0030] Figure 2 This is a schematic block diagram of a system according to an embodiment of the present invention.

[0031] Figure 3 This is a schematic diagram of the structure of a device provided in an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0034] The digital twin energy simulation processing method based on data credibility provided in this embodiment of the invention is executed by a computer device, and correspondingly, the digital twin energy simulation processing system based on data credibility runs in the computer device.

[0035] Figure 1This is a schematic flowchart illustrating a method according to an embodiment of the present invention. Wherein, Figure 1 The executing entity can be a digital twin energy simulation processing system based on data credibility. Depending on different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0036] like Figure 1 As shown, the method includes: S1. Obtain raw energy data for similar equipment.

[0037] S2. Based on the original energy data and the preset confidence function, calculate the confidence value corresponding to each piece of original energy data.

[0038] S3. Perform confidence interpolation on the original energy data with a confidence value of 0 to obtain interpolated energy data. Record the original energy data and interpolated energy data with a confidence value of non-zero as valid operating data.

[0039] S4. Based on the credibility-weighted energy data and credibility values, the operating status of similar energy equipment is fused with credibility weight to obtain the equivalent energy load of the site.

[0040] S5. Based on the valid operating data and the credibility value, calculate the credibility weighting coefficient, and then weight the valid operating data based on the credibility weighting coefficient to obtain credibility-weighted energy data.

[0041] S6. Based on the site's equivalent energy load and credibility values, update the state variables of the pre-built energy digital twin model so that energy data with different credibility levels have different degrees of influence on the state variable updates.

[0042] In one embodiment of the present invention, based on step S1, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0043] Obtain raw energy data for similar equipment, including: Within the same sampling time, operational data is collected from each energy device in the multi-energy power station according to a preset time interval. The operational data includes power consumption data of charging devices, power generation data of generating devices, charging and discharging power data of energy storage devices, and power consumption data of station fixed loads.

[0044] Specifically, within the same sampling time, operational data is collected from each energy device in the multi-energy power station at preset time intervals. The preset time interval can be set according to the operation and management needs of the multi-energy power station, for example, 5 minutes, 10 minutes, or 15 minutes, to ensure the synchronization of the operational data of each energy device in the time dimension.

[0045] The multi-energy station includes charging equipment, power generation equipment, energy storage equipment, and station fixed loads. All types of energy equipment are connected to the data acquisition system through on-site acquisition devices or monitoring terminals. During each sampling time slot, the operating status of each energy equipment is collected according to the preset time interval to form corresponding raw energy data.

[0046] Specifically, for charging equipment, power consumption data is collected during the current sampling time to characterize the energy consumption of the charging equipment at the site; for power generation equipment, power generation data is collected during the current sampling time to characterize the power generation equipment's ability to provide energy to the site; for energy storage equipment, charging or discharging power data is collected during the current sampling time to characterize the energy storage and retrieval status of the energy storage equipment in the current state; and for the site's fixed load, power consumption data is collected during the current sampling time to characterize the power consumption level of the site's basic load.

[0047] During the data acquisition process, for each type of energy equipment, its corresponding operating power data is uniformly collected and time-marked within the same sampling time slice to form a raw energy data set aligned in the time dimension. This provides a data foundation for subsequent confidence numerical calculation, confidence interpolation, and confidence weighted fusion processing based on the confidence function.

[0048] In one embodiment of the present invention, based on step S2, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0049] Based on the raw energy data and a preset confidence function, the confidence value corresponding to each piece of raw energy data is calculated, including: Determine if each data point in each original energy data set is missing. If not, calculate the reliability score for each original energy data set.

[0050] If missing, the confidence level is 0.

[0051] Specifically, firstly, each piece of raw energy data is examined to determine whether the operating power data in each piece of raw energy data is missing. If it is determined that a piece of raw energy data is missing data within the current sampling time, the credibility value corresponding to that piece of raw energy data is directly set to 0, which is used to indicate that the raw energy data does not have effective credibility within the current sampling time.

[0052] If it is determined that a certain piece of original energy data is not missing, the credibility value corresponding to that piece of original energy data is calculated based on a preset credibility function. The credibility function takes the operating power value corresponding to the original energy data, as well as the minimum and maximum reasonable power thresholds formed by the energy equipment corresponding to the original energy data during historical operation, as inputs to evaluate the rationality of the value of the original energy data.

[0053] Specifically, for the i-th piece of original energy data, its reliability value is... Calculate as follows: When the operating power value corresponding to this original energy data Located at the minimum reasonable power threshold With the maximum reasonable power threshold If the original energy data is considered to be within a reasonable operating range, then its credibility value is set accordingly. Set to 1; When the operating power value corresponding to this original energy data Greater than the maximum reasonable power threshold When the degree of deviation from the maximum reasonable power threshold is exceeded, the confidence value is attenuated to obtain a confidence value between 0 and 1. When the operating power value corresponding to this original energy data Less than the minimum reasonable power threshold When the value deviates from the minimum reasonable power threshold, the confidence value is attenuated to obtain a confidence value between 0 and 1.

[0054] The greater the deviation of the operating power value from the minimum or maximum reasonable power threshold, the smaller the corresponding calculated reliability value, reflecting the degree to which the rationality of the original energy data value has decreased.

[0055] In one embodiment of the present invention, based on step S3, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0056] The original energy data with a confidence level of 0 is subjected to confidence interpolation to obtain interpolated energy data, including: Interpolation calculations are performed on the raw energy data with a confidence level of 0:

[0057] in, This represents the interpolated energy data for the kth missing original energy data. This represents the set of data indexes that belong to the same type of energy equipment as the k-th original energy data. This represents the operating power value corresponding to the original energy data at index j. This represents the credibility value corresponding to the original energy data at index j.

[0058] Specifically, firstly, after calculating the confidence values ​​corresponding to the original energy data, all original energy data are traversed, and those with a confidence value of 0 are selected and used as the energy data to be interpolated. A confidence value of 0 indicates that the original energy data is missing within the current sampling time and cannot be directly used for subsequent calculations.

[0059] For each piece of original energy data with a confidence value of 0, perform interpolation calculations. For the k-th missing piece of original energy data, determine the data index set that belongs to the same type of energy equipment as that piece of original energy data. The aforementioned similar energy equipment refers to energy equipment with the same equipment type and the same operating attributes in multiple energy stations.

[0060] Determining the data index set Then, from the data index set Read the operating power values ​​of the raw energy data corresponding to each index in the middle. and the corresponding credibility value Among them, the credibility value Used to characterize the reliability of each piece of raw energy data within the current sampling time.

[0061] Based on the aforementioned operating power value And credibility value The missing original energy data for the kth data point is interpolated using a reliability-weighted method to obtain the interpolated energy data. .

[0062] By using the above interpolation calculation method, the original energy data with higher credibility has a greater weight in the interpolation result, while the impact of the original energy data with lower credibility on the interpolation result is reduced accordingly, thereby avoiding the excessive influence of abnormal data on the interpolation result.

[0063] Finally, the interpolated energy data The operating power value of the k-th original energy data with a confidence value of 0 within the current sampling time is used to generate the corresponding interpolated energy data, which together with the original energy data with a confidence value of non-zero constitutes the original energy data set for subsequent processing.

[0064] Through the above process, missing original energy data can be imputed based on credibility information without introducing a mean to replace the assumptions, providing a reliable data foundation for subsequent credibility-weighted energy data calculation and site equivalent energy load calculation.

[0065] In one embodiment of the present invention, based on step S4, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0066] Based on valid operational data and credibility values, a credibility weighting coefficient is calculated. The valid operational data is then weighted based on this coefficient to obtain credibility-weighted energy data, including:

[0067] in, This represents the credibility weighting coefficient for the i-th piece of original energy data. This represents the credibility value corresponding to the i-th piece of original energy data. This represents the valid operational data of the i-th original energy data, when... hour, ,when hour, , This represents the predicted operating value of the energy digital twin model for the i-th data point at the current moment. This represents the residual scaling parameter, used to characterize the allowable range of deviation. This represents the gating shape parameter, used to adjust the rate at which the weighted value decays as the deviation increases; i represents the energy data index.

[0068] Specifically, firstly, the effective operational data for each piece of raw energy data is determined. Among them, when the corresponding confidence value At that time, the effective operating data Take interpolated energy data; when the corresponding confidence value At that time, the effective operating data Obtain the original energy data. Through the above method, the interpolated energy data and the original energy data with non-zero confidence values ​​will use a unified data representation format in subsequent calculations.

[0069] Read the predicted running value of the energy digital twin model for the i-th energy data at the current moment. And calculate the effective operating data. Compared with predicted running values The magnitude of the deviation between them.

[0070] Based on credibility values Effective operating data Predicted running values Residual scaling parameters And the gate shape parameter p, calculate the credibility weighting coefficient for the i-th original energy data. .

[0071] When the credibility value High and effective operating data Compared with predicted running values When the deviation is small, the confidence weighting coefficient The larger the value, the greater the impact of the original energy data in subsequent calculations; when the confidence value... Lower or effective operating data Compared with predicted running values When the deviation is large, the credibility weighting coefficient The corresponding reduction will decrease the impact of the original energy data on subsequent calculation results.

[0072] Finally, the credibility weighting coefficients corresponding to each piece of raw energy data are calculated. As a component of the credibility-weighted energy data, the output is used for subsequent credibility-weighted fusion calculations of the operating status of similar energy equipment.

[0073] The valid operational data is weighted based on a credibility weighting coefficient to obtain credibility-weighted energy data:

[0074] in, This represents the credibility-weighted energy data for the i-th energy data point.

[0075] In one embodiment of the present invention, based on step S5, a possible embodiment will be given below, and its specific implementation will be described in a non-limiting manner.

[0076] Based on credibility-weighted energy data and credibility values, the operating status of similar energy equipment is fused using credibility-weighted methods to obtain the equivalent energy load of the site, including:

[0077] in, Indicates the equivalent energy load of the site. A collection of data indexes representing similar energy equipment.

[0078] Specifically, firstly, data belonging to the same type of energy equipment within the same sampling time are categorized to form a data index set Ω of the same type of energy equipment, wherein the same type of energy equipment can be any of the following: charging equipment, power generation equipment, or energy storage equipment.

[0079] By employing the above method, in the process of calculating the equivalent energy load of a site, energy data with different confidence levels have varying degrees of impact on the site's equivalent energy load. Energy data with higher confidence levels carries greater weight in the fusion calculation, while the impact of energy data with lower confidence levels on the fusion results is correspondingly weakened. This avoids abnormal or low-quality energy data from having an excessive impact on the overall operating status of the site. In one embodiment of the present invention, based on step S6, a possible embodiment will be given below, and its specific implementation will be described in a non-limiting manner.

[0080] Read the state variables of the pre-built energy digital twin model at the current sampling time t. Read the equivalent energy load of the site corresponding to the current sampling time t. Read the set of reliable numerical values ​​of the equivalent energy load of the site. ; A set of reliable numerical values ​​based on the equivalent energy load of a site Calculate the site credibility coefficient :

[0081] in, This indicates that the calculation result will be truncated to... interval; Based on the equivalent energy load of the site at the current sampling time t State variables and site credibility coefficient Construction state update quantity :

[0082] in, This represents the preset state update step size coefficient; Based on state update amount For state variables Perform the update to obtain the updated state variables. : .

[0083] Specifically, firstly, at the current sampling time, the state variables of the pre-built energy digital twin model corresponding to the sampling time slice are read to characterize the operating state of the energy digital twin model at the current moment; at the same time, the equivalent energy load of the site corresponding to the current sampling time is read, and the set of credibility values ​​used to form the equivalent energy load of the site is read, wherein the set of credibility values ​​is used to characterize the overall credibility of multiple energy data participating in the calculation of the equivalent energy load of the site.

[0084] The site credibility coefficient is calculated based on the set of credibility values, and this coefficient varies with the overall level of credibility values. When the overall level of credibility values ​​is high, the site credibility coefficient is correspondingly large; when the overall level of credibility values ​​is low, the site credibility coefficient is correspondingly small. By normalizing the set of credibility values, the site credibility coefficient is always kept within a preset effective range, thereby ensuring the stability of subsequent state update processes.

[0085] Based on the site's equivalent energy load at the current sampling time, the state variables, and the site's reliability coefficient, a state update quantity is constructed. This state update quantity is numerically modulated by the site's reliability coefficient, thereby allowing energy data at different reliability levels to have varying degrees of impact on the state variable updates. Specifically, when the site's reliability coefficient is high, the state update quantity increases accordingly, enabling the state variables to converge more quickly to the site's equivalent energy load; when the site's reliability coefficient is low, the state update quantity decreases accordingly, reducing the impact of low-reliability energy data on the state variable updates.

[0086] The state variable is updated based on the state update amount to obtain the updated state variable, and the updated state variable is used as the initial state of the energy digital twin model in the next sampling time slice.

[0087] Finally, through the above methods, the energy digital twin model can adaptively adjust the update magnitude of state variables based on the credibility level of the equivalent energy load at each site during operation. When the overall credibility of the energy data used in the calculation is high, the model's response speed to the equivalent energy load at each site is faster; when the overall credibility of the energy data used in the calculation is low, the model's response speed to the equivalent energy load at each site is slower. This achieves the technical effect that energy data with different credibility levels have varying degrees of impact on the update of state variables in the energy digital twin model.

[0088] In some embodiments, the data-trust-based digital twin energy simulation processing system may include multiple functional modules composed of computer program segments. The computer programs for each program segment in the data-trust-based digital twin energy simulation processing system may be stored in the memory of a computer device and executed by at least one processor to perform (see details). Figure 1 (Description) Functionality of digital twin energy simulation processing based on data credibility.

[0089] In this embodiment, the data-credibility-based digital twin energy simulation processing system can be divided into multiple functional modules according to its functions, such as... Figure 2As shown. The module referred to in this invention is a series of computer program segments that can be executed by at least one processor and perform a fixed function, and is stored in memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0090] The data acquisition module is used to acquire raw energy data from similar equipment. The credibility calculation module is used to calculate the credibility value corresponding to each piece of original energy data based on the original energy data and the preset credibility function. The credibility interpolation module is used to interpolate the credibility of the original energy data with a credibility value of 0 to obtain interpolated energy data. The original energy data with a credibility value of non-zero and the interpolated energy data are recorded as valid operating data. The credibility weighting module is used to calculate the credibility weighting coefficient based on the valid operating data and credibility value, and to perform weighting processing on the valid operating data based on the credibility weighting coefficient to obtain credibility weighted energy data; The site energy load calculation module is used to perform credibility-weighted fusion of the operating status of similar energy equipment based on credibility-weighted energy data and credibility values ​​to obtain the site's equivalent energy load. The state variable update module is used to update the state variables of a pre-built energy digital twin model based on the site's equivalent energy load and credibility values, so that energy data with different credibility levels have different degrees of impact on the state variable update.

[0091] Figure 3 The data-trust-based digital twin energy simulation processing method provided in the embodiments of this application can be applied to devices. Those skilled in the art will understand that the device structure involved in the embodiments of this invention does not constitute a limitation on the device. A device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the device includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0092] The device 300 may include a processor 310, a memory 320, and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will understand that the server structure shown in the figure does not constitute a limitation of the present invention. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0093] The memory 320 can be used to store execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the device 300 is able to perform some or all of the steps in the above method embodiments.

[0094] The processor 310 serves as the control center of the storage device, connecting various parts of the electronic device via various interfaces and lines. It executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 310 may consist only of a central processing unit (CPU). In this embodiment of the invention, the CPU may have a single processing core or include multiple processing cores.

[0095] The communication unit 330 is used to establish a communication channel, enabling the storage device to communicate with other devices. It can receive user data sent by other devices or send user data to other devices.

[0096] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, which, when executed, may include some or all of the steps provided in the embodiments of the present invention. The storage medium may be a magnetic disk, an optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0097] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other medium capable of storing program code. It includes several instructions to cause a computer device (which may be a personal computer, a server, or a second device, network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0098] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0099] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or modules may be electrical, mechanical, or other forms.

[0100] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0102] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the present invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the present invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should also be covered within the protection scope of the present invention.

Claims

1. A digital twin energy simulation processing method based on data credibility, characterized in that, include: Obtain raw energy data for similar equipment; Based on the raw energy data and the preset confidence function, the confidence value corresponding to each piece of raw energy data is calculated. The original energy data with a confidence value of 0 is interpolated to obtain interpolated energy data. The original energy data with a confidence value of non-zero and the interpolated energy data are recorded as valid operating data. Based on valid operational data and credibility values, a credibility weighting coefficient is calculated, and the valid operational data is weighted based on the credibility weighting coefficient to obtain credibility-weighted energy data. Based on credibility-weighted energy data and credibility values, the operating status of similar energy equipment is fused with credibility weight to obtain the equivalent energy load of the site. Based on the site's equivalent energy load and credibility values, the state variables of the pre-built energy digital twin model are updated, so that energy data with different credibility levels have different degrees of impact on the state variable updates.

2. The method according to claim 1, characterized in that, Obtain raw energy data for similar equipment, including: Within the same sampling time, operational data is collected from each energy device in the multi-energy power station according to a preset time interval. The operational data includes power consumption data of charging devices, power generation data of generating devices, charging and discharging power data of energy storage devices, and power consumption data of station fixed loads.

3. The method according to claim 1, characterized in that, Based on the raw energy data and a preset confidence function, the confidence value corresponding to each piece of raw energy data is calculated, including: Determine if each data point in each original energy data set is missing. If not, calculate the reliability score for each original energy data set. in, This represents the operating power value of the i-th piece of raw energy data. This represents the minimum reasonable power threshold of the energy device corresponding to the i-th original energy data point during its historical operation. This represents the maximum reasonable power threshold of the energy device corresponding to the i-th piece of original energy data during its historical operation. This represents the credibility value of the i-th piece of original energy data; If missing, the confidence level is 0.

4. The method according to claim 1, characterized in that, The original energy data with a confidence level of 0 is subjected to confidence interpolation to obtain interpolated energy data, including: Interpolation calculations are performed on the raw energy data with a confidence level of 0: in, This represents the interpolated energy data for the kth missing original energy data. This represents the set of data indexes that belong to the same type of energy equipment as the k-th original energy data. This represents the operating power value corresponding to the original energy data at index j. This represents the credibility value corresponding to the original energy data at index j.

5. The method according to claim 1, characterized in that, Based on valid operational data and credibility values, a credibility weighting coefficient is calculated. The valid operational data is then weighted based on this coefficient to obtain credibility-weighted energy data, including: Based on valid operational data and credibility values, calculate the credibility weighting coefficient: in, This represents the credibility weighting coefficient for the i-th piece of original energy data. This represents the credibility value corresponding to the i-th piece of original energy data. This represents the valid operational data of the i-th original energy data, when... hour, ,when hour, , This represents the predicted operating value of the energy digital twin model for the i-th data point at the current moment. This represents the residual scaling parameter, used to characterize the allowable range of deviation. This represents the gating shape parameter, used to adjust the rate at which the weighted value decays as the deviation increases; i represents the energy data index. The valid operational data is weighted based on a credibility weighting coefficient to obtain credibility-weighted energy data: in, This represents the credibility-weighted energy data for the i-th energy data point.

6. The method according to claim 1, characterized in that, Based on credibility-weighted energy data and credibility values, the operating status of similar energy equipment is fused using credibility-weighted methods to obtain the equivalent energy load of the site, including: in, Indicates the equivalent energy load of the site. This represents a set of data indexes for similar energy devices. This represents the credibility-weighted energy data for the i-th energy data point. This represents the credibility value corresponding to the i-th piece of original energy data.

7. The method according to claim 1, characterized in that, Based on the site's equivalent energy load and reliability values, the state variables of a pre-built energy digital twin model are updated, ensuring that energy data with different reliability levels have varying degrees of impact on the state variable updates, including: Read the state variables of the pre-built energy digital twin model at the current sampling time t. Read the equivalent energy load of the site corresponding to the current sampling time t. Read the set of reliable numerical values ​​of the equivalent energy load of the site. ; A set of reliable numerical values ​​based on the equivalent energy load of a site Calculate the site credibility coefficient : in, This indicates that the calculation result will be truncated to... interval; Based on the equivalent energy load of the site at the current sampling time t State variables and site credibility coefficient Construction state update quantity : in, This represents the preset state update step size coefficient; Based on state update amount For state variables Perform the update to obtain the updated state variables. : 。 8. A digital twin energy simulation processing system based on data credibility, characterized in that, include: The data acquisition module is used to acquire raw energy data from similar equipment. The credibility calculation module is used to calculate the credibility value corresponding to each piece of original energy data based on the original energy data and the preset credibility function. The credibility interpolation module is used to interpolate the credibility of the original energy data with a credibility value of 0 to obtain interpolated energy data. The original energy data with a credibility value of non-zero and the interpolated energy data are recorded as valid operating data. The credibility weighting module is used to calculate the credibility weighting coefficient based on the valid operating data and credibility value, and to perform weighting processing on the valid operating data based on the credibility weighting coefficient to obtain credibility weighted energy data; The site energy load calculation module is used to perform credibility-weighted fusion of the operating status of similar energy equipment based on credibility-weighted energy data and credibility values ​​to obtain the site's equivalent energy load. The state variable update module is used to update the state variables of a pre-built energy digital twin model based on the site's equivalent energy load and credibility values, so that energy data with different credibility levels have different degrees of impact on the state variable update.

9. A device, characterized in that, include: A memory used to store digital twin energy simulation processing programs based on data credibility; A processor, configured to implement the steps of the data-trust-based digital twin energy simulation processing method as described in any one of claims 1-7 when executing the data-trust-based digital twin energy simulation processing program.

10. A computer-readable storage medium storing a computer program, characterized in that, The readable storage medium stores a digital twin energy simulation processing program based on data credibility, which, when executed by a processor, implements the steps of the digital twin energy simulation processing method based on data credibility as described in any one of claims 1-7.

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