Satellite model type spectrum product selection rate statistical method

Abstract

The invention discloses a statistical method for the selection rate of satellite model spectrum products. According to the method, the hierarchical weight, the task importance weight, the function selection degree weight and the life cycle weight are introduced, the product hierarchical difference, the task priority, the partial selection scene and the dynamic life cycle are covered, accurate and comprehensive statistics of the profile spectrum product selection rate is achieved, and a reliable basis is provided for profile spectrum optimization and development decision making.

Description

Technical Field

[0001] This invention relates to the field of aerospace product quality and reliability technology, specifically to a statistical method for the selection rate of satellite model product range, and to evaluation methods for key technologies and technology maturity level evaluation. Background Technology

[0002] Satellite product lineage is the core carrier for the standardization and serialization of satellite products, encompassing various validated and reusable satellite subsystems (such as control subsystems, power supply and distribution systems, and telemetry and control systems). During satellite development, selecting mature products from this product lineage can significantly shorten the development cycle, reduce R&D costs, and improve product reliability. Therefore, accurately calculating the selection rate of satellite model product lineages is a key indicator for measuring the practicality of the product lineage and guiding its iterative optimization.

[0003] Existing methods typically calculate selection rates based on a single dimension such as time or frequency of use, ignoring key parameters of satellite product profiles; data acquisition lacks multi-source fusion, leading to statistical biases; synergistic effects and technical status baselines of product profiles are not considered; and static calculation models cannot adapt to real-time changes. Summary of the Invention

[0004] In view of this, the present invention provides a statistical method for the selection rate of satellite model product range, which realizes accurate and comprehensive statistics on the selection rate of product range, and provides a reliable basis for product range optimization and development decision-making.

[0005] This invention provides a statistical method for the selection rate of satellite model product range, comprising the following steps: Step 1: Construct a database of satellite product hierarchy and attributes. Step 1.1: Based on the hierarchical structure of the satellite product line, divide the product line into three levels: system-level products (such as satellite platform systems), subsystem-level products (such as control subsystems and power supply and distribution subsystems), and unit-level products (such as telemetry, tracking, and command transponders and flywheels). Assign a "hierarchical weight coefficient" to each level. W 1”: Among them, system level W 1 = 0.4, subsystem level W 1 = 0.3, single-machine level W 1 = 0.3 (The weighting coefficient can be dynamically adjusted according to the mission requirements of the satellite development unit, and satisfies Σ) W 1=1); Step 1.2: Label the core attributes for each product type: including product type code, product name, hierarchical level, and effective lifecycle. T 0, which refers to the time from when a product is added to the product catalog to when it is phased out (in years), and the applicable satellite mission type (such as remote sensing satellite, communication satellite, navigation satellite). Step 1.3: Establish a "Satellite Mission - Product Requirements" association table to record the quantity of products required for each level of product portfolio for each satellite mission. N 总 This refers to the total number of times a certain type of product is required for the task, and the actual selection status (including the number of times all products are selected). N 1. Number of times some items were selected N 2. Number of times not selected N 3, and satisfy N 总 = N1+N2+N3).

[0006] Step 2: Determine the product selection weighting coefficient Step 2.1, Task Importance Weight W2: Based on the priority of satellite missions (such as military missions, civilian missions, and commercial missions), assign task importance weights to different missions: military missions W2=1.0, civilian missions W2=0.8, and commercial missions W2=0.6 (the weight values ​​can be adjusted according to the unit's mission management specifications, and must satisfy 0<W2≤1). Step 2.2, Product Function Selection Weight W3: For the "partial selection" scenario, calculate the product function selection degree: Suppose a certain product series contains K functional modules, of which k are selected, then the function selection degree W3 = k / K (when the product is fully selected, W3 = 1; when not selected, W3 = 0). Step 2.3, Product Life Cycle Weight W4: Calculate the life cycle weight based on the current effective life cycle stage of the product: Let t be the time the product has been in service in the product portfolio, then W4 = 1 - t / T0 (when t≤T0, W4≥0; when t>T0, the product is removed from the product portfolio and no longer participates in the selection rate calculation).

[0007] Step 3: Calculate the satellite model selection rate. Step 3.1, calculate the "single task selection contribution value S" for a certain product type under a single task: S = W1×W2×[N1×1 + N2×W3] / N 总 Where "N1×1" represents the contribution of full selection (the function selection degree is 1 when full selection is achieved), "N2×W3" represents the contribution of partial selection, and the denominator is... N 总 To determine the total number of times this task requires this type of product, ensure the calculation result is within the range of 0-1; Step 3.2: Calculate the "total selection contribution value" of a certain type of product within the statistical period. S 总 ": S 总 = Σ(S i ×W 4i ) Where i represents the i-th satellite mission within the statistical period, S i W is the contribution value selected for a single task of this product under the i-th task. 4i The lifecycle weight of the product when the i-th task is executed; Step 3.3, calculate the final selection rate R of this product type: R = S 总 / M Where M is the total number of satellite missions involving this type of product within the statistical period (ensuring that the selection rate R ranges from 0 to 1, and can ultimately be converted into a percentage for display).

[0008] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Multi-dimensional and accurate calculation: By introducing "hierarchical weight, task importance weight, function selection weight, and lifecycle weight", it covers product hierarchical differences, task priority, some selection scenarios and dynamic lifecycle, solving the problem of single dimension of traditional methods; 2. Complete data statistics: For the first time, the "partial selection" scenario is included in the statistics. The contribution of partial selection is quantified by the "feature selection degree weight W3", avoiding the underestimation of partial selection products by traditional methods; 3. Alignment with satellite type characteristics: Based on the hierarchical characteristics of satellite type spectrum, differentiated weights are assigned to products at different levels to ensure that the calculation results conform to the actual logic in satellite model development that "core level products receive more attention"; 4. Strong dynamic adaptability: By incorporating the effective service time of the product into the "lifecycle weight W4", the selection trend of the product in different periods can be reflected in real time, providing data support for the dynamic updating of the product portfolio. Detailed Implementation

[0009] The present invention will now be described in detail with reference to the embodiments.

[0010] Taking the product selection data of a satellite development unit from 2022 to 2024 as an example, the specific implementation process of this invention will be described in detail: The selection rate of the system-level product "Satellite Platform A" is calculated as follows: S01 Build Database 1.1 Satellite platform A is a system-level product with a hierarchical weight W1=0.4; 1.2 Core Attributes: Product number S002, effective lifespan T0=10 years (entered into the warehouse in 2019, phased out in 2029), applicable mission type is high-orbit communication satellite, functional module is "platform as a whole (indivisible, K=1)"; 1.3 During the statistical period of 2022-2024, there were M=3 tasks involving this product, and the correlation table is shown in Table 1: Table 1

[0011] S02 Determine the weighting coefficients 2.1 W2: Task 6, W2=1.0; Task 7, W2=0.8; Task 8, W2=1.0; 2.2 W3: All are fully selected, W3=1; 2.3 W4: Task 6 will be executed in 2022, t=3 years, W4=1-3 / 10=0.7; Task 7 will be executed in 2023, t=4 years, W4=0.6; Task 8 will be executed in 2024, t=5 years, W4=0.5.

[0012] S03 Calculate the selection rate 3.1 S6=0.4×1.0×(1×1) / 1=0.4; S7=0.4×0.8×(2×1) / 2=0.32; S8=0.4×1.0×(1×1) / 1=0.4; 3.2 S 总 = 0.4×0.7 + 0.32×0.6 + 0.4×0.5=0.28 + 0.192 + 0.2=0.672; 3.3 R=0.672 / 3=0.224, that is, the selection rate is 22.4%. In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A statistical method for determining the selection rate of satellite model product lines, characterized in that, include: S1, according to the hierarchical structure of the satellite product line, divide the product line into system-level products, subsystem-level products and single-unit products, and label the attributes; Based on the mission requirements of the satellite development unit, hierarchical weight coefficients are set for each level of product series. W 1; S2. Establish a "Satellite Mission - Product Requirements" association table to calculate the total demand for each satellite mission's product portfolio at each level, as well as the actual selection status. S3, determine the weighting coefficients for selecting product types, including: The mission importance weight W2 is determined by the priority of the satellite mission; The product function selection weight W3 is determined by the ratio of the number of selected function modules to the total number of function modules of the product series. The product lifecycle weight W4 is determined by the ratio of the service time of this product type to its effective lifecycle. S4, Calculate the single-task selection contribution value of this product type under a single task. S : S = W1×W2×[N1×1 + N2×W3] / N 总 Where N1 represents the number of times this product type was fully selected in this task. N 1; N2 represents the number of times this product type was selected in this task. N 总 This represents the total number of times this task requires this type of product. N 总 = N1 + N2 + N3, N 3 represents the number of times this product type was not selected in this task; S5, Calculate the total selection contribution value of this product type within the statistical period. S 总 : S 总 = Σ(S i ×W 4i ) Where i represents the i-th satellite mission within the statistical period, S i W represents the contribution value selected for a single task of this product type under the i-th task. 4i The lifecycle weight of this product type during the execution of the i-th task; S6, calculate the final selection rate R of this product type: R = S 总 / M Where M is the total number of satellite missions involving this type of spectrum product within the statistical period.

2. The method as described in claim 1, characterized in that, In S1, the labeled attributes include: product type code, product name, level, effective life cycle, and applicable satellite mission type.

3. The method as described in claim 1, characterized in that, In S1, the system-level product W 1 = 0.4, subsystem-level product W 1 = 0.3, stand-alone product W 1 = 0.

3.

4. The method as described in claim 1, characterized in that, In S3, military mission W2=1.0, civilian mission W2=0.8, and commercial mission W2=0.

6.

5. The method as described in claim 1, characterized in that, In S3, suppose a certain product series contains K functional modules, of which k are selected, then W3 = k / K; when the product series is fully selected, W3 = 1; when not selected, W3 = 0.

6. The method as described in claim 1, characterized in that, In S3, let the effective life cycle of a certain product series be T0, and its service time in the series be t. Then W4 = 1 - t / T0. When t ≤ T0, W4 ≥ 0. When t > T0, the product is removed from the series and no longer participates in the selection rate calculation.

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