Aerial material supply chain risk management method, device and equipment and readable storage medium

Abstract

The invention provides an aerial material supply chain risk management method, device and equipment and a readable storage medium, and relates to the technical field of risk analysis, and the method comprises the steps: obtaining risk event parameter information of an aerial material supply chain; constructing an aerial material supply chain risk pulse model, and obtaining a risk pulse signal graph based on the aerial material supply chain risk pulse model; performing Fourier transform on the risk pulse signal diagrams to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal diagram; performing risk analysis based on the amplitude spectrum and the phase spectrum corresponding to each risk pulse signal diagram to obtain a risk analysis result; and carrying out risk early warning and constructing prevention measures based on the risk analysis result, and sending the risk early warning and prevention measures to communication equipment of management personnel to prompt the management personnel to carry out risk prevention. According to the method, dynamic data analysis and modern signal processing technologies are combined, the limitation of a traditional risk management method is broken through, and potential risks in a supply chain can be predicted more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of risk analysis, and in particular to a method and device for aviation material supply chain risk management. Background Art

[0002] With the rapid development of globalization and the aviation industry, the aviation material supply chain has become a key link in the operation of aviation companies. However, the links involved in the aviation material supply chain are complex and diverse, covering supplier management, logistics distribution, quality control and other aspects. Therefore, frequent risk events in the supply chain, such as delivery delays, quality problems, supplier bankruptcy, etc., may have a significant impact on the normal operation of aviation companies. Traditional aviation material supply chain risk management methods mainly rely on experience judgment and static analysis, which makes it difficult to accurately predict the time of occurrence and extent of risk. In addition, most of the existing risk management tools are based on simple statistical analysis of historical data and cannot effectively deal with dynamic changes and complex risk transmission relationships in the supply chain. When faced with frequent risk events, these methods often show problems such as slow response, inaccurate predictions, and delayed preventive measures, resulting in a low level of overall risk management in the supply chain.

[0003] Therefore, there is an urgent need for an aviation material supply chain risk prediction method that breaks through the limitations of traditional risk management methods, can more accurately predict potential risks in the supply chain, and provide effective response strategies. Summary of the invention

[0004] The purpose of the present invention is to provide an aviation material supply chain risk management method, device, equipment and readable storage medium to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows: In a first aspect, the present application provides an aviation material supply chain risk management method, including: Acquire risk event parameter information of the aviation material supply chain, wherein the parameter information includes the duration of the risk event, the risk peak time and the damage coefficient; Constructing an aviation material supply chain risk pulse model, and sending risk event parameter information of the aviation material supply chain to the aviation material supply chain risk pulse model for processing, to obtain a risk pulse signal diagram corresponding to the risk event of the aviation material supply chain; Performing Fourier transformation on the risk pulse signal graph to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph; Perform risk analysis based on the amplitude spectrum and phase spectrum corresponding to each risk pulse signal graph to obtain a risk analysis result, wherein the risk analysis result is to determine the aviation material supply event with the greatest risk in each time period; Based on the risk analysis results, risk warnings are issued and preventive measures are constructed, which are sent to the management personnel's communication devices to prompt the management personnel to take risk prevention measures.

[0005] In a second aspect, the present application also provides an aviation material supply chain risk management device, including: An acquisition unit, used to acquire risk event parameter information of the aviation material supply chain, wherein the parameter information includes the duration of the risk event, the risk peak time and the damage coefficient; A processing unit, used to construct an aviation material supply chain risk pulse model, and send risk event parameter information of the aviation material supply chain to the aviation material supply chain risk pulse model for processing to obtain a risk pulse signal diagram corresponding to the risk event of the aviation material supply chain; A conversion unit, used for performing Fourier transform on the risk pulse signal graph to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph; An analysis unit, configured to perform risk analysis based on the amplitude spectrum and phase spectrum corresponding to each risk pulse signal graph to obtain a risk analysis result, wherein the risk analysis result is to determine the aviation material supply event with the greatest risk in each time period; The prevention unit is used to issue risk warnings and construct preventive measures based on the risk analysis results, and send them to the management personnel's communication equipment to prompt the management personnel to take risk prevention measures.

[0006] In a third aspect, the present application also provides an aviation material supply chain risk management device, including: Memory for storing computer programs; A processor is used to implement the steps of the aviation material supply chain risk management method when executing the computer program.

[0007] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned aviation material supply chain risk management method are implemented.

[0008] The beneficial effects of the present invention are: The present invention obtains the parameter information of risk events in the aviation material supply chain, and uses the risk pulse model to dynamically process these parameters to generate a corresponding risk pulse signal graph. The corresponding amplitude spectrum and phase spectrum are extracted by Fourier transforming the risk pulse signal graph, and then risk analysis is performed in combination with these frequency domain characteristics to determine the main risk events in each time period. Based on the risk analysis results, the present invention can achieve real-time risk warning for the aviation material supply chain, and propose corresponding preventive measures to further reduce the probability and impact of risk occurrence. The present invention combines dynamic data analysis with modern signal processing technology, breaks through the limitations of traditional risk management methods, can more accurately predict potential risks in the supply chain, and provide effective response strategies, which helps to improve the risk management level of aviation companies in complex supply chain environments.

[0009] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 It is a schematic diagram of the flow of the aviation material supply chain risk management method described in an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of the aviation material supply chain risk management device described in an embodiment of the present invention; Figure 3 is the amplitude spectrum corresponding to the risk pulse signal diagram described in the embodiment of the present invention; Figure 4 is a phase spectrum corresponding to the risk pulse signal diagram described in an embodiment of the present invention; Figure 5 It is a schematic diagram of the structure of the aviation material supply chain risk management device described in an embodiment of the present invention.

[0012] In the figure: 701, acquisition unit; 702, processing unit; 703, conversion unit; 704, analysis unit; 705, prevention unit; 7021, first processing subunit; 7022, second processing subunit; 7023, third processing subunit; 7024, fourth processing subunit; 7031, first conversion subunit; 7032, second conversion subunit; 7033, third conversion subunit; 7041, first analysis subunit; 7042, second analysis subunit; 7043, third analysis subunit; 7044, fourth analysis subunit; 7051, first prevention subunit; 7052, second prevention subunit; 7053, third prevention subunit; 7054, fourth prevention subunit; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0014] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0015] Embodiment 1: This embodiment provides an aviation material supply chain risk management method.

[0016] See also Figure 1 , Figure 3 and Figure 4 , the figure shows that the method includes step S1, step S2, step S3, step S4 and step S5.

[0017] Step S1, obtaining risk event parameter information of the aviation material supply chain, wherein the parameter information includes the duration of the risk event, the risk peak time and the damage coefficient; It is understandable that in this step, the duration of a risk event refers to the length of time from the occurrence to the end of a risk event. For example, the period of time when a supplier cannot deliver aviation materials on time for some reason. Risk events with longer duration usually have a greater impact on the supply chain. The peak time of risk refers to the time point when the risk event has the most severe impact. For example, in the case of delayed delivery by suppliers, the peak time may be the time point when the inventory is exhausted, resulting in a production line shutdown. The precise determination of the peak time helps to prevent in advance. The damage coefficient is used to quantify the impact of risk events on the supply chain. It can be a comprehensive indicator that takes into account the severity of the risk event, the degree of interference with operations, economic losses, etc. Risk events with high damage coefficients require special attention. By obtaining and processing the risk event parameter information of the aviation material supply chain, accurate and comprehensive input data can be provided for the risk pulse model. The high quality and accuracy of these data directly affect the accuracy and reliability of subsequent risk analysis. By accurately obtaining and analyzing the duration, peak time and damage coefficient of risk events, enterprises can better understand and predict potential risks in the supply chain, thereby formulating more effective preventive measures to reduce the negative impact of risk events on the supply chain.

[0018] Step S2, constructing an aviation material supply chain risk pulse model, and sending risk event parameter information of the aviation material supply chain to the aviation material supply chain risk pulse model for processing, to obtain a risk pulse signal diagram corresponding to the risk event of the aviation material supply chain; It can be understood that in this step, by constructing an aviation material supply chain risk pulse model and inputting risk event parameter information into it for processing, the enterprise can intuitively see the timing characteristics and propagation paths of various risk events in the supply chain. This analysis method based on a dynamic model is more predictive and accurate than traditional static analysis, and can better reflect the actual operating conditions of the supply chain. The generated risk pulse signal diagram provides strong data support for subsequent risk analysis and decision-making, enabling enterprises to identify potential risks earlier and more accurately, formulate countermeasures in advance, and ensure the continuity and stability of the supply chain. In this step, step S2 includes step S21 and step S22.

[0019] Step S21, constructing a functional relationship between amplitude and standard deviation in the risk event parameter information of the aviation material supply chain; It can be understood that by constructing the functional relationship between amplitude and standard deviation in this step, the characteristics of risk events can be more accurately described. This functional relationship enables the risk pulse model to more accurately simulate the spread and impact of risk events in the supply chain, providing a solid foundation for subsequent risk analysis and prediction. Specifically, this method can effectively capture the spatiotemporal characteristics of different risk events, help enterprises identify high-risk events that may occur in advance, and provide a scientific basis for formulating preventive measures. The functional relationship between amplitude and standard deviation is shown below: , ; in, is the amplitude of the risk pulse function waveform, is the standard deviation of the risk impulse function, Indicates risk source, Represents pi.

[0020] Step S22: combining the preset waveform function of the aviation material supply chain with the functional relationship to obtain the pulse function of the aviation material supply chain, and then constructing an aviation material supply chain risk pulse model based on the pulse function of the aviation material supply chain.

[0021] It is understandable that by combining the preset aviation material supply chain waveform function with the functional relationship between amplitude and standard deviation, the resulting pulse function can more accurately describe the dynamic behavior of risk events. The risk pulse model constructed based on this pulse function enables enterprises to comprehensively analyze the superposition effect of multiple risk events and generate accurate supply chain risk pulse signal graphs. This model provides enterprises with powerful tools to better predict and manage various risk events in the supply chain, thereby improving the risk resistance of the supply chain and ensuring the stable operation of the aviation material supply chain. Among them, the risk pulse function is as follows: , ; in, is the risk impulse function; The damage coefficient of the risk source to the aviation material supply chain system; is the standard deviation of the risk impulse function; is the time point when the amplitude of the risk pulse function waveform is the largest, that is, the time point when the probability of risk pulse outbreak is the highest. Indicates risk source, represents pi, Represents a time variable.

[0022] In this step, step S2 also includes step S23 and step S24.

[0023] Step S23, preprocessing the risk event parameter information of the aviation material supply chain, wherein the preprocessing includes data cleaning, denoising and normalization to obtain the preprocessed risk event parameter information; It can be understood that this step can greatly improve the quality of the model's input data by preprocessing the risk event parameter information of the aviation material supply chain, thereby enhancing the accuracy and reliability of the risk pulse model. Data cleaning ensures the integrity and consistency of the data, denoising eliminates random interference signals, and normalization ensures the scale consistency between different data. Ultimately, these preprocessing steps enable the risk pulse model to more accurately capture potential risks in the supply chain, thereby improving the overall risk prediction and management capabilities.

[0024] Step S24: convert the pre-processed risk event parameter information into a preset format through the device interface, and send it to the aviation material supply chain risk pulse model for calculation to obtain a risk pulse signal diagram corresponding to each pre-processed risk event parameter information.

[0025] It can be understood that this step converts and transmits data formats through the device interface, ensuring that the parameter information of aviation material supply chain risk events can be seamlessly input into the risk pulse model for calculation. This process greatly improves the automation level of data processing and reduces errors and delays caused by manual operations. In addition, the accuracy and consistency of the data are guaranteed through format conversion, so that the model can generate accurate risk pulse signal diagrams based on high-quality data, providing reliable data support for risk prediction and management.

[0026] Step S3, performing Fourier transform on the risk pulse signal graph to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph; It is understandable that if Figure 3 and Figure 4 As shown, this step can conduct an in-depth analysis of risk events in the aviation material supply chain from the perspective of the frequency domain by Fourier transforming the risk pulse signal graph and obtaining the amplitude spectrum and phase spectrum. The amplitude spectrum can reveal the intensity distribution and periodic characteristics of risk events, which helps to identify high-frequency and high-risk events; the phase spectrum provides the phase relationship between risk events, which helps to understand the time dependence and correlation of events. Through this step, enterprises can more accurately identify potential high-risk time periods and events, provide a scientific basis for subsequent risk warnings and preventive measures, and thus improve the security and stability of the supply chain. In this step, step S3 includes step S31, step S32, and step S33.

[0027] Step S31, classifying and marking the data in the risk pulse signal diagram in chronological order to obtain risk pulse signal data in different time periods; It can be understood that this step first determines the time interval according to the accuracy requirements of the risk analysis, and divides the data of the entire risk pulse signal graph according to the preset time interval. For example, the risk event data of a month is divided into 30 time periods by day, each time period contains 24 hours of data points, and then the risk pulse signal data in each time period is classified and organized into independent time series. The classification results of each time period should include the pulse signal data of all risk events that occurred in the time period. By classifying and marking the data in the risk pulse signal graph in chronological order, the device can effectively organize and manage a large amount of time series data. This method not only improves the efficiency of data management, but also provides clear basic data for subsequent spectrum analysis, trend analysis, and risk prediction. Through this time series classification and labeling, enterprises can more accurately identify and respond to risk events in the supply chain, optimize risk management processes, and improve the robustness and risk resistance of the supply chain.

[0028] Step S32, sending the risk pulse signal data as discrete data to a preset Fourier transform formula for calculation to obtain a frequency domain signal of the risk pulse signal data; It can be understood that in this step, by inputting discrete risk pulse signal data into the Fourier transform formula for calculation, the device can effectively convert the complex fluctuation pattern in the time domain into the frequency components in the frequency domain. In this way, risk managers can more clearly understand and analyze the periodic risk events in the supply chain and identify the frequency components that have the most significant impact on the supply chain. This method helps to accurately identify and predict potential risks, improve the scientificity and effectiveness of risk management, and also provides solid data support for the formulation of targeted preventive measures.

[0029] ; in, is the value of the frequency domain signal, representing the signal at frequency The amplitude and phase information at is the sequence number of the discrete frequency, is the sampling point index of the time domain signal, is the total number of sampling points of the signal, is the time domain signal at the sampling point The value at is an imaginary unit, It is the basic unit of angular frequency, indicating that a complete circle is distributed in On a point.

[0030] Step S33: Calculate the frequency domain signal based on a preset amplitude spectrum calculation formula and a phase spectrum calculation formula to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph.

[0031] ; in, For the The amplitude at discrete frequencies, For the The real part at discrete frequencies, For the The imaginary part at discrete frequencies.

[0032] The calculation formula of the phase spectrum is as follows: ; in, For the The phase angle at discrete frequencies, is the four-quadrant inverse tangent function. For the The real part at discrete frequencies, For the The imaginary part at discrete frequencies.

[0033] It is understandable that by calculating the amplitude spectrum and phase spectrum, the device can deeply analyze the frequency characteristics and phase relationships of risk events in the aviation material supply chain. The amplitude spectrum reveals the intensity of different frequency components, which helps to identify periodic fluctuations that affect the stability of the supply chain. The phase spectrum provides information on the temporal relationship between events, helping to understand the dependency and transmission path of risk events. This multi-dimensional analysis provides managers with comprehensive risk insights and supports the formulation of more accurate risk warning and prevention and control strategies.

[0034] Step S4, performing risk analysis based on the amplitude spectrum and phase spectrum corresponding to each risk pulse signal graph to obtain a risk analysis result, wherein the risk analysis result is to determine the aviation material supply event with the greatest risk in each time period; It can be understood that in this step, by performing risk analysis based on the amplitude spectrum and phase spectrum, the device can accurately identify key risk events in different periods of the supply chain. This analysis method not only reveals the intensity of risk events, but also takes into account their time dependence, so that potential risk outbreak points can be predicted more accurately. This multi-dimensional analysis provides supply chain managers with a scientific basis for decision-making, supporting them to take appropriate preventive measures before critical time nodes, thereby effectively reducing supply chain risks and ensuring a stable supply of aviation materials. In this step, step S4 includes step S41, step S42, step S43 and step S44.

[0035] Step S41: based on the amplitude spectrum corresponding to each risk pulse signal graph and a preset threshold, classify the corresponding risk events to obtain three categories of risk events, wherein the three categories of risk events include high-frequency risk events, medium-frequency risk events and low-frequency risk events; It is understood that this step considers events whose peaks in the amplitude spectrum exceed the high-frequency event threshold as high-frequency risk events. These events usually represent the most threatening risks in the supply chain and may require immediate action. Consider events whose peaks in the amplitude spectrum are between the medium-frequency event threshold and the high-frequency event threshold as medium-frequency risk events. These events usually require monitoring and preventive measures, but their urgency is lower than that of high-frequency events. Consider events whose peaks in the amplitude spectrum are lower than the low-frequency event threshold. These events have less impact on the supply chain and can usually be dealt with through conventional management means.

[0036] Step S42, calculating the phase difference corresponding to each risk event based on the phase spectrum corresponding to each risk pulse signal graph, and using the phase difference as the dependency value of the corresponding risk event on the event; It can be understood that in this step, by calculating the phase difference and using it as the dependency value, the device can deeply understand the temporal relationship between risk events. This method can reveal the potential causal relationship and dependency between events, making risk management more accurate and comprehensive. It can identify key events that may lead to chain reactions, allowing the management team to better predict and respond to potential risks, thereby reducing the possibility of supply chain disruptions and reducing the overall risk level.

[0037] The calculation formula of the phase difference is as follows: ; in, For the Events and The phase difference of each event, Indicates The phase spectrum value of an event, Indicates The phase spectrum value of an event.

[0038] Step S43: performing risk grading based on the categories and dependency values ​​of the risk events, wherein a grade evaluation matrix is ​​constructed based on the categories and dependency values ​​of the risk events; It can be understood that this step performs risk grading based on the category and dependency value of risk events, and constructs a grade assessment matrix, which can integrate and evaluate complex risk information in an instrumental way. This method not only clarifies the risk level of each event, but also provides targeted management strategies. Risk grading can help managers prioritize the most important risk events, thereby more effectively allocating resources and formulating risk control measures, and improving the overall efficiency and security of supply chain management. Among them, the horizontal axis of the grade assessment matrix is ​​high frequency, medium frequency, and low frequency, and the vertical axis is high dependency and low dependency. Dependence can be divided based on the absolute value of the phase difference.

[0039] Step S44: Calculate the risk level corresponding to each risk event based on the level assessment matrix and the level calculation formula.

[0040] It can be understood that this step can classify risk events in a device-based manner by calculating the risk level of each risk event based on the level assessment matrix and the level calculation formula. This method provides a clear priority and management strategy, making risk management more targeted and efficient. It can effectively identify the risks that need the most attention and treatment, optimize resource allocation, and provide a basis for formulating response measures, thereby improving the overall safety and reliability of supply chain management. In this step, the level calculation formula is as follows: ; in, For the The risk level of the event, For the The category of the event, For the The dependency value of events, where is a risk level assessment function, which specifically determines the first The corresponding score of the category of the event is obtained by multiplying the score by the dependency value of the event. The risk level of an event.

[0041] Step S5: Based on the risk analysis results, risk warnings are issued and preventive measures are constructed, and the results are sent to the management personnel's communication equipment to prompt the management personnel to take risk prevention measures.

[0042] It is understandable that in this step, by providing risk warnings and constructing preventive measures based on the analysis results, the device can take action as soon as the risk is identified. Timely warnings and specific preventive measures can effectively reduce the impact of risks on the supply chain and enhance the supply chain's ability to resist risks. After receiving detailed warning information and preventive measures, managers can respond quickly and optimize resource allocation, thereby reducing potential losses and operational interruption risks and improving the stability and reliability of the overall supply chain. In this step, step S5 includes step S51, step S52, step S53, and step S54.

[0043] Step S51: input the preset historical risk level, historical risk events, and historical preventive measures as input information into the BP neural network model, and predict the historical preventive measures for each risk event to obtain a prediction result; It can be understood that this step uses historical risk levels, risk events, and preventive measures as input information to train the BP neural network model, and can use the rules in historical data to predict effective preventive measures for new risk events. This method can provide scientific prevention suggestions based on a data-driven approach and improve the accuracy and efficiency of risk management. Through this prediction, managers can prepare and respond to potential risks more effectively, thereby optimizing resource allocation and reducing potential losses.

[0044] Step S52: Calculate the matching degree between the prediction results and the historical preventive measures respectively to determine the matching degree between the prediction results and the historical preventive measures; It can be understood that this step can verify the effectiveness and practicality of the predicted measures by calculating the matching degree between the predicted results and the historical preventive measures. This method ensures that the preventive measures predicted by the model are consistent with the measures implemented historically, thereby improving the accuracy and reliability of the prediction. Through the matching degree calculation, managers can determine whether the predicted measures are worth adopting, optimize risk response strategies, and ensure that the best preventive measures are taken when facing similar risk events. Among them, the matching degree calculation is to calculate the cosine similarity between the predicted results and the historical measures, and use the cosine similarity between the predicted results and the historical measures as the matching degree between the two.

[0045] Step S53, adjusting the weight and bias information of the BP neural network based on the gradient descent algorithm and the matching degree until the matching degree is greater than a preset threshold, thereby obtaining a trained BP neural network model; It can be understood that this step optimizes the weights and biases of the BP neural network model through the gradient descent algorithm, which can improve the prediction accuracy and stability of the model. Through continuous adjustment, it ensures that the predicted preventive measures have a high degree of match with the historical preventive measures, so that the model can effectively predict the preventive measures for future risk events. This optimization process enables the model to provide more reliable and practical suggestions when dealing with new risk events, thereby improving the efficiency and effectiveness of risk management.

[0046] Step S54: Send all risk events and their risk levels to the trained BP neural network model to predict preventive measures for each risk event, and predict corresponding preventive measures for each risk event.

[0047] It can be understood that this step can achieve the automation and accurate prevention of various risk events by applying the trained BP neural network model to the prediction of preventive measures for all risk events. This method can efficiently process large amounts of data, provide data-driven decision support, and optimize the process of formulating preventive measures, thereby improving the effectiveness and response speed of overall risk management.

[0048] Embodiment 2: like Figure 2 As shown, this embodiment provides an aviation material supply chain risk management device, see Figure 2 The device includes an acquisition unit 701 , a processing unit 702 , a conversion unit 703 , an analysis unit 704 and a prevention unit 705 .

[0049] An acquisition unit 701 is used to acquire risk event parameter information of the aviation material supply chain, wherein the parameter information includes the duration of the risk event, the risk peak time and the damage coefficient; The processing unit 702 is used to construct an aviation material supply chain risk pulse model, and send the risk event parameter information of the aviation material supply chain to the aviation material supply chain risk pulse model for processing to obtain a risk pulse signal diagram corresponding to the risk event of the aviation material supply chain; The processing unit 702 includes a first processing sub-unit 7021 and a second processing sub-unit 7022 .

[0050] The first processing subunit 7021 is used to construct a functional relationship between the amplitude and the standard deviation in the risk event parameter information of the aviation material supply chain; The second processing subunit 7022 is used to combine the preset waveform function of the aviation material supply chain with the functional relationship to obtain the pulse function of the aviation material supply chain, and then construct an aviation material supply chain risk pulse model based on the pulse function of the aviation material supply chain.

[0051] The processing unit 702 further includes a third processing sub-unit 7023 and a fourth processing sub-unit 7024 .

[0052] The third processing subunit 7023 is used to pre-process the risk event parameter information of the aviation material supply chain, wherein the pre-processing includes data cleaning, denoising and normalization to obtain the pre-processed risk event parameter information; The fourth processing sub-unit 7024 is used to convert the pre-processed risk event parameter information into a preset format through the device interface, and send it to the aviation material supply chain risk pulse model for calculation to obtain a risk pulse signal diagram corresponding to each pre-processed risk event parameter information.

[0053] A conversion unit 703 is used to perform Fourier transform on the risk pulse signal graph to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph; The conversion unit 703 includes a first conversion subunit 7031 , a second conversion subunit 7032 and a third conversion subunit 7033 .

[0054] The first conversion subunit 7031 is used to classify and mark the data in the risk pulse signal diagram in chronological order to obtain risk pulse signal data in different time periods; The second conversion subunit 7032 is used to send the risk pulse signal data as discrete data to a preset Fourier transform formula for calculation to obtain a frequency domain signal of the risk pulse signal data; The third conversion subunit 7033 is used to calculate the frequency domain signal based on a preset amplitude spectrum calculation formula and a phase spectrum calculation formula to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph.

[0055] An analysis unit 704 is used to perform risk analysis based on the amplitude spectrum and phase spectrum corresponding to each risk pulse signal graph to obtain a risk analysis result, wherein the risk analysis result is to determine the aviation material supply event with the greatest risk in each time period; The analysis unit 704 includes a first analysis subunit 7041 , a second analysis subunit 7042 , a third analysis subunit 7043 and a fourth analysis subunit 7044 .

[0056] The first analysis subunit 7041 is used to classify the corresponding risk events of each risk pulse signal graph based on the amplitude spectrum corresponding to the risk pulse signal graph and the preset threshold value, and obtain three categories of risk events, wherein the three categories of risk events include high-frequency risk events, medium-frequency risk events and low-frequency risk events; The second analysis subunit 7042 is used to calculate the phase difference corresponding to each risk event based on the phase spectrum corresponding to each risk pulse signal diagram, and use the phase difference as the dependency value of the corresponding risk event on the event; The third analysis subunit 7043 is used to perform risk grading based on the categories and dependency values ​​of the risk events, wherein a grade evaluation matrix is ​​constructed based on the categories and dependency values ​​of the risk events; The fourth analysis subunit 7044 is used to calculate the risk level corresponding to each risk event based on the level assessment matrix and the level calculation formula.

[0057] The prevention unit 705 is used to issue risk warnings and construct prevention measures based on the risk analysis results, and send them to the management personnel's communication equipment to prompt the management personnel to take risk prevention measures.

[0058] The prevention unit 705 includes a first prevention subunit 7051 , a second prevention subunit 7052 , a third prevention subunit 7053 and a fourth prevention subunit 7054 .

[0059] The first prevention subunit 7051 is used to input the preset historical risk level, historical risk events, and historical prevention measures as input information into the BP neural network model, and predict the historical prevention measures for each risk event to obtain a prediction result; The second prevention subunit 7052 is used to calculate the matching degree between the prediction result and the historical prevention measures respectively, and determine the matching degree between the prediction result and the historical prevention measures; The third prevention subunit 7053 is used to adjust the weight and bias information of the BP neural network based on the gradient descent algorithm and the matching degree until the matching degree is greater than a preset threshold, thereby obtaining a trained BP neural network model; The fourth prevention subunit 7054 is used to send all risk events and risk levels of all risk events to the trained BP neural network model to predict the preventive measures for each risk event, and predict the corresponding preventive measures for each risk event.

[0060] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0061] Embodiment 3: Corresponding to the above method embodiment, this embodiment also provides an aviation material supply chain risk management device. The aviation material supply chain risk management device described below and the aviation material supply chain risk management method described above can refer to each other.

[0062] Figure 5FIG. 8 is a block diagram of an aviation material supply chain risk management device 800 according to an exemplary embodiment. Figure 5 As shown, the aviation material supply chain risk management device 800 may include: a processor 801 and a memory 802. The aviation material supply chain risk management device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0063] The processor 801 is used to control the overall operation of the aviation material supply chain risk management device 800 to complete all or part of the steps in the aviation material supply chain risk management method. The memory 802 is used to store various types of data to support the operation of the aviation material supply chain risk management device 800. These data may include, for example, instructions for any application or method operated on the aviation material supply chain risk management device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the aviation material supply chain risk management device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0064] In an exemplary embodiment, the aviation material supply chain risk management device 800 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned aviation material supply chain risk management method.

[0065] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned aviation material supply chain risk management method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the aviation material supply chain risk management device 800 to complete the above-mentioned aviation material supply chain risk management method.

[0066] Embodiment 4: Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the aviation material supply chain risk management method described above can refer to each other.

[0067] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the aviation material supply chain risk management method of the above method embodiment.

[0068] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0070] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for managing aviation material supply chain risk, characterized in that: include: Acquire risk event parameter information of the aviation material supply chain, wherein the parameter information includes the duration of the risk event, the risk peak time and the damage coefficient; Constructing an aviation material supply chain risk pulse model, and sending risk event parameter information of the aviation material supply chain to the aviation material supply chain risk pulse model for processing, to obtain a risk pulse signal diagram corresponding to the risk event of the aviation material supply chain; Performing Fourier transformation on the risk pulse signal graph to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph; Perform risk analysis based on the amplitude spectrum and phase spectrum corresponding to each risk pulse signal graph to obtain a risk analysis result, wherein the risk analysis result is to determine the aviation material supply event with the greatest risk in each time period; Based on the risk analysis results, risk warnings are issued and preventive measures are constructed, which are sent to the management personnel's communication devices to prompt the management personnel to take risk prevention measures.

2. The aviation material supply chain risk management method according to claim 1, characterized in that: The construction of the aviation material supply chain risk pulse model includes: Constructing a functional relationship between amplitude and standard deviation in the risk event parameter information of the aviation material supply chain; The preset waveform function of the aviation material supply chain is combined with the functional relationship to obtain the pulse function of the aviation material supply chain, and then the aviation material supply chain risk pulse model is constructed based on the pulse function of the aviation material supply chain.

3. The aviation material supply chain risk management method according to claim 1, characterized in that: The risk event parameter information of the aviation material supply chain is sent to the aviation material supply chain risk pulse model for processing to obtain a risk pulse signal diagram corresponding to the risk event of the aviation material supply chain, including: Preprocessing the risk event parameter information of the aviation material supply chain, wherein the preprocessing includes data cleaning, denoising and normalization to obtain the preprocessed risk event parameter information; The pre-processed risk event parameter information is converted into a preset format through the device interface and sent to the aviation material supply chain risk pulse model for calculation to obtain a risk pulse signal diagram corresponding to each pre-processed risk event parameter information.

4. The aviation material supply chain risk management method according to claim 1, characterized in that: The risk pulse signal graph is subjected to Fourier transformation to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph, including: Classifying and marking the data in the risk pulse signal graph in chronological order to obtain risk pulse signal data in different time periods; Sending the risk pulse signal data as discrete data to a preset Fourier transform formula for calculation to obtain a frequency domain signal of the risk pulse signal data; The frequency domain signal is calculated based on a preset amplitude spectrum calculation formula and a phase spectrum calculation formula to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph.

5. An aviation material supply chain risk management device, characterized in that: include: An acquisition unit, used to acquire risk event parameter information of the aviation material supply chain, wherein the parameter information includes the duration of the risk event, the risk peak time and the damage coefficient; A processing unit, used to construct an aviation material supply chain risk pulse model, and send risk event parameter information of the aviation material supply chain to the aviation material supply chain risk pulse model for processing to obtain a risk pulse signal diagram corresponding to the risk event of the aviation material supply chain; A conversion unit, used for performing Fourier transform on the risk pulse signal graph to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph; An analysis unit, configured to perform risk analysis based on the amplitude spectrum and phase spectrum corresponding to each risk pulse signal graph to obtain a risk analysis result, wherein the risk analysis result is to determine the aviation material supply event with the greatest risk in each time period; The prevention unit is used to issue risk warnings and construct preventive measures based on the risk analysis results, and send them to the management personnel's communication equipment to prompt the management personnel to take risk prevention measures.

6. The aviation material supply chain risk management device according to claim 5, characterized in that: The processing unit comprises: A first processing subunit is used to construct a functional relationship between amplitude and standard deviation in the risk event parameter information of the aviation material supply chain; The second processing subunit is used to combine the preset waveform function of the aviation material supply chain with the functional relationship to obtain the pulse function of the aviation material supply chain, and then construct an aviation material supply chain risk pulse model based on the pulse function of the aviation material supply chain.

7. The aviation material supply chain risk management device according to claim 5, characterized in that: The processing unit comprises: A third processing subunit is used to pre-process the risk event parameter information of the aviation material supply chain, wherein the pre-processing includes data cleaning, denoising and normalization to obtain the pre-processed risk event parameter information; The fourth processing sub-unit is used to convert the pre-processed risk event parameter information into a preset format through the device interface, and send it to the aviation material supply chain risk pulse model for calculation to obtain a risk pulse signal diagram corresponding to each pre-processed risk event parameter information.

8. The aviation material supply chain risk management device according to claim 5, characterized in that: The conversion unit comprises: A first conversion subunit is used to classify and mark the data in the risk pulse signal diagram in chronological order to obtain risk pulse signal data in different time periods; A second conversion subunit is used to send the risk pulse signal data as discrete data to a preset Fourier transform formula for calculation to obtain a frequency domain signal of the risk pulse signal data; The third conversion subunit is used to calculate the frequency domain signal based on a preset amplitude spectrum calculation formula and a phase spectrum calculation formula to obtain an amplitude spectrum and a phase spectrum corresponding to each risk pulse signal graph.

9. An aviation material supply chain risk management device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the aviation material supply chain risk management method as claimed in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the aviation material supply chain risk management method according to any one of claims 1 to 4 are implemented.

Images