Configuration method, device and node based on digital twinning
Through digital twin technology, the configuration of business systems and refrigeration systems in large computer rooms or data centers is solved, and more efficient energy consumption management and system matching is achieved.
Patent Information
- Application Number
- CN202411951721.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-27
- Publication Date
- 2025-04-11
AI Technical Summary
In large computer rooms or data centers, the energy consumption of business systems and refrigeration systems is high, and the refrigeration systems often do not match the business systems, resulting in inefficiency.
Through digital twin technology, the operation configuration information and operation data of the business system are obtained, the operation status of the business system is predicted in the future, the cooling configuration information and/or updated operation configuration information are generated, and the configuration of the business system and the cooling system are adjusted.
It reduces the energy consumption of business systems and refrigeration systems, reduces the data volume and real-time requirements of the digital twin system, and improves the efficiency and matching of the system.
Smart Images
Figure CN119815783A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing, and in particular to a configuration method, device and node based on digital twins. Background Art
[0002] Large computer rooms and data centers are core facilities of business systems such as the Internet, communication systems, and digital systems, and are used to transmit, calculate, and store data information. Large computer rooms or data centers usually include servers, communication equipment, and environmental equipment, among which environmental equipment includes refrigeration systems, monitoring systems, and other equipment. In the prior art, business systems such as large computer rooms or data centers still have the problem of high energy consumption, and the supporting refrigeration systems often have the problem of not matching with the business systems. Summary of the invention
[0003] The present application provides a configuration method, device and node based on digital twins, which can effectively predict and adjust the configuration of the business system and the refrigeration system, thereby reducing the energy consumption of the system, and at the same time reducing the data volume and real-time requirements of the digital twin system.
[0004] An embodiment of the present application provides a configuration method based on digital twins, which is applied to a first node, and includes: obtaining operation configuration information and operation data of a business system, wherein the operation data includes data obtained in a first time period when the business system is operated using the operation configuration information; predicting the operation status of the business system in a second time period through a digital twin system based on the operation configuration information and the operation data, and obtaining a prediction result; generating refrigeration configuration information and / or updated operation configuration information for the business system based on the prediction result; sending the refrigeration configuration information to a refrigeration system that provides refrigeration for the business system, and sending the updated operation configuration information to the business system.
[0005] An embodiment of the present application also provides a configuration device based on digital twins, which is located at a first node and includes: an acquisition module, used to acquire operation configuration information and operation data of a business system, wherein the operation data includes data obtained in a first time period when the business system operates using the operation configuration information; a prediction module, used to predict the operation status of the business system in a second time period through a digital twin system based on the operation configuration information and the operation data, and obtain a prediction result; a generation module, used to generate refrigeration configuration information and / or updated operation configuration information for the business system based on the prediction result; a sending module, used to send the refrigeration configuration information to a refrigeration system that provides refrigeration for the business system, and to send the updated operation configuration information to the business system.
[0006] An embodiment of the present application also provides a first node, and the steps executed by the first node include: obtaining operation configuration information and operation data of the business system, wherein the operation data includes data obtained in a first time period when the business system adopts the operation configuration information to operate; predicting the operation status of the business system in a second time period through a digital twin system based on the operation configuration information and the operation data, and obtaining a prediction result; generating refrigeration configuration information and / or updated operation configuration information for the business system based on the prediction result; sending the refrigeration configuration information to a refrigeration system that provides refrigeration for the business system, and sending the updated operation configuration information to the business system.
[0007] In the configuration method based on digital twins provided in the embodiment of the present application, the first node first obtains the operation configuration information and operation data of the business system, and the operation data includes the data obtained in the first time period when the business system is running using the operation configuration information. Then, the first node predicts the operation status of the business system in the second time period based on the operation configuration information and the operation data through the digital twin system, obtains the prediction result, and generates refrigeration configuration information and / or updated operation configuration information for the business system based on the prediction result. Finally, the first node sends the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, and sends the updated operation configuration information to the business system. This method can effectively predict and adjust the configuration of the business system and the refrigeration system, thereby reducing the energy consumption of the system, and at the same time can also reduce the data volume and real-time requirements of the digital twin system. BRIEF DESCRIPTION OF THE DRAWINGS
[0008] Figure 1 It is a flow chart of a configuration method based on digital twins provided in an embodiment of the present application.
[0009] Figure 2 This is a schematic diagram of the relevant duration in a digital twin-based configuration method provided in an embodiment of the present application.
[0010] Figure 3 It is a flow chart of another configuration method based on digital twins provided in an embodiment of the present application.
[0011] Figure 4 It is a schematic diagram of a configuration device based on digital twins provided in an embodiment of the present application.
[0012] Figure 5 It is a schematic diagram of a digital twin-based configuration system provided in an embodiment of the present application.
[0013] Figure 6 It is a schematic diagram of another digital twin-based configuration system provided in an embodiment of the present application. DETAILED DESCRIPTION
[0014] The present application is described below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined arbitrarily with each other.
[0015] Large computer rooms and data centers are core facilities of business systems such as the Internet, communication systems, and digital systems, and are used to transmit, calculate, and store data information. Large computer rooms or data centers usually include servers, communication equipment, and environmental equipment, among which environmental equipment includes refrigeration systems, monitoring systems, and other equipment. In the prior art, business systems such as large computer rooms or data centers still have the problem of high energy consumption, and the supporting refrigeration systems often have the problem of not matching with the business systems.
[0016] Digital twin technology can be used to create a virtual model for a physical system, forming a digital twin system, which can obtain data from the physical system, simulate and predict the operation of the physical system, and then provide optimization decisions for the operation of the physical system. However, the digital twin system has high requirements for the real-time performance of data transmission.
[0017] In order to solve the above technical problems, the present application provides a configuration method based on digital twins, in which the first node obtains the operation configuration information and operation data of the business system, and the operation data includes the data obtained in the first time period when the business system is running using the operation configuration information. Then, the first node predicts the operation status of the business system in the second time period based on the operation configuration information and the operation data through the digital twin system, obtains the prediction result, and generates refrigeration configuration information and / or updated operation configuration information for the business system based on the prediction result. Finally, the first node sends the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, and sends the updated operation configuration information to the business system. This method can effectively predict and adjust the configuration of the business system and the refrigeration system, thereby reducing the energy consumption of the business system and the refrigeration system, and at the same time can reduce the digital twin system's requirements for the required data volume and real-time performance.
[0018] Figure 1 is a flow chart of a configuration method based on digital twins provided in an embodiment of the present application. The method is applied to the first node, such as Figure 1 As shown, the method includes step 110 , step 120 , step 130 and step 140 .
[0019] In step 110, operation configuration information and operation data of the business system are acquired, wherein the operation data includes data obtained in a first period of time when the business system is operated using the operation configuration information.
[0020] In step 120, the operation status of the business system in the second time period is predicted through the digital twin system according to the operation configuration information and the operation data to obtain a prediction result.
[0021] In step 130, refrigeration configuration information and / or updated operation configuration information are generated for the business system according to the prediction result.
[0022] In step 140, the refrigeration configuration information is sent to a refrigeration system that provides refrigeration for the business system, and the updated operation configuration information is sent to the business system.
[0023] The digital twin-based configuration method provided in this embodiment can effectively predict and adjust the configuration of the business system and the refrigeration system, thereby reducing the energy consumption of the business system and the refrigeration system, and at the same time reducing the digital twin system's requirements for the amount of data required and real-time performance.
[0024] In the above embodiments, the first node may be a server, an edge server, a digital twin node or other applicable node, and this application does not impose any limitation thereto.
[0025] In the above embodiments, the digital twin system may also be referred to as a digital twin. The digital twin system may be deployed on the first node, or independently configured and connected to the first node.
[0026] In one embodiment, the operation configuration information is generated according to the last prediction result. After the first node obtains the prediction result (referred to as the last prediction result) through the digital twin system for the last time, the operation configuration information is generated according to the last prediction result, and then the operation configuration information is sent to the business system, and the business system uses the operation configuration information for configuration and operation.
[0027] In one embodiment, the operation configuration information is determined based on the operation configuration information generated by the last prediction result. The first node generates the operation configuration information based on the last prediction result and sends it to the business system. The business system performs configuration based on the received operation configuration information. The actual operation configuration information used may be different from the received operation configuration information.
[0028] In one embodiment, the first node obtains the operation configuration information of the business system from the information it saves. In this embodiment, the first node can save the operation configuration information it sends to the business system, so that it is not necessary to obtain the operation configuration information from the business system. This is applicable to the case where the operation configuration information actually used by the business system is consistent with the operation configuration information sent by the first node.
[0029] In one embodiment, the first node obtains the operation configuration information from the business system. This is applicable to the case where the operation configuration information actually used by the business system may be different from the operation configuration information sent by the first node. In this case, when the first node obtains the operation data from the business system, it is necessary to simultaneously obtain the operation configuration information used by the business system, so that the first node can make correct predictions through the digital twin system based on the correct operation configuration information and operation data.
[0030] In one embodiment, the operation configuration information is generated based on at least one prediction result. The first node can perform multiple predictions through the digital twin system, obtain multiple prediction results, and then generate the operation configuration information based on at least one prediction result, and send the operation configuration information to the business system, and the business system configures and operates according to the operation configuration information. In this embodiment, equivalent to the current prediction, the multiple prediction results or at least one prediction result here can be regarded as the previous prediction result. When the at least one prediction result corresponding to the operation configuration information or the operation data is needed in the current prediction, the corresponding information between the operation configuration information or the operation data and the at least one prediction result, or the associated information, or the identification information that can reflect the corresponding relationship or association relationship between the two can be obtained, so that the at least one prediction result can be correctly obtained, so as to be used for the current prediction. Among them, the corresponding information or the associated information may include the corresponding information or associated information in index, or position, or time, and the identification information may include index information, or position information, or time information, or the relationship between the information of the starting data and other data relative to the starting data.
[0031] In one embodiment, the operation data is data obtained at a first time interval, and the prediction result is a prediction result obtained at a second time interval, wherein the first time interval is greater than the second time interval. In this embodiment, the business system operates using the operation configuration information, and collects data at a first time interval under a first duration, that is, collects data once every first time interval, and uses the obtained data as the operation data. When the operation status of the business system at a second duration is predicted through the digital twin system based on the operation configuration information and the operation data, the prediction is performed at the second time interval, that is, the prediction is performed once every second time interval to obtain the prediction result.
[0032] In the above embodiment, the second duration may be different from or the same as the first duration. In one example, the second duration is greater than the first duration. The starting time or position of the first duration or the second duration may be preset or determined according to a specified method.
[0033] The above-mentioned embodiments of the present application adopt the first duration and the second duration as well as the first time interval and the second time interval, which can reduce the digital twin system's requirements for the amount of data required and real-time performance, and is conducive to efficient prediction and configuration of the digital twin system.
[0034] In one embodiment, the operation status of the business system in the second time period is predicted through the digital twin system according to the operation configuration information and the operation data to obtain the prediction result, including: according to the operation configuration information, the operation data, and the difference between the operation data and the previous prediction result, the operation status of the business system in the second time period is predicted through the digital twin system to obtain the prediction result.
[0035] In one embodiment, the operation status of the business system in the second time period is predicted through the digital twin system according to the operation configuration information and the operation data to obtain the prediction result, including: predicting the operation status of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data to obtain a preliminary prediction result, and then correcting the preliminary prediction result according to the difference between the operation data and the previous prediction result to obtain a corrected prediction result, and using the corrected prediction result as the prediction result.
[0036] In the above embodiment, since the operation configuration information is generated according to the last prediction result, the operation data is the data obtained in the first time length when the business system adopts the operation configuration information to operate, and the time of obtaining the operation data may be within the time length range of the last prediction result, or the operation data has a corresponding relationship with the specified data in the last prediction result. Then, in this prediction, the operation status of the business system in the second time length can be predicted through the digital twin system according to the operation configuration information, the operation data, and the difference between the operation data and the last prediction result to obtain the prediction result; or, in this prediction, the operation status of the business system in the second time length is first predicted through the digital twin system according to the operation configuration information and the operation data to obtain a preliminary prediction result, and then the preliminary prediction result is corrected according to the difference between the operation data and the last prediction result to obtain a corrected prediction result, and the corrected prediction result is used as the prediction result.
[0037] The difference between the running data and the last prediction result may be a specific numerical value of the difference, or a statistical value of the difference, such as a mean and / or variance.
[0038] In one embodiment, predicting the operation of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data to obtain the prediction result includes the following steps: first, obtaining the operation change parameter from the operation data; then, predicting the operation change in the second time period according to the operation change parameter to obtain the operation change prediction result; then, simulating the operation of the business system in the second time period through the digital twin system according to the operation configuration information and the operation change prediction result to obtain the prediction result. In this embodiment, the operation change parameter includes parameters such as business load or energy consumption.
[0039] In the above embodiment, the operation change situation in the second time length is predicted according to the operation change parameters to obtain the operation change situation prediction result, including: interpolating or fitting the data of the operation change parameters to obtain a processing result, and then extrapolating the processing result in the second time length to obtain the operation change situation prediction result.
[0040] When predicting the operation change situation in the second time period according to the operation change parameter, the prediction can also be made in combination with the difference between the data of the operation change parameter and the corresponding data in the previous prediction result. For example, when interpolating or fitting the data of the operation change parameter to obtain the processing result, the difference can be used for processing, or the obtained processing result can be adjusted to obtain the adjusted processing result.
[0041] In one embodiment, the operation status of the business system in the second time period is predicted through the digital twin system according to the operation configuration information and the operation data to obtain the prediction result, including the following steps: first, the operation status of the business system is simulated through the digital twin system according to the operation configuration information to obtain the simulation operation result; then, the data change status is obtained according to the operation data; then, the operation status of the business system in the second time period is predicted according to the simulation operation result and the data change status to obtain the prediction result.
[0042] In this embodiment, when the operation status of the business system is simulated through the digital twin system according to the operation configuration information, the operation status of the business system under the second time length can be simulated, or the operation status of the business system at a specified time can be simulated, wherein the specified time includes at least one time.
[0043] In this embodiment, obtaining data change conditions according to the operating data includes: interpolating or fitting the operating data to obtain data change conditions.
[0044] In this embodiment, when obtaining data changes according to the operation data, the data changes can be obtained according to a part of the operation data or data of a specified parameter. For example, data of parameters such as business load or energy consumption. The data changes can include changes in data such as business load or energy consumption.
[0045] When obtaining the data change situation according to the operation data, the data change situation may also be obtained in combination with the difference between the operation data and the corresponding data in the previous prediction result. For example, when interpolating or fitting the operation data to obtain the data change situation, the difference may be used to obtain the data change situation, or the obtained data change situation may be adjusted to obtain the adjusted data change situation.
[0046] In this embodiment, the operation status of the business system in the second time period is predicted based on the simulation operation results and the data changes to obtain the prediction results, including: adjusting or extrapolating the specified data in the simulation operation results based on the data changes in the second time period to obtain the prediction results; or extrapolating the data changes to obtain the data change prediction results in the second time period, and then adjusting the specified data in the simulation operation results based on the data change prediction results to obtain the prediction results.
[0047] When predicting the operation status of the business system in the second time period based on the simulation operation results and the data changes, the digital twin system can be used for processing. For example, the specified data in the simulation operation results can be adjusted through the digital twin system.
[0048] In addition, when making predictions, the predictions can also be made in combination with the difference between the operating data and the corresponding data in the previous prediction results. For example, the simulation operation results can be adjusted according to the difference, or the prediction results can be adjusted according to the difference to obtain adjusted prediction results.
[0049] In the above embodiments, interpolation includes linear interpolation or polynomial interpolation, and the interpolation may be internal interpolation or external interpolation.
[0050] In the above embodiment, the fitting includes least square fitting or polynomial fitting. For example, a second-order polynomial fitting may be used, or a suitable fitting method or fitting polynomial may be determined by minimizing the sum of squares of errors.
[0051] In the above embodiment, the extrapolation includes extrapolation according to the adopted interpolation or fitting method, and the prediction data outside the range to which the existing data belongs can be obtained through the extrapolation.
[0052] In the above embodiment, the previous prediction result is a prediction result used to generate the operation configuration information or a prediction result having a corresponding relationship with the operation data. The previous prediction result may also be replaced by a previous prediction result, which includes a prediction result used to generate the operation configuration information or a prediction result having a corresponding relationship with the operation data, and the previous prediction result may include one or more prediction results.
[0053] In one embodiment, generating refrigeration configuration information and / or updated operating configuration information for the business system based on the prediction results includes: generating refrigeration configuration information for the business system based on the prediction results; or, generating updated operating configuration information for the business system based on the prediction results; or, generating refrigeration configuration information and updated operating configuration information for the business system based on the prediction results.
[0054] In the above embodiment, generating refrigeration configuration information for the business system according to the prediction result includes: determining the refrigeration demand of the business system according to the prediction result, and then generating refrigeration configuration information for the business system according to the refrigeration demand. This embodiment can provide effective and appropriate refrigeration for the business system, and appropriate refrigeration is conducive to reducing the energy consumption of the business system and avoiding over-refrigeration, thereby helping to reduce the energy consumption of the refrigeration system.
[0055] In the above embodiment, generating updated operation configuration information for the business system based on the prediction results is conducive to providing a more appropriate configuration for the business system, thereby ensuring that the energy consumption of the business system is in a more reasonable situation, which is conducive to reducing the energy consumption of the business system as a whole. Moreover, based on the appropriate configuration of the business system, a more appropriate configuration can be determined for the supporting system, such as determining a suitable configuration for the refrigeration system so that the refrigeration capacity it provides is more matched with the refrigeration demand of the business system.
[0056] In the above embodiment, refrigeration configuration information and updated operation configuration information are generated for the business system based on the prediction results, so that the joint configuration of the business system and the refrigeration system can be achieved, which is conducive to providing more matching configuration and adjustment for the two, thereby reducing the energy consumption of the business system and the refrigeration system as a whole.
[0057] In one embodiment, the first node sends the refrigeration configuration information to a refrigeration system that provides refrigeration for the business system, so that the refrigeration system performs configuration and operation according to the refrigeration configuration information.
[0058] In one embodiment, the first node sends the updated running configuration information to the business system, so that the business system performs configuration and operation according to the updated running configuration information.
[0059] In one embodiment, the first node may also generate configuration information for other supporting systems of the business system according to the prediction result, and send the configuration information to the other supporting systems so that the other supporting systems can configure and operate according to the configuration information.
[0060] In one embodiment, the configuration method based on digital twins also includes: obtaining updated operation configuration information and updated operation data adopted by the business system for the next round of prediction and configuration. Among them, the updated operation data is the data obtained in the first time period when the business system adopts the updated operation configuration information to run. Then, the method can perform a new round of prediction on the operation status of the business system through the digital twin system based on the adopted updated operation configuration information and updated operation data, and generate new configuration information based on the new round of prediction results, including new refrigeration configuration information and / or new operation configuration information. In this embodiment, the current prediction results can also be used for the next round of prediction and configuration, or the difference between the current prediction results and the updated operation data can be used for the next round of prediction and configuration.
[0061] It can be seen from the above embodiments that the digital twin-based configuration method provided in the embodiments of the present application can be executed iteratively, which is conducive to providing a more optimized configuration for the business system and the refrigeration system, thereby making the operation of the business system and the refrigeration system more optimized in terms of efficiency, energy consumption, etc.
[0062] According to the embodiments of the present application, the present application provides several examples, which are described in detail as follows.
[0063] In one example, a digital twin-based configuration method includes steps A1, A2, A3, and A4 as described below.
[0064] Step A1: Acquire operation configuration information and operation data of the business system, wherein the operation data includes data obtained in a first period of time when the business system is operated using the operation configuration information.
[0065] Step A2: Obtain operation change parameters from the operation data, predict the operation change status under the second time length according to the operation change parameters, obtain the operation change status prediction result, and then simulate the operation status of the business system under the second time length through the digital twin system according to the operation configuration information and the operation change status prediction result to obtain the prediction result.
[0066] Step A3: Generate refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result.
[0067] Step A4: Send the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, and send the updated operation configuration information to the business system.
[0068] In one example, a digital twin-based configuration method includes steps B1, B2, B3, and B4 as described below.
[0069] Step B1: Acquire operation configuration information and operation data of the business system, wherein the operation data includes data obtained in a first period of time when the business system is operated using the operation configuration information.
[0070] Step B2: Simulate the operation of the business system through the digital twin system according to the operation configuration information to obtain the simulation operation results, then obtain the data change according to the operation data, and predict the operation of the business system in the second time period according to the simulation operation results and the data change to obtain the prediction results.
[0071] Step B3: Generate refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result.
[0072] Step B4: Send the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, and send the updated operation configuration information to the business system.
[0073] In one example, a digital twin-based configuration method includes steps C1, C2, C3, C4, and C5 as described below.
[0074] Step C1: Acquire operation configuration information and operation data of the business system, wherein the operation data includes data obtained in a first period of time when the business system is operated using the operation configuration information.
[0075] Step C2: Obtaining a preliminary prediction result corresponding to the operation configuration information or the operation data.
[0076] Step C3: According to the operation configuration information, the operation data, and the difference between the operation data and the previous prediction result, the operation status of the business system in the second period is predicted through the digital twin system to obtain the prediction result.
[0077] Step C4: Generate refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result.
[0078] Step C5: Send the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, and send the updated operation configuration information to the business system.
[0079] In this example, the previous prediction result includes a prediction result used to generate the operation configuration information, or a prediction result having a corresponding relationship with the operation data. The previous prediction result may be a previous prediction result, or specified data in a previous prediction result. The previous prediction result may also include at least one prediction result, or specified data in at least one prediction result. When obtaining the previous prediction result corresponding to the operation configuration information or the operation data, the previous prediction result corresponding to the operation configuration information or the operation data may be obtained based on the corresponding information between the operation configuration information or the operation data and the previous prediction result, or the associated information, or the identification information that can reflect the corresponding relationship or the associated relationship between the two.
[0080] In one example, a digital twin-based configuration method includes steps D1, D2, D3, D4, and D5 as described below.
[0081] Step D1: Acquire operation configuration information and operation data of the business system, wherein the operation data includes data obtained in a first period of time when the business system is operated using the operation configuration information.
[0082] Step D2: Obtaining a preliminary prediction result corresponding to the operation configuration information or the operation data.
[0083] Step D3: Predict the operation status of the business system in the second period of time through the digital twin system according to the operation configuration information and the operation data, obtain a preliminary prediction result, and then correct the preliminary prediction result according to the difference between the operation data and the previous prediction result to obtain a corrected prediction result, and use the corrected prediction result as the prediction result.
[0084] Step D4: Generate refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result.
[0085] Step D5: Send the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, and send the updated operation configuration information to the business system.
[0086] Figure 2 Schematic diagram of the relevant duration in a configuration method based on digital twins provided in an embodiment of the present application. Figure 2As shown, T1 is the first duration, T2 is the second duration, Tr is the delay for the first node to obtain the operation configuration information and operation data of the business system, Tm is the processing duration for the first node to predict the operation status of the business system through the digital twin system based on the operation configuration information and operation data, Ts is the delay for the first node to send the updated operation configuration information to the business system, Tc is the configuration delay when the business system adopts the updated operation configuration information, Ta is the operation duration of the business system adopting the updated operation configuration information (until the next configuration update time), and Td is the time interval between the moment when the business system adopts the updated operation configuration information and the moment when the operation data is obtained next time.
[0087] In this embodiment, the first node first obtains the operation configuration information and operation data of the business system, wherein the operation data includes data obtained in the first time period T1 when the business system operates using the operation configuration information.
[0088] The first node obtains the operation configuration information and operation data of the business system after the time delay Tr, and then predicts the operation status of the business system in the second time length T2 through the digital twin system according to the operation configuration information and the operation data, obtains the prediction result, and generates refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result. The processing time for executing the prediction (and generating the configuration information) is Tm.
[0089] The first node sends the refrigeration configuration information to the refrigeration system providing refrigeration for the service system, and sends the updated operation configuration information to the service system. The service system receives the updated operation configuration information after a delay of Ts.
[0090] The business system is configured according to the updated operation configuration information, and the configuration is completed and the operation is completed after the delay Tc. The operation time after the business system completes the configuration is at least Ta. This operation time lasts at least until the next configuration update time. Therefore, within the operation time Ta range, the next (i.e. Figure 2 The second) T1 time window in the process obtains new operation data and performs the next round of prediction and configuration according to the configuration method provided in this application.
[0091] In this embodiment, the period of updating configuration of the service system is Ta, and Ta is equal to the sum of Td, T1, Tr, Tm, Ts, and Tc. The second duration T2 is equal to the sum of Tr, Tm, Ts, Tc, and Ta.
[0092] In this embodiment, the period Ta of updating configuration of the business system is usually greater than the first duration T1, and the second duration T2 may be greater than or equal to the period Ta of updating configuration of the business system.
[0093] In this embodiment, the range of each duration is an example, and the range of each duration can also be other situations. Taking the second duration T2 as an example, T2 can also be a range from the first Tm to the second Tc, or T2 can be equal to the period Ta of the business system update configuration. In addition, the ranges of the second durations T2 of two adjacent predictions can have overlapping areas, for example, Figure 2 The overlapping area of two adjacent second time lengths T2 includes Tr, Tm, Ts, and Tc. The range of each time length can be determined according to actual needs and is not limited here.
[0094] According to this embodiment, the first T1 duration window in the figure can be located within the last operation duration Ta, and the last prediction duration, i.e., the second duration T2. As described in the previous embodiment, when predicting the operation status of the business system through the digital twin system based on the operation configuration information and the operation data, the difference between the operation data and the last prediction result can be introduced, or the prediction result can be corrected according to the difference, which is conducive to obtaining more accurate and effective prediction results.
[0095] In the configuration method based on digital twins provided in the above-mentioned embodiment of the present application, the first node first obtains the operation configuration information and operation data of the business system, and the operation data includes the data obtained in the first time period when the business system is running using the operation configuration information. Then, the first node predicts the operation status of the business system in the second time period based on the operation configuration information and the operation data through the digital twin system, obtains the prediction result, and generates refrigeration configuration information and / or updated operation configuration information for the business system based on the prediction result. Finally, the first node sends the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, and sends the updated operation configuration information to the business system. This method can effectively predict and adjust the configuration of the business system and the refrigeration system, thereby reducing the energy consumption of the business system and the refrigeration system, and at the same time can reduce the digital twin system's requirements for the required data volume and real-time performance.
[0096] Figure 3 is a flow chart of another configuration method based on digital twins provided in an embodiment of the present application, such as Figure 3 As shown, the method includes step 310 , step 320 , step 330 and step 340 .
[0097] In step 310, operation configuration information and operation data of K business systems are obtained, wherein the operation data of each business system includes data obtained in a first period when each business system is operated using the operation configuration information of each business system, and K is an integer greater than 1.
[0098] In step 320, the operation status of M business systems among the K business systems in the second time period is predicted through the digital twin system according to the operation configuration information and operation data of the K business systems to obtain the prediction results, where M is an integer greater than or equal to 1 and less than or equal to K.
[0099] In step 330, refrigeration configuration information and / or updated operation configuration information is generated for at least one business system among the M business systems according to the prediction result.
[0100] In step 340, the refrigeration configuration information is sent to a refrigeration system that provides refrigeration for the at least one business system, and the updated operation configuration information is sent to the at least one business system.
[0101] In one embodiment, M is equal to K. The configuration method provided in this embodiment can predict the operation status of each business system to obtain a prediction result, and then generate configuration information for at least one business system according to the prediction result, including cooling configuration information and / or operation configuration information.
[0102] In one embodiment, M is equal to 1. The configuration method provided in this embodiment can predict the operation status of a business system according to the operation configuration information and operation data of K business systems to obtain a prediction result, and then generate configuration information for the business system according to the prediction result, including cooling configuration information and / or operation configuration information.
[0103] In one embodiment, the configuration method provided in this embodiment can predict the operating status of at least one business system based on the operating configuration information and operating data of K business systems to obtain prediction results, and then generate configuration information for at least one business system based on the prediction results, including cooling configuration information and / or operating configuration information.
[0104] In one embodiment, when the operation status of a business system is predicted through a digital twin system based on the operation configuration information and operation data of K business systems to obtain prediction results, the impact of other business systems on the operation status of the business system can be taken into account for simulation and prediction to obtain more accurate and effective prediction results.
[0105] The digital twin-based configuration method provided in this embodiment can effectively predict and adjust the configuration of the business system and the refrigeration system, thereby reducing the energy consumption of the business system and the refrigeration system, and at the same time reducing the digital twin system's requirements for the amount of data required and real-time performance.
[0106] The implementation principle and technical effects of the configuration method provided in this embodiment are similar to those of the above embodiment. For technical details not fully described in this embodiment, please refer to the above embodiment.
[0107] An embodiment of the present application provides a first node, and the steps performed by the first node include step 11, step 12, step 13 and step 14.
[0108] Step 11: Acquire operation configuration information and operation data of the business system, wherein the operation data includes data obtained in a first period of time when the business system is operated using the operation configuration information.
[0109] Step 12: Predict the operation status of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data, and obtain the prediction result.
[0110] Step 13: Generate refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result.
[0111] Step 14: Send the refrigeration configuration information to a refrigeration system that provides refrigeration for the business system, and send the updated operation configuration information to the business system.
[0112] The first node provided in this embodiment is used to implement the digital twin-based configuration method provided in the above embodiment. The steps and technical principles executed by the first node are similar to those of the above embodiment and have similar technical effects. For technical details not fully described in this embodiment, please refer to the above embodiment.
[0113] In one embodiment, the first node includes a configuration device, which executes the above steps 11, 12, 13 and 14 to implement the digital twin-based configuration method provided in the above embodiment.
[0114] In one embodiment, the first node may also be a first device, for example, a server, an edge server, a digital twin device, or other applicable device, which is not limited in this application.
[0115] In one embodiment, in step 14, the first node sends the refrigeration configuration information to a refrigeration system that provides refrigeration for the business system, so that the refrigeration system can be configured according to the refrigeration configuration information, and sends the updated operation configuration information to the business system, so that the business system can be configured according to the updated operation configuration information.
[0116] In one embodiment, the first node is connected to the business system and the refrigeration system, respectively. The first node obtains the operation configuration information adopted by the business system and the operation data obtained in the first time length. Then, the first node predicts the operation status of the business system in the second time length through the digital twin system based on the acquired operation configuration information and operation data to obtain the prediction result. The digital twin system here can be set in the first node. After generating the refrigeration configuration information and / or the updated operation configuration information, the first node sends the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, so that the refrigeration system can be configured according to the refrigeration configuration information, and sends the updated operation configuration information to the business system, so that the business system can be configured according to the updated operation configuration information.
[0117] In one embodiment, the first node is connected to the digital twin system, the business system, and the refrigeration system, respectively. The first node obtains the operation configuration information adopted by the business system and the operation data obtained under the first time length. Then, the first node transmits the obtained operation configuration information and operation data to the digital twin system, and predicts the operation status of the business system under the second time length through the digital twin system to obtain the prediction result. The digital twin system here can be set up separately. After generating refrigeration configuration information and / or updated operation configuration information, the first node sends the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, so that the refrigeration system can be configured according to the refrigeration configuration information, and sends the updated operation configuration information to the business system, so that the business system can be configured according to the updated operation configuration information.
[0118] In one embodiment, the first node includes one or more processors and a storage device. The processor and the storage device may be connected via a bus or other means. The storage device includes a computer-readable storage medium for storing one or more programs, or for storing data, wherein the program includes a software program, a computer executable program or a module. One or more processors can execute the various steps and processes of the first node by running the program, instruction or module stored in the storage device, so as to implement the configuration method based on digital twins in the above embodiment.
[0119] Figure 4 is a schematic diagram of a configuration device based on digital twins provided in an embodiment of the present application, such as Figure 4 As shown, the device includes an acquisition module 410, a prediction module 420, a generation module 430 and a sending module 440.
[0120] The acquisition module 410 is configured to acquire operation configuration information and operation data of the business system, wherein the operation data includes data obtained in a first period of time when the business system operates using the operation configuration information.
[0121] The prediction module 420 is configured to predict the operation status of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data, and obtain a prediction result.
[0122] The generating module 430 is configured to generate refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result.
[0123] The sending module 440 is configured to send the refrigeration configuration information to a refrigeration system that provides refrigeration for the business system, and to send the updated operation configuration information to the business system.
[0124] The device may be arranged in the first node to implement the configuration method based on digital twins or the steps performed by the first node provided in the embodiment of the present application. The steps or technical principles performed by the device are similar to those in the above embodiment and have similar technical effects. For technical details not described in detail in this embodiment, please refer to the above embodiment.
[0125] The device may be implemented by software and / or hardware, wherein the software includes software programs, computer executable programs or modules, etc., and the hardware includes processors or integrated circuits, etc.
[0126] Figure 5 is a schematic diagram of a configuration system based on digital twins provided in an embodiment of the present application, such as Figure 5 As shown, the configuration system includes a first node 510. The first node is connected to a business system 520 and a refrigeration system 530 respectively.
[0127] The first node 510 obtains the operating configuration information adopted by the first node 510 and the operating data obtained in the first duration from the business system 520 .
[0128] The first node 510 predicts the operation status of the business system 520 in the second time period through the digital twin system 5101 according to the acquired operation configuration information and operation data, and obtains the prediction result.
[0129] The first node 510 generates cooling configuration information and / or updated operation configuration information for the business system 520 according to the prediction result.
[0130] The first node 510 sends the refrigeration configuration information to the refrigeration system 530 that provides refrigeration for the business system 520, so that the refrigeration system 530 can configure according to the refrigeration configuration information, and sends the updated operation configuration information to the business system 520, so that the business system 520 can configure according to the updated operation configuration information.
[0131] In the digital twin-based configuration system provided by this embodiment, the digital twin system 5101 is set in the first node 510.
[0132] Figure 6 is a schematic diagram of another configuration system based on digital twins provided in an embodiment of the present application, such as Figure 6 As shown, the configuration system includes a first node 610. The first node is connected to a business system 620, a refrigeration system 630, and a digital twin system 640 respectively.
[0133] The first node 610 obtains the operating configuration information adopted by the first node 610 and the operating data obtained in the first duration from the business system 620 .
[0134] The first node 610 transmits the acquired operation configuration information and operation data to the digital twin system 640, and the digital twin system 640 predicts the operation status of the business system 620 in the second time period to obtain a prediction result.
[0135] The first node 610 generates cooling configuration information and / or updated operation configuration information for the business system 620 according to the prediction result.
[0136] The first node 610 sends the refrigeration configuration information to the refrigeration system 630 that provides refrigeration for the business system 620, so that the refrigeration system 630 can configure according to the refrigeration configuration information, and sends the updated operation configuration information to the business system 620, so that the business system 620 can configure according to the updated operation configuration information.
[0137] In the digital twin-based configuration system provided by this embodiment, the digital twin system 640 is independently configured relative to the first node 610 .
[0138] In the configuration system provided in the above embodiment, the business system and the refrigeration system may also be connected to each other.
[0139] In the configuration system provided in the above embodiment, the business system may include one or more systems.
[0140] The first node in the configuration system provided in the above embodiment is used to implement the digital twin-based configuration method provided in the above embodiment of the present application, or includes the digital twin-based configuration device provided in the above embodiment of the present application. The steps and technical principles performed by the first node are similar to those of the above embodiment, and have similar technical effects. For technical details not described in detail in this embodiment, please refer to the above embodiment.
[0141] An embodiment of the present application also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to implement the digital twin-based configuration method provided in the embodiment of the present application, or to implement the method or steps performed by the first node, or to implement the modules or steps included in the configuration device provided in the embodiment of the present application.
[0142] Through the description of the above embodiments, those skilled in the art can understand that the present application can be implemented with the aid of software and general hardware. The technical solution of the present application can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk, a floppy disk or an optical disk, etc., including a number of instructions for enabling a computing device (such as a computer, a server, a workstation, or a network device, etc.) to execute the method described in any embodiment of the present application.
[0143] It should be noted that in the embodiment of the above-mentioned software device, the various units or modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.
[0144] The present application can be implemented by software and / or hardware, and all or some steps in the above method, the functional modules / units in the above device or equipment can be implemented as software, firmware, hardware and appropriate combinations thereof. In hardware implementation, the division between the functional modules / units mentioned in the above embodiments does not necessarily correspond to the division of physical components. For example, a physical component can have multiple functions, or a function or step can be performed by several physical components in cooperation. Some physical components or all physical components can be implemented as software executed by a processor (such as a central processing unit, a graphics processor, a digital signal processor or a microprocessor, etc.), or implemented as hardware (such as an integrated circuit, an application-specific integrated circuit, a field programmable gate array, etc.). Among them, the software can be distributed on a computer-readable medium, and the computer-readable medium includes a computer-readable storage medium or a computer-readable communication medium.
[0145] The above embodiments describe the present invention in detail, but the present invention is not limited to the above embodiments. Various modifications or adjustments may be made to the present invention without departing from the spirit of the present invention.
Claims
1. A configuration method based on digital twins, applied to a first node, characterized in that: include: Acquire operation configuration information and operation data of the business system, wherein the operation data includes data obtained in a first period of time when the business system is operated using the operation configuration information; Predicting the operation status of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data, and obtaining a prediction result; generating refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result; The refrigeration configuration information is sent to a refrigeration system that provides refrigeration for the business system, and the updated operation configuration information is sent to the business system.
2. The method according to claim 1, characterized in that The operating data is data obtained at a first time interval, and the prediction result is a prediction result obtained at a second time interval, wherein the first time interval is greater than the second time interval.
3. The method according to claim 1, characterized in that The predicting the operation status of the business system in the second time period by using the digital twin system according to the operation configuration information and the operation data to obtain the prediction result includes: Acquiring an operation change parameter from the operation data; Predicting the operation change condition under the second duration according to the operation change parameter, and obtaining an operation change condition prediction result; The operation status of the business system in the second time period is simulated through the digital twin system according to the operation configuration information and the operation change prediction result to obtain the prediction result.
4. The method according to claim 3, characterized in that: The step of predicting the operation change situation in the second time period according to the operation change parameters to obtain the operation change situation prediction result includes: interpolating or fitting the data of the operation change parameters to obtain a processing result, and then extrapolating the processing result in the second time period to obtain the operation change situation prediction result.
5. The method according to claim 1, characterized in that The predicting the operation status of the business system in the second time period by using the digital twin system according to the operation configuration information and the operation data to obtain the prediction result includes: Simulating the operation of the business system through the digital twin system according to the operation configuration information to obtain a simulation operation result; Acquiring data change status according to the operation data; The operation status of the business system in the second time period is predicted according to the simulation operation result and the data change status, and the prediction result is obtained.
6. The method according to claim 5, characterized in that The predicting the operation status of the business system in the second time period according to the simulation operation result and the data change status, and obtaining the prediction result, includes: Adjust or extrapolate the specified data in the simulation operation result according to the data change in the second time period to obtain the prediction result; or The data change situation is extrapolated to obtain the data change prediction result under the second time length, and then the specified data in the simulation operation result is adjusted according to the data change prediction result to obtain the prediction result.
7. The method according to claim 1, characterized in that The predicting the operation status of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data to obtain the prediction result includes: obtaining a previous prediction result corresponding to the operation configuration information or the operation data, and predicting the operation status of the business system in the second time period through the digital twin system according to the operation configuration information, the operation data, and the difference between the operation data and the previous prediction result to obtain the prediction result; wherein the previous prediction result includes a prediction result used to generate the operation configuration information or a prediction result having a corresponding relationship with the operation data.
8. The method according to claim 1, characterized in that The method of predicting the operation status of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data to obtain the prediction result includes: obtaining the previous prediction result corresponding to the operation configuration information or the operation data; predicting the operation status of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data to obtain a preliminary prediction result; then, correcting the preliminary prediction result according to the difference between the operation data and the previous prediction result to obtain a corrected prediction result, and using the corrected prediction result as the prediction result.
9. A configuration device based on digital twins, located at a first node, characterized in that: include: An acquisition module, used to acquire operation configuration information and operation data of a business system, wherein the operation data includes data obtained in a first period of time when the business system is operated using the operation configuration information; A prediction module, used to predict the operation status of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data, and obtain a prediction result; A generating module, configured to generate refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result; The sending module is used to send the refrigeration configuration information to the refrigeration system that provides refrigeration for the business system, and to send the updated operation configuration information to the business system.
10. A first node, characterized in that: The steps performed by the first node include: Acquire operation configuration information and operation data of the business system, wherein the operation data includes data obtained in a first period of time when the business system is operated using the operation configuration information; Predicting the operation status of the business system in the second time period through the digital twin system according to the operation configuration information and the operation data, and obtaining a prediction result; generating refrigeration configuration information and / or updated operation configuration information for the business system according to the prediction result; The refrigeration configuration information is sent to a refrigeration system that provides refrigeration for the business system, and the updated operation configuration information is sent to the business system.