Vehicle-mounted broadcasting system health assessment method, system, device and medium

Abstract

The invention discloses a vehicle-mounted broadcasting system health assessment method, system and device and a medium, and the method comprises the steps: carrying out the hierarchical division of a vehicle-mounted broadcasting system, and constructing a health assessment architecture diagram; respectively constructing a judgment matrix set of each layer, and respectively carrying out consistency verification on the judgment matrix set of each layer; under the condition that the judgment matrix set of each level meets the consistency verification requirement, determining a weight vector set of the corresponding level according to the corresponding judgment matrix set; and normalizing the bottommost layer, and according to the normalized bottommost layer and the weight vector set of each layer, sequentially performing health assessment on the second layer to the kth layer from bottom to top to obtain a target health assessment value of the vehicle-mounted broadcasting system. According to the invention, comprehensive and accurate health assessment of the vehicle-mounted broadcasting system is realized, the accuracy and reliability are improved, and an effective basis is provided for maintenance decision making.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, specifically to a method, system, device, and medium for health assessment of an onboard broadcasting system. Background Technology

[0002] The onboard broadcasting system is a comprehensive multimedia information system that relies on multimedia network technology and uses a computer system as its core. It allows passengers to access timely and accurate train operation information and public media information through the train's display terminals. Through the onboard broadcasting system, passengers can promptly learn about train arrival times, transfer information, and more, improving travel efficiency and convenience. Simultaneously, the onboard broadcasting system also provides rail transit operators with real-time monitoring and emergency response capabilities, ensuring the safe operation of rail transit.

[0003] In practical applications of vehicle broadcasting systems, due to long-term operation and untimely maintenance, issues such as broadcast voice announcement malfunctions and display screen failures at operating stations or for travel directions may occur during online operation, leading to passenger complaints and impacting their travel experience. Currently, vehicle broadcasting systems are maintained only after-the-fact repairs, failing to provide on-demand maintenance based on the equipment's current health status.

[0004] Therefore, a method is needed to comprehensively and accurately assess the health status of vehicle broadcasting systems in order to promptly identify potential faults, rationally arrange maintenance and repairs, and ensure the safe and stable operation of the system. Summary of the Invention

[0005] In view of this, the present invention provides a method, system, device and medium for health assessment of vehicle broadcasting systems, in order to solve the problem that existing vehicle broadcasting systems cannot assess their health status in real time, resulting in the inability to detect potential faults in a timely manner and poor operational stability.

[0006] This invention provides a health assessment method for a vehicle-mounted broadcasting system, the method comprising:

[0007] The vehicle broadcasting system is hierarchically divided to construct a health assessment architecture diagram of the vehicle broadcasting system; wherein the health assessment architecture diagram includes k levels from bottom to top, where k≥3;

[0008] Construct the judgment matrix set for each level of the health assessment architecture diagram, and perform consistency verification on the judgment matrix set for each level.

[0009] For each level in the health assessment architecture diagram, if the corresponding set of judgment matrices meets the consistency verification requirements, the set of weight vectors for the corresponding level is determined based on the corresponding set of judgment matrices.

[0010] The bottom layer of the health assessment architecture diagram is normalized. Based on the normalized bottom layer and the weight vector set of each layer, health assessments are performed sequentially from the bottom up on layers 2 to k of the health assessment architecture diagram to obtain the target health assessment value corresponding to the vehicle broadcasting system.

[0011] Optionally, in the health assessment architecture diagram, each level includes multiple hierarchical factors;

[0012] In the health assessment architecture diagram, the set of weight vectors for each level is specifically composed of the weight vectors corresponding to all level factors in each level.

[0013] Optionally, the lowest level after normalization specifically includes the normalized value of each factor in the first level of the health assessment architecture diagram after normalization.

[0014] The health assessment is performed sequentially from layer 2 to layer k in the health assessment architecture diagram, based on the normalized bottom layer and the weight vector set of each layer, to obtain the target health assessment value corresponding to the vehicle broadcasting system, including:

[0015] In the first layer of the health assessment architecture diagram, the hierarchical factors associated with each hierarchical factor in the second layer are predetermined, resulting in a first set of associated factors corresponding to each hierarchical factor in the second layer; and based on the first set of associated factors corresponding to each hierarchical factor in the second layer, the first set of associated weight vectors and the set of normalized values ​​corresponding to each hierarchical factor in the second layer are extracted from the set of weight vectors in the first layer and the normalized values ​​obtained after normalization.

[0016] Select the a-th hierarchical factor in the second layer, and calculate the first health value corresponding to the a-th hierarchical factor in the second layer based on the corresponding first association weight vector set and normalized value vector set; wherein, the number of components in the first association weight vector set corresponding to each hierarchical factor in the second layer is the same as the number of components in the normalized value vector set.

[0017] Iterate through each level factor in the second layer and calculate the first health value corresponding to each level factor in the second layer using the same method.

[0018] In the second layer of the health assessment architecture diagram, the hierarchical factors associated with each hierarchical factor in the third layer are predetermined to obtain the second associated factor set corresponding to each hierarchical factor in the third layer; and based on the second associated factor set corresponding to each hierarchical factor in the third layer, the second associated weight vector set and health value vector set corresponding to each hierarchical factor in the third layer are extracted from the weight vector set in the second layer and the calculated first health value, respectively.

[0019] Select the b-th level factor in the third layer, and calculate the second health value corresponding to the b-th level factor in the third layer based on the corresponding second association weight vector set and health value vector set; wherein, the number of components in the second association weight vector set corresponding to each level factor in the third layer is the same as the number of components in the health value vector set.

[0020] Iterate through each level factor in the third level and calculate the second health value corresponding to each level factor in the third level using the same method.

[0021] When the number of layers in the health assessment architecture diagram is greater than 3, the health value corresponding to each level factor in the 4th to kth layers of the health assessment architecture diagram is calculated sequentially; and the target health assessment value corresponding to the PIS system is calculated based on the weight vector and health value corresponding to all level factors in the kth layer.

[0022] When the number of layers in the health assessment architecture diagram is equal to 3, the target health assessment value corresponding to the vehicle broadcasting system is calculated directly based on the weight vectors and second health values ​​corresponding to all layer factors in the 3rd layer.

[0023] Optionally, let the first association weight vector set and the normalized value vector set corresponding to the a-th level factor in the second layer be respectively... and The specific formula for calculating the first health value corresponding to the a-th level factor in the second layer is as follows:

[0024]

[0025] Wherein, H2(a) is the first health value corresponding to the a-th level factor in the second layer;

[0026] Suppose that the set of health value vectors corresponding to the b-th hierarchical factor in the third layer is obtained from the first health values ​​corresponding to all hierarchical factors in the second layer. The specific formula for calculating the second health value corresponding to the b-th level factor in the third level is as follows:

[0027]

[0028] Wherein, H3(b) is the second health value corresponding to the b-th level factor in the third layer. This is the second association weight vector set of the b-th level factor in the third layer;

[0029] The specific formula for calculating the target health assessment value corresponding to the vehicle broadcasting system is as follows:

[0030]

[0031] Wherein, HI is the target health assessment value corresponding to the vehicle broadcasting system, and W k (l) and H k (l) represents the weight vector and health value corresponding to the l-th hierarchical factor in the k-th layer, respectively, and L is the number of hierarchical factors in the k-th layer.

[0032] Optionally, the specific formula for normalizing the lowest level of the health assessment architecture diagram is as follows:

[0033]

[0034] in, The normalized value is the s-th hierarchical factor in the lowest level of the health assessment architecture diagram after normalization. This represents the initial normalized value of the s-th hierarchical factor in the lowest level of the health assessment architecture diagram before normalization. and These are the limit value and the optimal value corresponding to the s-th level factor in the bottom layer of the health assessment architecture diagram, respectively, where S is the number of sub-factors in the bottom layer of the health assessment architecture diagram.

[0035] Optionally, each level of the health assessment architecture diagram has at least one set of associated factors, specifically referring to the set of hierarchical factors in each level that are associated with a hierarchical factor in the adjacent upper level; wherein, the k-th level of the health assessment architecture diagram has only one set of associated factors associated with the vehicle broadcasting system.

[0036] The set of judgment matrices for each level of the health assessment architecture diagram is constructed, including:

[0037] Select any level in the health assessment architecture diagram, and in the selected level, select any set of associated factors that are associated with a level factor in the adjacent upper level. Compare every two elements in the selected set of associated factors to obtain the importance comparison value between every two elements.

[0038] Based on all the importance comparison values ​​obtained from the selected set of association factors, construct the judgment matrix corresponding to the set of association factors selected in the selected level;

[0039] When the selected level has more than one set of related factors, iterate through each set of related factors in the selected level and construct the judgment matrix corresponding to each set of related factors in the selected level in the same way; and obtain the set of judgment matrices corresponding to the selected level based on the judgment matrices of all the related factor sets in the selected level; when the selected level has only one set of related factors, directly determine the judgment matrix corresponding to the related factor set as the set of judgment matrices corresponding to the selected level.

[0040] By traversing each level of the health assessment architecture diagram, and using the same method, a set of judgment matrices corresponding to each level of the health assessment architecture diagram is constructed.

[0041] Optionally, let the selected set of related factors be the t-th set of related factors in the r-th layer of the health assessment architecture diagram, where r satisfies 1≤r≤k and t satisfies t≥1;

[0042] The specific expression for the judgment matrix corresponding to the selected level factor in the selected level is then constructed as follows:

[0043]

[0044] in, Let b be the judgment matrix corresponding to the t-th correlation factor set in the r-th layer of the health assessment architecture diagram. ij Let n be the element in the i-th row and j-th column of the judgment matrix corresponding to the t-th associated factor set in the r-th layer of the health assessment architecture diagram, where n is the number of factors in the t-th associated factor set in the r-th layer of the health assessment architecture diagram.

[0045] Optionally, the consistency check of the judgment matrix set at each level includes:

[0046] Select the judgment matrix corresponding to the t-th associated factor set in the r-th layer of the health assessment architecture diagram, and obtain the corresponding maximum eigenvalue;

[0047] The consistency index corresponding to the selected judgment matrix is ​​calculated based on the largest eigenvalue of the selected judgment matrix.

[0048] The random consistency index corresponding to the selected judgment matrix is ​​found in advance. Based on the found random consistency index and the calculated consistency index, the consistency ratio corresponding to the selected judgment matrix is ​​calculated.

[0049] Determine whether the consistency ratio corresponding to the selected judgment matrix is ​​less than a preset threshold. If so, determine that the selected judgment matrix meets the consistency test requirements. Otherwise, determine that the selected judgment matrix does not meet the consistency test requirements, adjust the selected judgment matrix, and re-determine whether the adjusted judgment matrix meets the consistency test requirements until the adjusted judgment matrix meets the consistency test requirements.

[0050] Traverse the judgment matrix of each associated factor set in the r-th layer of the health assessment architecture diagram, and perform consistency verification on the judgment matrix of each associated factor set in the r-th layer in the same way;

[0051] Traverse each level of the health assessment architecture diagram and perform consistency verification on the judgment matrix of each set of related factors under each level.

[0052] Optionally, for the judgment matrix corresponding to the t-th correlation factor set in the r-th layer of the health assessment architecture diagram, the specific formula for calculating the consistency index corresponding to the selected judgment matrix is ​​as follows:

[0053]

[0054] in, Let be the consistency index corresponding to the judgment matrix of the t-th association factor set in the r-th layer. Let be the largest eigenvalue corresponding to the judgment matrix of the t-th association factor set in the r-th layer;

[0055] The specific formula for calculating the consistency ratio corresponding to the selected judgment matrix is ​​as follows:

[0056]

[0057] in, Let be the consistency ratio corresponding to the judgment matrix of the t-th association factor set in the r-th layer. This is the random consistency index corresponding to the judgment matrix of the t-th correlation factor set in the r-th layer that was found in advance.

[0058] Optionally, for the r-th layer in the health assessment architecture diagram, if the set of judgment matrices at the r-th layer satisfies the consistency check requirement, determining the set of weight vectors for the corresponding layer based on the corresponding set of judgment matrices includes:

[0059] If the set of judgment matrices at the r-th layer in the health assessment architecture diagram meets the consistency verification requirements, the t-th correlation factor set at the r-th layer is selected, and the selected judgment matrix is ​​normalized column by column to obtain the normalized matrix corresponding to the t-th correlation factor set at the r-th layer.

[0060] The normalized matrix of the t-th correlation factor set in the r-th layer is summed element by element along each row to obtain the initial weight vector of the t-th correlation factor set in the r-th layer.

[0061] Normalize the initial weight vector of the t-th correlation factor set in the r-th layer to obtain the weight vector of the t-th correlation factor set in the r-th layer.

[0062] Traverse each set of associated factors in the r-th layer of the health assessment architecture diagram, and obtain the weight vector corresponding to each set of associated factors in the r-th layer using the same method; and obtain the set of weight vectors in the r-th layer of the health assessment architecture diagram based on the weight vectors of all associated factor sets in the r-th layer.

[0063] Optionally, the specific formula for normalizing the judgment matrix of the t-th association factor set in the r-th layer by column is as follows:

[0064]

[0065] Among them, b′ ij The element b in the i-th row and j-th column of the judgment matrix corresponding to the t-th association factor set in the r-th layer. ij The value after column normalization represents the element in the i-th row and j-th column of the normalized matrix of the t-th association factor set in the r-th layer;

[0066] The specific formula for summing the elements of the normalized matrix of the t-th correlation factor set in the r-th layer by row is as follows:

[0067]

[0068] Among them, w i ′ is the value of the i-th row of the normalized matrix of the t-th association factor set in the r-th layer after summing the elements by row, which represents the component of the i-th row of the initial weight vector of the t-th association factor set in the r-th layer;

[0069] The specific formula for normalizing the initial weight vector of the t-th correlation factor set in the r-th layer is as follows:

[0070]

[0071] Among them, w i w is the component of the i-th row in the initial weight vector of the t-th association factor set in the r-th layer. i The normalized value represents the component of the i-th row of the weight vector of the t-th association factor set in the r-th layer.

[0072] Optionally, when the set of judgment matrices at each level of the health assessment architecture diagram satisfies the consistency verification requirement, the method further includes:

[0073] Perform a comprehensive sorting consistency check on the judgment matrix set at all levels;

[0074] If the judgment matrix sets at all levels meet the consistency verification requirements of the comprehensive ranking, the weight vector set for the corresponding level is determined based on the judgment matrix sets at each level; otherwise, the judgment matrix sets at each level are readjusted until the readjusted judgment matrix sets at each level all meet the consistency verification requirements and the readjusted judgment matrix sets at all levels meet the consistency verification requirements of the comprehensive ranking. Then, the weight vector set for the corresponding level is determined based on the readjusted judgment matrix sets at each level.

[0075] Optionally, a comprehensive sorting consistency check is performed on the judgment matrix set at all levels, including:

[0076] Calculate the consistency ratio between layer 1 and layer k in the health assessment architecture diagram;

[0077] Determine whether the consistency ratio between the first layer and the kth layer in the health assessment architecture diagram is less than a preset threshold. If so, determine that the set of judgment matrices for all layers meets the comprehensive sorting consistency verification requirement; otherwise, determine that the set of judgment matrices for all layers does not meet the comprehensive sorting consistency verification requirement.

[0078] Optionally, the health assessment architecture diagram specifically includes an indicator layer, a device layer, and a system layer from bottom to top.

[0079] Furthermore, the present invention also provides a vehicle-mounted broadcasting system health assessment system, applied in the aforementioned vehicle-mounted broadcasting system health assessment method, comprising:

[0080] The hierarchical partitioning module is used to partition the vehicle broadcasting system into hierarchical levels and construct a health assessment architecture diagram of the vehicle broadcasting system; wherein, the health assessment architecture diagram includes k levels from bottom to top, where k≥3;

[0081] The hierarchical analysis module is used to construct the judgment matrix set for each level of the health assessment architecture diagram and perform consistency verification on the judgment matrix set for each level; it is also used to determine the weight vector set for each level of the health assessment architecture diagram, provided that the corresponding judgment matrix set meets the consistency verification requirements.

[0082] The health assessment module is used to normalize the bottom layer of the health assessment architecture diagram. Based on the normalized bottom layer and the weight vector set of each layer, the module performs health assessments on layers 2 to k of the health assessment architecture diagram from bottom to top to obtain the target health assessment value corresponding to the vehicle broadcasting system.

[0083] In addition, the present invention also provides a vehicle broadcasting system health assessment device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, it implements the method steps in the aforementioned vehicle broadcasting system health assessment method.

[0084] In addition, the present invention also provides a computer storage medium comprising: at least one instruction that, when executed, implements the method steps in the aforementioned vehicle broadcasting system health assessment method.

[0085] The beneficial effects of this invention are:

[0086] By dividing the vehicle broadcasting system into layers, a health assessment architecture diagram with k layers from bottom to top is obtained. This facilitates subsequent hierarchical analysis of each layer, decomposing the complex vehicle broadcasting system into multiple layers for layer-by-layer analysis and assessment, reducing the difficulty of assessment. After constructing this health assessment architecture diagram with k layers, the judgment matrix set for each layer can comprehensively consider the mutual influence and correlation between various factors within the vehicle broadcasting system, contributing to more accurate assessment results. After constructing the judgment matrix set for each layer, consistency verification can filter out unreasonable judgments, ensuring the consistency between elements in the judgment matrix and improving the reliability of the assessment results. Only when the judgment matrix set for each layer meets the consistency verification requirements is the corresponding weight vector set calculated. Based on the weight vector set for each layer, a layer-by-layer health assessment of the health assessment architecture diagram is performed from bottom to top, ensuring that the health status of each layer from the bottom to the top is fully considered and assessed, resulting in a final accurate target health assessment value for the vehicle broadcasting system. This assessment value can comprehensively and accurately reflect the health status of the vehicle broadcasting system, providing an effective basis for maintenance decisions. Attached Figure Description

[0087] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings:

[0088] Figure 1 A flowchart of a health assessment method for a vehicle broadcasting system according to Embodiment 1 of the present invention is shown;

[0089] Figure 2 A structural diagram of the health assessment architecture in Embodiment 1 of the present invention is shown;

[0090] Figure 3 A structural diagram of a vehicle broadcasting system health assessment system according to Embodiment 2 of the present invention is shown. Detailed Implementation

[0091] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0092] Example 1

[0093] like Figure 1 As shown, a health assessment method for a vehicle-mounted broadcasting system includes:

[0094] S1: Divide the vehicle broadcasting system into layers and construct a health assessment architecture diagram of the vehicle broadcasting system; wherein, the health assessment architecture diagram includes k layers from bottom to top, k≥3;

[0095] S2: Construct the judgment matrix set for each level of the health assessment architecture diagram, and perform consistency verification on the judgment matrix set for each level.

[0096] S3: For each level in the health assessment architecture diagram, if the corresponding judgment matrix set meets the consistency verification requirements, determine the weight vector set of the corresponding level according to the corresponding judgment matrix set.

[0097] S4: Normalize the bottom layer of the health assessment architecture diagram. Based on the normalized bottom layer and the weight vector set of each layer, perform health assessments on layers 2 to k of the health assessment architecture diagram from bottom to top to obtain the target health assessment value corresponding to the vehicle broadcasting system.

[0098] In this embodiment, by dividing the vehicle broadcasting system into layers, a health assessment architecture diagram with k layers from bottom to top is obtained. This facilitates subsequent hierarchical analysis of each layer, decomposing the complex vehicle broadcasting system into multiple layers for layer-by-layer analysis and assessment, reducing the difficulty of assessment. After constructing the health assessment architecture diagram with k layers, by building a set of judgment matrices for each layer, the mutual influence and correlation between various factors within the vehicle broadcasting system can be comprehensively considered, contributing to more accurate assessment results. After constructing the set of judgment matrices for each layer, consistency verification can filter out unreasonable judgments, ensuring the consistency between elements in the judgment matrix and improving the reliability of the assessment results. Only when the set of judgment matrices for each layer meets the consistency verification requirements are the corresponding weight vector sets calculated. Based on the weight vector sets for each layer, the health assessment architecture diagram is assessed layer by layer from bottom to top, ensuring that the health status of each layer from the bottom to the top is fully considered and assessed, resulting in the final accurate target health assessment value for the vehicle broadcasting system. This assessment value can comprehensively and accurately reflect the health status of the vehicle broadcasting system, providing an effective basis for maintenance decisions of the vehicle broadcasting system.

[0099] The method in this embodiment is applied to rail transit, including buses, subways, bullet trains, high-speed trains, and other means of transportation. These means of transportation are equipped with onboard broadcasting systems, through which passengers can obtain timely and accurate information about train operations.

[0100] The following provides a detailed description of each step in the vehicle broadcasting system health assessment method of this embodiment.

[0101] In this embodiment S1, after the vehicle broadcasting system is divided into layers, the health assessment architecture diagram constructed includes at least 3 layers from bottom to top. For ease of explanation, this embodiment takes 3 layers as an example, and the other layers are the same as the 3-layer case.

[0102] Specifically, the constructed health assessment architecture diagram includes, from bottom to top, an indicator layer, a device layer, and a system layer. The system layer, as the highest layer of the health assessment architecture diagram, represents the entire vehicle broadcasting system; its health status can be obtained through health assessment. The device layer, as the middle layer of the health assessment architecture diagram, includes multiple devices (i.e., devices are hierarchical factors of the device layer). These devices constitute the system layer and can be used to assess its health status. The indicator layer, as the lowest layer of the health assessment architecture diagram, includes multiple indicators (i.e., indicators are hierarchical factors of the indicator layer). These indicators describe the physical characteristics, operating status, and other features of the corresponding devices and can be used to assess their health status. In this embodiment, by evaluating each layer sequentially upwards from the indicator layer, the final health status assessment of the entire vehicle broadcasting system can be achieved.

[0103] In the health assessment architecture diagram described in this embodiment, each level includes multiple hierarchical factors.

[0104] Specifically, the health assessment architecture constructed in this embodiment is shown in the diagram below. Figure 2 As shown. In Figure 2 In the diagram, the broadcast system represents the system layer of the health assessment architecture, signifying the entire in-vehicle broadcast system. The passenger compartment control unit, driver's compartment control unit, dynamic map screen, broadcast control box, and emergency alarm are all located at the device layer of the health assessment architecture. Specifically, the passenger compartment control unit, driver's compartment control unit, dynamic map screen, broadcast control box, and emergency alarm are all hierarchical factors within the device layer that are associated with the broadcast system. Performance of passenger compartment control host boards (including CPU temperature and utilization), passenger compartment control host network status (including traffic and packet loss rate), passenger compartment control host power amplifier status (including voltage status and link status), passenger compartment control host power supply status (including voltage and temperature), driver's cab control host boards (including CPU utilization and temperature), driver's cab control host network performance (including traffic and packet loss rate), driver's cab control host storage status (including usage time, temperature, and remapped sectors), driver's cab control host power supply status (including temperature and voltage), dynamic map screen boards (including CPU utilization, temperature, and usage time), broadcast control box boards (including CPU utilization and temperature), and emergency alarm boards. Performance (including CPU utilization, temperature, etc.) are all indicators in the health assessment architecture diagram. Among them, the performance of the passenger compartment control host board, the network performance of the passenger compartment control host, the power amplifier status of the passenger compartment control host, and the power status of the passenger compartment control host are hierarchical factors associated with the passenger compartment control host in the indicator layer. The performance of the driver's cab control host board, the network performance of the driver's cab control host, the storage status of the driver's cab control host, and the power status of the driver's cab control host are hierarchical factors associated with the driver's cab control host in the indicator layer. The performance of the dynamic map screen board is a hierarchical factor associated with the dynamic map screen in the indicator layer. The performance of the broadcast control box board is a hierarchical factor associated with the broadcast control box in the indicator layer. The performance of the emergency alarm board is a hierarchical factor associated with the emergency alarm in the indicator layer.

[0105] In this embodiment S2, each level of the health assessment architecture diagram has at least one set of associated factors. The set of associated factors specifically refers to the set of hierarchical factors in each level that are associated with a hierarchical factor in the adjacent upper level. Among them, the k-th level of the health assessment architecture diagram has only one set of associated factors associated with the vehicle broadcasting system.

[0106] For any level in the health assessment framework diagram, each level factor is necessarily associated with a level factor in the adjacent higher level. These associated level factors constitute a set of associated factors in that level, and this set of associated factors corresponds to the set of associated level factors in the adjacent higher level. In each set of associated factors, there is at least one factor; that is, there may be one or more level factors associated with a level factor in the adjacent higher level.

[0107] It should be understood that the highest level of the health assessment architecture diagram does not have an adjacent upper level. It only has one hierarchical factor, namely the vehicle broadcasting system. This hierarchical factor can be regarded as the set of related factors corresponding to this level. This set of related factors is used for the health assessment of the entire vehicle broadcasting system.

[0108] Specifically, with Figure 2 Taking the health assessment architecture diagram shown as an example, the performance of the broadcast control box board is a hierarchical factor in the indicator layer that is associated with the broadcast control box in the device layer. Therefore, the performance of the broadcast control box board constitutes a set of associated factors in the indicator layer (this set includes only one hierarchical factor), and it is also the set of associated factors corresponding to the broadcast control box in the device layer. As another example, the performance of the guest room control host board, the network performance of the guest room control host, the power amplifier status of the guest room control host, and the power status of the guest room control host are hierarchical factors in the indicator layer that are associated with the guest room control host. Therefore, the set of these factors constitutes a set of associated factors in the indicator layer, and it is also the set of associated factors corresponding to the guest room control host in the device layer. Other hierarchical factors are similar and will not be elaborated upon here.

[0109] In this embodiment S2, a set of judgment matrices for each level of the health assessment architecture diagram is constructed, including:

[0110] S21: Select any level in the health assessment architecture diagram, and select any set of associated factors in the selected level that is associated with a level factor in the adjacent upper level. Compare every two elements in the selected set of associated factors to obtain the importance comparison value between every two elements.

[0111] S22: Based on all the importance comparison values ​​obtained from the selected set of association factors, construct the judgment matrix corresponding to the set of association factors selected in the selected level;

[0112] S23: When the selected level has more than one set of related factors, traverse each set of related factors in the selected level and construct the judgment matrix corresponding to each set of related factors in the selected level in the same way; and obtain the set of judgment matrices corresponding to the selected level based on the judgment matrices of all sets of related factors in the selected level; when the selected level has only one set of related factors, directly determine the judgment matrix corresponding to the set of related factors as the set of judgment matrices corresponding to the selected level.

[0113] S24: Traverse each level of the health assessment architecture diagram and construct a set of judgment matrices corresponding to each level of the health assessment architecture diagram in the same way.

[0114] In the hierarchical analysis of the health assessment architecture diagram, the first step is to construct a set of judgment matrices for each level. For any given level, each judgment matrix represents the relative importance of a hierarchical factor within that level relative to a related hierarchical factor in the previous level. This allows for a quantitative expression of the decision-maker's subjective judgment on the importance of factors at different levels, facilitating subsequent application in health assessments of in-vehicle broadcasting systems with complex decision-making processes. Therefore, in constructing the judgment matrix set for any level, the first step is to obtain the set of related factors within that level (i.e., the set of all hierarchical factors in that level that are related to a certain hierarchical factor in the previous level). This facilitates subsequent analysis of the importance comparison values ​​between these related hierarchical factors, forming a quantitative expression. Furthermore, it facilitates the extraction of the set of related factors associated with higher-level hierarchical factors during health assessment, thereby enabling the calculation of the corresponding health value for that higher-level hierarchical factor and achieving a bottom-up, layer-by-layer health assessment.

[0115] Once a set of related factors is selected, every two elements in that set are compared, and the importance comparison value between each pair of elements is quantitatively expressed. Combining all the importance comparison values ​​yields the judgment matrix corresponding to that set of related factors. The judgment matrices of all related factor sets at a given level constitute the judgment matrix set for that level. By constructing the judgment matrix for each related factor set at each level using this method, a set of judgment matrices corresponding to each level of the health assessment architecture diagram can be obtained.

[0116] This embodiment constructs a set of judgment matrices using the above method, which can quantify subjective judgments and facilitate subsequent determination of weight vector sets, consistency checks, and layer-by-layer health assessments in vehicle broadcasting systems.

[0117] It should be understood that when the set of related factors contains only one hierarchical factor, the importance comparison value obtained by comparing the hierarchical factor with itself constitutes the corresponding judgment matrix.

[0118] Specifically, in S21, let the selected set of related factors be the t-th set of related factors in the r-th layer of the health assessment architecture diagram, where r satisfies 1≤r≤k and t satisfies t≥1. For example, a certain set of related factors in the indicator layer is: {B1,B2,…,B...} n}(n is the number of factors in the associated factor set). In S22, the elements in this set are compared pairwise, and the comparison function is denoted as b. ij =f(B i / B j ), b ij The value of can be determined according to Table 1 below.

[0119] Table 1 Comparison of Importance Values ​​Among Hierarchical Factors

[0120] <![CDATA[B i Importance level compared to Bj]]> <![CDATA[f(B i / B j Values]]> <![CDATA[f(B j / B i Values]]> <![CDATA[B i Equally important as Bj 1 1 <![CDATA[B i Slightly more important than Bj 3 1 / 3 <![CDATA[B i It is clearly more important than Bj. 5 1 / 5 <![CDATA[B i [Strongly important to Bj]]> 7 1 / 7 <![CDATA[B i Bj is absolutely important. 9 1 / 9 <![CDATA[B i The importance of Bj falls between the above levels. 2, 4, 6, 8 in sequence 1 / 2、1 / 4、1 / 6、1 / 8

[0121] Following the above method, in S22, the specific expression for constructing the judgment matrix corresponding to the selected set of association factors in the selected layer (specifically, the t-th set of association factors in the r-th layer) is as follows:

[0122]

[0123] in, Let b be the judgment matrix corresponding to the t-th correlation factor set in the r-th layer of the health assessment architecture diagram. ij Let n be the element in the i-th row and j-th column of the judgment matrix corresponding to the t-th associated factor set in the r-th layer of the health assessment architecture diagram, where n is the number of factors in the t-th associated factor set in the r-th layer of the health assessment architecture diagram.

[0124] It should be noted that the correlation factors between the device layer and the system layer mentioned in this invention are the main devices that make up the vehicle broadcasting system and its functions. Taking the system layer as an example, the various devices included in the system layer are the correlation factors of the system layer.

[0125] The importance of a correlation factor can be determined based on the degree of influence of its current state on its parent correlation factors. For example, the correlation factor set could be: {B1, B2, ..., B...} n (where n is the number of factors in the set of related factors), the influence of each element in this set on its parent related factor is compared pairwise, and the comparison function is denoted as b. ij =f(B i / B jIf the elements being compared are equally important, the value is 1; if they are slightly more important, the value is 3 (the value of the factor being compared is its reciprocal, i.e., 1 / 3). Similarly, values ​​can be assigned to Table 1 based on their importance to construct a judgment matrix.

[0126] Additionally, it's worth noting that when a system has more than three layers, adjustments should be made to account for changes in the system. The layers need to be redefined based on the system's structure and functions. For example, it could be divided into a system layer, a subsystem layer, an equipment layer, and an indicator layer. Each layer's related factors consist of subsystems, subsystems containing major equipment, and specific indicators for that equipment.

[0127] The other level factors in S23 are constructed using the same method to obtain the corresponding judgment matrix, which will not be listed here.

[0128] In this embodiment S2, consistency checks are performed on the judgment matrix set at each level, including:

[0129] S25: Select the judgment matrix corresponding to the t-th associated factor set in the r-th layer of the health assessment architecture diagram, and obtain the corresponding maximum eigenvalue;

[0130] S26: Calculate the consistency index corresponding to the selected judgment matrix based on the largest eigenvalue of the selected judgment matrix;

[0131] S27: Pre-find the random consistency index corresponding to the selected judgment matrix, and calculate the consistency ratio corresponding to the selected judgment matrix based on the found random consistency index and the calculated consistency index.

[0132] S28: Determine whether the consistency ratio corresponding to the selected judgment matrix is ​​less than the preset threshold. If so, determine that the selected judgment matrix meets the consistency test requirements. Otherwise, determine that the selected judgment matrix does not meet the consistency test requirements, adjust the selected judgment matrix, and re-determine whether the adjusted judgment matrix meets the consistency test requirements until the adjusted judgment matrix meets the consistency test requirements.

[0133] S29: Traverse the judgment matrix of each associated factor set in the r-th layer of the health assessment architecture diagram, and perform consistency verification on the judgment matrix of each associated factor set in the r-th layer in the same way;

[0134] S210: Traverse each level of the health assessment architecture diagram and perform consistency verification on the judgment matrix of each set of related factors under each level.

[0135] Because the subjective factors of decision-makers were incorporated into the construction of the aforementioned judgment matrix, and given the complexity of actual in-vehicle broadcasting systems and the limitations of human cognitive abilities, these subjective factors can be biased. Therefore, in the process of implementing the health assessment of in-vehicle broadcasting systems, after constructing the judgment matrix for each set of related factors at each level, the consistency verification of each single-level judgment matrix is ​​performed through the above steps. This ensures that the judgment matrices are logically consistent, reduces bias caused by subjective judgments, and also ensures that the relationships between the elements in the judgment matrix are coordinated and without contradictions. This ensures that the judgment matrix is ​​reasonable and reliable, contributing to the reliability and stability of subsequent health assessments.

[0136] In S25, since the largest eigenvalue is an important attribute of the judgment matrix and can reflect its consistency to a certain extent, solving the characteristic equation to find the largest eigenvalue provides basic data for subsequent consistency checks. In S26, the consistency index can also be used to measure the consistency of the judgment matrix. By calculating the consistency index, inconsistencies in the judgment matrix can be detected in a timely manner, and corresponding adjustment measures can be taken. In S27, the random consistency index is an average consistency index value that depends on the order of the judgment matrix. It can be used to assess whether the consistency level of the judgment matrix is ​​within an acceptable range. Based on the found random consistency index and the consistency index calculated in S27, the consistency ratio is calculated, which can accurately assess whether the consistency level of the judgment matrix meets the requirements.

[0137] Specifically, the judgment matrix corresponding to the t-th correlation factor set in the r-th layer of the health assessment architecture diagram is also selected. In S25, the characteristic equation of this matrix is:

[0138]

[0139] Where E is the identity matrix. Let be the largest eigenvalue corresponding to the judgment matrix of the t-th association factor set in the r-th layer.

[0140] By solving the above characteristic equation, the maximum eigenvalue corresponding to the t-th correlation factor set in the r-th layer can be calculated.

[0141] Specifically, in S26, based on the calculated maximum eigenvalue... The specific formula for calculating the consistency index corresponding to the selected judgment matrix is ​​as follows:

[0142]

[0143] in, Let be the consistency index corresponding to the judgment matrix of the t-th association factor set in the r-th layer. Let be the largest eigenvalue corresponding to the judgment matrix of the t-th association factor set in the r-th layer.

[0144] Specifically, in S27, the random consistency index corresponding to the judgment matrix of the t-th association factor set in the r-th layer can be obtained through Table 2 below.

[0145] Table 2 Random Consistency Indices for Different Matrix Orders

[0146] n 1 2 3 4 5 6 7 8 9 10 11 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.52

[0147] Specifically, in S27, the formula for calculating the consistency ratio corresponding to the selected judgment matrix is ​​as follows:

[0148]

[0149] in, Let be the consistency ratio corresponding to the judgment matrix of the t-th association factor set in the r-th layer. The random consistency index is the judgment matrix corresponding to the t-th association factor set of the r-th layer found in advance;

[0150] The consistency index calculated in S27 And the random consistency index obtained from querying Table 2 Substituting into the above formula, we can obtain the consistency ratio corresponding to the judgment matrix of the t-th association factor set in the r-th layer.

[0151] Specifically, in S28, the preset threshold can be preset and adjusted according to the actual situation. For example, in this embodiment, the preset threshold is 0.1, then when the consistency ratio corresponding to the judgment matrix of the t-th association factor set in the r-th layer is... If the condition is met, the judgment matrix satisfies the consistency check requirement; otherwise, it does not. The judgment matrix is ​​then adjusted, and steps S26–S29 are repeated until the consistency ratio of the adjusted judgment matrix is ​​reached.

[0152] The calculation process for the consistency ratio of other judgment matrices and the comparison process with the preset threshold are the same as those described in S25 to S28 above, and will not be listed here again.

[0153] In this embodiment S3, for the r-th layer in the health assessment architecture diagram, if the set of judgment matrices at the r-th layer meets the consistency verification requirements, determining the set of weight vectors for the corresponding layer based on the corresponding set of judgment matrices includes:

[0154] S31: When the set of judgment matrices at the r-th layer in the health assessment architecture diagram meets the consistency verification requirements, select the t-th correlation factor set at the r-th layer, normalize the selected judgment matrix by column, and obtain the normalized matrix corresponding to the t-th correlation factor set at the r-th layer.

[0155] S32: Add the elements of the normalized matrix of the t-th correlation factor set in the r-th layer row by row to obtain the initial weight vector of the t-th correlation factor set in the r-th layer.

[0156] S33: Normalize the initial weight vector of the t-th correlation factor set in the r-th layer to obtain the weight vector of the t-th correlation factor set in the r-th layer.

[0157] S34: Traverse each set of related factors in the r-th layer of the health assessment architecture diagram, and obtain the weight vector corresponding to each set of related factors in the r-th layer in the same way; and obtain the set of weight vectors of the r-th layer in the health assessment architecture diagram based on the weight vectors of all related factor sets in the r-th layer.

[0158] In calculating the weight vector, the judgment matrix is ​​first normalized column-wise to eliminate dimensional differences between columns, allowing elements in the judgment matrix to be compared and calculated on the same scale, reducing calculation errors and improving the accuracy and reliability of subsequent calculations. Then, the normalized matrix is ​​summed row-wise, resulting in an initial weight vector that intuitively reflects the importance of each row in the overall decision, providing foundational data for the final weight vector set. Finally, the initial weight vector is normalized again to ensure the sum of its elements is 1, meeting the basic requirements for weight allocation. This allows the weights of each objective to be compared and evaluated on the same scale, providing a scientific basis for the final decision.

[0159] Specifically, in S31, the formula for normalizing the judgment matrix of the t-th association factor set in the r-th layer by column is as follows:

[0160]

[0161] Among them, b′ ij The element b in the i-th row and j-th column of the judgment matrix corresponding to the t-th association factor set in the r-th layer. ij The value after column normalization represents the element in the i-th row and j-th column of the normalized matrix of the t-th association factor set in the r-th layer;

[0162] In S32, the specific formula for summing the elements of the normalized matrix of the t-th correlation factor set in the r-th layer by row is as follows:

[0163]

[0164] Among them, w i ′ is the value of the i-th row of the normalized matrix of the t-th association factor set in the r-th layer after summing the elements by row, which represents the component of the i-th row of the initial weight vector of the t-th association factor set in the r-th layer;

[0165] In S33, the specific formula for normalizing the initial weight vector of the t-th correlation factor set in the r-th layer is as follows:

[0166]

[0167] Among them, w i w is the component of the i-th row in the initial weight vector of the t-th association factor set in the r-th layer. i The normalized value represents the component of the i-th row of the weight vector of the t-th association factor set in the r-th layer.

[0168] The calculation process of the weight vectors of other related factor sets in S34 is similar to that described in S31 to S33 above, and will not be repeated here.

[0169] In this embodiment S3, when the set of judgment matrices at each level of the health assessment architecture diagram meets the consistency verification requirements, the method further includes:

[0170] S51: Perform a comprehensive sorting consistency check on the judgment matrix set at all levels;

[0171] S52: If the judgment matrix sets at all levels meet the consistency verification requirements of the comprehensive ranking, determine the weight vector set of the corresponding level based on the judgment matrix sets at each level; otherwise, readjust the judgment matrix sets at each level until the readjusted judgment matrix sets at each level all meet the consistency verification requirements and the readjusted judgment matrix sets at all levels meet the consistency verification requirements of the comprehensive ranking. Then, determine the weight vector set of the corresponding level based on the readjusted judgment matrix sets at each level.

[0172] Although the individual decision matrices pass the consistency check, the inconsistencies of the individual decision matrices may accumulate after being integrated into the target, leading to serious biases in the final analysis. Therefore, the consistency of the overall integration at each level should also be checked. That is, a consistency check should be performed on the integrated sorting of the decision matrix sets at all levels. On the basis that the decision matrix sets at each individual level pass the consistency check, the decision matrix sets at all levels should also pass the integrated sorting consistency check. The decision matrix set obtained under this condition is used as the data basis for the final weight vector set, which can further ensure the accuracy and reliability of the final analysis structure.

[0173] Preferably, S51 includes:

[0174] S511: Calculate the consistency ratio between layer 1 and layer k in the health assessment architecture diagram;

[0175] S512: Determine whether the consistency ratio between the first layer and the kth layer in the health assessment architecture diagram is less than a preset threshold. If so, determine that the judgment matrix set of all layers meets the comprehensive sorting consistency verification requirement; otherwise, determine that the judgment matrix set of all layers does not meet the comprehensive sorting consistency verification requirement.

[0176] The consistency ratio between layer 1 and layer k (i.e., the lowest layer to the highest layer) is an important indicator for measuring the logical consistency of the entire hierarchical structure. It reflects whether the relative importance of factors at each level is coordinated and consistent, from the specific methods or measures at the lowest level to the overall goal at the highest level. If this consistency ratio is less than a preset threshold, it indicates that the entire hierarchical structure is logically consistent and there are no contradictions or unreasonable situations. This embodiment directly calculates the consistency ratio between layer 1 and layer k and determines whether the consistency ratio is less than a preset threshold to achieve comprehensive ranking consistency verification. The method is simple and effective, and can effectively ensure the reliability and scientific nature of the entire hierarchical analysis.

[0177] Specifically, in steps S25 to S27 above, the consistency index of the t-th associated factor set at level r represents the consistency index of all hierarchical factors in that associated factor set relative to its parent level (i.e., level r+1). Correspondingly, all consistency indices at that level represent the consistency index of that level relative to its parent level (i.e., level r+1). The same applies to other levels.

[0178] Therefore, steps S25 to S27 can sequentially calculate the consistency index of layer 1 relative to layer 2, layer 2 relative to layer 3, ..., and the consistency index of layer (k-1) relative to layer k. Similarly, the random consistency index of layer 1 relative to layer 2, layer 2 relative to layer 3, ..., and the random consistency index of layer (k-1) relative to layer k can be sequentially retrieved. By first synthesizing these consistency indices and random consistency indices level by level, the consistency index and random consistency index between layer 1 and layer k can be obtained. Then, based on the synthesized consistency index and random consistency index between layer 1 and layer k, the consistency ratio between layer 1 and layer k can be calculated.

[0179] Specifically, in S511, the consistency index between layer 1 and layer k is first synthesized, and its calculation formula is as follows:

[0180] CI 1,k =∑CI 1,2 ·w 2 +∑CI 2,3 ·w3 +...+∑CI k-2,k-1 ·w k-1 ;

[0181] Among them, CI 1,k CI is a consistency metric between layer 1 and layer k. 1,2 CI 2,3 …and CI k-2,k-1 These are the consistency metrics for layer 1 relative to layer 2, layer 2 relative to layer 3, ..., and layer (k-2) relative to layer (k-1), respectively. 2 w 3 …and w k-1 The weight vector sets for the second layer, the third layer, ..., and the (k-1)th layer are calculated respectively in the aforementioned steps.

[0182] The random consistency index between the first layer and the kth layer is then synthesized, and its calculation formula is as follows:

[0183] RI 1,k =∑RI 1,2 ·w 2 +∑RI 2,3 ·w 3 +...+∑RI k-2,k-1 ·w k-1 ;

[0184] Among them, RI 1,k RI is the consistency index between layer 1 and layer k. 1,2 RI 2,3 …and RI k-2,k-1 These are the random consistency indices for layer 1 relative to layer 2, layer 2 relative to layer 3, ..., and layer (k-2) relative to layer (k-1).

[0185] Finally, the formula for calculating the consistency ratio between layer 1 and layer k is:

[0186]

[0187] Among them, CR 1,k This represents the consistency ratio between layer 1 and layer k.

[0188] against Figure 2 For the three-layer health assessment architecture diagram of system layer, device layer, and indicator layer shown, the formula for calculating the consistency ratio of indicator layer relative to system layer is:

[0189]

[0190] Among them, CR 1,3CI represents the consistency ratio between Layer 1 and Layer 3, specifically the consistency ratio of the indicator layer relative to the system layer. 1,3 This refers to the consistency index between Layer 1 and Layer 3, specifically representing the consistency index of the index layer relative to the system layer, and the consistency index CI of the index layer relative to the device layer. 1,2 The set of weight vectors w between the device layer and the system layer 2 Obtained by synthesis; RI 1,3 This refers to the random consistency index between Layer 1 and Layer 3, specifically representing the random consistency index of the index layer relative to the system layer, and derived from the random consistency index RI of the index layer relative to the device layer. 1,2 The set of weight vectors w between the device layer and the system layer 2 It is synthesized.

[0191] The calculation of the consistency ratio between layer 1 and layer k in the health assessment architecture diagram for other layer structures is similar and will not be listed here.

[0192] Specifically, in S512, after calculating the consistency ratio between the first layer and the kth layer, the consistency ratio is compared with a preset threshold to achieve comprehensive sorting consistency verification. The preset threshold in this process can be the same as or different from the preset threshold used in the consistency verification in S28. In this embodiment, the same threshold is used, i.e., 0.1.

[0193] Once the set of judgment matrices at each level satisfies the consistency check requirement and the set of judgment matrices at all levels satisfies the overall ranking consistency check requirement, the set of weight vectors under this condition will be used as the set of weight vectors for health assessment in the subsequent step S4.

[0194] Specifically, in the health assessment architecture diagram, the set of weight vectors for each level is composed of the weight vectors corresponding to all level factors in each level.

[0195] In this embodiment S4, the lowest level after normalization specifically includes the normalized value of each level factor in the first layer of the health assessment architecture diagram after normalization.

[0196] The specific formula for normalizing the lowest level of the health assessment architecture diagram is as follows:

[0197]

[0198] in, The normalized value is the s-th hierarchical factor in the lowest level of the health assessment architecture diagram after normalization. This represents the initial normalized value of the s-th hierarchical factor in the lowest level of the health assessment architecture diagram before normalization. and These are the limit value and the optimal value corresponding to the s-th level factor in the bottom layer of the health assessment architecture diagram, respectively, where S is the number of sub-factors in the bottom layer of the health assessment architecture diagram.

[0199] Through the above normalization, data of different types and dimensions can be uniformly converted into dimensionless data, which facilitates subsequent data analysis and calculation.

[0200] for Figure 2 The diagram shows a three-layer health assessment architecture consisting of a system layer, a device layer, and an indicator layer. The bottom layer is the indicator layer. Each factor (i.e., each indicator) in the indicator layer has a corresponding indicator value obtained from detection. After normalization, the indicator value of each indicator in this indicator layer is obtained as a corresponding normalized value.

[0201] In this embodiment S4, based on the normalized bottom layer and the weight vector set of each layer, health assessments are performed sequentially from the bottom up on layers 2 to k in the health assessment architecture diagram to obtain the target health assessment value corresponding to the vehicle broadcasting system, including:

[0202] S41: In the first layer of the health assessment architecture diagram, the hierarchical factors associated with each hierarchical factor in the second layer are predetermined to obtain the first associated factor set corresponding to each hierarchical factor in the second layer; and based on the first associated factor set corresponding to each hierarchical factor in the second layer, the first associated weight vector set and the normalized value vector set corresponding to each hierarchical factor in the second layer are extracted from the weight vector set in the first layer and the normalized value obtained after normalization.

[0203] S42: Select the a-th level factor in the second layer, and calculate the first health value corresponding to the a-th level factor in the second layer based on the corresponding first association weight vector set and normalized value vector set; wherein, the number of components in the first association weight vector set corresponding to each level factor in the second layer is the same as the number of components in the normalized value vector set.

[0204] S43: Traverse each level factor in the second level and calculate the first health value corresponding to each level factor in the second level in the same way;

[0205] S44: In the second layer of the health assessment architecture diagram, the hierarchical factors associated with each hierarchical factor in the third layer are predetermined to obtain the second associated factor set corresponding to each hierarchical factor in the third layer; and based on the second associated factor set corresponding to each hierarchical factor in the third layer, the second associated weight vector set and health value vector set corresponding to each hierarchical factor in the third layer are extracted from the weight vector set in the second layer and the calculated first health value, respectively.

[0206] It should be noted that the third layer in the health assessment architecture diagram is the system layer (i.e., a hierarchical factor). The hierarchical factors associated with each hierarchical factor in the third and second layers are the devices contained in the system layer.

[0207] S45: Select the b-th level factor in the third layer, and calculate the second health value corresponding to the b-th level factor in the third layer according to the corresponding second association weight vector set and health value vector set; wherein, the number of components in the second association weight vector set corresponding to each level factor in the third layer is the same as the number of components in the health value vector set.

[0208] S46: Traverse each level factor in the 3rd level and calculate the second health value corresponding to each level factor in the 3rd level using the same method;

[0209] S47A: When the number of layers in the health assessment architecture diagram is greater than 3, the health value corresponding to each layer factor in the 4th to kth layers of the health assessment architecture diagram is calculated sequentially; and the target health assessment value corresponding to the vehicle broadcasting system is calculated based on the weight vector and health value corresponding to all layer factors in the kth layer.

[0210] S47B: When the number of layers in the health assessment architecture diagram is equal to 3, the target health assessment value corresponding to the vehicle broadcasting system is calculated directly based on the weight vectors and second health values ​​corresponding to all layer factors in the 3rd layer.

[0211] In the aforementioned layer-by-layer health assessment, since each higher level is composed of its corresponding lower level and is related to it, the first health value corresponding to the second level is directly calculated based on the normalized value vector and corresponding weight vector of the first level. The health assessment of the second level can be achieved by further refining the normalized value vector of the first level. Then, in the health assessment of subsequent levels, the health value is calculated based on the health value and weight vector of the previous level, which can accumulate the assessment results of the previous level to form a more comprehensive health assessment system, thereby more accurately reflecting the overall health status. In addition, the above-mentioned layer-by-layer health assessment method is also convenient for dynamic monitoring and tracking. When the health status of each level changes, the corresponding assessment results will also change, and the overall health assessment will also change. It can reflect the changing trend of the health status of the vehicle broadcasting system and is more in line with the actual situation of the vehicle broadcasting system.

[0212] Specifically, in S41, let the first associated weight vector set (i.e., the set of weight vectors of all hierarchical factors associated with this hierarchical factor in the second layer, such as a device in the device layer) and the normalized value vector set corresponding to the a-th hierarchical factor in the second layer be respectively... and The specific formula for calculating the first health value corresponding to the a-th level factor in the second layer in S42 is as follows:

[0213]

[0214] H2(a) is the first health value corresponding to the a-th level factor in the second layer.

[0215] It should be understood that there is at least one weight vector in the first associated weight vector set, and correspondingly, there is also at least one component in the normalized value vector set, which corresponds one-to-one with the weight vector in the first associated weight vector set. When calculating the first health value, the components in the first associated weight vector set and the components in the normalized value vector set are multiplied one-to-one, and then summed.

[0216] In S43, the first health value of other level factors is calculated in the same way, which will not be repeated here.

[0217] In S44, let the set of health value vectors for the b-th hierarchical factor (e.g., a subsystem in the system layer) in the third layer be obtained from the first health values ​​corresponding to all hierarchical factors in the second layer. The specific formula for calculating the second health value corresponding to the b-th level factor in the third layer in S45 is as follows:

[0218]

[0219] Wherein, H3(b) is the second health value corresponding to the b-th level factor in the third layer. This is the second association weight vector set of the b-th level factor in the third layer.

[0220] Similarly, the weight vector in the second associated weight vector set and the component in the health value vector set are at least one and correspond one-to-one. When calculating the second health value, the components in the second associated weight vector set and the components in the health value vector set are multiplied one-to-one and then summed.

[0221] In S46, the second health value of other level factors is calculated in the same way, which will not be repeated here.

[0222] In S47A, if the number of layers in the health assessment architecture diagram exceeds 3, the health value corresponding to each level factor in layers 4 to k is calculated in a similar way to S44 to S46 and used for the calculation of subsequent target health assessment values.

[0223] In S47B, if the health assessment architecture diagram has 3 layers, the second health value corresponding to all hierarchical factors in the 3rd layer is directly used to calculate the subsequent target health assessment value.

[0224] Specifically, the formula for calculating the target health assessment value corresponding to the vehicle broadcasting system is as follows:

[0225]

[0226] Wherein, HI is the target health assessment value corresponding to the vehicle broadcasting system, and W k (l) and H k (l) represents the weight vector and health value corresponding to the l-th hierarchical factor in the k-th layer, respectively, and L is the number of hierarchical factors in the k-th layer.

[0227] for Figure 2 The health assessment architecture diagram shown has k specifically set to 3. The second layer (i.e., the device layer) includes 5 hierarchical factors, and 5 first health values ​​are calculated in S41. The third layer (i.e., the system layer) has L specifically set to 1 (indicating that the system layer only includes the broadcast system hierarchical factor), and 1 second health value is calculated in S44. Since the entire architecture has only 3 layers, and the highest layer has only one hierarchical factor, its corresponding weight vector must be 1. Therefore, in S47B, the second health value of the third layer can be directly used as the final target health assessment value.

[0228] Preferably, after S4 in this embodiment, the method further includes:

[0229] S6: Rating the health status of the vehicle broadcasting system based on the target health assessment value corresponding to the vehicle broadcasting system.

[0230] By rating the health status, decision-makers can more intuitively and efficiently grasp the health status of the vehicle broadcasting system, which facilitates more accurate maintenance decisions.

[0231] Specifically, the correspondence between target health assessment values ​​and health status ratings is shown in Table 3 below.

[0232] Table 3. Correspondence between target health assessment values ​​and health status ratings

[0233]

[0234] Example 2

[0235] like Figure 3 As shown, a vehicle-mounted broadcasting system health assessment system, applied in the vehicle-mounted broadcasting system health assessment method of Embodiment 1, includes:

[0236] The hierarchical partitioning module is used to partition the vehicle broadcasting system into hierarchical levels and construct a health assessment architecture diagram of the vehicle broadcasting system; wherein, the health assessment architecture diagram includes k levels from bottom to top, where k≥3;

[0237] The hierarchical analysis module is used to construct the judgment matrix set for each level of the health assessment architecture diagram and perform consistency verification on the judgment matrix set for each level; it is also used to determine the weight vector set for each level of the health assessment architecture diagram, provided that the corresponding judgment matrix set meets the consistency verification requirements.

[0238] The health assessment module is used to normalize the bottom layer of the health assessment architecture diagram. Based on the normalized bottom layer and the weight vector set of each layer, the module performs health assessments on layers 2 to k of the health assessment architecture diagram from bottom to top to obtain the target health assessment value corresponding to the vehicle broadcasting system.

[0239] In this embodiment, the vehicle broadcasting system is hierarchically divided using a hierarchical partitioning module, resulting in a health assessment architecture diagram with k levels from bottom to top. This facilitates subsequent hierarchical analysis of each level, decomposing the complex vehicle broadcasting system into multiple levels for easier layer-by-layer analysis and evaluation, thus reducing the assessment difficulty. After constructing this health assessment architecture diagram with k levels, a set of judgment matrices for each level is built using the hierarchical analysis module. This comprehensively considers the mutual influence and correlation between various factors within the vehicle broadcasting system, contributing to more accurate assessment results. After constructing the set of judgment matrices for each level, a consistency check is performed using the hierarchical analysis module to screen the results. To eliminate unreasonable judgments, the consistency between elements in the judgment matrix is ​​ensured to improve the reliability of the evaluation results. Only when the judgment matrix sets at each level meet the consistency verification requirements are the corresponding weight vector sets calculated. Then, the health assessment module performs a layer-by-layer health assessment of the health assessment architecture diagram from bottom to top based on the weight vector sets at each level. This ensures that the health status of each level from the bottom to the top is fully considered and assessed, resulting in the final accurate target health assessment value for the vehicle broadcasting system. This assessment value can comprehensively and accurately reflect the health status of the vehicle broadcasting system, providing an effective basis for maintenance decisions.

[0240] The functions of each module in the vehicle broadcasting system health assessment system described in this embodiment correspond to the steps of the vehicle broadcasting system health assessment method in Embodiment 1. For details not covered in this embodiment, please refer to Embodiment 1 and... Figures 1 to 2 The specific details will not be repeated here.

[0241] Example 3

[0242] A vehicle-mounted broadcasting system health assessment device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, it implements the method steps of the vehicle-mounted broadcasting system health assessment method of Embodiment 1.

[0243] By using a computer program stored in memory and running on a processor, based on the analytic hierarchy process (AHP), the complex vehicle broadcasting system is decomposed into multiple layers for layer-by-layer analysis and evaluation. This reduces the difficulty of the evaluation, allows for comprehensive consideration of multiple factors, and enables a complete and accurate health assessment of the vehicle broadcasting system. This improves the accuracy and reliability of the assessment and provides an effective basis for vehicle broadcasting system maintenance decisions.

[0244] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting all parts of the computer device through various interfaces and lines.

[0245] Memory can be used to store computer programs and / or models. The processor performs various functions of the computer device by running or executing the computer programs and / or models stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0246] It should be understood that each block of a flowchart and / or block diagram, and combinations of blocks in a flowchart and / or block diagram, can be implemented by a computer program. These computer programs can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that instructions executable by the processor of the computer or other programmable data processing device generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0247] These computer programs may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0248] These computer programs may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0249] This embodiment also provides a computer storage medium, which includes at least one instruction that, when executed, implements the method steps in the vehicle broadcasting system health assessment method of Embodiment 1.

[0250] By executing a computer storage medium containing at least one instruction, and based on the analytic hierarchy process (AHP), the complex vehicle broadcasting system is decomposed into multiple layers for layer-by-layer analysis and evaluation. This reduces the difficulty of the evaluation, allows for comprehensive consideration of multiple factors, and enables a complete and accurate health assessment of the vehicle broadcasting system. This improves the accuracy and reliability of the assessment and provides an effective basis for vehicle broadcasting system maintenance decisions.

[0251] Similarly, for details not covered in Embodiment 3, please refer to Embodiments 1 and 2. Figures 1 to 2 The specific details will not be repeated here.

[0252] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for health assessment of a vehicle-mounted broadcasting system, characterized in that, The method includes: The vehicle broadcasting system is hierarchically divided to construct a health assessment architecture diagram of the vehicle broadcasting system; wherein the health assessment architecture diagram includes k levels from bottom to top, where k≥3; Construct the judgment matrix set for each level of the health assessment architecture diagram, and perform consistency verification on the judgment matrix set for each level. For each level in the health assessment architecture diagram, if the corresponding set of judgment matrices meets the consistency verification requirements, the set of weight vectors for the corresponding level is determined based on the corresponding set of judgment matrices. The bottom layer of the health assessment architecture diagram is normalized. Based on the normalized bottom layer and the weight vector set of each layer, health assessments are performed sequentially from the bottom up on layers 2 to k of the health assessment architecture diagram to obtain the target health assessment value corresponding to the vehicle broadcasting system.

2. The method according to claim 1, characterized in that, In the aforementioned health assessment architecture diagram, each level includes multiple hierarchical factors; In the health assessment architecture diagram, the set of weight vectors for each level is specifically composed of the weight vectors corresponding to all level factors in each level.

3. The method according to claim 2, characterized in that, The lowest level after normalization specifically includes the normalized values ​​of each factor in the first layer of the health assessment architecture diagram after normalization. The health assessment is performed sequentially from layer 2 to layer k in the health assessment architecture diagram, based on the normalized bottom layer and the weight vector set of each layer, to obtain the target health assessment value corresponding to the vehicle broadcasting system, including: In the first layer of the health assessment architecture diagram, the hierarchical factors associated with each hierarchical factor in the second layer are predetermined, resulting in a first set of associated factors corresponding to each hierarchical factor in the second layer; and based on the first set of associated factors corresponding to each hierarchical factor in the second layer, the first set of associated weight vectors and the set of normalized values ​​corresponding to each hierarchical factor in the second layer are extracted from the set of weight vectors in the first layer and the normalized values ​​obtained after normalization. Select the a-th hierarchical factor in the second layer, and calculate the first health value corresponding to the a-th hierarchical factor in the second layer based on the corresponding first association weight vector set and normalized value vector set; wherein, the number of components in the first association weight vector set corresponding to each hierarchical factor in the second layer is the same as the number of components in the normalized value vector set. Iterate through each level factor in the second layer and calculate the first health value corresponding to each level factor in the second layer using the same method. In the second layer of the health assessment architecture diagram, the hierarchical factors associated with each hierarchical factor in the third layer are predetermined to obtain the second associated factor set corresponding to each hierarchical factor in the third layer; and based on the second associated factor set corresponding to each hierarchical factor in the third layer, the second associated weight vector set and health value vector set corresponding to each hierarchical factor in the third layer are extracted from the weight vector set in the second layer and the calculated first health value, respectively. Select the b-th level factor in the third layer, and calculate the second health value corresponding to the b-th level factor in the third layer based on the corresponding second association weight vector set and health value vector set; wherein, the number of components in the second association weight vector set corresponding to each level factor in the third layer is the same as the number of components in the health value vector set. Iterate through each level factor in the third level and calculate the second health value corresponding to each level factor in the third level using the same method. When the number of layers in the health assessment architecture diagram is greater than 3, the health value corresponding to each layer factor in the 4th to kth layers of the health assessment architecture diagram is calculated sequentially; and the target health assessment value corresponding to the vehicle broadcasting system is calculated based on the weight vector and health value corresponding to all layer factors in the kth layer. When the number of layers in the health assessment architecture diagram is equal to 3, the target health assessment value corresponding to the vehicle broadcasting system is calculated directly based on the weight vectors and second health values ​​corresponding to all layer factors in the 3rd layer.

4. The method according to claim 3, characterized in that, Let the first association weight vector set and the normalized value vector set corresponding to the a-th level factor in the second layer be respectively... and The specific formula for calculating the first health value corresponding to the a-th level factor in the second layer is as follows: Wherein, H2(a) is the first health value corresponding to the a-th level factor in the second layer; Let H be the set of health value vectors corresponding to the b-th hierarchical factor in the third layer, obtained from the first health values ​​corresponding to all hierarchical factors in the second layer. 3 The specific formula for calculating the second health value corresponding to the b-th level factor in the third level is as follows: H3=W 3 ·H 3 ; Where H3(b) is the second health value corresponding to the b-th level factor in the third layer, and W 3 This is the second association weight vector set of the b-th level factor in the third layer; The specific formula for calculating the target health assessment value corresponding to the vehicle broadcasting system is as follows: Wherein, HI is the target health assessment value corresponding to the vehicle broadcasting system, and W k (l) and H k (l) represents the weight vector and health value corresponding to the l-th hierarchical factor in the k-th layer, respectively, and L is the number of hierarchical factors in the k-th layer.

5. The method according to claim 3, characterized in that, The specific formula for normalizing the lowest level of the health assessment architecture diagram is as follows: in, The normalized value is the s-th hierarchical factor in the lowest level of the health assessment architecture diagram after normalization. This represents the initial normalized value of the s-th hierarchical factor in the lowest level of the health assessment architecture diagram before normalization. and These are the limit value and the optimal value corresponding to the s-th level factor in the bottom layer of the health assessment architecture diagram, respectively, where S is the number of sub-factors in the bottom layer of the health assessment architecture diagram.

6. The method according to claim 3, characterized in that, Each level of the health assessment architecture diagram has at least one set of associated factors. Specifically, the set of associated factors refers to the set of hierarchical factors in each level that are associated with a hierarchical factor in the adjacent upper level. Among them, the k-th level of the health assessment architecture diagram has only one set of associated factors associated with the vehicle broadcasting system. The set of judgment matrices for each level of the health assessment architecture diagram is constructed, including: Select any level in the health assessment architecture diagram, and in the selected level, select any set of associated factors that are associated with a level factor in the adjacent upper level. Compare every two elements in the selected set of associated factors to obtain the importance comparison value between every two elements. Based on all the importance comparison values ​​obtained from the selected set of association factors, construct the judgment matrix corresponding to the set of association factors selected in the selected level; When the selected level has more than one set of related factors, iterate through each set of related factors in the selected level and construct the judgment matrix corresponding to each set of related factors in the selected level in the same way; and obtain the set of judgment matrices corresponding to the selected level based on the judgment matrices of all the related factor sets in the selected level; when the selected level has only one set of related factors, directly determine the judgment matrix corresponding to the related factor set as the set of judgment matrices corresponding to the selected level. By traversing each level of the health assessment architecture diagram, and using the same method, a set of judgment matrices corresponding to each level of the health assessment architecture diagram is constructed.

7. The method according to claim 6, characterized in that, Let the selected set of related factors be the t-th set of related factors in the r-th layer of the health assessment architecture diagram, where r satisfies 1≤r≤k and t satisfies t≥1; The specific expression for constructing the judgment matrix corresponding to the selected set of association factors in the selected hierarchy is: in, Let b be the judgment matrix corresponding to the t-th correlation factor set in the r-th layer of the health assessment architecture diagram. ij Let n be the element in the i-th row and j-th column of the judgment matrix corresponding to the t-th associated factor set in the r-th layer of the health assessment architecture diagram, where n is the number of factors in the t-th associated factor set in the r-th layer of the health assessment architecture diagram.

8. The method according to claim 7, characterized in that, The consistency check of the judgment matrix set at each level includes: Select the judgment matrix of the t-th associated factor set in the r-th layer of the health assessment architecture diagram, and obtain the corresponding maximum eigenvalue; The consistency index corresponding to the selected judgment matrix is ​​calculated based on the largest eigenvalue of the selected judgment matrix. The random consistency index corresponding to the selected judgment matrix is ​​found in advance. Based on the found random consistency index and the calculated consistency index, the consistency ratio corresponding to the selected judgment matrix is ​​calculated. Determine whether the consistency ratio corresponding to the selected judgment matrix is ​​less than a preset threshold. If so, determine that the selected judgment matrix meets the consistency test requirements. Otherwise, determine that the selected judgment matrix does not meet the consistency test requirements, adjust the selected judgment matrix, and re-determine whether the adjusted judgment matrix meets the consistency test requirements until the adjusted judgment matrix meets the consistency test requirements. Traverse the judgment matrix of each associated factor set in the r-th layer of the health assessment architecture diagram, and perform consistency verification on the judgment matrix of each associated factor set in the r-th layer in the same way; Traverse each level of the health assessment architecture diagram and perform consistency verification on the judgment matrix of each set of related factors under each level.

9. The method according to claim 8, characterized in that, For the judgment matrix corresponding to the t-th associated factor set in the r-th layer of the health assessment architecture diagram, the specific formula for calculating the consistency index corresponding to the selected judgment matrix is ​​as follows: in, Let be the consistency index corresponding to the judgment matrix of the t-th association factor set in the r-th layer. Let be the largest eigenvalue corresponding to the judgment matrix of the t-th association factor set in the r-th layer; The specific formula for calculating the consistency ratio corresponding to the selected judgment matrix is ​​as follows: in, Let be the consistency ratio corresponding to the judgment matrix of the t-th association factor set in the r-th layer. This is the random consistency index corresponding to the judgment matrix of the t-th correlation factor set in the r-th layer that was found in advance.

10. The method according to claim 7, characterized in that, For the r-th layer of the health assessment architecture diagram, if the set of judgment matrices at the r-th layer satisfies the consistency check requirement, determining the set of weight vectors for the corresponding layer based on the corresponding set of judgment matrices includes: If the set of judgment matrices at the r-th layer in the health assessment architecture diagram meets the consistency verification requirements, the t-th correlation factor set at the r-th layer is selected, and the selected judgment matrix is ​​normalized column by column to obtain the normalized matrix corresponding to the t-th correlation factor set at the r-th layer. The normalized matrix of the t-th correlation factor set in the r-th layer is summed element by element along each row to obtain the initial weight vector of the t-th correlation factor set in the r-th layer. Normalize the initial weight vector of the t-th correlation factor set in the r-th layer to obtain the weight vector of the t-th correlation factor set in the r-th layer. Traverse each set of associated factors in the r-th layer of the health assessment architecture diagram, and obtain the weight vector corresponding to each set of associated factors in the r-th layer using the same method; and obtain the set of weight vectors in the r-th layer of the health assessment architecture diagram based on the weight vectors of all associated factor sets in the r-th layer.

11. The method according to claim 10, characterized in that, The specific formula for normalizing the judgment matrix of the t-th association factor set in the r-th layer by column is as follows: Among them, b′ ij The element b in the i-th row and j-th column of the judgment matrix corresponding to the t-th association factor set in the r-th layer. ij The value after column normalization represents the element in the i-th row and j-th column of the normalized matrix of the t-th association factor set in the r-th layer; The specific formula for summing the elements of the normalized matrix of the t-th correlation factor set in the r-th layer by row is as follows: Among them, w i ′ is the value of the i-th row of the normalized matrix of the t-th association factor set in the r-th layer after summing the elements by row, which represents the component of the i-th row of the initial weight vector of the t-th association factor set in the r-th layer; The specific formula for normalizing the initial weight vector of the t-th correlation factor set in the r-th layer is as follows: Among them, w i w is the component of the i-th row in the initial weight vector of the t-th association factor set in the r-th layer. i The normalized value represents the component of the i-th row of the weight vector of the t-th association factor set in the r-th layer.

12. The method according to claim 1, characterized in that, When the set of judgment matrices at each level of the health assessment architecture diagram meets the consistency verification requirements, the method further includes: Perform a comprehensive sorting consistency check on the judgment matrix set at all levels; If the judgment matrix sets at all levels meet the consistency verification requirements of the comprehensive ranking, the weight vector set for the corresponding level is determined based on the judgment matrix sets at each level; otherwise, the judgment matrix sets at each level are readjusted until the readjusted judgment matrix sets at each level all meet the consistency verification requirements and the readjusted judgment matrix sets at all levels meet the consistency verification requirements of the comprehensive ranking. Then, the weight vector set for the corresponding level is determined based on the readjusted judgment matrix sets at each level.

13. The method according to claim 11, characterized in that, A comprehensive sorting consistency check is performed on the judgment matrix set at all levels, including: Calculate the consistency ratio between layer 1 and layer k in the health assessment architecture diagram; Determine whether the consistency ratio between the first layer and the kth layer in the health assessment architecture diagram is less than a preset threshold. If so, determine that the set of judgment matrices for all layers meets the comprehensive sorting consistency verification requirement; otherwise, determine that the set of judgment matrices for all layers does not meet the comprehensive sorting consistency verification requirement.

14. The method according to any one of claims 1 to 13, characterized in that, The health assessment architecture diagram, from bottom to top, specifically includes an indicator layer, an equipment layer, and a system layer.

15. A health assessment system for a vehicle-mounted broadcasting system, characterized in that, The method for health assessment of a vehicle broadcasting system as described in any one of claims 1 to 14 includes: The hierarchical partitioning module is used to partition the vehicle broadcasting system into hierarchical levels and construct a health assessment architecture diagram of the vehicle broadcasting system; wherein, the health assessment architecture diagram includes k levels from bottom to top, where k≥3; The hierarchical analysis module is used to construct the judgment matrix set for each level of the health assessment architecture diagram and perform consistency verification on the judgment matrix set for each level; it is also used to determine the weight vector set for each level of the health assessment architecture diagram, provided that the corresponding judgment matrix set meets the consistency verification requirements. The health assessment module is used to normalize the bottom layer of the health assessment architecture diagram. Based on the normalized bottom layer and the weight vector set of each layer, the module performs health assessments on layers 2 to k of the health assessment architecture diagram from bottom to top to obtain the target health assessment value corresponding to the vehicle broadcasting system.

16. A health assessment device for a vehicle-mounted broadcasting system, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed, implements the steps of the method as claimed in any one of claims 1 to 14.

17. A computer storage medium, characterized in that, The computer storage medium includes at least one instruction that, when executed, implements the steps of the method as described in any one of claims 1 to 14.

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