A data processing method, device and equipment for digital twinning

Abstract

The application discloses a data processing method for digital twinning, which comprises the following steps: acquiring a plurality of existing continuous state information parameters; constructing a state information prediction model according to the plurality of existing continuous state information parameters; acquiring predicted state information through the state information prediction model; performing threshold filtering on the predicted state information through a similarity algorithm to obtain optimal predicted state information; constructing a state information fusion model according to the optimal predicted state information; obtaining fused state information through the state information fusion model; constructing a state information aggregation model according to the predicted state information, the optimal predicted state information and the fused state information; and obtaining aggregated state information through the state information aggregation model; and sending the aggregated state information to a twin end.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data processing method, apparatus and equipment for digital twins. Background Technology

[0002] With traffic conditions becoming increasingly complex and autonomous driving technology emerging, there is a greater and more comprehensive understanding of the scope and technological level of connected vehicle services. As connected vehicles become the primary traffic participants, the requirements for the accuracy of visual tracking of their operations are also rising. To achieve intuitive and precise identification of the operational status information of traffic participants, the industry has proposed "digital twin" technology to meet the visualization needs of connected vehicle services.

[0003] In order to better express and reproduce the state information of participants, existing "digital twin" technology adopts a state information buffering mechanism, which sacrifices the authenticity of the state information itself. Summary of the Invention

[0004] This specification provides a data processing method, apparatus, and device for digital twins, which addresses the problem of ghosting in the twinning process caused by the diversity of existing state information extraction technologies and the loss of state information in signal blind zones.

[0005] The embodiments in this specification adopt the following technical solutions:

[0006] Firstly, embodiments of this specification provide a data processing method for digital twins, the method comprising:

[0007] A number of existing continuous state information parameters are obtained, a state information prediction model is constructed based on the number of existing continuous state information parameters, and the predicted state information is obtained through the state information prediction model.

[0008] The predicted state information is then subjected to threshold filtering using a similarity algorithm to obtain the optimal predicted state information.

[0009] A state information fusion model is constructed based on the optimal predicted state information, and the fused state information is obtained through the state information fusion model.

[0010] A state information aggregation model is constructed based on the predicted state information, the optimal predicted state information, and the fused state information. Aggregated state information is obtained through the state information aggregation model.

[0011] The aggregated state information is sent to the twin terminal, which refers to a device used to map the state information of the target object.

[0012] Secondly, embodiments of this specification also provide a data processing method for digital twins, the method comprising:

[0013] Obtain the start and end timestamps of the received aggregation status information;

[0014] Set the slice volume between the start timestamp and the end timestamp;

[0015] Obtain aggregate status information data parameters;

[0016] The aggregation state information data parameters are sliced ​​equally according to the slice size, and the aggregation state information sequence is output.

[0017] The aggregated state information sequence is sent to the user interface.

[0018] Thirdly, embodiments of this specification also provide a data processing apparatus for digital twins, comprising:

[0019] The prediction model module is used to acquire several existing continuous state information parameters, construct a state information prediction model based on the several existing continuous state information parameters, and obtain predicted state information through the state information prediction model.

[0020] The filtering module is used to perform threshold filtering on the predicted state information using a similarity algorithm to obtain the optimal predicted state information;

[0021] The fusion model module is used to construct a state information fusion model based on the optimal predicted state information, and obtain fused state information through the state information fusion model.

[0022] The aggregation model module is used to construct a state information aggregation model based on the predicted state information, the optimal predicted state information, and the fused state information, and to obtain aggregated state information through the state information aggregation model.

[0023] A communication module is used to send the aggregated state information to a twin terminal, wherein the twin terminal refers to a device used to map the state information of a target object.

[0024] Fourthly, embodiments of this specification also provide a data processing apparatus for digital twins, comprising:

[0025] The acquisition module is used to obtain the start and end timestamps of the received aggregation status information;

[0026] The settings module is used to set the slice volume between the start timestamp and the end timestamp;

[0027] The extraction module is used to obtain aggregated status information data parameters;

[0028] The slicing module is used to slice the aggregation state information data parameters equally according to the slice volume and output the aggregation state information sequence.

[0029] The delivery module is used to send the aggregation state information sequence to the user interface.

[0030] Fifthly, embodiments of this specification also provide an electronic device, including at least one processor and a memory, the memory storing a program and configured such that at least one processor executes a data processing method for digital twins according to embodiments of this specification.

[0031] The above-mentioned at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: by constructing a state information prediction model, the purpose of predicting state information in signal blind spots can be achieved, thereby improving the continuity of state information twins; and by constructing a state information aggregation model, the purpose of aggregating state information from multiple sources can be achieved. Attached Figure Description

[0032] The accompanying drawings, which are included to provide a further understanding of the embodiments of this specification and form part of the embodiments of this specification, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0033] Figure 1 A flowchart illustrating a data processing method for digital twins provided in an embodiment of this specification;

[0034] Figure 2 This is a flowchart illustrating a method for sending aggregated state information to the twin in a data processing method for digital twins provided in an embodiment of this specification.

[0035] Figure 3 This specification provides a schematic diagram of a data transmission method in a data processing method for digital twins, as illustrated in an embodiment of the present specification.

[0036] Figure 4 This specification provides a schematic diagram of the structure of a data processing device for digital twins, as illustrated in an embodiment.

[0037] Figure 5 A flowchart illustrating a data processing method for digital twins provided in an embodiment of this specification;

[0038] Figure 6 This is a schematic diagram of a data processing device for digital twins provided in an embodiment of this specification. Detailed Implementation

[0039] The concept of the Internet of Vehicles (IoV) originates from the Internet of Things (IoT), which uses vehicles in motion as information sensing objects and leverages next-generation information and communication technologies to achieve network connections between vehicles and X (i.e., vehicles, people, roads, and service platforms). This enhances the overall intelligent driving level of vehicles, provides users with a safe, comfortable, intelligent, and efficient driving experience and transportation services, and improves traffic operation efficiency and the level of intelligence in social transportation services.

[0040] The Internet of Vehicles (IoV) utilizes next-generation information and communication technologies to achieve comprehensive network connectivity between vehicles and cloud platforms, between vehicles themselves, between vehicles and roads, between vehicles and people, and within vehicles. It primarily achieves "triple play," integrating the in-vehicle network, the inter-vehicle network, and the in-vehicle mobile internet. The IoV uses sensing technology to perceive vehicle status information and leverages wireless communication networks and modern intelligent information processing technologies to achieve intelligent traffic management, intelligent decision-making for traffic information services, and intelligent vehicle control.

[0041] Intelligent connected vehicles (ICVs) are developed based on traditional vehicle-to-everything (V2X) technology. As traffic conditions become increasingly complex, traditional traffic control and optimization methods have reached their limits. Only through intelligent connected vehicle technology can traffic be further controlled and optimized with high quality, and safety be improved. Since intelligent connected vehicles are a set of traffic AI technologies based on IT technology, which involves rule analysis and model optimization, there is bound to be a process of understanding and acceptance during their development. In order to vividly and intuitively express the maturity of intelligent connected vehicle capabilities in the early stages of application or during the stable guarantee period, the need becomes relatively urgent. Digital twins have become a powerful technical means to meet this need.

[0042] Due to the complexity and variability of traffic conditions, coupled with the diverse range of participants in various situations, higher demands are placed on the quality assurance of digital twins. In order to provide timely and accurate feedback on the operational status of individual participants in traffic conditions, and to express the effect of intelligent capability output based on driving optimization, the industry has proposed various technical architectures for business indicators such as state information latency, entity model, capability model, and state information process.

[0043] The basic principle of existing technology is based on the technical processing latency requirements of each level of the twin. It lacks the necessary compensation mechanism for the delay in state information. In order to better express and restore the state information process of the participants, a state information buffering mechanism is adopted, which actually sacrifices the authenticity of the state information itself, as detailed below.

[0044] Existing technologies do not consider the discontinuity of the state information twin process caused by the signal blind zone in the state information process area; existing technologies do not consider the multi-source state information caused by the diversity of participant state information extraction technologies, resulting in ghosting phenomenon in the twin process; the state information buffering mechanism provided by existing technologies causes state information delay, ultimately sacrificing the real-time performance and authenticity of the state information; existing technologies have a low state information extraction frequency, resulting in unsmooth twin effect due to discrete state information, and no optimization solution is provided.

[0045] Therefore, the embodiments of this specification provide a data processing method, apparatus, and electronic device for digital twins. By constructing a state information prediction model, the method aims to predict state information in signal blind spots and improve the continuity of state information twins. Furthermore, by constructing a state information aggregation model, the method aims to aggregate state information from multiple sources. It also solves the problem of state information distortion through state information compensation and improves the smoothing effect of state information twins through state information slicing.

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments in this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0047] Example 1

[0048] Figure 1 This is a flowchart illustrating a data processing method for digital twins, provided as an embodiment of the specification.

[0049] Please see Figure 1 As shown, Embodiment 1 provides a data processing method for digital twins, which can be applied to the platform side. The method includes the following steps:

[0050] S101. Obtain several existing continuous state information parameters, construct a state information prediction model based on the several existing continuous state information parameters, and obtain predicted state information through the state information prediction model.

[0051] Specifically, state information can be understood as operational state information. Existing continuous state information can be understood as already known operational state information. Therefore, the methods for constructing a state information prediction model include, but are not limited to, obtaining continuous time corresponding to several existing continuous state information parameters; and constructing a state information prediction model based on a fitting algorithm using the several existing continuous state information parameters and continuous time.

[0052] The methods for obtaining predicted state information based on the state information prediction model include, but are not limited to, constructing a weighted state information parameter sequence based on several existing continuous state information parameters; inputting the weighted state information parameter sequence into the state information prediction model to obtain fitted state information coefficients; obtaining a predicted state information time period and dividing the predicted state information time period into several predicted state information time slices; generating a predicted state information parameter sequence based on the fitted state information coefficients and the predicted state information time slices; constructing a predicted state information sequence based on the predicted state information parameter sequence; and obtaining predicted state information from the predicted state information sequence. The operational state information can be understood as traffic environment operational state information, moving object operational state information, and stationary object operational state information. For example, moving object operational state information includes, but is not limited to, people, animals, birds, and vehicles moving on roads.

[0053] As an application example, the state information prediction model can be expressed as follows:

[0054]

[0055] in:

[0056] Q j (x) is the basis function. A polynomial order n is set to obtain the curve polynomial function. The point sequence to be fitted (which in this scheme is also the state information sequence to be fitted) is input into the curve polynomial function to satisfy the overall error value function. The polynomial coefficients are then obtained. (That is, the state information coefficients to be fitted).

[0057] Obtain the predicted state information time period and divide the predicted state information time period into several predicted state information time slices. For example, if the predicted state information time period is 1 second, and each predicted state information time slice is 200 ms, it can be divided into five predicted state information time slices.

[0058] Obtain continuous state information N, N-1, N-2;

[0059] Construct a weighted state information sequence [(1, 0, X)] n (1, 1000, X) n-1 (1, 2000, X) n-2 The input is fed into the polynomial state information curve fitting algorithm; the fitted state information coefficients (PX) are obtained. n PX n+1 PX n+2 );

[0060] Based on the fitted state information coefficients and the predicted state information time slices, the predicted state information (Xn+1, Xn+2, Xn+3, Xn+4, Xn+5) is constructed.

[0061] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0062] S103. The predicted state information is subjected to threshold filtering using a similarity algorithm to obtain the optimal predicted state information;

[0063] Specifically, filtering methods include, but are not limited to, using Kalman filtering to filter the predicted state information. The optimal predicted state information is obtained after Kalman filtering. This can also be understood as obtaining the optimal predicted state information X′ corresponding to the prediction time. n+1 .

[0064] As an application example, assuming the previous state information is k, the current predicted state information is constructed based on the system model (Formula 1, Formula 2):

[0065] X(k|k-1)=AX(k-1|k-1)+BU(k)(Formula 1)

[0066] Note: X(k|k-1) is the prediction result of the previous state information, X(k-1|k-1) is the optimal result of the previous state information, and U(k) is the control quantity of the current state information. If there is no control quantity, it can be 0.

[0067] P(k|k-1)=AP(k-1|k-1)A′+Q (Formula 2)

[0068] Note: P(k|k-1) is the covariance of X(k|k-1), P(k-1|k-1) is the covariance of X(k-1|k-1), A′ represents the transpose of A, and Q is the covariance of the system process.

[0069] Having obtained the predicted current state information, we then collect measured values ​​of the current state information. Combining the predicted and measured values, we obtain the optimal estimate X(k|k) of the current state information (k) using (Formulas 3 and 4):

[0070] X(k|k)=X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1))(Formula 3)

[0071] Where Kg is the Kalman gain:

[0072] Kg(k)=P(k|k-1)H′ / (HP(k|k-1)H′+R) (Formula 4)

[0073] By using the optimal estimated value X(k|k) under the k-state information, and combining it with (Equation 5) the Kalman filter to run continuously until the system process ends, the final filtered optimal predicted state information X′ is obtained. n+1 :

[0074] P(k|k)=(I-Kg(k)H)P(k|k-1)(Formula 5)

[0075] Note: I is a matrix of 1. For a single model and single measurement, I = 1. When the system enters the k+1 state information, P(k|k) is P(k-1|k-1) in equation (2).

[0076] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0077] S105. Construct a state information fusion model based on the optimal predicted state information, and obtain fused state information through the state information fusion model;

[0078] Specifically, the state information fusion model can be expressed as follows:

[0079] Y n+1 =X′ n+1 P n+1 +X′ (n+1)2 P n+1

[0080] Where, X′ n+1 It is the optimal predicted state information after threshold filtering using a similarity algorithm, P n+1 Represents the optimal predicted state information X′ n+1 The covariance matrix, Y n+1 To integrate state information.

[0081] It should be understood that the specific details listed above are for illustrative purposes only and should not be construed as limiting the present invention in any way.

[0082] S107. Construct a state information aggregation model based on the predicted state information, the optimal predicted state information, and the fused state information, and obtain aggregated state information through the state information aggregation model.

[0083] Specifically, the state information aggregation model can be expressed as follows:

[0084] Z n+1 =X n+1 +Y n+1 -X′ n+1

[0085] Among them, X n+1 To predict state information, X′n+1 It is the optimal predicted state information after threshold filtering using a similarity algorithm, P n+1 Represents the optimal predicted state information X′ n+1 The covariance matrix, Y n+1 To fuse state information, Z n+1 This refers to the final aggregated state information after data compensation processing of the merged state information.

[0086] As an application example, the input is the sequence of collected status information: X n1 X n2 X nn In the state information prediction model, a time-aligned predicted state information sequence is obtained: X (n+1)1 X (n+1)2 X (n+1)n .

[0087] The optimal predicted state information sequence X′ is obtained by applying a threshold filter using a similarity algorithm to the predicted state information sequence. (n+1)1 、X′ (n+1)2 、X′ (n+1)n .

[0088] Input the optimal predicted state information sequence X′ n+1 The fused state information Y is obtained from the state information fusion equation. n+1 ;

[0089] Input fusion status information Y n+1 The aggregated state information Z is obtained from the state information aggregation model. n+1 .

[0090] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0091] S109. Send the aggregated state information to the twin terminal, where the twin terminal refers to a device used to map the state information of the target object.

[0092] Specifically, the method of sending to the twin can be the same as usual, such as directly mapping the aggregated state information to the twin.

[0093] This embodiment of the disclosure can construct a state information prediction model and obtain predicted state information through the state information prediction model; perform state information prediction on signal blind spots, solve the problem of discontinuous state information caused by state information blind spots, and improve the continuity of state information.

[0094] The optimal predicted state information is obtained by threshold filtering the predicted state information using a similarity algorithm; a state information fusion model is constructed based on the optimal predicted state information, and fused state information is obtained through the state information fusion model; a state information aggregation model is constructed based on the predicted state information, the optimal predicted state information, and the fused state information, and aggregated state information is obtained through the state information aggregation model; the aggregated state information is then sent to the twin device. This achieves the goal of aggregating multi-source state information into a single state information, enabling clear display of the target state information on the twin device.

[0095] To better implement this embodiment, a state information prediction model is constructed to predict the target state information in advance and perform state information compensation, which solves the problem of state information distortion caused by the state information compensation mechanism and improves the authenticity of the state information twin.

[0096] Specifically, the compensation state information time period is obtained, and then divided into several compensation state information time slices; a weighted state information sequence is generated based on the several compensation state information time slices; the weighted state information sequence is input into the state information prediction model to obtain fitted state information coefficients; and compensation state information is generated based on the fitted state information coefficients and the compensation state information time slices. The method of obtaining compensation state information is basically the same as that of obtaining prediction state information, and will not be described again here.

[0097] Furthermore, because existing technologies extract state information in milliseconds, the discrete state information leads to a lack of smoothness in the twinning effect. For this purpose, please refer to... Figure 2 As shown, Figure 2 This is a flowchart illustrating a method for sending aggregated state information to the digital twin in a data processing method for digital twins, as provided in an embodiment of the specification. The method includes:

[0098] S201. Obtain the start and end timestamps of sending aggregation status information;

[0099] Specifically, the start timestamp can be understood as the time when the platform first sends aggregated status information to the twin client; the end timestamp can be understood as the time when the platform last sends aggregated status information to the twin client. Each aggregated status message sent corresponds to a timestamp, and the timestamps corresponding to the start and end status messages are obtained.

[0100] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0101] S203. Set the slice volume between the start timestamp and the end timestamp;

[0102] Specifically, input the frequency of state information and obtain the slice volume between the start state information timestamp and the end state information timestamp.

[0103] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0104] S205. Obtain the aggregation status information data parameters;

[0105] Specifically, the aggregated status information data parameters include, but are not limited to: GPS, heading angle, speed, or timestamp.

[0106] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0107] S207. The aggregation state information data parameters are sliced ​​equally according to the slice volume, and the aggregation state information sequence is output.

[0108] Specifically, equally slicing the aggregated state information data parameters according to the slice size can be understood as equally slicing the GPS, heading angle, speed, and timestamp in the aggregated state information data parameters according to the slice size.

[0109] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0110] S209. Send the aggregated state information sequence to the twin terminal.

[0111] In this embodiment of the disclosure, by slicing the transmitted aggregated state information data parameters, the smoothness of the state information twin can be improved.

[0112] Furthermore, existing technologies rely on communication latency at various technical layers, which can easily lead to latency jitter. For this purpose, please refer to... Figure 3 As shown, Figure 3 This is a schematic flowchart illustrating a data transmission method in a data processing method for digital twins, provided as an embodiment of the specification. The method includes:

[0113] S301. Determine whether the aggregation status information sent to the twin terminal has overflowed. If so, determine whether the overflow amount exceeds the set overflow valve slot. If so, back up the communication status information. The overflow valve slot refers to the overflow amount that can be processed within the overflow duration.

[0114] Specifically, overflow can be understood as data overflowing the channel during transmission, and the overflow flow can be understood as the data that overflows the channel.

[0115] In one application embodiment, a high water level (80% of overflow valve slot) and a low water level (20% of overflow valve slot) are set for the overflow flow. When the communication status information exceeds the high water level, an overflow is marked and backed up, and the overflow timestamp is recorded.

[0116] S303. Start transmitting the backup communication status information in the new communication status information. The new communication status information runs synchronously with the old communication status information. The old communication status information refers to the communication status information that sends the aggregated status information to the twin end.

[0117] Specifically, a switching timestamp for the overflow valve slot is set; the overflow time can be understood as the time when the overflow flow reaches the threshold. The base time for the new communication status information is set as the overflow timestamp. When the overflow timestamp of the new communication status information exceeds the switching timestamp of the overflow valve slot, the old communication status information is stopped, and the communication status information switching is completed.

[0118] This embodiment of the invention can determine whether the overflow rate exceeds the set overflow rate by using a set overflow valve slot. If it does, a new communication status information is initiated to output the flow. The reference time of the new communication status information is set as the overflow timestamp, and the status information is restored from the previous backup. The old and new communication status information operate synchronously. When the timestamp of the new communication status information exceeds the current time, the old communication status information is stopped, ultimately completing the communication status information switching. This achieves stable data transmission and reduces flow loss.

[0119] For further details, please refer to Figure 4 As shown, Figure 4 This is a schematic diagram of a data processing apparatus for digital twins, provided as an embodiment of the specification. The apparatus includes:

[0120] The prediction model module 401 is used to acquire several existing continuous state information parameters, construct a state information prediction model based on the several existing continuous state information parameters, and obtain predicted state information through the state information prediction model.

[0121] The filtering module 403 is used to perform threshold filtering on the predicted state information using a similarity algorithm to obtain the optimal predicted state information.

[0122] The fusion model module 405 is used to construct a state information fusion model based on the optimal predicted state information, and obtain fused state information through the state information fusion model.

[0123] The aggregation model module 407 is used to construct a state information aggregation model based on the predicted state information, the optimal predicted state information, and the fused state information, and to obtain aggregated state information through the state information aggregation model.

[0124] The communication module 409 is used to send the aggregated state information to the twin terminal, wherein the twin terminal refers to a device used to map the state information of the target object.

[0125] The data acquisition module is used to acquire several continuous state information parameters; it is also used to acquire the continuous time corresponding to the several continuous state information parameters.

[0126] The model building module is used to construct a state information prediction model based on a fitting algorithm using several continuous state information parameters and continuous time.

[0127] The sequence construction module is used to construct a weighted state information parameter sequence based on several continuous state information.

[0128] The calculation module is used to input the weighted state information parameter sequence into the state information prediction model to obtain the fitted state information coefficients.

[0129] The data acquisition module is also used to acquire the predicted state information time period and divide the predicted state information time period into several predicted state information time slices.

[0130] The sequence construction module is also used to generate a predicted state information parameter sequence based on the fitted state information coefficients and the predicted state information time slices; and to construct a predicted state information sequence based on the predicted state information parameter sequence.

[0131] The judgment module is used to determine whether the aggregated status information sent to the twin end has overflowed. If so, it determines whether the overflow amount exceeds the set overflow valve slot. If so, it backs up the communication status information. The overflow valve slot refers to the overflow amount that can be processed within the overflow duration.

[0132] The communication module is used to initiate new communication status information and transmit the backup communication status information. The new communication status information operates synchronously with the old communication status information. The old communication status information refers to the communication status information that sends the aggregated status information to the twin end.

[0133] Furthermore, an electronic device is provided, including at least one processor and a memory, the memory storing a program and configured such that at least one processor executes a data processing method for digital twins according to an embodiment.

[0134] Furthermore, a computer-readable storage medium is provided that stores computer instructions for causing the computer to execute a data processing method for digital twins according to an embodiment.

[0135] Example 2

[0136] Please see Figure 5 As shown, Figure 5 The first example is a flowchart illustrating a data processing method for digital twins provided in the specification. Example 2 provides a data processing method for digital twins, applied to the twin end, the method comprising:

[0137] S501. Obtain the start and end timestamps of the received aggregation status information;

[0138] Specifically, the start timestamp can be understood as the time when the twin first receives the aggregated state information; the end timestamp can be understood as the time when the twin last receives the aggregated state information. Each received aggregated state information corresponds to a timestamp, and the timestamps corresponding to the start and end state information are obtained.

[0139] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0140] S503, Set the slice volume between the start timestamp and the end timestamp;

[0141] Specifically, input the frequency of state information and obtain the slice volume between the start state information timestamp and the end state information timestamp.

[0142] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0143] S505. Obtain aggregate status information data parameters;

[0144] Specifically, the aggregated status information data parameters include, but are not limited to: GPS, heading angle, speed, or timestamp.

[0145] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0146] S507. The data parameters of the aggregated state information number are equally sliced ​​according to the slice volume, and the aggregated state information sequence is output.

[0147] Specifically, the aggregated state information data parameters are sliced ​​equally according to the slice size. This can be understood as slicing the state information parameters GPS, heading angle, speed, and timestamp equally according to the slice size, and outputting the aggregated state information sequence.

[0148] It should be understood that the specific content described above is for illustrative purposes only and should not be construed as limiting the scope of this application.

[0149] S509. Send the aggregated state information sequence to the user interface.

[0150] In this embodiment of the disclosure, by slicing the data parameters of the sent aggregated state information, the smoothness of the state information twinning can be improved, thereby enhancing the twinning effect.

[0151] For further details, please refer to Figure 6 As shown, Figure 6 A data processing apparatus for digital twins, as provided in the embodiments of the specification, includes:

[0152] The acquisition module 601 is used to acquire the start and end timestamps of the received aggregation status information;

[0153] Setting module 602 is used to set the slice volume between the start timestamp and the end timestamp;

[0154] Extraction module 603 is used to obtain aggregated status information data parameters;

[0155] Slicing module 604 is used to slice the aggregation state information data parameters equally according to the slice volume and output the aggregation state information sequence;

[0156] The transmission module 605 is used to send the aggregation status information sequence to the user interface.

[0157] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0158] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0159] The systems, devices, modules, or units described in the embodiments of the above specification can be implemented by computer chips or physical entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0160] For ease of description, the above devices are described in terms of function, divided into various modules or units. Of course, in implementing this application, the functions of each module or unit can be implemented in one or more software and / or hardware.

[0161] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0162] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0163] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a processing flow implemented by the computer, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0165] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0166] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory (NVM), like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0167] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0168] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0169] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0170] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0171] The above description is merely an embodiment of the specification of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A data processing method for digital twins, characterized in that, The method includes: A number of existing continuous state information parameters of the target object are obtained, a state information prediction model is constructed based on the number of existing continuous state information parameters, and the predicted state information of the target object is obtained through the state information prediction model. The state information is operational state information, which includes traffic environment operational state information, mobile object operational state information, and fixed object operational state information. The predicted state information is then subjected to threshold filtering using a similarity algorithm to obtain the optimal predicted state information. A state information fusion model is constructed based on the optimal predicted state information, and the fused state information is obtained through the state information fusion model. A state information aggregation model is constructed based on the predicted state information, the optimal predicted state information, and the fused state information. Aggregated state information is obtained through the state information aggregation model. The aggregated state information is sent to the twin terminal, where the twin terminal refers to a device used to map the state information of the target object; The state information aggregation model adopts the following expression: = + - in, To predict state information, It is the optimal predicted state information after threshold filtering using a similarity algorithm. To integrate state information, This refers to the final aggregated state information after data compensation processing of the merged state information; After sending the aggregation state information to the twin, the process also includes: Determine whether the aggregation status information sent to the twin terminal has overflowed. If so, determine whether the overflow amount exceeds the set overflow valve slot. If so, back up the communication status information. The overflow valve slot refers to the overflow amount that can be processed within the overflow duration. The new communication status information is initiated to transmit the backup communication status information. The new communication status information operates synchronously with the old communication status information. The old communication status information refers to the communication status information that sends the aggregated status information to the twin end. The method includes: Obtain the start and end timestamps of the received aggregation status information; Set the slice volume between the start timestamp and the end timestamp; Obtain aggregate status information data parameters; The aggregation state information data parameters are sliced ​​equally according to the slice size, and the aggregation state information sequence is output. The aggregated state information sequence is sent to the user interface.

2. The data processing method for digital twins according to claim 1, characterized in that, The constructed state information prediction model includes: Obtain the continuous time corresponding to several of the existing continuous state information parameters; A state information prediction model is constructed based on a fitting algorithm using several existing continuous state information parameters and the continuous time.

3. The data processing method for digital twins according to claim 2, characterized in that, The step of obtaining the predicted state information through the state information prediction model includes: Construct a weighted state information parameter sequence based on several existing continuous state information parameters; The weighted state information parameter sequence is input into the state information prediction model to obtain the fitted state information coefficients. Obtain the predicted state information time period and divide the predicted state information time period into several predicted state information time slices; Generate a sequence of predicted state information parameters based on the fitted state information coefficients and the predicted state information time slices; Construct a predicted state information sequence based on the predicted state information parameter sequence; Predicted state information is obtained from the predicted state information sequence.

4. The data processing method for digital twins according to claim 1, characterized in that, The backup communication status information also includes: recording overflow timestamps.

5. A data processing method for digital twins according to claim 4, characterized in that, After the new communication status information is synchronized with the old communication status information, it also includes: Set the switching timestamp for the overflow valve slot; The base time for the new communication status information is set as the overflow timestamp. When the overflow timestamp of the new communication status information exceeds the switching timestamp of the overflow valve slot, the old communication status information is stopped and the communication status information switching is completed.

6. A data processing device for digital twins, characterized in that, include: The prediction model module is used to acquire several existing continuous state information parameters of the target object, construct a state information prediction model based on the several existing continuous state information parameters, and obtain predicted state information through the state information prediction model. The state information is operational state information, which includes traffic environment operational state information, moving object operational state information, and fixed object operational state information. The filtering module is used to perform threshold filtering on the predicted state information using a similarity algorithm to obtain the optimal predicted state information; The fusion model module is used to construct a state information fusion model based on the optimal predicted state information, and obtain fused state information through the state information fusion model. The aggregation model module is used to construct a state information aggregation model based on the predicted state information, the optimal predicted state information, and the fused state information, and to obtain aggregated state information through the state information aggregation model. A communication module is used to send the aggregated state information to a twin terminal, wherein the twin terminal refers to a device used to map the state information of the target object; The state information aggregation model adopts the following expression: = + - in, To predict state information, It is the optimal predicted state information after threshold filtering using a similarity algorithm. To integrate state information, This refers to the final aggregated state information after data compensation processing of the merged state information; The judgment module is used to determine whether the aggregated status information sent to the twin end has overflowed. If so, it determines whether the overflow amount exceeds the set overflow valve slot. If so, it backs up the communication status information. The overflow valve slot refers to the overflow amount that can be processed within the overflow duration. The communication module is used to initiate new communication status information and transmit the backup communication status information. The new communication status information operates synchronously with the old communication status information. The old communication status information refers to the communication status information that sends the aggregated status information to the twin end. The device is also used for: Obtain the start and end timestamps of the received aggregation status information; Set the slice volume between the start timestamp and the end timestamp; Obtain aggregate status information data parameters; The aggregation state information data parameters are sliced ​​equally according to the slice size, and the aggregation state information sequence is output. The aggregated state information sequence is sent to the user interface.

7. An electronic device, characterized in that, It includes at least one processor and a memory, the memory storing a program and configured such that at least one processor executes a data processing method for digital twins according to any one of claims 1-5.

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