Equipment reliability risk early warning method and device, terminal equipment and storage medium

Abstract

The invention discloses an equipment reliability risk early warning method and device, terminal equipment and a storage medium, and belongs to the technical field of equipment risk early warning, and the method comprises the steps: determining the supply risk degree value of each component according to the supplier number of each component and the supply risk feature data of all corresponding suppliers, and determining the supply risk degree value of each component according to the supply risk degree value; and performing multi-dimensional analysis based on the supply risk degree value of each component, the importance degree value of each component in the equipment and the supply dependence degree value between each supplier and the equipment to obtain a reliability degree value corresponding to the equipment. According to the method, the supply risk of each component can be obtained, the importance difference of the components in the equipment and the dependency relationship between suppliers and the equipment are considered, the real reliability of the equipment can be accurately obtained, and accurate risk early warning can be carried out. According to the invention, the problem of low risk early warning accuracy caused by difficulty in accurately judging the reliability of the equipment in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment risk warning, and in particular to an equipment reliability risk warning method, device, terminal equipment and storage medium. Background Art

[0002] Equipment reliability generally refers to the quality and performance of equipment throughout its design, manufacturing, installation, use, and maintenance. Ensuring equipment reliability is crucial for ensuring safe and stable system operation. The stability of the supply of electronic components is closely related to equipment reliability. The overall operational reliability of a device is often assessed by assessing the risks faced by components during the supply process, based on the supply risks of each component within the device. If the overall operational reliability of a device is low, proactive measures can be taken to ensure stable operation, avoid equipment failures caused by component supply issues, and minimize the impact on power system communications.

[0003] However, when traditional technology provides risk warnings for equipment, it directly collects the supplier data of all components of the equipment and uniformly assesses the component supply risk of the equipment based on all supplier data, thereby deriving the reliability of the equipment. Traditional technology cannot measure the impact of each component supply risk on the overall reliability of the equipment, but rather makes a rough assessment, resulting in a large deviation between the reliability judgment results of the equipment and the actual situation.

[0004] Therefore, because traditional technology does not perform detailed supply risk calculations and analyses for each component, and does not take into account the importance of different components in the equipment and the differences in supply dependence between different suppliers and equipment, traditional technology has difficulty accurately judging the true overall reliability of the equipment, resulting in low early warning accuracy and the inability to take effective preventive measures in a timely manner. Summary of the Invention

[0005] The embodiments of the present invention provide a reliability risk warning method, apparatus, terminal device and storage medium for equipment, which can not only focus on the supply risk of each component itself, but also take into account the differences in the importance of components in the equipment, as well as the dependencies between suppliers and equipment. It can comprehensively and accurately reflect the true overall reliability of the equipment, improve the accuracy of equipment reliability assessment, and effectively solve the problem in the existing technology that it is difficult to accurately judge the true overall reliability of the equipment, resulting in low warning accuracy.

[0006] An embodiment of the present invention provides a device reliability risk early warning method, comprising:

[0007] Obtain a list of component suppliers for the device; wherein the list of component suppliers includes: a plurality of components and a plurality of different suppliers corresponding to each component; each supplier includes: supply risk characteristic data and a supply dependency value between the supplier and the device;

[0008] For each component, determine the supply risk level corresponding to the component based on the number of suppliers of the component and the supply risk characteristic data of all suppliers corresponding to the component;

[0009] Determine the reliability value of the equipment based on the supply risk value of each component, the preset importance value of each component in the equipment, and the supply dependency value between each supplier and the equipment;

[0010] When it is determined that the reliability value is less than a preset reliability threshold, a first risk warning is generated to indicate that the reliability of the device is low.

[0011] Preferably, the supply risk characteristic data includes: a supply interruption risk level value, a plurality of delivery reports for recording historical delivery data of components, and a plurality of test reports for recording safety performance test results of components; wherein different components correspond to different delivery reports and different test reports;

[0012] Determining the supply risk level value corresponding to a component based on the number of component suppliers and the supply risk characteristic data of all suppliers corresponding to the component includes:

[0013] Determine the concentration of the component supply chain based on the total number of component suppliers;

[0014] Determine the supply risk of components based on the supply interruption risk values ​​of each supplier corresponding to the components;

[0015] Determine the supply stability of components based on the corresponding delivery reports of components;

[0016] Determine the technical safety reliability of components based on their corresponding test reports;

[0017] The supply risk level value corresponding to the components is determined based on the supply chain concentration, supply risk, supply stability and technical security credibility.

[0018] Preferably, the delivery report includes: delivery time records, delivery quantity deviation data, quality pass rate data and transportation loss rate data;

[0019] Determining the supply stability of components based on the delivery reports corresponding to the components includes:

[0020] Based on the delivery time records and delivery quantity deviation data, a target number of times the delivery quantity deviation value is within a preset deviation range within a preset delivery time window is calculated, and a component supply rate is generated based on the target number and the total number of deliveries within the preset delivery time window;

[0021] Based on the delivery time records, quality pass rate data, and transportation loss rate data, the number of times within a preset delivery time window that the quality pass rate is greater than a preset pass rate threshold and the transportation loss rate is less than a preset transportation loss rate threshold is used as the number of qualified deliveries; based on the number of qualified deliveries and the total number of deliveries within the preset delivery time window, the supply pass rate of components is generated;

[0022] The supply stability of components is determined based on the supply rate of components and the supply qualification rate of components.

[0023] Preferably, it also includes:

[0024] Divide each supplier into several supply groups based on their supply interruption risk level. Different supply groups have different average supply interruption risk levels.

[0025] The supply group with the smallest average supply interruption risk level is selected as the first target supply group; the supply group with the largest average supply interruption risk level is selected as the second target supply group;

[0026] Determine the number of first components corresponding to the first target supply group based on all components in the first target supply group; determine the proportion of the number of first components corresponding to the first target supply group based on the number of first components and the total number of components in all supply groups;

[0027] Determine the number of second components corresponding to the second target supply group based on all components in the second target supply group; determine the proportion of the number of second components corresponding to the second target supply group based on the number of second components and the total number of components in all supply groups;

[0028] When it is determined that the proportion of the number of the first components is not greater than the proportion of the first preset number of components, a second risk warning is generated to indicate that the supply controllability of the equipment is low; or

[0029] When it is determined that the proportion of the number of the second components is greater than the proportion of the number of the second preset components, a second risk warning is generated to indicate that the supply controllability of the device is low;

[0030] Among them, the proportion of the number of the first preset components is greater than the proportion of the number of the second preset components.

[0031] Preferably, each component also corresponds to a device model;

[0032] After determining the first target supply group and the second target supply group, the method further includes:

[0033] Obtaining the number of first device models and types of all components in the first target supply group, and the number of second device models and types of all components in the second target supply group;

[0034] Determine the proportion of the first type of device corresponding to the first target supply group based on the number of the first device model types and the total number of device model types of all supply groups;

[0035] Determine the proportion of the second type of device corresponding to the second target supply group based on the number of the second device model types and the total number of device model types in all supply groups;

[0036] When it is determined that the proportion of the number of the first category is not greater than the proportion of the number of the first preset category, a third risk warning is generated to characterize low supply stability of the device component category; or

[0037] When it is determined that the proportion of the second category is greater than the proportion of the second preset category, generating a third risk warning for characterizing low supply stability of the device component category;

[0038] The proportion of the number of the first preset categories is greater than the proportion of the number of the second preset categories.

[0039] Preferably, the generation of the supply dependency value between each supplier and device includes:

[0040] For each supplier, count the number of target component types corresponding to the device under the supplier based on the component supplier list;

[0041] Generate a target category ratio corresponding to the supplier based on the number of target component types and the total number of component types in the component supplier list;

[0042] According to the proportion of the target categories, the supply dependence value between the supplier and the equipment is determined.

[0043] Preferably, it also includes:

[0044] When it is determined that the reliability value is not less than a preset reliability threshold, prompt information is generated to indicate that the current reliability of the device is high and the device is less affected by the supplier supply risk.

[0045] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0046] An embodiment of the present invention provides a device reliability risk warning device, comprising: a supplier list acquisition module, a supply risk level determination module, a reliability level determination module, and a risk warning module;

[0047] The supplier list acquisition module is used to obtain a list of component suppliers for the device; wherein the component supplier list includes: a plurality of components and a plurality of different suppliers corresponding to each component; each supplier includes: supply risk characteristic data and a supply dependency value between the supplier and the device;

[0048] The supply risk level determination module is configured to determine, for each component, a supply risk level value corresponding to the component based on the number of suppliers of the component and the supply risk characteristic data of all suppliers corresponding to the component;

[0049] The reliability determination module is configured to determine a reliability value corresponding to a device based on a supply risk value of each component, a preset importance value of each component in the device, and a supply dependency value between each supplier and the device;

[0050] The risk warning module is configured to generate a first risk warning indicating that the reliability of the device is low when it is determined that the reliability value is less than a preset reliability threshold.

[0051] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0052] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the reliability risk warning method of a device described in the above-mentioned embodiment of the invention.

[0053] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0054] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a device reliability risk warning method described in the above-mentioned embodiment of the invention.

[0055] The following beneficial effects are achieved by implementing the present invention:

[0056] Embodiments of the present invention provide a device reliability risk early warning method, apparatus, terminal device, and storage medium. The present invention can obtain a list of component suppliers for a device, thereby determining the supply risk level corresponding to each component based on the number of suppliers and the corresponding supply risk characteristic data of all suppliers. The present invention can then refine the risk to each component and accurately derive the supply risk of a single component based on the number of suppliers and the supply risk characteristic data of each supplier. Furthermore, after performing a detailed supply risk calculation and analysis for each component, the present invention can perform a multi-dimensional analysis based on the supply risk level of each component, the importance of each component in the device, and the supply dependency between each supplier and the device to derive the corresponding reliability level of the device. Compared to the prior art, the present invention not only focuses on the supply risk of each component itself, but also takes into account the differences in the importance of components in the device and the dependency between suppliers and the device. This can comprehensively and accurately reflect the true overall reliability of the device, thereby improving the accuracy of the device reliability assessment. Based on an early warning mechanism based on accurate assessment results, the present invention can promptly detect reduced device reliability, quickly take measures to ensure stable operation of the device, avoid device failures caused by component supply issues, and reduce the impact on power system communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The present invention is a flowchart of a device reliability risk early warning method provided by an embodiment of the present invention.

[0058] Figure 2 1 is a schematic diagram of a calculation flow for calculating the proportion of the number of specifications of components provided in one embodiment of the present invention.

[0059] Figure 3 The figure is a schematic structural diagram of a reliability risk warning device for equipment provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0061] like Figure 1 As shown, in order to solve the problem in the prior art that it is difficult to accurately determine the true overall reliability of a device, resulting in low warning accuracy and inability to take effective preventive measures in a timely manner, an embodiment of the present invention provides a device reliability risk warning method, including:

[0062] Step S1: Obtain a list of component suppliers for the device; wherein the list of component suppliers includes: a number of components and a number of different suppliers corresponding to each component; each supplier includes: supply risk characteristic data and a supply dependency value between the supplier and the device;

[0063] Schematically, in order to evaluate the reliability of equipment and issue risk warnings, the present invention can extract supplier data related to the components of the equipment through the component supplier list of the equipment, and then conduct targeted risk assessments on the components based on the risk characteristic data corresponding to each supplier and the supply dependence value between each supplier and the equipment, so as to achieve further equipment reliability assessment and risk warnings.

[0064] The component supplier list not only lists the suppliers of each component required by the device, but also records key information about each supplier. Supply risk characteristic data can be characterized by potential risk factors such as the supplier's risk of supply interruption, historical delivery history, and product quality stability. The supply dependency value reflects the device's reliance on that supplier. For example, if certain core components are only available from a few suppliers, the device will be highly dependent on these suppliers.

[0065] Step S2: For each component, determine the supply risk level value corresponding to the component based on the number of suppliers of the component and the supply risk characteristic data of all suppliers corresponding to the component;

[0066] Indicatively, in order to derive the supply risk level for each component, the number of component suppliers is an important consideration. If a component has only one or two suppliers, it means that the supply channel is relatively single. Once these suppliers encounter problems, such as production failures or logistics interruptions, the component may face the risk of supply interruption. On the contrary, when there are more suppliers, the supply stability is relatively high.

[0067] Furthermore, the present invention also uses the supply risk characteristic data of each supplier. For example, a supplier with a high supply interruption risk value will lead to a higher overall risk of components, while a supplier with a good delivery record and stable product quality will have a lower risk. Therefore, by analyzing the supply risk characteristic data of the supplier, the present invention can derive a quantitative supply risk level value to describe the risk status faced by each component in the supply process, thereby more accurately evaluating the overall reliability of the equipment.

[0068] Step S3: Determine the reliability value corresponding to the equipment based on the supply risk value of each component, the preset importance value of each component in the equipment, and the supply dependency value between each supplier and the equipment;

[0069] Indicatively, after obtaining the supply risk value of each component, the reliability of the equipment can be further evaluated based on the supply risk value of the component. Since the preset importance value of each component in the equipment reflects the criticality of the component to the normal operation of the equipment, components with different importance and different supply risk values ​​can deeply affect the corresponding reliability value of the equipment. For example, if a component with high importance has a supply problem, it will have a great impact on the operation of the equipment, resulting in lower reliability of the equipment, and thus a risk warning is needed to take intervention measures in advance.

[0070] In addition, for components supplied by highly dependent suppliers, their risk changes have a great impact on equipment reliability. The present invention integrates and calculates the three dimensions of factors: the supply risk level of each component, the preset importance value of each component in the equipment, and the supply dependence value between each supplier and the equipment. It can then derive a value that can comprehensively reflect the overall reliability of the equipment, that is, the reliability level of the equipment under the current supply conditions.

[0071] Step S4: When it is determined that the reliability value is less than a preset reliability threshold, a first risk warning is generated to indicate that the reliability of the device is low.

[0072] Illustratively, the present invention can generate a first risk warning when it determines that the reliability value is less than a preset reliability threshold, thereby informing equipment maintenance personnel, management personnel and other relevant personnel through pop-up prompts, SMS notifications or email reminders that there is a reliability risk in the equipment, prompting them to take timely measures to ensure the normal operation of power system communications and other services.

[0073] Regarding step S1, in a preferred embodiment, the component supplier list of the device also includes supply dependency values ​​between the supplier and the device, and the process of generating each supply dependency value between the supplier and the device specifically includes:

[0074] For each supplier, count the number of target component types corresponding to the device under the supplier based on the component supplier list;

[0075] Generate a target category ratio corresponding to the supplier based on the number of target component types and the total number of component types in the component supplier list;

[0076] According to the proportion of the target categories, the supply dependence value between the supplier and the equipment is determined.

[0077] Specifically, for each supplier in the component supplier list, you can count the number of different component types provided by that supplier for the device. This number is the target number of component types for the device under that supplier. For example, if a supplier provides three different types of components for a device: resistors, capacitors, and chips, then the target number of component types for that supplier is 3.

[0078] Divide the previously calculated number of target component types for each supplier by the total number of component types in the component supplier's list. The resulting ratio is the supplier's target category share. For example, if the component supplier's list contains 10 different types of components and a supplier's target number of component types is 3, then its target category share is 3 ÷ 10 = 0.3.

[0079] Therefore, the supply dependency value between the supplier and the device can be determined based on the calculated target category ratio. Generally speaking, the higher the target category ratio, the higher the device's dependency on that supplier; conversely, the lower the dependency, the lower the dependency. In embodiments of the present invention, the target category ratio can be converted into a supply dependency value through a certain mapping relationship, such as directly using the target category ratio as the supply dependency value, or setting a function to perform the conversion based on different business needs.

[0080] The embodiment of the present invention can quantify the originally vague dependency relationship between equipment and suppliers and express it with a specific numerical value. Therefore, by calculating the proportion of target categories, it can intuitively reflect the importance of each supplier to the equipment, thereby helping to obtain the corresponding reliability value of the equipment.

[0081] In a preferred embodiment, when calculating the number of target component types for each supplier, the actual calculation is the proportion of the number of specifications of the manufacturing enterprise corresponding to the supplier. The proportion of the number of specifications refers to the proportion of the number of components of different models and specifications provided by a supplier (corresponding to its manufacturing enterprise) in the component system of the equipment to the total number of all component models and specifications.

[0082] For example, the entire equipment BOM list contains 100 different types and specifications of components (this is the total number of all component types and specifications), and the research and development company corresponding to a supplier provides 20 different types and specifications of components. Then the proportion of the number of specifications of this supplier (research and development company) is: 20÷100=20%, which reflects the richness and importance of the components provided by this supplier (research and development company) in the entire equipment component system.

[0083] like Figure 2 The calculation process diagram of the proportion of the number of specifications of components shown in the figure is as follows. The steps for calculating the proportion in the embodiment of the present invention are as follows:

[0084] Identify the supply chain development units involved in the BOM: Identify the development companies (i.e., suppliers) for all components in the equipment's BOM. The BOM, short for Bill of Material (BOM), is also known as a product structure sheet or material structure sheet.

[0085] Model and specification uniqueness processing: Components that appear repeatedly on different boards in the BOM list are deduplicated, that is, the model and specification are unique, and the number of components with different models and specifications is obtained.

[0086] Count the number of unique specifications under different research and development enterprise classifications: It is possible to combine multiple data sources for searching research and development enterprises to determine the category to which the research and development enterprises of various types of specifications of components belong, and obtain multiple types of research and development enterprise classification sets. According to the number of different types of specifications of components in each research and development enterprise classification set, the number of unique electronic component specifications corresponding to each type of research and development enterprise is obtained. Schematically, in an embodiment of the present invention, four types of research and development enterprise sets can be classified (first type research and development enterprise set, second type research and development enterprise set, third type research and development enterprise set, fourth type research and development enterprise set), and different types of research and development enterprises (i.e., suppliers) have different corresponding supply interruption risks. The supply interruption risk can be ranked from low to high as follows: first type research and development enterprise < second type research and development enterprise < third type research and development enterprise < fourth type research and development enterprise; schematically, first type research and development enterprises can belong to high-autonomy enterprises, and fourth type research and development enterprises belong to low-autonomy enterprises. High-autonomy enterprises refer to those with complete design and production capabilities and high controllability of the supply chain, and thus have low supply interruption risks. Low-autonomy enterprises refer to those that rely on external suppliers to provide core technologies and materials and have low controllability of the supply chain, and thus have high supply interruption risks.

[0087] Calculate the total number of unique electronic component specifications: Assuming that all components used in the equipment are products on the list, the total number of unique electronic component specifications is the sum of the component specifications of all research and development companies;

[0088] If products outside the list are actually selected, the total number of unique electronic component specifications is the sum of the component specifications of each type of research and development enterprise in the list and the products outside the list.

[0089] Calculate the proportion of unique specifications in different classifications: Calculate the proportion of unique specifications in different research and development enterprise classification sets respectively. The calculation formula is: Among them, P i is the number of unique specifications in the set of research and development enterprises of type i, N is the total number of unique electronic component specifications, Ni The number of components of different models and specifications in the set of type i research and development enterprises.

[0090] Statistical analysis: Based on the calculated percentage of unique specifications for different research and development enterprise classifications, analysis is conducted. For example, if the percentage of unique specifications in the first category of research and development enterprises is higher, it means that most of the components in the equipment come from the first category of research and development enterprises with low supply interruption risks, indicating that the equipment has a high degree of safety and controllability. If the number of unique specifications in the fourth category of research and development enterprises exceeds a certain proportion (such as 15%), it means that most of the components in the equipment come from the fourth category of research and development enterprises with high supply interruption risks, the safety and controllability of the equipment is low, and there is a need for rectification of safety and controllability risks. At the same time, according to the numerical value of the percentage of unique specifications corresponding to each research and development enterprise, sorted from high to low, a specific distribution analysis table of research and development enterprises ranked by their dependence on the supply chain can be obtained.

[0091] For step S2, in a preferred embodiment, the supply risk characteristic data includes: a supply interruption risk level value, several delivery reports for recording historical delivery data of components, and several test reports for recording safety performance test results of components; wherein different components correspond to different delivery reports and different test reports.

[0092] Then, when determining the supply risk level value corresponding to the components, it specifically includes:

[0093] Determine the concentration of the component supply chain based on the total number of component suppliers;

[0094] Determine the supply risk of components based on the supply interruption risk values ​​of each supplier corresponding to the components;

[0095] Determine the supply stability of components based on the corresponding delivery reports of components;

[0096] Determine the technical safety reliability of components based on their corresponding test reports;

[0097] The supply risk level value corresponding to the components is determined based on the supply chain concentration, supply risk, supply stability and technical security credibility.

[0098] It's understandable that the fewer suppliers there are, the higher the supply chain concentration. For example, if a component has only one or two suppliers, the supply chain concentration is higher than if it has five or six. High concentration means that if these few suppliers encounter problems (such as production failures or operational crises), the supply of components is likely to be affected, increasing supply risk. Low concentration, on the other hand, disperses supply risk to a certain extent; if one supplier encounters problems, other suppliers may still be able to maintain supply.

[0099] The supply interruption risk score measures the likelihood and impact of a supplier halting supply. By comprehensively considering the supply interruption risk scores of each supplier for a component, such as averaging or weighted averaging (weighting suppliers based on their importance), the supply risk level of that component can be determined. For example, if three suppliers supply a component, one with a high supply interruption risk score and the other two with low scores, calculating the combined supply risk score can reflect the overall risk level faced by that component due to a supplier interruption.

[0100] Furthermore, delivery reports record historical component delivery data, including information such as on-time delivery and accurate delivery quantities. By analyzing delivery reports, a supplier's delivery stability can be assessed. For example, if a supplier has repeatedly delayed deliveries or frequently delivers quantities that don't match orders, then the stability of their component supply is low. Conversely, a supplier that consistently delivers on time and accurately has a high stability of component supply. Therefore, based on the delivery reports corresponding to the components, the stability of the component supply can be determined.

[0101] Test reports record component safety performance testing results, such as electrical, mechanical, and reliability. Embodiments of the present invention can assess the reliability of a component's technical and safety performance based on component test reports. For example, if a component fails safety performance tests multiple times or has a high probability of failure, its technical safety reliability is low. On the other hand, components that have passed rigorous testing and have stable performance have high technical safety reliability.

[0102] In a preferred embodiment, the test report includes: functional performance test data, which is used to record the achievement of various functional indicators of components under standard working conditions; environmental adaptability test data, which is used to record the performance stability of components in high temperature / low temperature / humid heat / vibration environments; reliability life test data, which is used to record the life indicators of components such as mean time between failures (MTBF) and failure rate; safety compliance test data, which is used to record the certification results of whether the components comply with industry safety standards (such as IEC, UL, etc.);

[0103] Then, the technical safety reliability of the components is determined based on the test reports corresponding to the components, specifically including:

[0104] Generate functional compliance rate based on functional performance test data: Count the actual test values ​​of various functional indicators of components in the functional performance test data, compare the actual test values ​​of each functional indicator with the design standard value, and calculate the number of times the standard is met; generate the functional compliance rate of the component based on the number of times the standard is met and the total number of test indicators;

[0105] Generate environmental reliability based on environmental adaptability test data: Count the number of component tests under different environmental conditions, calculate the number of times the component's performance indicator fluctuations under each environmental condition do not exceed the preset threshold (as the number of times it meets the standard), and generate the component's environmental reliability based on the number of times it meets the standard and the total number of tests;

[0106] Generate life confidence based on reliability life test data: Obtain mean time between failures (MTBF) data from reliability life test data, compare the actual MTBF value with the industry standard value or design expected value, and generate a life confidence score based on the comparison results (e.g., actual MTBF ≥ 120% of the standard value is excellent);

[0107] Generate compliance credibility based on safety compliance test data: Check whether the safety compliance test data contains complete safety standard certification documents (such as CE, RoHS, etc.), verify the validity and timeliness of the certification documents, and generate a compliance credibility score based on the completeness and validity of the certification documents;

[0108] Assign weights (such as 40%, 25%, 20%, and 15%) to the functional compliance rate, environmental reliability, life confidence, and compliance credibility, and calculate the final technical safety credibility through a weighted sum formula.

[0109] Finally, by combining the indicators of the above-mentioned dimensions of supply chain concentration, supply risk, supply stability, and technical security credibility, different weights can be assigned to the importance of each indicator through weighted summation, thereby determining the supply risk level value corresponding to the components. The embodiment of the present invention can comprehensively and systematically reflect the various risk factors faced by components in the supply process through the supply risk level value.

[0110] Therefore, the present invention can assess supply chain structure (supply chain concentration), the possibility of supplier supply disruptions (supply risk), delivery execution (supply stability), and the technical safety performance of the components themselves (technical safety credibility). This avoids the one-sidedness of judging supply risk from only a single factor and can more comprehensively capture potential risk points in the component supply process. By quantifying indicators in various dimensions and comprehensively calculating them, the supply risk level can more accurately reflect the actual supply risk status of components.

[0111] In a preferred embodiment, the delivery report includes: delivery time records, delivery quantity deviation data, quality pass rate data and transportation loss rate data;

[0112] Determining the supply stability of components based on the delivery reports corresponding to the components includes:

[0113] Based on the delivery time records and delivery quantity deviation data, a target number of times the delivery quantity deviation value is within a preset deviation range within a preset delivery time window is calculated, and a component supply rate is generated based on the target number and the total number of deliveries within the preset delivery time window;

[0114] Based on the delivery time records, quality pass rate data, and transportation loss rate data, the number of times within a preset delivery time window that the quality pass rate is greater than a preset pass rate threshold and the transportation loss rate is less than a preset transportation loss rate threshold is used as the number of qualified deliveries; based on the number of qualified deliveries and the total number of deliveries within the preset delivery time window, the supply pass rate of components is generated;

[0115] The supply stability of components is determined based on the supply rate of components and the supply qualification rate of components.

[0116] As you can understand, first, the fill rate is calculated. Delivery time records can be used to determine whether deliveries are made within the preset delivery window, and delivery quantity deviation data can be used to determine whether the delivery quantity deviation is within the preset tolerance range. If the preset delivery window is three days before or after the order's agreed delivery date, the preset tolerance range is ±5%. If there are 10 total deliveries within a certain period, and 7 of them fall within these three days, and the delivery quantity deviation is within ±5%, then the target number is 7. Divide the target number by the total number of deliveries within the preset delivery window, and you have the fill rate = target number / total number of deliveries. In the above example, the fill rate could be: 7 ÷ 10 = 70%. This 70% fill rate reflects the supplier's ability and performance in delivering the agreed quantity within the specified timeframe.

[0117] Secondly, the supply qualification rate can be calculated by using delivery time records to determine whether delivery is within the preset delivery time window, using quality qualification data to determine whether it is greater than a preset qualification rate threshold (such as 95%), and using transportation loss rate data to determine whether it is less than a preset transportation loss rate threshold (such as 2%). If, within the preset delivery time window, the quality qualification rate of a delivery is 98% and the transportation loss rate is 1.5%, then the delivery meets the requirements, and the number of such qualified deliveries can be counted as the number of qualified deliveries.

[0118] The supply qualification rate is calculated by dividing the number of qualified deliveries by the total number of deliveries within the preset delivery time window: number of qualified deliveries ÷ total number of deliveries. For example, if there are 6 qualified deliveries and 10 total deliveries, the supply qualification rate is 6 ÷ 10 = 60%. A 60% supply qualification rate reflects the overall degree to which the supplier meets delivery requirements in terms of time, quality, and transportation loss.

[0119] Finally, the supply stability can be determined by comprehensively considering the supply rate and the supply qualification rate. The weighted average method can be used. For example, the supply rate and the supply qualification rate can be given the same weight, and the sum of the two can be divided by 2 to obtain the supply stability. Alternatively, based on actual demand, a higher weight (such as 0.6) can be given to the supply rate and a lower weight (such as 0.4) can be given to the supply qualification rate. The supply stability can be obtained through weighted calculation to reflect the quantitative indicator of the supplier's delivery stability.

[0120] Schematically, the supply rate mainly measures the supplier's performance from the perspective of delivery time and quantity, and the supply qualification rate comprehensively considers multiple aspects such as delivery time, quality and transportation loss. By combining the two, the present invention can more comprehensively evaluate the stability of the supplier in the delivery process, avoiding focusing on only one aspect and ignoring other important factors.

[0121] In a preferred embodiment, the present invention can conduct risk assessment of components based on a quantitative risk assessment model for different categories of suppliers corresponding to components according to the following indicators: supply chain concentration (reflecting the degree of dependence on a single supplier), supply stability (based on historical delivery data and market fluctuation analysis) and technical security (combined with component design documents, test reports, etc.).

[0122] By comprehensively calculating the above indicators, a risk score can be generated for each component, and the corresponding supply risk level value can be derived accordingly. Based on the supply risk level value, the overall component supply chain can be sorted and optimized. For example, for high-risk components (such as key chips with high concentration and poor stability), alternative suppliers or technology replacements can be introduced; for low-risk components (such as standard parts with stable supply and high autonomy), the existing procurement strategy can be maintained.

[0123] In step S3, the supply risk level for each component reflects the various risks faced by the component during the supply process and serves as a risk indicator for the component's own supply. A higher supply risk level indicates a greater likelihood of component supply issues and a greater potential threat to equipment reliability. For example, a high supply risk level for a key chip may be due to a limited number of suppliers, a high risk of supply disruption, and unstable delivery, posing a significant risk to the normal operation of the equipment.

[0124] The preset importance value of each component in a device reflects its importance to the overall performance and proper operation of the device. For example, the core processor is crucial to the device and has a high importance value; while some auxiliary small resistors, while also part of the device, have a smaller impact on the core function and therefore have a lower importance value.

[0125] The supply dependency value between each supplier and the device indicates the device's reliance on that particular supplier. A high level of dependence on a particular supplier indicates that the components supplied by that supplier are difficult to replace, and if that supplier encounters a supply issue, the device will be more severely impacted. For example, if a device has only one supplier that can provide a specific key component, the device's dependence on that supplier is high.

[0126] By taking these three factors into consideration, the embodiment of the present invention can comprehensively evaluate the reliability of the equipment in terms of component supply. For example, weighted calculation and other methods can be used to assign corresponding weights to each factor according to its importance, and then calculate the corresponding reliability value of the equipment. Therefore, the embodiment of the present invention comprehensively considers multiple key factors and comprehensively and accurately evaluates the reliability of the equipment in terms of component supply. Compared with considering only a single factor, the present invention can more accurately reflect the actual supply risks faced by the equipment and provide a more reliable basis for equipment maintenance, procurement decisions, etc.

[0127] For step S4, the preset reliability threshold mentioned can be pre-set based on factors such as the equipment's operating requirements, industry standards, and the company's own risk tolerance. When the calculated equipment reliability value is less than this threshold, it means that the equipment's reliability has dropped to a dangerous level, and failure or performance degradation may occur due to component supply problems.

[0128] Then, when the equipment reliability value is lower than the preset reliability threshold, a first risk warning can be generated, so that maintenance personnel can avoid equipment failures caused by supply problems by looking for alternative suppliers, increasing inventory, and other measures to ensure the stable operation of the power system.

[0129] In a preferred embodiment, when it is determined that the reliability value is not less than a preset reliability threshold, prompt information may be generated to indicate that the current reliability of the device is high and the device is less affected by supplier supply risks.

[0130] To sum up, the embodiment of the present invention can provide comprehensive and timely reliability management means for the equipment by setting a preset reliability threshold and generating corresponding warnings or prompts based on the equipment reliability value, thereby ensuring the stable operation of the equipment and assisting equipment maintenance personnel in optimizing resource allocation.

[0131] Illustratively, based on the first risk warning, the embodiment of the present invention can also formulate a targeted supply chain optimization strategy:

[0132] For components with high supply risks, design redundant circuits or backup modules; for key components that rely on a single supplier, introduce alternative suppliers to reduce concentration risks, and promote the improvement of independent research and development capabilities, gradually reducing dependence on low-autonomy suppliers.

[0133] In a preferred embodiment, since different suppliers correspond to different supply interruption risk values, the present invention can further judge and evaluate the supply controllability of the equipment, clarify whether there is a risk in the quantity of component supply, and evaluate the stability of the equipment in terms of component type supply based on the supply interruption risk values ​​of different suppliers, so as to realize the early discovery of potential type supply risks.

[0134] The specific process for early warning of component supply quantity risks is as follows:

[0135] Divide each supplier into several supply groups based on their supply interruption risk level. Different supply groups have different average supply interruption risk levels.

[0136] The supply group with the smallest average supply interruption risk level is selected as the first target supply group; the supply group with the largest average supply interruption risk level is selected as the second target supply group;

[0137] Determine the number of first components corresponding to the first target supply group based on all components in the first target supply group; determine the proportion of the number of first components corresponding to the first target supply group based on the number of first components and the total number of components in all supply groups;

[0138] Determine the number of second components corresponding to the second target supply group based on all components in the second target supply group; determine the proportion of the number of second components corresponding to the second target supply group based on the number of second components and the total number of components in all supply groups;

[0139] When it is determined that the proportion of the number of the first components is not greater than the proportion of the first preset number of components, a second risk warning is generated to indicate that the supply controllability of the equipment is low; or

[0140] When it is determined that the proportion of the number of the second components is greater than the proportion of the number of the second preset components, a second risk warning is generated to indicate that the supply controllability of the device is low;

[0141] Among them, the proportion of the number of the first preset components is greater than the proportion of the number of the second preset components.

[0142] Schematically, the present invention can first group suppliers according to their supply interruption risk values ​​to determine a first target supply group and a second target supply group, and then generate a second risk warning by calculating the proportion of the number of components in the two groups to evaluate the quantity supply controllability of the equipment.

[0143] It is understandable that by grouping suppliers according to their respective supply interruption risk values, supply groups with different average supply interruption risk values ​​can be formed, and suppliers can be differentiated according to their risk levels.

[0144] For example, for a component manufacturer (i.e., supplier), factors such as the intellectual property rights of its core products, controlling capital, place of registration, and legal entity nationality fundamentally determine the company's R&D management, market strategy, and legal framework. When faced with major geopolitical conflicts, the attributes of these factors will determine whether the manufacturer can maintain normal production and stable supply.

[0145] Illustratively, the present invention can derive a supplier's supply interruption risk value based on factors such as the intellectual property status of the main products, the supplier's domestic capital holdings, place of registration, and legal person nationality, thereby classifying each supplier into several groups based on their supply interruption risk values. Different groups have different autonomy and supply stability, that is, different average supply interruption risk values. For example, an embodiment of the present invention can derive four supply groups, namely, a first-category supply group, a second-category supply group, a third-category supply group, and a fourth-category supply group. It is understandable that the average supply interruption risk values ​​of different groups are different, and the order from low to high according to the average supply interruption risk values ​​is: first-category supply group < second-category supply group < third-category supply group < fourth-category supply group. The first three supplier groups are primarily determined by analyzing whether the research and development enterprise (i.e., supplier) holds less than 50% capital in China, whether it is legally registered in China, and the nationality of its legal representative. These groups are then categorized into three groups: the first, second, and third. The fourth group, which typically supplies primarily to Europe, the United States, Japan, and South Korea, operates primarily under the influence of foreign policies, facing a higher and uncontrollable risk of supply disruption. Consequently, they are assigned to the fourth category, representing the highest average supply disruption risk. Indicatively, the first group has the lowest average supply disruption risk, corresponding to the lowest supply disruption risk.

[0146] Furthermore, the present invention can select the supply group with the smallest average value of supply interruption risk from each supply group as the first target supply group, and the supply group with the largest average value as the second target supply group, so as to focus on the two extremes of the risk level, thereby analyzing the impact of the lowest risk and highest risk supply groups on equipment supply.

[0147] The corresponding number of components is determined by the number of components in the first target supply group and the second target supply group, and then compared with the total number of components in all supply groups to obtain the corresponding component number ratio (such as the first component number ratio and the second component number ratio). The first component number ratio and the second component number ratio reflect the ratio of the number of components provided by supply groups with different risk levels in the overall component supply quantity of the equipment. The first and second component number ratios are compared with the corresponding preset component number ratios to determine whether to generate a second risk warning for characterizing the low supply controllability of the equipment.

[0148] Specifically, when the proportion of the number of first components is not greater than the proportion of the first preset number of components, or the proportion of the number of second components is greater than the proportion of the second preset number of components, a second risk warning is generated.

[0149] Assume that a device's components are provided by 10 suppliers. These 10 suppliers are divided into three supply groups based on their supply disruption risk. Group 1, consisting of three suppliers, has the lowest average supply disruption risk; Group 2, consisting of two suppliers, has the highest average supply disruption risk; and Group 3, consisting of five suppliers, has a moderate supply disruption risk.

[0150] According to statistics, the total number of components across all supply groups is 1000. The three suppliers in the first target supply group (i.e., the first supply group) each produced 300 components, with their first component share being 300 ÷ 1000 = 30%. The two suppliers in the second target supply group (i.e., the second supply group) each produced 400 components, with their second component share being 400 ÷ 1000 = 40%.

[0151] Assuming the first preset component count accounts for 90% and the second preset component count accounts for 15%, a second risk warning is generated because the first component count of 30% is not greater than the first preset component count of 90%. Alternatively, if the second component count of 40% is greater than the second preset component count of 15%, a second risk warning is generated, indicating that the controllability of the device's component supply quantity is low.

[0152] Illustratively, the first preset component quantity percentage is for a low-risk supply group (e.g., the first target supply group). A larger value indicates a higher expectation for the number of components provided by the low-risk supply group. If the component quantity percentage of the first target supply group fails to meet this higher standard, it indicates that the low-risk supply group is providing insufficient components and may need to rely on high-risk supply groups, increasing supply risk, and a warning is required.

[0153] The second preset component count percentage targets the high-risk supply group (the second target supply group). A smaller value indicates a lower tolerance for the number of components provided by the high-risk supply group. Once the component count percentage of the high-risk supply group exceeds this lower threshold, it indicates that the device is overly dependent on the high-risk supply group, and there are supply controllability issues, necessitating an early warning.

[0154] By setting different preset values, the embodiments of the present invention can more accurately capture potential risks in equipment supply quantity. Different preset values ​​consider low-risk and high-risk factors respectively, avoiding misjudgments or omissions that may result from a single standard. For example, if the same preset value is used, it may not be possible to distinguish between insufficient supply in a low-risk supply group and excessive reliance in a high-risk supply group. However, setting different preset values ​​can more carefully judge risks, thereby improving the accuracy of early warnings.

[0155] In a preferred embodiment, if it is determined that the proportion of the number of the first components is greater than the proportion of the number of the first preset components, a prompt message is generated to indicate that the current reliability of the device is high and the device is less affected by the supplier supply risk; or,

[0156] When it is determined that the proportion of the number of the second components is not greater than the proportion of the number of the second preset components, prompt information is generated to indicate that the current reliability of the device is high and the device is less affected by the supplier supply risk.

[0157] Furthermore, considering the device models of components, the present invention can also generate a third risk warning by calculating the proportion of the number of device model types in the two groups, which is used to evaluate the device device type supply stability. The specific process is as follows:

[0158] Each component also corresponds to a device model;

[0159] After determining the first target supply group and the second target supply group, the method further includes:

[0160] Obtaining the number of first device models and types of all components in the first target supply group, and the number of second device models and types of all components in the second target supply group;

[0161] Determine the proportion of the first type of device corresponding to the first target supply group based on the number of the first device model types and the total number of device model types of all supply groups;

[0162] Determine the proportion of the second type of device corresponding to the second target supply group based on the number of the second device model types and the total number of device model types in all supply groups;

[0163] When it is determined that the proportion of the number of the first category is not greater than the proportion of the number of the first preset category, a third risk warning is generated to characterize low supply stability of the device component category; or

[0164] When it is determined that the proportion of the second category is greater than the proportion of the second preset category, generating a third risk warning for characterizing low supply stability of the device component category;

[0165] The proportion of the number of the first preset categories is greater than the proportion of the number of the second preset categories.

[0166] Illustratively, the embodiment of the present invention can accurately measure the impact of device types provided by supply groups with different risk levels on the overall supply stability of the equipment by calculating the proportion of device model types in the first and second target supply groups.

[0167] The first target supply group represents suppliers with low supply interruption risk. If the proportion of the first category provided by them is low, it means that the low-risk supply group provides fewer device types, and the equipment may rely on more device types from high-risk suppliers.

[0168] The second target supply group represents suppliers with high supply interruption risks. If the proportion of the second category they provide is high, it indicates that the equipment is highly dependent on high-risk suppliers in terms of component types.

[0169] Both of the above situations reflect that the supply of equipment and device types is unstable. A third risk warning can be generated to characterize the low supply stability of the equipment and device types, thereby assisting equipment maintenance personnel to promptly discover potential supply risks.

[0170] like Figure 3 As shown, based on the embodiments of the reliability risk early warning method of various equipments mentioned above, the present invention provides corresponding device embodiments;

[0171] An embodiment of the present invention provides a device reliability risk warning device, comprising: a supplier list acquisition module, a supply risk level determination module, a reliability level determination module, and a risk warning module;

[0172] The supplier list acquisition module is used to obtain a list of component suppliers for the device; wherein the component supplier list includes: a plurality of components and a plurality of different suppliers corresponding to each component; each supplier includes: supply risk characteristic data and a supply dependency value between the supplier and the device;

[0173] The supply risk level determination module is configured to determine, for each component, a supply risk level value corresponding to the component based on the number of suppliers of the component and the supply risk characteristic data of all suppliers corresponding to the component;

[0174] The reliability determination module is configured to determine a reliability value corresponding to a device based on a supply risk value of each component, a preset importance value of each component in the device, and a supply dependency value between each supplier and the device;

[0175] The risk warning module is configured to generate a first risk warning indicating that the reliability of the device is low when it is determined that the reliability value is less than a preset reliability threshold.

[0176] It should be noted that the device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without paying any creative effort.

[0177] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0178] Based on the above embodiments of the reliability risk early warning method for various devices, the present invention provides corresponding embodiments of terminal devices.

[0179] An embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a reliability risk warning method for a device described in any method embodiment of the present invention.

[0180] The terminal device may be a computing terminal device such as a desktop computer, a notebook computer, a palmtop computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0181] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0182] The memory can be used to store the computer program, and the processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0183] Based on the above embodiments of the reliability risk early warning method for various devices, the present invention provides corresponding embodiments of storage media items.

[0184] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a reliability risk warning method for a device described in any method embodiment of the present invention.

[0185] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0186] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A reliability risk early warning method for equipment, characterized in that: include: Obtain a list of component suppliers for the device; wherein the list of component suppliers includes: a plurality of components and a plurality of different suppliers corresponding to each component; each supplier includes: supply risk characteristic data and a supply dependency value between the supplier and the device; For each component, determine the supply risk level corresponding to the component based on the number of suppliers of the component and the supply risk characteristic data of all suppliers corresponding to the component; Determine the reliability value of the equipment based on the supply risk value of each component, the preset importance value of each component in the equipment, and the supply dependency value between each supplier and the equipment; When it is determined that the reliability value is less than a preset reliability threshold, a first risk warning is generated to indicate that the reliability of the device is low.

2. The reliability risk early warning method for equipment according to claim 1, characterized in that: The supply risk characteristic data includes: a supply interruption risk value, a number of delivery reports for recording historical delivery data of components, and a number of test reports for recording safety performance testing of components; different components correspond to different delivery reports and different test reports; Determining the supply risk level value corresponding to a component based on the number of component suppliers and the supply risk characteristic data of all suppliers corresponding to the component includes: Determine the concentration of the component supply chain based on the total number of component suppliers; Determine the supply risk of components based on the supply interruption risk values ​​of each supplier corresponding to the components; Determine the supply stability of components based on the corresponding delivery reports of components; Determine the technical safety reliability of components based on their corresponding test reports; The supply risk level value corresponding to the components is determined based on the supply chain concentration, supply risk, supply stability and technical security credibility.

3. The reliability risk early warning method for equipment according to claim 2, characterized in that: The delivery report includes: delivery time records, delivery quantity deviation data, quality pass rate data, and transportation loss rate data; Determining the supply stability of components based on the delivery reports corresponding to the components includes: Based on the delivery time records and delivery quantity deviation data, a target number of times the delivery quantity deviation value is within a preset deviation range within a preset delivery time window is calculated, and a component supply rate is generated based on the target number and the total number of deliveries within the preset delivery time window; Based on the delivery time records, quality pass rate data, and transportation loss rate data, the number of times within a preset delivery time window that the quality pass rate is greater than a preset pass rate threshold and the transportation loss rate is less than a preset transportation loss rate threshold is used as the number of qualified deliveries; based on the number of qualified deliveries and the total number of deliveries within the preset delivery time window, the supply pass rate of components is generated; The supply stability of components is determined based on the supply rate of components and the supply qualification rate of components.

4. The reliability risk early warning method for equipment according to claim 3, characterized in that: Also includes: Divide each supplier into several supply groups based on their supply interruption risk level. Different supply groups have different average supply interruption risk levels. The supply group with the smallest average supply interruption risk level is selected as the first target supply group; the supply group with the largest average supply interruption risk level is selected as the second target supply group; Determine the number of first components corresponding to the first target supply group based on all components in the first target supply group; determine the proportion of the number of first components corresponding to the first target supply group based on the number of first components and the total number of components in all supply groups; Determine the number of second components corresponding to the second target supply group based on all components in the second target supply group; determine the proportion of the number of second components corresponding to the second target supply group based on the number of second components and the total number of components in all supply groups; When it is determined that the proportion of the number of the first components is not greater than the proportion of the first preset number of components, a second risk warning is generated to indicate that the supply controllability of the equipment is low; or When it is determined that the proportion of the number of the second components is greater than the proportion of the number of the second preset components, a second risk warning is generated to indicate that the supply controllability of the device is low; Among them, the proportion of the number of the first preset components is greater than the proportion of the number of the second preset components.

5. The reliability risk early warning method for equipment according to claim 4, characterized in that: Each component also corresponds to a device model; After determining the first target supply group and the second target supply group, the method further includes: Obtaining the number of first device models and types of all components in the first target supply group, and the number of second device models and types of all components in the second target supply group; Determine the proportion of the first type of device corresponding to the first target supply group based on the number of the first device model types and the total number of device model types of all supply groups; Determine the proportion of the second type of device corresponding to the second target supply group based on the number of the second device model types and the total number of device model types in all supply groups; When it is determined that the proportion of the number of the first category is not greater than the proportion of the number of the first preset category, a third risk warning is generated to characterize low supply stability of the device component type; or When it is determined that the proportion of the second category is greater than the proportion of the second preset category, generating a third risk warning for characterizing low supply stability of the device component category; The proportion of the number of the first preset categories is greater than the proportion of the number of the second preset categories.

6. The reliability risk early warning method for equipment according to claim 5, characterized in that: Generating a supply dependency value between each supplier and device includes: For each supplier, count the number of target component types corresponding to the device under the supplier based on the component supplier list; Generate a target category ratio corresponding to the supplier based on the number of target component types and the total number of component types in the component supplier list; According to the proportion of the target categories, the supply dependence value between the supplier and the equipment is determined.

7. The reliability risk early warning method for equipment according to claim 1, characterized in that: Also includes: When it is determined that the reliability value is not less than a preset reliability threshold, prompt information is generated to indicate that the current reliability of the device is high and the device is less affected by the supplier supply risk.

8. A reliability risk early warning device for equipment, characterized in that: include: Supplier list acquisition module, supply risk level determination module, reliability level determination module and risk warning module; The supplier list acquisition module is used to obtain a list of component suppliers for the device; wherein the component supplier list includes: a plurality of components and a plurality of different suppliers corresponding to each component; each supplier includes: supply risk characteristic data and a supply dependency value between the supplier and the device; The supply risk level determination module is configured to determine, for each component, a supply risk level value corresponding to the component based on the number of suppliers of the component and the supply risk characteristic data of all suppliers corresponding to the component; The reliability determination module is configured to determine a reliability value corresponding to a device based on a supply risk value of each component, a preset importance value of each component in the device, and a supply dependency value between each supplier and the device; The risk warning module is configured to generate a first risk warning indicating that the reliability of the device is low when it is determined that the reliability value is less than a preset reliability threshold.

9. A terminal device, characterized in that: The device comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the device reliability risk warning method according to any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the reliability risk early warning method for a device according to any one of claims 1 to 7.

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