A method and system for digital twinning of heavy assets

Abstract

The present application relates to a method and system for digital twinning of heavy assets, and belongs to the field of digital twin technology. The method includes: obtaining relevant data information of infrastructure equipment in the target field and performing preprocessing; constructing a digital twin corresponding to the target infrastructure equipment; forming a one-to-one mapping relationship between the constructed digital twin model and multiple digital twins, and determining the attenuation curve of each digital twin based on the digital twin model; in response to detecting that the attenuation change rate of the target time node is greater than the first preset threshold, determining whether to trigger the warning information corresponding to the target digital twin according to the correlation of different digital twins; in response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending the warning information to the terminal. The present application can accurately and effectively judge and predict safety hazards and other issues, so as to remind relevant personnel to deal with them in time, thereby avoiding unnecessary losses.

Description

Technical Field

[0001] The present application relates to the field of digital twin technology, and in particular to a method and system for digital twinning of heavy assets. Background Art

[0002] Urban infrastructure equipment refers to the physical structures and equipment that support the normal operation of the city, provide basic services, and ensure the quality of life of urban residents. These infrastructure equipment are key components of urban life, including various transportation facilities, various facilities for residents' lives, and other basic equipment and facilities that enable the city to operate normally.

[0003] The quality and reliability of urban infrastructure equipment are crucial to the economic, social and environmental sustainability of cities. Currently, the digital transformation of the infrastructure industry is in full swing. How to accurately judge safety hazards and other issues by digitally twinning infrastructure equipment is an issue that needs to be addressed urgently. Summary of the invention

[0004] Based on this, it is necessary to provide a method and system for digital twinning of heavy assets that can accurately judge safety hazards and other issues in response to the above technical problems.

[0005] On the one hand, a method for digital twinning of heavy assets is provided, the method comprising:

[0006] Acquire relevant data information of infrastructure equipment in a target area and perform preprocessing, wherein the target area includes at least one of the following: gas, heating or power grid;

[0007] Based on the pre-processed data information of the infrastructure equipment, a digital twin corresponding to the target infrastructure equipment is constructed;

[0008] A one-to-one mapping relationship is formed between the constructed digital twin model and multiple digital twins, and based on the digital twin model, an attenuation curve of each digital twin is determined;

[0009] In response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger warning information corresponding to the target digital twin according to the correlation between different digital twins;

[0010] In response to detecting the need to trigger the warning information corresponding to the target digital twin, the warning information is sent to the terminal.

[0011] Optionally, obtain relevant data information of infrastructure equipment in the target area and perform preprocessing including:

[0012] Acquire relevant data information of infrastructure equipment in the target area, wherein the relevant data information at least includes environmental data information, equipment parameter information and operation data information;

[0013] Performing data cleaning on the relevant data information, and classifying the relevant data information after data cleaning to obtain static data information and dynamic data information;

[0014] Based on the classification tags, the classified related data information is marked and stored in the database.

[0015] Optionally, based on the preprocessed data information of the infrastructure equipment, constructing a digital twin corresponding to the target infrastructure equipment includes:

[0016] Obtain classified static data information and dynamic data information;

[0017] The static data information is mapped to generate an initial digital twin, and the dynamic data information is assigned to the initial digital twin to generate a target digital twin, that is, a digital twin corresponding to the target infrastructure device.

[0018] Optionally, the method for constructing the digital twin model includes:

[0019] Obtaining data information related to infrastructure equipment in multiple fields from a historical database, characterizing static data information and dynamic data information to obtain characterization data information;

[0020] Dividing the representation data information into a training set and a test set according to a preset ratio, and training and testing the initial digital twin model based on the training set and the test set;

[0021] In response to detecting that the test accuracy meets the preset standard, the digital twin model is output, wherein the digital twin model includes:

[0022]

[0023] in, represents the attenuation value, Represents the static data information representation value, represents the actual attenuation coefficient, represents the theoretical attenuation coefficient, represents the correction factor, Indicates the number of devices. represents a linear function, Indicates the dynamic data information representation value, represents the theoretical life coefficient, Indicates devices.

[0024] Optionally, forming a one-to-one mapping relationship between the constructed digital twin model and the multiple digital twins, and determining the attenuation curve of each digital twin based on the digital twin model includes:

[0025] Mapping the digital twin model to multiple digital twins respectively to form a one-to-one mapping relationship;

[0026] Based on the collected real-time dynamic data information of the target digital twin, the data is input into the digital twin model to obtain the attenuation value of each time node;

[0027] Based on the attenuation values ​​of multiple time nodes, the attenuation curve of the target digital twin is generated.

[0028] Optionally, in response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger the warning information corresponding to the target digital twin according to the correlation between different digital twins includes:

[0029] In response to detecting that the attenuation change rate of the target time node in the attenuation curve is greater than a first preset threshold, the correlation between the target digital twin and the other digital twins is obtained according to the correlation relationship between the target digital twin and the other digital twins, wherein the calculation method of the correlation includes:

[0030]

[0031] in, Represents the associated value, Indicates the change value of the amount of data affecting the parameter. Indicates time, represents the correlation coefficient, represents the weight coefficient, Represents the sum of the mutually influencing parameter data, Indicates the amount of parameter data that will be transferred at runtime that affects the target;

[0032] Sort the correlation values ​​corresponding to different correlations from large to small, select the correlation values ​​of the first n other digital twins in the sorting, and calculate the first average value;

[0033] In response to detecting that the first average value is greater than or equal to a second preset threshold, selecting the attenuation change rates of the top m other digital twins at the target time node, and calculating a second average value, where m<n;

[0034] In response to detecting that the second average value is greater than or equal to a third preset threshold, determining that a warning information corresponding to the target digital twin needs to be triggered;

[0035] In response to detecting that the second average value is less than a third preset threshold, it is determined that there is no need to trigger the warning information corresponding to the target digital twin.

[0036] Optionally, in response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending the warning information to the terminal includes:

[0037] In response to detecting the need to trigger the warning information corresponding to the target digital twin, based on the level to which the target digital twin belongs;

[0038] Send a warning message to the terminal corresponding to the level to which the target digital twin belongs.

[0039] On the other hand, a system for digital twinning of heavy assets is provided, the system comprising:

[0040] A preprocessing module, used to obtain and preprocess relevant data information of infrastructure equipment in a target field, wherein the target field includes at least one of the following: gas, heating or power grid;

[0041] A digital twin construction module is used to construct a digital twin corresponding to the target infrastructure equipment based on the preprocessed relevant data information of the infrastructure equipment;

[0042] An attenuation curve determination module is used to form a one-to-one mapping relationship between the constructed digital twin model and multiple digital twins, and determine the attenuation curve of each digital twin based on the digital twin model;

[0043] A warning information trigger determination module, configured to determine whether to trigger warning information corresponding to a target digital twin according to the correlation between different digital twins in response to detecting that the attenuation change rate of a target time node is greater than a first preset threshold;

[0044] The warning information sending module is used to send warning information to the terminal in response to detecting the need to trigger the warning information corresponding to the target digital twin.

[0045] Optionally, the system further comprises a model building module, wherein the model building module is used to:

[0046] Obtaining data information related to infrastructure equipment in multiple fields from a historical database, characterizing static data information and dynamic data information to obtain characterization data information;

[0047] Dividing the representation data information into a training set and a test set according to a preset ratio, and training and testing the initial digital twin model based on the training set and the test set;

[0048] In response to detecting that the test accuracy meets the preset standard, the digital twin model is output, wherein the digital twin model includes:

[0049]

[0050] in, represents the attenuation value, Represents the static data information representation value, represents the actual attenuation coefficient, represents the theoretical attenuation coefficient, represents the correction factor, Indicates the number of devices. represents a linear function, Indicates the dynamic data information representation value, represents the theoretical life coefficient, Indicates devices.

[0051] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0052] Acquire relevant data information of infrastructure equipment in a target area and perform preprocessing, wherein the target area includes at least one of the following: gas, heating or power grid;

[0053] Based on the pre-processed data information of the infrastructure equipment, a digital twin corresponding to the target infrastructure equipment is constructed;

[0054] A one-to-one mapping relationship is formed between the constructed digital twin model and multiple digital twins, and based on the digital twin model, an attenuation curve of each digital twin is determined;

[0055] In response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger warning information corresponding to the target digital twin according to the correlation between different digital twins;

[0056] In response to detecting the need to trigger the warning information corresponding to the target digital twin, the warning information is sent to the terminal.

[0057] In another aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0058] Acquire relevant data information of infrastructure equipment in a target area and perform preprocessing, wherein the target area includes at least one of the following: gas, heating or power grid;

[0059] Based on the pre-processed data information of the infrastructure equipment, a digital twin corresponding to the target infrastructure equipment is constructed;

[0060] A one-to-one mapping relationship is formed between the constructed digital twin model and multiple digital twins, and based on the digital twin model, an attenuation curve of each digital twin is determined;

[0061] In response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger warning information corresponding to the target digital twin according to the correlation between different digital twins;

[0062] In response to detecting the need to trigger the warning information corresponding to the target digital twin, the warning information is sent to the terminal.

[0063] The above-mentioned method and system for digital twinning of heavy assets, the method includes: obtaining relevant data information of infrastructure equipment in the target field and performing preprocessing, the target field including at least one of the following: gas, heating or power grid; constructing a digital twin corresponding to the target infrastructure equipment based on the relevant data information of the preprocessed infrastructure equipment; forming a one-to-one mapping relationship between the constructed digital twin model and multiple digital twins, and determining the attenuation curve of each digital twin based on the digital twin model; in response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger the warning information corresponding to the target digital twin according to the correlation between different digital twins; in response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending a warning information to the terminal. By digitally twinning infrastructure equipment, the present application can accurately judge and predict safety hazards and other problems, so as to remind relevant personnel to deal with them in time, thereby avoiding unnecessary losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is an application environment diagram of a method for digitally twinning heavy assets in one embodiment;

[0065] Figure 2 A schematic diagram of a process for performing digital twinning of heavy assets in one embodiment;

[0066] Figure 3 It is a structural block diagram of a system for digitally twinning heavy assets in one embodiment;

[0067] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0069] It should be understood that in the description of the present application, unless the context clearly requires otherwise, words such as "include", "comprises", and the like throughout the specification should be interpreted as including rather than being exclusive or exhaustive; that is, as including but not limited to.

[0070] It should also be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, "plurality" means two or more.

[0071] It should be noted that the terms "S1", "S2", etc. are only used for the purpose of describing the steps, and do not specifically refer to the order or sequence, nor are they used to limit the present application. They are only for the convenience of describing the method of the present application, and cannot be understood as indicating the order of the steps. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0072] The method for digital twinning of heavy assets provided in this application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the data processing platform set on the server 104 through the network, wherein the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0073] In one embodiment, Figure 2 As shown in the figure, a method for digital twinning of heavy assets is provided, and this method is applied to Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0074] S1: Obtain relevant data information of infrastructure equipment in the target area and perform preprocessing;

[0075] It should be noted that, because urban construction in the fields of municipal infrastructure such as water, gas, and heating, problems such as aging equipment and facilities, rising operation and maintenance costs, high energy consumption, and prominent safety hazards have gradually emerged, so the preferred target field of the embodiment of the present application is the field of aging facilities and equipment such as gas, heating or power grid. The relevant data information may include environmental data information, equipment parameter information and operation data information, among which the environmental data information may include information such as the geographical location, temperature, humidity, etc. of the equipment. The equipment parameter information includes parameter information attached to the equipment when it is produced, such as the theoretical life parameters, specifications, models, etc. of the equipment. The operation data information refers to the data generated when the equipment is running, such as parameters such as current and voltage. The operation data information can be classified as dynamic data information, and the environmental data information and equipment parameter information can be classified as static data information. Among them, when the environmental data information is classified as static data information, the parameters such as temperature and humidity are taken as the average value of the geographical location of the equipment over a period of time.

[0076] In some specific implementations, obtaining relevant data information of infrastructure equipment in the target field and performing preprocessing includes:

[0077] Obtain relevant data information on infrastructure equipment in the target area;

[0078] Perform data cleaning on the relevant data information, and classify the relevant data information after data cleaning to obtain static data information and dynamic data information, wherein data cleaning is a common technique, and the specific cleaning process will not be described in detail here;

[0079] Based on the classification tags, the classified related data information is marked and stored in the database, wherein the classification tags only need to be able to clear the attributes that describe the corresponding data information, such as 1 represents the tag of static data information, 2 represents the tag of dynamic data information, etc.

[0080] S2: Based on the preprocessed data information of the infrastructure equipment, a digital twin corresponding to the target infrastructure equipment is constructed.

[0081] It should be noted that digital twin refers to a data model formed by mapping infrastructure equipment in the physical world into the digital space in the form of data.

[0082] In some specific implementations, constructing a digital twin corresponding to a target infrastructure device based on preprocessed relevant data information of the infrastructure device includes:

[0083] Obtain classified static data information and dynamic data information;

[0084] The static data information is mapped to generate an initial digital twin, and the dynamic data information is assigned to the initial digital twin to generate a target digital twin, that is, a digital twin corresponding to the target infrastructure device.

[0085] S3: Form a one-to-one mapping relationship between the constructed digital twin model and multiple digital twins, and determine the attenuation curve of each digital twin based on the digital twin model.

[0086] It should be noted that the digital twin model is used to determine the attenuation value of the digital twin, and each digital twin has its corresponding digital twin model.

[0087] In some specific implementations, the method for constructing the digital twin model includes:

[0088] Obtaining data information related to infrastructure equipment in multiple fields from a historical database, characterizing static data information and dynamic data information to obtain characterization data information, wherein characterizing different data information can make them in the same dimension for easy calculation, and the characterization method is a common method, such as an encoder, a decoder, etc., which will not be repeated here;

[0089] Dividing the representation data information into a training set and a test set according to a preset ratio, and training and testing the initial digital twin model based on the training set and the test set, wherein the preset ratio can be set according to actual needs, such as 7:3;

[0090] In response to detecting that the test accuracy meets the preset standard, the digital twin model is output, wherein the preset standard can be set according to actual needs, such as the accuracy reaches 95%, etc. The digital twin model includes:

[0091]

[0092] in, represents the attenuation value, Represents the static data information representation value, represents the actual attenuation coefficient, represents the theoretical attenuation coefficient, represents the correction factor, Indicates the number of devices. Indicates the dynamic data information representation value, represents the theoretical life coefficient, Represents a linear function. If the linear function is a linear function y=ax+b, then y= , a and b are learnable parameters, x is the representation value assignment, Indicates devices.

[0093] In some specific implementations, forming a one-to-one mapping relationship between the constructed digital twin model and the multiple digital twins, and determining the attenuation curve of each digital twin based on the digital twin model includes:

[0094] The digital twin model is mapped to multiple digital twins respectively to form a one-to-one mapping relationship, wherein, according to the actual scene requirements, the digital twin model can also be further trained using the data corresponding to the mapping target to obtain a more targeted digital twin model and improve the accuracy of the calculated attenuation value;

[0095] Based on the real-time dynamic data information of the target digital twin collected, it is input into the digital twin model to obtain the attenuation value of each time node, wherein the real-time dynamic data information collected here needs to be characterized, and the characterization value is obtained to be input into the digital twin model to determine the corresponding attenuation value;

[0096] Based on the attenuation values ​​of multiple time nodes, the attenuation curve of the target digital twin is generated.

[0097] S4: In response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger the warning information corresponding to the target digital twin according to the correlation between different digital twins.

[0098] It should be noted that the first preset threshold can be set according to actual needs, such as 15%, etc. The attenuation change rate refers to the change rate value calculated by comparing the attenuation value of the current time node with the attenuation value of the previous time node. The specific calculation process is a common method and will not be repeated here.

[0099] In some specific embodiments, in response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger the warning information corresponding to the target digital twin according to the correlation between different digital twins includes:

[0100] In response to detecting that the attenuation change rate of the target time node in the attenuation curve is greater than the first preset threshold, the correlation between the target digital twin and the other digital twins is obtained according to the correlation relationship between the target digital twin and the other digital twins, wherein the correlation relationship of the digital twins means that when a digital twin changes, the associated digital twin will also change accordingly, such as when the resistance changes, the current or voltage will also change accordingly, etc. The correlation value corresponding to the correlation can describe the degree of change to a certain extent. The calculation method of the correlation includes:

[0101]

[0102] in, Represents the associated value, Indicates the change value of the amount of data affecting the parameter. Indicates time, represents the correlation coefficient, represents the weight coefficient, Represents the sum of the mutually influencing parameter data, Indicates the amount of parameter data that will be transferred at runtime that affects the target;

[0103] Sort the association values ​​corresponding to different associations in descending order, select the association values ​​of the top n other digital twins in the sorting, and calculate the first average value, where n can be set according to actual needs, and the preferred value is 5, that is, the association values ​​of the top five digital twins;

[0104] In response to detecting that the first average value is greater than or equal to the second preset threshold, the attenuation change rates of the top m other digital twins at the target time node are selected, and the second average value is calculated, where m<n, m can be set according to actual needs, and the preferred value is 3, that is, the correlation value of the top three digital twins. The second preset threshold can be set according to actual needs. If it is detected that the first average value is less than the second preset threshold, no further processing is required;

[0105] In response to detecting that the second average value is greater than or equal to a third preset threshold, determining that a warning message corresponding to the target digital twin needs to be triggered, wherein the third preset threshold can be set according to actual needs;

[0106] In response to detecting that the second average value is less than a third preset threshold, it is determined that there is no need to trigger the warning information corresponding to the target digital twin.

[0107] S5: In response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending the warning information to the terminal.

[0108] It should be noted that the terminal can be a mobile phone, a computer, etc.

[0109] In some specific implementations, in response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending the warning information to the terminal includes:

[0110] In response to detecting the need to trigger the warning information corresponding to the target digital twin, based on the level to which the target digital twin belongs, where managers at different levels may manage different digital twins, their corresponding association relationships can be obtained from the database;

[0111] Send warning information to the terminal corresponding to the level to which the target digital twin belongs, wherein the information receiving terminals corresponding to different levels are also different, and their corresponding association relationships can be obtained from the database. Therefore, the warning information of different digital twins needs to be sent to the corresponding terminals for effective reminders, thereby improving the processing speed.

[0112] In the above-mentioned method of digital twinning of heavy assets, the method includes: obtaining relevant data information of infrastructure equipment in the target field and performing preprocessing, and the target field includes at least one of the following: gas, heating or power grid; based on the relevant data information of the preprocessed infrastructure equipment, constructing a digital twin corresponding to the target infrastructure equipment; forming a one-to-one mapping relationship between the constructed digital twin model and multiple digital twins, and determining the attenuation curve of each digital twin based on the digital twin model; in response to detecting that the attenuation change rate of the target time node is greater than the first preset threshold, determining whether to trigger the warning information corresponding to the target digital twin according to the correlation between different digital twins; in response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending a warning message to the terminal. By digitally twinning infrastructure equipment, the present application can accurately judge and predict safety hazards and other problems, so as to remind relevant personnel to deal with them in time, thereby avoiding unnecessary losses.

[0113] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0114] In one embodiment, Figure 3 As shown, a system for digital twinning of heavy assets is provided, including: a preprocessing module, a digital twin building module, an attenuation curve determination module, a warning information trigger determination module and a warning information sending module, wherein:

[0115] A preprocessing module, used to obtain and preprocess relevant data information of infrastructure equipment in a target field, wherein the target field includes at least one of the following: gas, heating or power grid;

[0116] A digital twin construction module is used to construct a digital twin corresponding to the target infrastructure equipment based on the preprocessed relevant data information of the infrastructure equipment;

[0117] An attenuation curve determination module is used to form a one-to-one mapping relationship between the constructed digital twin model and multiple digital twins, and determine the attenuation curve of each digital twin based on the digital twin model;

[0118] A warning information trigger determination module, configured to determine whether to trigger warning information corresponding to a target digital twin according to the correlation between different digital twins in response to detecting that the attenuation change rate of a target time node is greater than a first preset threshold;

[0119] The warning information sending module is used to send warning information to the terminal in response to detecting the need to trigger the warning information corresponding to the target digital twin.

[0120] As a preferred implementation, in the embodiment of the present invention, the system further includes a model building module, and the model building module is used to:

[0121] Obtaining data information related to infrastructure equipment in multiple fields from a historical database, characterizing static data information and dynamic data information to obtain characterization data information;

[0122] Dividing the representation data information into a training set and a test set according to a preset ratio, and training and testing the initial digital twin model based on the training set and the test set;

[0123] In response to detecting that the test accuracy meets the preset standard, the digital twin model is output, wherein the digital twin model includes:

[0124]

[0125] in, represents the attenuation value, Represents the static data information representation value, represents the actual attenuation coefficient, represents the theoretical attenuation coefficient, represents the correction factor, Indicates the number of devices. represents a linear function, Indicates the dynamic data information representation value, represents the theoretical life coefficient, Indicates devices.

[0126] As a preferred implementation, in the embodiment of the present invention, the preprocessing module is specifically used for:

[0127] Acquire relevant data information of infrastructure equipment in the target area, wherein the relevant data information at least includes environmental data information, equipment parameter information and operation data information;

[0128] Performing data cleaning on the relevant data information, and classifying the relevant data information after data cleaning to obtain static data information and dynamic data information;

[0129] Based on the classification tags, the classified related data information is marked and stored in the database.

[0130] As a preferred implementation, in the embodiment of the present invention, the digital twin construction module is specifically used for:

[0131] Obtain classified static data information and dynamic data information;

[0132] The static data information is mapped to generate an initial digital twin, and the dynamic data information is assigned to the initial digital twin to generate a target digital twin, that is, a digital twin corresponding to the target infrastructure device.

[0133] As a preferred implementation, in the embodiment of the present invention, the attenuation curve determination module is specifically used for:

[0134] Mapping the digital twin model to multiple digital twins respectively to form a one-to-one mapping relationship;

[0135] Based on the collected real-time dynamic data information of the target digital twin, the data is input into the digital twin model to obtain the attenuation value of each time node;

[0136] Based on the attenuation values ​​of multiple time nodes, the attenuation curve of the target digital twin is generated.

[0137] As a preferred implementation, in the embodiment of the present invention, the warning information triggering determination module is specifically used for:

[0138] In response to detecting that the attenuation change rate of the target time node in the attenuation curve is greater than a first preset threshold, the correlation between the target digital twin and the other digital twins is obtained according to the correlation relationship between the target digital twin and the other digital twins, wherein the calculation method of the correlation includes:

[0139]

[0140] in, Represents the associated value, Indicates the change value of the amount of data affecting the parameter. Indicates time, represents the correlation coefficient, represents the weight coefficient, Represents the sum of the mutually influencing parameter data, Indicates the amount of parameter data that will be transferred at runtime that affects the target;

[0141] Sort the correlation values ​​corresponding to different correlations from large to small, select the correlation values ​​of the first n other digital twins in the sorting, and calculate the first average value;

[0142] In response to detecting that the first average value is greater than or equal to a second preset threshold, selecting the attenuation change rates of the top m other digital twins at the target time node, and calculating a second average value, where m<n;

[0143] In response to detecting that the second average value is greater than or equal to a third preset threshold, determining that a warning information corresponding to the target digital twin needs to be triggered;

[0144] In response to detecting that the second average value is less than a third preset threshold, it is determined that there is no need to trigger the warning information corresponding to the target digital twin.

[0145] As a preferred implementation, in the embodiment of the present invention, the warning information sending module is specifically used for:

[0146] In response to detecting the need to trigger the warning information corresponding to the target digital twin, based on the level to which the target digital twin belongs;

[0147] Send a warning message to the terminal corresponding to the level to which the target digital twin belongs.

[0148] For the specific definition of the system for digital twinning of heavy assets, please refer to the definition of the method for digital twinning of heavy assets above, which will not be repeated here. Each module in the above-mentioned system for digital twinning of heavy assets can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0149] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for digital twinning of heavy assets is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0150] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0151] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0152] S1: Obtain relevant data information of infrastructure equipment in a target area and perform preprocessing, wherein the target area includes at least one of the following: gas, heating or power grid;

[0153] S2: Based on the preprocessed data information of the infrastructure equipment, a digital twin corresponding to the target infrastructure equipment is constructed;

[0154] S3: forming a one-to-one mapping relationship between the constructed digital twin model and multiple digital twins, and determining the attenuation curve of each digital twin based on the digital twin model;

[0155] S4: In response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger warning information corresponding to the target digital twin according to the correlation between different digital twins;

[0156] S5: In response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending the warning information to the terminal.

[0157] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0158] Acquire relevant data information of infrastructure equipment in the target area, wherein the relevant data information at least includes environmental data information, equipment parameter information and operation data information;

[0159] Performing data cleaning on the relevant data information, and classifying the relevant data information after data cleaning to obtain static data information and dynamic data information;

[0160] Based on the classification tags, the classified related data information is marked and stored in the database.

[0161] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0162] Obtain classified static data information and dynamic data information;

[0163] The static data information is mapped to generate an initial digital twin, and the dynamic data information is assigned to the initial digital twin to generate a target digital twin, that is, a digital twin corresponding to the target infrastructure device.

[0164] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0165] Obtaining data information related to infrastructure equipment in multiple fields from a historical database, characterizing static data information and dynamic data information to obtain characterization data information;

[0166] Dividing the representation data information into a training set and a test set according to a preset ratio, and training and testing the initial digital twin model based on the training set and the test set;

[0167] In response to detecting that the test accuracy meets the preset standard, the digital twin model is output, wherein the digital twin model includes:

[0168]

[0169] in, represents the attenuation value, Represents the static data information representation value, represents the actual attenuation coefficient, represents the theoretical attenuation coefficient, represents the correction factor, Indicates the number of devices. represents a linear function, Indicates the dynamic data information representation value, represents the theoretical life coefficient, Indicates devices.

[0170] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0171] Mapping the digital twin model to multiple digital twins respectively to form a one-to-one mapping relationship;

[0172] Based on the collected real-time dynamic data information of the target digital twin, the data is input into the digital twin model to obtain the attenuation value of each time node;

[0173] Based on the attenuation values ​​of multiple time nodes, the attenuation curve of the target digital twin is generated.

[0174] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0175] In response to detecting that the attenuation change rate of the target time node in the attenuation curve is greater than a first preset threshold, the correlation between the target digital twin and the other digital twins is obtained according to the correlation relationship between the target digital twin and the other digital twins, wherein the calculation method of the correlation includes:

[0176]

[0177] in, Represents the associated value, Indicates the change value of the amount of data affecting the parameter. Indicates time, represents the correlation coefficient, represents the weight coefficient, Represents the sum of the mutually influencing parameter data, Indicates the amount of parameter data that will be transferred at runtime that affects the target;

[0178] Sort the correlation values ​​corresponding to different correlations from large to small, select the correlation values ​​of the first n other digital twins in the sorting, and calculate the first average value;

[0179] In response to detecting that the first average value is greater than or equal to a second preset threshold, selecting the attenuation change rates of the top m other digital twins at the target time node, and calculating a second average value, where m<n;

[0180] In response to detecting that the second average value is greater than or equal to a third preset threshold, determining that a warning information corresponding to the target digital twin needs to be triggered;

[0181] In response to detecting that the second average value is less than a third preset threshold, it is determined that there is no need to trigger the warning information corresponding to the target digital twin.

[0182] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0183] In response to detecting the need to trigger the warning information corresponding to the target digital twin, based on the level to which the target digital twin belongs;

[0184] Send a warning message to the terminal corresponding to the level to which the target digital twin belongs.

[0185] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0186] S1: Obtain relevant data information of infrastructure equipment in a target area and perform preprocessing, wherein the target area includes at least one of the following: gas, heating or power grid;

[0187] S2: Based on the preprocessed data information of the infrastructure equipment, a digital twin corresponding to the target infrastructure equipment is constructed;

[0188] S3: forming a one-to-one mapping relationship between the constructed digital twin model and multiple digital twins, and determining the attenuation curve of each digital twin based on the digital twin model;

[0189] S4: In response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger warning information corresponding to the target digital twin according to the correlation between different digital twins;

[0190] S5: In response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending the warning information to the terminal.

[0191] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0192] Acquire relevant data information of infrastructure equipment in the target area, wherein the relevant data information at least includes environmental data information, equipment parameter information and operation data information;

[0193] Performing data cleaning on the relevant data information, and classifying the relevant data information after data cleaning to obtain static data information and dynamic data information;

[0194] Based on the classification tags, the classified related data information is marked and stored in the database.

[0195] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0196] Obtain classified static data information and dynamic data information;

[0197] The static data information is mapped to generate an initial digital twin, and the dynamic data information is assigned to the initial digital twin to generate a target digital twin, that is, a digital twin corresponding to the target infrastructure device.

[0198] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0199] Obtaining data information related to infrastructure equipment in multiple fields from a historical database, characterizing static data information and dynamic data information to obtain characterization data information;

[0200] Dividing the representation data information into a training set and a test set according to a preset ratio, and training and testing the initial digital twin model based on the training set and the test set;

[0201] In response to detecting that the test accuracy meets the preset standard, the digital twin model is output, wherein the digital twin model includes:

[0202]

[0203] in, represents the attenuation value, Represents the static data information representation value, represents the actual attenuation coefficient, represents the theoretical attenuation coefficient, represents the correction factor, Indicates the number of devices. represents a linear function, Indicates the dynamic data information representation value, represents the theoretical life coefficient, Indicates devices.

[0204] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0205] Mapping the digital twin model to multiple digital twins respectively to form a one-to-one mapping relationship;

[0206] Based on the collected real-time dynamic data information of the target digital twin, the data is input into the digital twin model to obtain the attenuation value of each time node;

[0207] Based on the attenuation values ​​of multiple time nodes, the attenuation curve of the target digital twin is generated.

[0208] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0209] In response to detecting that the attenuation change rate of the target time node in the attenuation curve is greater than a first preset threshold, the correlation between the target digital twin and the other digital twins is obtained according to the correlation relationship between the target digital twin and the other digital twins, wherein the calculation method of the correlation includes:

[0210]

[0211] in, Represents the associated value, Indicates the change value of the amount of data affecting the parameter. Indicates time, represents the correlation coefficient, represents the weight coefficient, Represents the sum of the mutually influencing parameter data, Indicates the amount of parameter data that will be transferred at runtime that affects the target;

[0212] Sort the correlation values ​​corresponding to different correlations from large to small, select the correlation values ​​of the first n other digital twins in the sorting, and calculate the first average value;

[0213] In response to detecting that the first average value is greater than or equal to a second preset threshold, selecting the attenuation change rates of the top m other digital twins at the target time node, and calculating a second average value, where m<n;

[0214] In response to detecting that the second average value is greater than or equal to a third preset threshold, determining that a warning information corresponding to the target digital twin needs to be triggered;

[0215] In response to detecting that the second average value is less than a third preset threshold, it is determined that there is no need to trigger the warning information corresponding to the target digital twin.

[0216] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0217] In response to detecting the need to trigger the warning information corresponding to the target digital twin, based on the level to which the target digital twin belongs;

[0218] Send a warning message to the terminal corresponding to the level to which the target digital twin belongs.

[0219] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0220] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0221] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for digital twinning of heavy assets, characterized in that: The method comprises: Acquire relevant data information of infrastructure equipment in a target area and perform preprocessing, wherein the target area includes at least one of the following: gas, heating or power grid; Based on the pre-processed data information of the infrastructure equipment, a digital twin corresponding to the target infrastructure equipment is constructed; A one-to-one mapping relationship is formed between the constructed digital twin model and multiple digital twins, and based on the digital twin model, an attenuation curve of each digital twin is determined; In response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold, determining whether to trigger warning information corresponding to the target digital twin according to the correlation between different digital twins; In response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending the warning information to the terminal; The method for constructing the digital twin model includes: Obtaining data information related to infrastructure equipment in multiple fields from a historical database, characterizing static data information and dynamic data information to obtain characterization data information; Dividing the representation data information into a training set and a test set according to a preset ratio, and training and testing the initial digital twin model based on the training set and the test set; In response to detecting that the test accuracy meets the preset standard, the digital twin model is output, wherein the digital twin model includes: in, represents the attenuation value, Represents the static data information representation value, represents the actual attenuation coefficient, represents the theoretical attenuation coefficient, represents the correction factor, Indicates the number of devices. represents a linear function, Indicates the dynamic data information representation value, represents the theoretical life coefficient, Indicates devices.

2. The method for digital twinning of heavy assets according to claim 1 is characterized in that: Obtain relevant data information of infrastructure equipment in the target area and perform preprocessing including: Acquire relevant data information of infrastructure equipment in the target area, wherein the relevant data information at least includes environmental data information, equipment parameter information and operation data information; Performing data cleaning on the relevant data information, and classifying the relevant data information after data cleaning to obtain static data information and dynamic data information; Based on the classification tags, the classified related data information is marked and stored in the database.

3. The method for digital twinning of heavy assets according to claim 2 is characterized in that: Based on the preprocessed data information of the infrastructure equipment, the digital twin corresponding to the target infrastructure equipment is constructed, including: Obtain classified static data information and dynamic data information; The static data information is mapped to generate an initial digital twin, and the dynamic data information is assigned to the initial digital twin to generate a target digital twin, that is, a digital twin corresponding to the target infrastructure device.

4. The method for digital twinning of heavy assets according to claim 3 is characterized in that: The constructed digital twin model forms a one-to-one mapping relationship with multiple digital twins, and based on the digital twin model, the attenuation curve of each digital twin is determined, including: Mapping the digital twin model to multiple digital twins respectively to form a one-to-one mapping relationship; Based on the collected real-time dynamic data information of the target digital twin, the data is input into the digital twin model to obtain the attenuation value of each time node; Based on the attenuation values ​​of multiple time nodes, the attenuation curve of the target digital twin is generated.

5. The method for digital twinning of heavy assets according to claim 4 is characterized in that: In response to detecting that the attenuation change rate of the target time node is greater than the first preset threshold, determining whether to trigger the warning information corresponding to the target digital twin according to the correlation between different digital twins includes: In response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold value in the attenuation curve, the correlation between the target digital twin and the other digital twins is obtained according to the correlation relationship between the target digital twin and the other digital twins, wherein the calculation method of the correlation includes: in, Represents the associated value, Indicates the change value of the amount of data affecting the parameter. Indicates time, represents the correlation coefficient, represents the weight coefficient, represents the sum of the mutually influencing parameter data, Indicates the amount of parameter data that will be transferred at runtime that affects the target; Sort the correlation values ​​corresponding to different correlations from large to small, select the correlation values ​​of the first n other digital twins in the sorting, and calculate the first average value; In response to detecting that the first average value is greater than or equal to a second preset threshold, selecting the attenuation change rates of the top m other digital twins at the target time node, and calculating a second average value, where m<n; In response to detecting that the second average value is greater than or equal to a third preset threshold, determining that a warning information corresponding to the target digital twin needs to be triggered; In response to detecting that the second average value is less than a third preset threshold, it is determined that there is no need to trigger the warning information corresponding to the target digital twin.

6. The method for digital twinning of heavy assets according to claim 5, characterized in that: In response to detecting that the warning information corresponding to the target digital twin needs to be triggered, sending the warning information to the terminal includes: In response to detecting the need to trigger the warning information corresponding to the target digital twin, based on the level to which the target digital twin belongs; Send a warning message to the terminal corresponding to the level to which the target digital twin belongs.

7. A system for digital twinning of heavy assets, characterized in that: The system comprises: A preprocessing module, used to obtain and preprocess relevant data information of infrastructure equipment in a target field, wherein the target field includes at least one of the following: gas, heating or power grid; A digital twin construction module is used to construct a digital twin corresponding to the target infrastructure equipment based on the relevant data information of the preprocessed infrastructure equipment; An attenuation curve determination module is used to form a one-to-one mapping relationship between the constructed digital twin model and multiple digital twins, and determine the attenuation curve of each digital twin based on the digital twin model; A warning information trigger determination module, used to determine whether to trigger the warning information corresponding to the target digital twin according to the correlation between different digital twins in response to detecting that the attenuation change rate of the target time node is greater than a first preset threshold; A warning information sending module, configured to send warning information to a terminal in response to detecting that warning information corresponding to a target digital twin needs to be triggered; The system further comprises a model building module, and the model building module is used to: Obtaining data information related to infrastructure equipment in multiple fields from a historical database, characterizing static data information and dynamic data information to obtain characterization data information; Dividing the representation data information into a training set and a test set according to a preset ratio, and training and testing the initial digital twin model based on the training set and the test set; In response to detecting that the test accuracy meets the preset standard, the digital twin model is output, wherein the digital twin model includes: in, represents the attenuation value, Represents the static data information representation value, represents the actual attenuation coefficient, represents the theoretical attenuation coefficient, represents the correction factor, Indicates the number of devices. represents a linear function, Indicates the dynamic data information representation value, represents the theoretical life coefficient, Indicates devices.

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