A statistical method for delivery timeliness of remote sensing satellite ground systems

Abstract

This application discloses a statistical method for the delivery timeliness of a remote sensing satellite ground system. The method includes: identifying each stage of the ground system delivery process and constructing a ground system delivery timeliness model. The ground system delivery timeliness model characterizes the delivery time value of the ground system, which is obtained by weighting the time consumption and stability coefficient of each stage. A time sequence is obtained based on the recorded time values ​​for processing each task at each stage, and the time sequence for each stage is normalized to obtain a normalized time sequence. The time consumption and stability coefficient of each stage are calculated based on the normalized time sequence, and the time consumption and stability coefficient of each stage are substituted into the ground system delivery timeliness model to calculate the delivery time value of the ground system. This application solves the technical problem of the lack of a statistical scheme for ground system delivery timeliness capability in the prior art.

Description

Technical Field

[0001] This application relates to the field of remote sensing satellite technology, and in particular to a statistical method for the delivery timeliness of remote sensing satellite ground systems. Background Technology

[0002] The core capabilities of commercial remote sensing satellite data products and services are directly reflected in the timeliness and quality of these services for users. With the development of the commercial remote sensing industry, the requirements for rapid response to sub-meter resolution satellites and the timeliness of rapid downlink and production delivery after image formation are becoming increasingly stringent in emergency response, disaster mitigation, and national defense reconnaissance services.

[0003] As commercial remote sensing satellite operators' service and product systems become increasingly sophisticated, users have more widespread and specific requirements for the timeliness of data product delivery, making delivery timeliness a crucial factor in product competitiveness. As a vital link in delivery timeliness, the delivery timeliness capability of the ground system determines the total time from receiving raw onboard data to delivering the specified level of product to the user. A comprehensive and systematic analysis of the ground system's delivery timeliness capability will help commercial remote sensing satellite operators design stable and accurate product delivery timeliness systems, enhancing their market competitiveness in product delivery timeliness within a controllable range of delivery risks. However, current published articles and publicly available information, such as "Construction of Commercial Aerospace Remote Sensing Service System" (Zhong Xing et al., Satellite Application, March 2020), mainly provide an overview of the overall delivery timeliness level, lacking research on statistical analysis methods for the delivery timeliness capability of commercial remote sensing satellite ground systems. In other words, existing technology lacks quantitative analysis methods for the delivery timeliness capability of the ground system production stage in commercial remote sensing satellite data services. Summary of the Invention

[0004] The technical problem solved by this application is: addressing the lack of a statistical method for the delivery timeliness of ground systems in the existing technology. This application provides a statistical method for the delivery timeliness of remote sensing satellite ground systems. The solution provided in the embodiments of this application constructs a normalized duration sequence of timeliness samples and quantitatively analyzes the delivery timeliness of ground systems based on the normalized duration sequence, thus providing a solution for quantitatively analyzing the delivery timeliness of ground systems.

[0005] In a first aspect, embodiments of this application provide a statistical method for the delivery timeliness of a remote sensing satellite ground system. The method includes: determining each stage corresponding to the delivery of the ground system; and constructing a ground system delivery timeliness model, wherein the ground system delivery timeliness model is used to characterize the time value of the ground system delivery, which is obtained by weighting the time consumption and stability coefficient corresponding to each stage; obtaining a time sequence based on the recorded time values ​​corresponding to each task processed in each stage, and normalizing the time sequence corresponding to each stage to obtain a normalized time sequence; calculating the time consumption and stability coefficient corresponding to each stage based on the normalized time sequence corresponding to each stage, and substituting the time consumption and stability coefficient corresponding to each stage into the ground system delivery timeliness model to calculate the time value of the ground system delivery.

[0006] Optionally, the ground system delivery timeliness model is expressed by the following formula:

[0007]

[0008] Where F(t) represents the delivery time of the ground system; T p Let W represent the time taken for the p-th stage, where p = 1, 2, 3, ..., i, and i represents the number of stages corresponding to the delivery of the ground system, which is a positive integer not less than 1; p This represents the stability coefficient corresponding to the p-th stage.

[0009] Optionally, the normalized duration sequence corresponding to each stage is processed to obtain a normalized duration sequence, including: determining the maximum and minimum values ​​in the duration sequence corresponding to each stage; normalizing each duration value in the duration sequence based on the maximum and minimum values ​​to obtain a normalized value corresponding to each duration value, and obtaining a normalized duration sequence based on the normalized values.

[0010] Optionally, based on the maximum and minimum values, each duration value in the duration sequence is normalized to obtain a normalized value corresponding to each duration value, including:

[0011] The normalized value is obtained using the following formula:

[0012]

[0013] Among them, t k，nor t represents the normalized value of the k-th duration value, where k = 1, 2, 3, ..., n; n represents the number of duration values ​​in the duration sequence; k Represents the k-th duration value; t max Indicates the maximum duration; t min This represents the minimum duration.

[0014] Optionally, calculating the time consumption and stability coefficient of each stage based on the normalized time sequence corresponding to each stage includes: calculating the window step size value corresponding to each stage based on the normalized time sequence corresponding to each stage, and calculating the window density factor corresponding to each window on each normalized time sequence based on the window step size value, obtaining the window density factor sequence corresponding to each normalized time sequence based on the window density factor corresponding to each window; constructing the oscillation curve corresponding to the density factor based on the window density factor sequence, calculating the amplitude of multiple sets of neighboring points corresponding to each normalized time sequence based on the oscillation curve; calculating the time consumption corresponding to each stage based on the amplitude of multiple sets of neighboring points, and counting the number of times greater than the time consumption in each normalized time sequence, and calculating the stability coefficient based on the number.

[0015] Optionally, the window step size value corresponding to each stage is calculated based on the normalized duration sequence corresponding to each stage, including:

[0016] The window step size for each stage is calculated using the following formula:

[0017]

[0018] Where w represents the window step size; This represents the mean of the top 10% of the normalized values ​​in the normalized time series.

[0019] Optionally, the window density factor corresponding to each window in each normalized time sequence is calculated based on the window step size value, including:

[0020] The window density factor is calculated using the following formula:

[0021]

[0022] Where, d w，j This represents the density factor of the j-th window. n w，j This represents the number of normalized duration values ​​within the j-th window.

[0023] Optionally, constructing an oscillation curve corresponding to the density factor based on the window density factor sequence includes:

[0024] The oscillation curve corresponding to the density factor is constructed using the following formula:

[0025] f(d w )=d w，j+1 -d w，j

[0026] Where f(dx) represents the oscillation curve.

[0027] Optionally, based on the oscillation curve, multiple sets of neighboring point amplitudes corresponding to each normalized duration sequence are calculated, including:

[0028] A l =|f(d w，j+1 )-f(d w，j )|

[0029] Among them, A l Let be the amplitude of the group of near-peak values.

[0030] Optionally, the time consumption corresponding to each stage is calculated based on the amplitudes of the multiple sets of neighboring points, including:

[0031] Select the first set of nearest point amplitudes that meets the preset conditions from the plurality of nearest point amplitudes;

[0032] Take the d corresponding to the amplitude of the first group of nearby points w，j The upper limit of the window represents the time consumed in each step.

[0033] Optionally, the preset condition is:

[0034]

[0035]

[0036] Optionally, the stability coefficient is calculated based on the number of times the normalized duration sequence exceeds the specified time, by counting the number of times the duration exceeds the specified time in each normalized duration sequence, including:

[0037] The stability coefficient is calculated using the following formula:

[0038]

[0039] in, This represents the number of times in the normalized time sequence that are greater than the stated time.

[0040] Secondly, this application provides a computer device, the computer device comprising:

[0041] Memory, used to store at least one instruction executed by a processor;

[0042] A processor is configured to execute instructions stored in memory to perform the method described in the first aspect.

[0043] Compared with the prior art, the embodiments of this application have at least the following beneficial effects:

[0044] The solution provided in this application provides a method for quantitatively analyzing the delivery timeliness of ground systems by constructing a normalized time sequence of timeliness samples and quantitatively analyzing the delivery timeliness capability of ground systems based on the normalized time sequence. Attached Figure Description

[0045] Figure 1 A flowchart illustrating a statistical method for the delivery time of a remote sensing satellite ground system provided in this application embodiment;

[0046] Figure 2 A flowchart illustrating another statistical method for the delivery time of a remote sensing satellite ground system provided in this application embodiment;

[0047] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0048] The embodiments described in this application are only a part of the embodiments, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0049] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0050] The following description, in conjunction with the accompanying drawings, provides a more detailed explanation of a statistical method for the delivery time of a remote sensing satellite ground system provided in this application. The specific implementation of this method may include the following steps (method flow as follows): Figure 1 As shown):

[0051] Step 101: Determine each stage of the ground system delivery and construct a ground system delivery timeliness model. The ground system delivery timeliness model is used to characterize the time value of the ground system delivery, which is obtained by weighting the time consumption and stability coefficient of each stage.

[0052] Ground systems are used to transmit signals to satellites, receive data transmitted by satellites, and process that data to obtain the data products to be delivered. The timeliness of this delivery directly affects the overall delivery timeliness of the data products. The process of a ground system receiving data from satellites and processing that data to obtain the data products to be delivered involves one or more stages, and the processing timeliness of each stage affects the delivery timeliness of the ground system. Since the stages differ between different ground systems, to statistically analyze the timeliness of a ground system, it is necessary to first determine each stage of the ground system's delivery process and then construct a ground system delivery timeliness model based on the relevant parameters (such as time consumption and stability coefficients) for each stage.

[0053] For example, the delivery timeliness capability of a ground system can be broken down into the sum of the timeliness of multiple individual stages. Specifically, the ground system delivery timeliness model can be constructed using the following formula:

[0054]

[0055] Where F(t) represents the delivery time of the ground system; T p Let W represent the time taken for the p-th stage, where p = 1, 2, 3, ..., i, and i represents the number of stages corresponding to the delivery of the ground system, which is a positive integer not less than 1; p This represents the stability coefficient corresponding to the p-th stage.

[0056] Step 102: Obtain the duration sequence based on the duration value corresponding to each task processed in each stage, and normalize the duration sequence corresponding to each stage to obtain the normalized duration sequence.

[0057] In the ground system, each component participates in data product delivery by processing tasks in the smallest unit (or basic unit). The processing time (statistical sample) for each basic unit is recorded during processing. Based on the processing time value corresponding to each basic unit, a time sequence is obtained for each component, where the time sequence includes multiple time values. For example, the statistical samples of a single component are arranged in ascending order according to their processing time values ​​to form a time sequence {t1, t2, t3, ..., t...}. k}

[0058] Furthermore, after obtaining the duration sequence corresponding to each stage, the duration values ​​in the duration sequence are normalized to obtain a normalized duration sequence corresponding to each stage. For example, normalizing the duration sequence corresponding to each stage to obtain a normalized duration sequence includes: determining the maximum and minimum values ​​in the duration sequence corresponding to each stage; normalizing each duration value in the duration sequence based on the maximum and minimum values ​​to obtain a normalized value corresponding to each duration value; and obtaining a normalized duration sequence based on the normalized values.

[0059] For example, the following formula (2) is used to normalize each duration value in the duration sequence:

[0060]

[0061] Among them, t k，nor t represents the normalized value of the k-th duration value, where k = 1, 2, 3, ..., n; n represents the number of duration values ​​in the duration sequence; k Represents the k-th duration value; t max Indicates the maximum duration; t min This represents the minimum duration.

[0062] Step 103: Calculate the time consumption and stability coefficient corresponding to each step based on the normalized time sequence corresponding to each step, and substitute the time consumption and stability coefficient corresponding to each step into the ground system delivery timeliness model to calculate the time value of ground system delivery.

[0063] As an example, calculating the time consumption and stability coefficient of each stage based on the normalized duration sequence corresponding to each stage includes: calculating the window step size value corresponding to each stage based on the normalized duration sequence corresponding to each stage, and calculating the window density factor corresponding to each window on each normalized duration sequence based on the window step size value, obtaining the window density factor sequence corresponding to each normalized duration sequence based on the window density factor corresponding to each window; constructing the oscillation curve corresponding to the density factor based on the window density factor sequence, calculating the amplitude of multiple sets of neighboring points corresponding to each normalized duration sequence based on the oscillation curve; calculating the time consumption corresponding to each stage based on the amplitude of multiple sets of neighboring points, and counting the number of times greater than the time consumption in each normalized duration sequence, calculating the stability coefficient based on the number.

[0064] Specifically, the window step size for each step is calculated using the following formula:

[0065]

[0066] Where w represents the window step size; This represents the mean of the top 10% of the normalized values ​​in the normalized time series.

[0067] Furthermore, the window density factor is calculated using the following formula:

[0068]

[0069] Where, d w，j This represents the density factor of the j-th window. n w，j This represents the number of normalized duration values ​​within the j-th window.

[0070] Furthermore, the oscillation curve corresponding to the density factor is constructed using the following formula:

[0071] f(d w )=d w，j+1 -d w，j (5)

[0072] Where, f(d) w ) represents the oscillation curve.

[0073] Furthermore, based on the oscillation curve, multiple sets of nearest-neighbor amplitudes corresponding to each normalized duration sequence are calculated, including:

[0074] A l =|f(d w，j+1 )-f(d w，j (6)

[0075] Among them, A l Let be the amplitude of the group of near-peak values.

[0076] Furthermore, as an example, the amplitudes of neighboring points that meet preset conditions are selected from the multiple sets of neighboring point amplitudes; for example, the preset conditions are:

[0077]

[0078]

[0079] Furthermore, take the d corresponding to the amplitude of the nearest point in this group. w，j The upper limit of the window is used as the time T corresponding to each step. p The time consumed in each step represents the timeliness of each step.

[0080] Furthermore, the stability coefficient is calculated using the following formula:

[0081]

[0082] in, This represents the number of times in the normalized time sequence that are greater than the stated time.

[0083] Then, the time consumption and stability coefficient corresponding to each step are substituted into equation (1) above to obtain the delivery time value of the ground system, where the delivery time value of the ground system characterizes the delivery timeliness capability of the ground system. For a brief overview of the statistical process for ground system delivery timeliness, please refer to [link to relevant documentation]. Figure 2 As shown.

[0084] The solution provided in this application provides a method for quantitatively analyzing the delivery timeliness of ground systems by constructing a normalized duration sequence of timeliness samples, calculating the density factor of the duration window, and quantitatively analyzing the delivery timeliness capability of ground systems based on the density factor index.

[0085] See Figure 3 This application provides a computer device, the computer device comprising:

[0086] Memory 301 is used to store at least one instruction executed by a processor;

[0087] Processor 302 is used to execute instructions stored in memory. Figure 1 The method described.

[0088] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0089] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] 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.

[0091] 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 computer-implemented process, 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.

[0092] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A statistical method for the delivery timeliness of a remote sensing satellite ground system, characterized in that, include: The various stages corresponding to the delivery of the ground system are identified, such as the data transmission stage, the data processing stage, and the data delivery stage, and a ground system delivery timeliness model is constructed. The ground system delivery timeliness model is used to characterize the time value of the ground system delivery, which is obtained by weighting the time consumption and stability coefficient corresponding to each stage. The duration sequence is obtained by processing the corresponding duration value of each task in each recorded stage, and the duration sequence corresponding to each stage is normalized to obtain the normalized duration sequence. Based on the normalized duration sequence corresponding to each step, the time consumption and stability coefficient corresponding to each step are calculated. The time consumption and stability coefficient corresponding to each step are then substituted into the ground system delivery timeliness model to calculate the time value of ground system delivery. The calculation of the time consumption and stability coefficient for each stage, based on the normalized time sequence corresponding to each stage, includes: Calculate the window step size value corresponding to each step based on the normalized duration sequence corresponding to each step, and calculate the window density factor corresponding to each window on each normalized duration sequence based on the window step size value, and obtain the window density factor sequence corresponding to each normalized duration sequence based on the window density factor corresponding to each window. Based on the window density factor sequence, an oscillation curve corresponding to the density factor is constructed, and based on the oscillation curve, multiple sets of neighboring point amplitudes corresponding to each normalized duration sequence are calculated. The time consumption corresponding to each stage is calculated based on the amplitude of the multiple nearest points, and the number of times greater than the time consumption is counted in each normalized time sequence. The stability coefficient is then calculated based on the number of times the time consumption is greater than the time consumption.

2. The method as described in claim 1, characterized in that, The delivery timeliness model for the ground system is expressed by the following formula: in, This indicates the time value for the delivery of the ground system; Indicates the first p The time spent on each step, among which, , This represents the number of stages corresponding to the delivery of the ground system, and it is a positive integer not less than 1; Indicates the first p The stability coefficient corresponding to each link.

3. The method as described in claim 2, characterized in that, The duration sequence corresponding to each stage is normalized to obtain a normalized duration sequence, including: Determine the maximum and minimum values ​​in the time sequence corresponding to each stage; Based on the maximum value and the minimum value, normalization is performed on each duration value in the duration sequence to obtain the normalized value corresponding to each duration value, and a normalized duration sequence is obtained based on the normalized value.

4. The method as described in claim 3, characterized in that, Based on the maximum and minimum values, normalization is performed on each duration value in the duration sequence to obtain a normalized value corresponding to each duration value, including: The normalized value is obtained using the following formula: in, Indicates the first The normalized values ​​of the duration values, among which... ; This indicates the number of duration values ​​in the duration sequence; Representing the Each duration value; Indicates the maximum duration; This represents the minimum duration.

5. The method as described in claim 4, characterized in that, Calculate the window step size value for each stage based on the normalized duration sequence corresponding to each stage, including: The window step size for each stage is calculated using the following formula: in, Indicates the window step size value; This represents the mean of the top 10% of normalized values ​​in the normalized time series.

6. The method as described in claim 5, characterized in that, Based on the window step size value, calculate the window density factor corresponding to each window in each normalized time sequence, including: The window density factor is calculated using the following formula: in, This represents the density factor of the j-th window. ; This represents the number of normalized duration values ​​within the j-th window.

7. The method as described in claim 6, characterized in that, Based on the window density factor sequence, an oscillation curve corresponding to the density factor is constructed, including: The oscillation curve corresponding to the density factor is constructed using the following formula: in, This represents the oscillation curve.

8. The method as described in claim 7, characterized in that, Based on the oscillation curve, multiple sets of nearest-neighbor amplitudes corresponding to each normalized duration sequence are calculated, including: in, For the first l The amplitude of the group near its peak. l =1,2,3,…, .

9. The method as described in claim 8, characterized in that, The time consumed for each stage is calculated based on the amplitude of the multiple sets of nearest points, including: Select the first set of nearest point amplitudes that meets the preset conditions from the plurality of nearest point amplitudes; Take the amplitude corresponding to the first group of nearby points The upper limit of the window represents the time consumed in each step.

10. The method as described in claim 9, characterized in that, The preset conditions are: 。 11. The method as described in claim 10, characterized in that, The stability coefficient is calculated based on the number of times the normalized duration sequence exceeds the specified time, by counting the number of times the duration exceeds the specified time in each normalized duration sequence. The stability coefficient is calculated using the following formula: in, This represents the number of times in the normalized time sequence that are greater than the stated time.

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