Screening method and device for alarm design schemes in complex task scene and medium

Abstract

The invention discloses a method and device for screening alarm design schemes in a complex task scene and a medium, and relates to the technical field of physical reckoning, and the method mainly comprises the steps: obtaining the distance deviation between each evaluation index actual value in multi-attribute evaluation indexes and a corresponding preset optimal value, and carrying out the standardization processing of the distance deviation; integrating the standardized distance deviation by using an analytic hierarchy process to generate a comprehensive distance deviation of the multi-attribute evaluation index; constructing a bipartite graph network structure which takes the task type and the alarm design scheme as nodes and takes the comprehensive distance deviation as an edge weight; network weighting centrality analysis is carried out on a bipartite graph network structure, and an alarm design scheme with the minimum comprehensive distance deviation under cross-task types is screened. According to the method, the problems of multi-dimensional contradiction and dynamic adaptability of alarm design scheme evaluation in a complex task scene are solved by integrating and mixing multi-criterion decision analysis and a network structure framework.

Description

Technical Field

[0001] The present invention relates to the field of physical deduction technology, and in particular to a method, device and medium for screening alarm design schemes in complex task scenarios. Background Art

[0002] In complex mission scenarios, existing evaluation methods for alarm design solutions have significant limitations. First, traditional statistical analysis methods (such as variance analysis) can typically only analyze a single evaluation metric in isolation and cannot effectively address the interactions and trade-offs between multi-dimensional metrics (such as response time, accuracy, workload, and user experience), resulting in one-sided evaluation results. Second, while the Top-of-Score Method (TOPSIS) can evaluate the quality of solutions through distance calculation, when the dimensions of multiple metrics are inconsistent, its Euclidean distance-based calculation method can easily over-amplify the deviations of certain metrics (such as response time and subjective ratings), thereby distorting the overall evaluation results. Furthermore, while goal planning methods can handle multi-objective conflicts, when there are significant contradictions between the goals (such as minimizing response time and optimizing user experience), it is difficult to find a balanced solution that takes all goals into account, and the final solution often requires sacrificing some key performance. More critically, existing technologies generally ignore the dynamic nature of task scenarios. In real-world applications, the types and workloads faced by operators can change at any time (e.g., switching from low-load monitoring to high-load emergency response). However, existing methods still rely on fixed alarm methods (e.g., a single auditory or visual alarm), resulting in optimized alarm designs lacking robustness across multiple mission scenarios. These shortcomings make it difficult for existing technologies to meet the stringent requirements for alarm system efficiency, adaptability, and reliability in complex mission scenarios. Summary of the Invention

[0003] In order to meet the requirements for efficiency, adaptability and reliability of alarm systems in complex mission scenarios, the present invention proposes a method for screening alarm design solutions in complex mission scenarios, comprising the steps of: S1: Preset several alarm design schemes containing single or combined sensory channels according to the task type, and define multi-attribute evaluation indicators according to the target optimization direction; S2: Obtain the distance deviation between the actual value of each evaluation indicator in the multi-attribute evaluation indicator and the corresponding preset optimal value, and standardize the distance deviation; S3: Using the analytic hierarchy process to integrate the distance deviations after standardization, a comprehensive distance deviation of the multi-attribute evaluation index is generated; S4: Construct a bipartite graph network structure with task type and alarm design scheme as nodes and comprehensive distance deviation as edge weight; S5: By performing network weighted degree centrality analysis on the bipartite graph network structure, the alarm design scheme with the smallest comprehensive distance deviation across task types is screened.

[0004] Furthermore, in step S1, the sensory channels include vision, hearing and touch.

[0005] Furthermore, in step S2, the distance deviation is calculated using Manhattan distance, which is expressed as follows: Where, is the label of the evaluation indicator, is a constant representing the total amount of evaluation indicators, For the The distance deviation of the evaluation indicators, For the The theoretical optimal value of the evaluation index, For the The actual value of the evaluation indicator, for exist Dimensional space, for exist Components in dimensional space.

[0006] Furthermore, in step S2, Z-score normalization is used to normalize the distance deviation.

[0007] Furthermore, in the step S3, the distance deviation formula after the integration and standardization processing using the hierarchical analysis method is expressed as: Where, For the Single-class distance deviation of class evaluation indicators, For the A collection of class evaluation metrics, For the The evaluation index is Local weights within class evaluation metrics, For the The distance deviation of the evaluation indicators The standard deviation after Z-score standardization is used. is a constant for the total number of categories of multi-attribute evaluation indicators, For the The global weight of the class evaluation metric, is the comprehensive distance deviation of the multi-attribute evaluation index.

[0008] Furthermore, in the step S4, the edge weight of the bipartite graph network structure is negatively correlated with the comprehensive distance deviation, and the smaller the edge weight, the better the comprehensive effectiveness of the alarm design solution in the corresponding task type.

[0009] Furthermore, in the step S5, the network weighted degree centrality analysis formula is expressed as: Where, For nodes The weighted degree centrality of For nodes The neighbor set of For nodes and nodes The edge weights between .

[0010] Furthermore, after the step S5, the method further includes the step of: performing reverse tracing through a network weighted degree centrality analysis formula.

[0011] The present invention also includes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for screening alarm design solutions in complex task scenarios.

[0012] The present invention also includes a device for processing data, comprising: a memory having a computer program stored thereon; The processor is used to execute the computer program in the memory to implement the steps of the screening method of alarm design solutions in complex task scenarios.

[0013] Compared with the prior art, the present invention has at least the following beneficial effects: (1) This paper proposes a screening method for alarm design schemes in complex mission scenarios. By integrating hybrid multi-criteria decision analysis with a network structure framework, it solves the multi-dimensional contradictions and dynamic adaptability problems in the evaluation of alarm design schemes in complex mission scenarios. (2) The one-way deviation calculation based on Manhattan distance combined with the Z-Score normalization method effectively unifies the evaluation criteria of indicators of different dimensions and avoids the deviation amplification problem of traditional Euclidean distance in multi-indicator scenarios; (3) By integrating the weights of multiple attributes such as task performance, workload, and user experience through the analytic hierarchy process, and combining the modeling advantages of goal planning and TOPSIS, a comprehensive distance deviation model was constructed. This model can achieve dynamic balance when multiple goals conflict, and solves the one-sided decision-making caused by isolated analysis indicators in traditional methods. (4) By introducing bipartite graph network structure and weighted degree centrality analysis, task types and alarm design schemes are mapped into network nodes, and the comprehensive performance differences under specific tasks are quantified by edge weights. This dynamic analysis framework breaks through the limitations of the traditional fixed evaluation model, so that in scenarios where task types frequently switch, the optimal scheme can maintain cross-task robustness and adaptability, significantly reducing the cognitive pressure of operators in high-load tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A step-by-step diagram of a screening method for alarm design solutions in a complex mission scenario; Figure 2 Schematic diagram of the edge connection between two types of nodes. DETAILED DESCRIPTION

[0015] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solutions of the present invention, but the present invention is not limited to these embodiments.

[0016] In complex human-computer interaction systems (such as nuclear power plant monitoring centers or aviation cockpits), operators often need to quickly respond to multi-channel alarm information in dynamic mission scenarios. Traditional alarm design scheme evaluation methods are limited to single indicator analysis and ignore changes in mission scenarios, resulting in problems such as response delays and cognitive overload in actual applications of the preferred scheme. For example, when a nuclear power plant's cooling system fails, fixed visual and auditory alarms may not be recognized in time because the operator is performing high-load tasks at the same time, thereby causing safety hazards. To address such problems, an embodiment of the present invention proposes a method for screening alarm design schemes in complex mission scenarios, such as Figure 1 As shown in the figure, robustness optimization in dynamic task scenarios is achieved through the following steps:

[0017] S1: Preset several alarm design schemes containing single or combined sensory channels according to the task type, and define multi-attribute evaluation indicators according to the target optimization direction; S2: Obtain the distance deviation between the actual value of each evaluation indicator in the multi-attribute evaluation indicator and the corresponding preset optimal value, and standardize the distance deviation; S3: Using the analytic hierarchy process to integrate the distance deviations after standardization, a comprehensive distance deviation of the multi-attribute evaluation index is generated; S4: Construct a bipartite graph network structure with task type and alarm design scheme as nodes and comprehensive distance deviation as edge weight; S5: By performing network weighted degree centrality analysis on the bipartite graph network structure, the alarm design scheme with the smallest comprehensive distance deviation across task types is screened.

[0018] Specifically, in complex mission scenarios, the predefined alarm design schemes must be closely aligned with the specific mission type and operational environment. First, based on the diverse mission types (e.g., aerospace, nuclear power plant monitoring, medical emergency response, or air traffic control), the core operator needs and potential risks in each scenario must be clearly defined. For example, in nuclear power plant monitoring, operators must simultaneously process multiple sources of information, requiring the alarm system to ensure rapid response even under high workloads. In medical emergency scenarios, on the other hand, alarm intuitiveness and false alarm rates are key considerations. Based on this, this step predefines several alarm design schemes, encompassing single sensory channels (e.g., purely visual, purely auditory, or purely tactile) and multi-sensory channel combinations (e.g., visual-auditory, visual-tactile, auditory-tactile, or visual-auditory-tactile multi-channel). Different combinations address specific mission pain points. For example, visual-tactile alarms are suitable for scenarios with limited visual information in noisy environments, while auditory-tactile combinations can reduce cognitive interference under high-load tasks.

[0019] Furthermore, multi-attribute evaluation indicators are defined based on the target optimization direction to ensure that the evaluation system comprehensively covers the key performance dimensions of the alarm system. These target optimization directions include, but are not limited to, maximizing the performance of the alarm response task (e.g., shortening reaction time and improving accuracy), minimizing the operator's workload (e.g., reducing mental and physical exertion), and maximizing the user experience (e.g., improving the intuitiveness and ease of use of alarms). Based on this, the multi-attribute evaluation indicators are refined into specific, quantifiable parameters, such as:

[0020] Task performance indicators: including average reaction time, error response rate, and information recognition accuracy; Workload indicators: including NASA-TLX scale scores and physiological load monitoring data (such as heart rate variability); User experience indicators: involve subjective ratings (such as intuitiveness and distraction), user satisfaction survey results, etc.

[0021] By dynamically associating task types with multi-attribute evaluation indicators, an extensible framework is provided for subsequent comprehensive evaluation and optimization, ensuring that the alarm design solution can not only meet the immediate needs of specific scenarios, but also adapt to dynamic changes across task scenarios.

[0022] After completing the pre-setting and evaluation index definition of the alarm design scheme, the actual performance data of each scheme needs to be quantitatively analyzed and standardized. First, for the pre-set multi-attribute evaluation indicators (such as response time, accuracy, workload and user experience), collect experimental data or simulation data in actual task scenarios to obtain the actual value of each indicator ( For example, by simulating nuclear power plant failure scenarios, operators' reaction times and error response rates under different alarm design schemes can be recorded; or EEG signal monitoring equipment can be used to measure the mental workload when performing alarm tasks.

[0023] Then, the actual value of each evaluation indicator and the preset optimal value are calculated based on the Manhattan distance ( ) between the one-way distance deviation ( Compared to Euclidean distance, Manhattan distance calculates deviation by summing absolute values, avoiding the problem of amplifying magnitude differences caused by square operations between metrics of different dimensions (such as reaction time in seconds and accuracy expressed in percentages), making it more suitable for fair comparison of multi-dimensional metrics. The specific calculation formula is:

[0024] in, is the label of the evaluation indicator, is a constant representing the total amount of evaluation indicators, For the The distance deviation of the evaluation indicators, For the The theoretical optimal value of the evaluation index, For the The actual value of the evaluation indicator, for exist Dimensional space, for exist For example, if the optimal response time for an alarm design is 0.5 seconds and the actual measured response time is 0.8 seconds, then the deviation is 0.3 seconds. If the optimal response time for an alarm design is 100% and the actual response time is 92%, then the deviation is 8%.

[0025] In order to eliminate the influence of the differences in the dimensions and numerical ranges of different indicators on the comprehensive evaluation, the Z-Score normalization method is further used to normalize the distance deviation. This method calculates the mean of the deviation of each indicator ( ) and standard deviation ( ), converting the original deviation into a standard distribution with a mean of 0 and a standard deviation of 1, the formula is:

[0026] For the The distance deviation of the evaluation indicators The standard deviation after Z-score normalization is used. For example, if the reaction time deviation has a mean of 0.4 seconds and a standard deviation of 0.1 seconds, and a solution has a deviation of 0.3 seconds, the normalized value is -1.0. On the other hand, if the accuracy deviation has a mean of 5% and a standard deviation of 2%, and a solution has a deviation of 8%, the normalized value is 1.5. This process allows indicators of different dimensions (such as time and percentage) to be uniformly mapped to the same scale, ensuring fairness in subsequent weight integration and comprehensive analysis.

[0027] After completing the standardization of each evaluation indicator, it is necessary to integrate the weights of the multi-attribute evaluation indicators through the Analytic Hierarchy Process (AHP) to generate a comprehensive distance deviation to fully reflect the overall effectiveness of the alarm design solution. The core of the AHP is to build a judgment matrix based on expert experience or historical data to quantify the relative importance of each indicator in the evaluation attributes such as task performance, workload and user experience, thereby determining the local weight ( ) and the global weight ( Specifically, for each evaluation attribute (e.g., task performance), the importance of its included indicators (e.g., reaction time and accuracy) is compared pairwise. A judgment matrix is ​​constructed and the consistency ratio (CR) is calculated to ensure the rationality of the weight distribution. For example, if the importance of reaction time is 1.5 times that of accuracy in the task performance attribute, the matrix operation results in their local weights of 0.6 and 0.4, respectively.

[0028] Then, the normalized distance deviation ( )) and the corresponding local weight ( ) are multiplied and aggregated layer by layer into the single-class distance deviation of each evaluation attribute ( ). Taking the task performance () as an example, its single-class distance deviation is calculated as:

[0029] Similarly, the deviation value between workload and user experience ( ) are integrated through the same logic.

[0030] Finally, the global weights of each evaluation attribute are combined ( ), the weighted sum of the three types of single-class distance deviations is obtained to obtain the comprehensive distance deviation (D): The global weight is determined by comparing the importance of different attributes. For example, if the global importance of task performance is 50%, workload is 30%, and user experience is 20%, then Through this process, the comprehensive distance deviation not only reflects the trade-offs within each attribute, but also reflects the priority of different attributes in the overall evaluation, ultimately providing a quantitative basis for cross-task optimization of alarm design solutions. In addition, the introduction of the hierarchical analysis method ensures the transparency and traceability of weight allocation. If the priority needs to be adjusted in actual applications (such as increasing the weight of user experience), it is only necessary to update the judgment matrix and recalculate the weights to quickly adapt to the new evaluation requirements.

[0031] After completing the calculation of the comprehensive distance deviation, the present invention converts the performance relationship between task types and alarm design schemes into a visual network model by constructing a bipartite graph network structure, providing a structured framework for robustness analysis in cross-task scenarios. Specifically, the nodes in the network structure are divided into two categories: one category represents different task types (such as nuclear power plant monitoring, aviation cockpit emergency response, medical first aid, etc.), and the other category represents preset alarm design schemes (such as visual-auditory alarms, tactile-visual combination alarms, etc.). The two types of nodes are connected by edges (such as Figure 2 ), the edge weight is determined by the comprehensive distance deviation (D) calculated in step S3. The edge weight is negatively correlated with the comprehensive distance deviation; that is, the smaller the weight, the closer the overall effectiveness of the alarm design for the corresponding task type is to the optimal target. For example, if the comprehensive distance deviation of a visual-tactile alarm in a nuclear power plant monitoring task is 0.8, while the deviation of another visual-auditory alarm is 1.2, the former has a lower edge weight, indicating better performance for that task type.

[0032] When building the network structure, it is necessary to ensure that each task type node is connected to all corresponding alarm design solution nodes, and to quantify their performance differences through edge weights. For example, for the "air traffic control" task type, the five preset alarm design solutions will generate five edges, and the weight of each edge is derived from the comprehensive distance deviation mapping of each solution under the task. This design allows the network to intuitively reflect the differences in the performance requirements of alarm design solutions for different tasks, while revealing the adaptability fluctuations of the same solution in multiple tasks. For example, a purely auditory alarm may perform well in a low-load medical monitoring task due to its low edge weight, but its performance may decrease in a high-load aviation emergency task due to the increased weight.

[0033] Based on the construction of a bipartite graph network structure, this paper quantifies the comprehensive performance robustness of alarm design solutions in cross-task scenarios through weighted degree centrality analysis and selects the optimal solution. Weighted degree centrality characterizes the influence of a node in the entire network by accumulating the edge weights of all its connections. Its core lies in converting the cross-task adaptability of the alarm design solution into a computable numerical indicator through the negative correlation of edge weights (i.e., the smaller the weight, the lower the comprehensive distance deviation). Specifically, for each alarm design solution node, its weighted degree centrality calculation formula is:

[0034] Where, For nodes (i.e., the weighted degree centrality of a certain alarm design), is the set of all task type nodes connected to the solution, Task type node and Alarm Design Node . Since smaller edge weights indicate better performance in the corresponding task, lower weighted degree centrality values ​​indicate that the solution can maintain a smaller overall deviation across multiple task types and has higher cross-task robustness.

[0035] For example, suppose a visual-audio-tactile multi-channel alarm design has edge weights of 0.5, 0.6, and 0.4 in three tasks: nuclear power plant monitoring, aviation emergency response, and medical emergency response. Its weighted degree centrality is 0.5+0.6+0.4=1.5. Meanwhile, a purely visual alarm design has edge weights of 1.2, 1.8, and 2.0 in the same tasks, resulting in a weighted degree centrality of 5.0. This comparison shows that the multi-channel design significantly outperforms the purely visual design, and is therefore selected as the optimal solution across these three tasks.

[0036] Furthermore, weighted degree centrality analysis not only supports solution screening in static mission scenarios but also dynamically adapts to changes in mission types. For example, when a new "air traffic control" mission type is added, simply adding that mission node to the network structure and recalculating edge weights can quickly update the weighted degree centrality values ​​of all alarm design solutions, ensuring the timeliness of the optimal selection results. This dynamic adaptability is particularly useful in complex systems (such as intelligent cockpits or medical emergency centers), where mission types may change in real time based on environmental conditions or operational requirements.

[0037] In addition, the present invention also realizes the transparency of the decision-making process through the reverse tracing mechanism and the linkage of the multi-level decision-making framework of the present invention, and the reverse deduction of the network weighted degree centrality analysis formula (including the decision amount of each decision layer). When a solution is selected as the best due to the lowest weighted degree centrality, its edge weights under different task types can be traced back through the network structure and further decomposed into the comprehensive distance deviation ( ) and specific data at the raw indicator level (such as reaction time deviation and user experience score) to verify the source of its robustness. For example, if a solution performs well across tasks, it may be due to its balanced performance in task performance (short reaction time) and user experience (highly intuitive), rather than the outstanding advantage of a single indicator.

[0038] Ultimately, through weighted degree centrality analysis, the present invention not only solves the problem of insufficient adaptability of alarm design schemes in cross-task scenarios in traditional methods, but also ensures that the preferred scheme always maintains a balance between high efficiency and low load in complex and dynamic task environments through quantitative evaluation and dynamic update mechanisms, significantly improving the reliability of the alarm system and the operator's emergency response efficiency.

[0039] In summary, the present invention proposes a screening method for alarm design schemes in complex mission scenarios, which solves the multi-dimensional contradictions and dynamic adaptability problems in the evaluation of alarm design schemes in complex mission scenarios by integrating hybrid multi-criteria decision analysis and a network structure framework.

[0040] The one-way deviation calculation based on Manhattan distance combined with the Z-Score normalization method effectively unifies the evaluation criteria of indicators of different dimensions and avoids the deviation amplification problem of traditional Euclidean distance in multi-indicator scenarios.

[0041] Abstract: In order to solve the problem of one-sided decision-making caused by isolated analysis indicators in traditional methods, a comprehensive distance deviation model was constructed by integrating the weights of multiple attributes such as task performance, workload and user experience through the hierarchical analysis method and combining the modeling advantages of goal planning and TOPSIS. The model can achieve dynamic balance when multiple objectives conflict, and solve the problem of one-sided decision-making caused by isolated analysis indicators in traditional methods.

[0042] By introducing a bipartite graph network structure and weighted degree centrality analysis, task types and alarm design schemes are mapped into network nodes, and the comprehensive performance differences under specific tasks are quantified through edge weights. This dynamic analysis framework breaks through the limitations of traditional fixed evaluation models, enabling the optimal scheme to maintain cross-task robustness and adaptability in scenarios where task types frequently switch, significantly reducing the cognitive pressure on operators in high-load tasks.

[0043] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0044] In addition, in the present invention, descriptions such as "first," "second," and "one" are for descriptive purposes only and should not be understood to indicate or imply their relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0045] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0046] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. A method for screening alarm design schemes in complex mission scenarios, characterized in that: Including steps: S1: Preset several alarm design schemes containing single or combined sensory channels according to the task type, and define multi-attribute evaluation indicators according to the target optimization direction; S2: Obtain the distance deviation between the actual value of each evaluation indicator in the multi-attribute evaluation indicator and the corresponding preset optimal value, and standardize the distance deviation; S3: Using the analytic hierarchy process to integrate the distance deviations after standardization, a comprehensive distance deviation of the multi-attribute evaluation index is generated; S4: Construct a bipartite graph network structure with task type and alarm design scheme as nodes and comprehensive distance deviation as edge weight; S5: By performing network weighted degree centrality analysis on the bipartite graph network structure, the alarm design scheme with the smallest comprehensive distance deviation across task types is screened.

2. The method for screening alarm design solutions in complex task scenarios according to claim 1, characterized in that: In step S1, the sensory channels include vision, hearing and touch.

3. The method for screening alarm design solutions in complex task scenarios according to claim 1, characterized in that: In the step S2, the distance deviation is calculated using the Manhattan distance, which is expressed as follows: Where, is the label of the evaluation indicator, is a constant representing the total amount of evaluation indicators, For the The distance deviation of the evaluation indicators, For the The theoretical optimal value of the evaluation index, For the The actual value of the evaluation indicator, for exist Dimensional space, for exist Components in dimensional space.

4. The method for screening alarm design solutions in complex task scenarios according to claim 1, characterized in that: In the step S2, Z-score standardization is used to standardize the distance deviation.

5. The method for screening alarm design solutions in complex task scenarios according to claim 4, characterized in that: In the step S3, the distance deviation formula after the integration and standardization processing using the hierarchical analysis method is expressed as: Where, For the Single-class distance deviation of class evaluation indicators, For the A collection of class evaluation metrics, For the The evaluation index is Local weights within class evaluation metrics, For the The distance deviation of the evaluation indicators The standard deviation after Z-score standardization is used. is a constant for the total number of categories of multi-attribute evaluation indicators, For the The global weight of the class evaluation metric, is the comprehensive distance deviation of the multi-attribute evaluation index.

6. The method for screening alarm design solutions in complex task scenarios according to claim 1, characterized in that: In the step S4, the edge weight of the bipartite graph network structure is negatively correlated with the comprehensive distance deviation. The smaller the edge weight, the better the comprehensive performance of the alarm design scheme in the corresponding task type.

7. The method for screening alarm design solutions in complex task scenarios according to claim 1, characterized in that: In the step S5, the network weighted degree centrality analysis formula is expressed as: Where, For nodes The weighted degree centrality of For nodes The neighbor set of For nodes and nodes The edge weights between .

8. The method for screening alarm design solutions in complex task scenarios according to claim 7, characterized in that: After the step S5, the method further includes the step of: performing reverse tracing through a network weighted degree centrality analysis formula.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for screening alarm design solutions in complex task scenarios described in any one of claims 1 to 8 are implemented.

10. A device for processing data, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the method for screening alarm design solutions in complex task scenarios as described in any one of claims 1 to 8.

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