A self-cognitive ability evaluation method and device for a work order system

Abstract

This disclosure proposes a method and apparatus for evaluating the self-sufficiency of a work order system, relating to the fields of data processing and artificial intelligence. The method includes: identifying key steps in the work order system's processing flow; constructing an automation rate matrix and an accuracy matrix in diagonal form for each key step; calculating a basic effectiveness vector characterizing the basic effectiveness of each key step based on the automation rate matrix and the accuracy matrix, combined with preset automation rate weights and accuracy weights; obtaining a normalized correlation matrix based on the pre- and post-pre-order correlation relationships between each key step; and generating a capability evaluation result for each key step through iterative calculation based on the basic effectiveness vector, the normalized correlation matrix, and preset damping factors.

Description

Technical Field

[0001] This application relates to the fields of data processing and artificial intelligence technology, and in particular to a method and apparatus for evaluating the self-intelligent capabilities of a work order system. Background Technology

[0002] Self-intelligence assessment is a quantitative analysis and comprehensive evaluation of the autonomous decision-making and self-optimization capabilities of intelligent systems. Its core function is to measure the system's intelligence level in different scenarios. It has been widely applied in fields such as autonomous driving and intelligent manufacturing, and is a key support for driving technological iteration. In the communications and enterprise services sector, the work order system, as a core tool for operators and enterprises in operations and customer service scenarios, directly determines service efficiency and cost control capabilities based on its level of self-intelligence. However, the current industry assessment of the self-intelligence of work order systems still has significant shortcomings.

[0003] The evaluation methods for related technologies often focus on a single indicator (such as automation rate) without combining quality dimensions such as accuracy rate, and fail to quantify the synergistic impact of preceding steps on subsequent steps. This results in significant differences in evaluation results among peers and limited reference value, failing to meet the need for accurate evaluation of work order systems. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, one objective of this application is to propose a method for evaluating the self-sufficiency of a work order system, comprising: identifying key steps in the work order system's processing flow; constructing an automation rate matrix and an accuracy matrix in diagonal form for each key step; wherein the main diagonal elements of the automation rate matrix represent the automation rate of each key step, and the main diagonal elements of the accuracy matrix represent the accuracy of each key step; calculating a basic effectiveness vector characterizing the basic effectiveness of each key step based on the automation rate matrix and the accuracy matrix, combined with preset automation rate weights and accuracy rate weights; obtaining a normalized correlation matrix based on the pre- and post-pre-order correlation relationships between each key step; and generating a capability evaluation result for each key step by iteratively calculating the importance weight corresponding to each key step based on the basic effectiveness vector, the normalized correlation matrix, and preset damping factors.

[0006] The second objective of this application is to propose an intelligent capability assessment device for a work order system.

[0007] The third objective of this application is to propose an electronic device.

[0008] The fourth objective of this application is to provide a non-transitory computer-readable storage medium.

[0009] The fifth objective of this application is to provide a computer program product.

[0010] To achieve the above objectives, the first aspect of this application proposes a method for evaluating the self-sufficiency of a work order system, comprising: identifying key links in the work order system's processing flow; constructing an automation rate matrix and an accuracy matrix in diagonal form for each key link; wherein the main diagonal elements of the automation rate matrix represent the automation rate of each key link, and the main diagonal elements of the accuracy matrix represent the accuracy of each key link; calculating a basic effectiveness vector characterizing the basic effectiveness of each key link based on the automation rate matrix and the accuracy matrix, combined with preset automation rate weights and accuracy rate weights; obtaining a normalized correlation matrix based on the pre- and post-pre-order correlation relationships between each key link; and generating a capability evaluation result for each key link by iteratively calculating the importance weight corresponding to each key link based on the basic effectiveness vector, the normalized correlation matrix, and a preset damping factor. According to one embodiment of this application, constructing an automation rate matrix and an accuracy matrix in diagonal form for key processes includes: for the i-th key process, calculating a first ratio of the number of automatically processed work orders corresponding to the i-th key process to the total number of work orders corresponding to the i-th key process, using the first ratio as the element in the i-th row and i-th column of the automation rate matrix, setting the non-main diagonal elements to 0, and constructing the automation rate matrix; for the i-th key process, calculating a second ratio of the number of accurately processed work orders corresponding to the i-th key process to the total number of work orders corresponding to the i-th key process, using the second ratio as the element in the i-th row and i-th column of the accuracy matrix, setting the non-main diagonal elements to 0, and constructing the accuracy matrix, where i is a positive integer and the value of i does not exceed the total number of key processes.

[0011] According to one embodiment of this application, a normalized correlation matrix is ​​obtained based on the preceding and following relationships between key links, including: constructing a synergistic influence matrix based on the preceding and following relationships between key links; and performing column normalization on the synergistic influence matrix to obtain the normalized correlation matrix.

[0012] According to one embodiment of this application, a synergistic influence matrix is ​​constructed based on the preceding and following relationships between key links, including: determining that if the i-th key link is handled correctly, the accuracy rate of the j-th key link is taken as the first accuracy rate; determining that if the i-th key link is handled incorrectly, the accuracy rate of the j-th key link is taken as the second accuracy rate; calculating the ratio of the second accuracy rate to the first accuracy rate as the influence factor of the i-th key link on the j-th key link, and taking the influence factor as the element in the i-th row and j-th column of the synergistic influence matrix, setting the main diagonal elements and lower triangular elements of the synergistic influence matrix to 0, and constructing the final synergistic influence matrix, where i < j, and i and j are both positive integers, and the values ​​of i and j do not exceed the total number of key links.

[0013] According to one embodiment of this application, column normalization processing is performed on the synergistic influence matrix to obtain a normalized correlation matrix, including: for each column in the synergistic influence matrix, calculating the sum of all elements in that column; if the sum is 0, the elements in that column remain unchanged at 0; if the sum is not 0, dividing each non-zero element in that column by the sum, and keeping the zero elements in that column unchanged, to obtain the normalized elements; after all columns have been processed, the normalized correlation matrix is ​​obtained.

[0014] According to one embodiment of this application, the sum of the automation rate weight and the accuracy rate weight is 1, and both are positive numbers greater than 0 and less than 1; the damping factor ranges from 0.8 to 0.9.

[0015] According to one embodiment of this application, generating capability assessment results for each key step includes: generating capability assessment results for each key step based on importance weights, combined with basic effectiveness, automation rate, and accuracy rate.

[0016] According to one embodiment of this application, the self-sufficiency assessment method of the work order system further includes: visualizing the assessment-related information corresponding to each key link in charts; wherein the assessment-related information includes at least one of automation rate, accuracy rate, importance weight, basic effectiveness and capability assessment results; based on the capability assessment results, identifying the weak links in each key link and generating targeted optimization suggestions.

[0017] To achieve the above objectives, a second aspect of this application proposes an intelligent capability assessment device for a work order system, comprising: a first construction module, used to determine key links in the work order system processing flow, and construct an automation rate matrix and an accuracy matrix in diagonal form for the key links; wherein the main diagonal elements of the automation rate matrix are the automation rates of each key link, and the main diagonal elements of the accuracy matrix are the accuracy rates of each key link; a first calculation module, used to calculate a basic effectiveness vector characterizing the basic effectiveness of each key link based on the automation rate matrix and the accuracy matrix, combined with preset automation rate weights and accuracy rate weights; a second construction module, used to obtain a normalized correlation matrix based on the pre- and post-pre-order correlation influence relationships between each key link; and a second calculation module, used to obtain the importance weights corresponding to each key link through iterative calculation based on the basic effectiveness vector, the normalized correlation matrix, and preset damping factors, and generate capability assessment results for each key link.

[0018] According to one embodiment of this application, the first construction module is further configured to: for the i-th key step, calculate a first ratio of the number of automatically processed work orders corresponding to the i-th key step to the total number of work orders corresponding to the i-th key step, and use the first ratio as the element of the i-th row and i-th column of the automation rate matrix, setting the non-main diagonal elements to 0, thereby constructing an automation rate matrix; for the i-th key step, calculate a second ratio of the number of accurately processed work orders corresponding to the i-th key step to the total number of work orders corresponding to the i-th key step, and use the second ratio as the element of the i-th row and i-th column of the accuracy matrix, setting the non-main diagonal elements to 0, thereby constructing an accuracy matrix, wherein i is a positive integer and the value of i does not exceed the total number of key steps.

[0019] According to one embodiment of this application, the second construction module is further configured to: construct a collaborative influence matrix based on the preceding and following relationships between key links; and perform column normalization on the collaborative influence matrix to obtain a normalized correlation matrix.

[0020] According to one embodiment of this application, the second construction module is further configured to: determine that if the i-th key step is handled correctly, use the accuracy rate at the j-th key step as the first accuracy rate, where i < j, and i and j are both positive integers, and the values ​​of i and j do not exceed the total number of key steps; determine that if the i-th key step is handled incorrectly, use the accuracy rate at the j-th key step as the second accuracy rate; calculate the ratio of the second accuracy rate to the first accuracy rate as the influence factor of the i-th key step on the j-th key step, and use the influence factor as the element in the i-th row and j-th column of the collaborative influence matrix, set the main diagonal elements and lower triangular elements of the collaborative influence matrix to 0, and construct the final collaborative influence matrix.

[0021] According to one embodiment of this application, the second construction module is further configured to: calculate the sum of all elements in each column of the synergistic influence matrix; if the sum is 0, the elements in the column remain unchanged at 0; if the sum is not 0, divide each non-zero element in the column by the sum, and keep the zero elements in the column unchanged, to obtain the normalized elements; after all columns have been processed, a normalized correlation matrix is ​​obtained.

[0022] According to one embodiment of this application, the sum of the automation rate weight and the accuracy rate weight is 1, and both are positive numbers greater than 0 and less than 1; the damping factor ranges from 0.8 to 0.9.

[0023] According to one embodiment of this application, the second calculation module is further configured to: generate capability assessment results for each key link based on importance weights, combined with basic effectiveness, automation rate, and accuracy rate.

[0024] According to one embodiment of this application, the second calculation module is further configured to: visualize the evaluation-related information corresponding to each key link; wherein the evaluation-related information includes at least one of automation rate, accuracy rate, importance weight, basic effectiveness and capability assessment results; based on the capability assessment results, identify the weak links in each key link and generate targeted optimization suggestions.

[0025] To achieve the above objectives, a third aspect of this application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to implement the self-sufficiency assessment method for a work order system as described in the first aspect of this application.

[0026] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the self-smart capability assessment method of the work order system as described in the first aspect of this application.

[0027] To achieve the above objectives, a fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the self-sufficiency assessment method for the work order system as described in the first aspect of this application.

[0028] The self-sufficiency assessment method for the work order system proposed in this application achieves at least the following beneficial effects: By constructing an automation rate matrix and an accuracy rate matrix, and combining them with preset weight coefficients to calculate a basic effectiveness vector, the method achieves a dual-dimensional quantitative assessment of the "automation level" and "processing accuracy" of each key link, avoiding the one-sidedness of traditional single-indicator assessments. By constructing a collaborative influence matrix and performing column normalization, the method clarifies the transmission effect of the processing quality of preceding links on subsequent links. Combined with the basic effectiveness vector, iterative calculations are completed to further accurately quantify the importance weight of each link, solving the pain point of existing technologies' difficulty in measuring the collaborative influence between links. By visually displaying core assessment information such as automation rate, accuracy rate, and importance weight, the method helps managers intuitively grasp the overall picture and shortcomings of the system's self-sufficiency. Simultaneously, based on the assessment results, targeted optimization suggestions are generated to improve the automation rate, increase accuracy, and strengthen link collaboration, effectively lowering the threshold for operation and maintenance management and reducing manual analysis costs. Attached Figure Description

[0029] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is an exemplary schematic diagram illustrating an intelligent capability assessment method for a work order system, as shown in some embodiments of this application.

[0030] Figure 2 This is a schematic diagram of a work order system shown in some embodiments of this application.

[0031] Figure 3 This is an exemplary schematic diagram illustrating an intelligent capability assessment method for a work order system, as shown in some embodiments of this application.

[0032] Figure 4 This is an exemplary schematic diagram of an intelligent capability assessment device for a work order system, as shown in some embodiments of this application.

[0033] Figure 5 This is a schematic diagram of an electronic device shown in some embodiments of this application. Detailed Implementation

[0034] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0035] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0036] Figure 1 This is an exemplary schematic diagram illustrating an intelligent capability assessment method for a work order system, as shown in some embodiments of this application. Figure 1 As shown, the self-sufficiency assessment method of this work order system includes the following steps: S101, identify the key steps in the work order system processing flow, and construct an automation rate matrix and an accuracy matrix in the form of a diagonal matrix for the key steps; wherein, the main diagonal elements of the automation rate matrix are the automation rates of each key step, and the main diagonal elements of the accuracy matrix are the accuracy rates of each key step.

[0037] Figure 2 This is a schematic diagram of a work order system shown in some embodiments of this application, such as... Figure 2 As shown, the work order system can include several key stages such as problem identification, root cause analysis, solution generation, solution implementation, and effect evaluation. Each key stage can be performed manually or automatically by the work order system.

[0038] In this application, after identifying the key steps in the work order system's processing flow, work order processing records within a specified time period can be extracted from the work order system via an Application Programming Interface (API) or database query statements. This allows for the collection of relevant data fields for each key step, including but not limited to: work order number, creation time, processing step, processing method (manual / automatic), processing result, processing duration, and processing accuracy. The processing method field is used to determine whether the key step is completed automatically by the system.

[0039] After collecting relevant data from each key stage, the data preprocessing stage begins. In this stage, data cleaning is performed first to remove duplicate, missing, or incorrectly formatted records. For example, if the "processing method" field of a record is empty, it is marked as invalid data and discarded.

[0040] In some embodiments of this application, both the automation rate matrix and the accuracy matrix are diagonal matrices.

[0041] Assuming the work order system processing flow contains n key steps, the specific steps for constructing the automation rate matrix include: for the i-th key step among the n key steps, calculate the first ratio of the number of automatically processed work orders corresponding to the i-th key step to the total number of work orders corresponding to the i-th key step, use the first ratio as the element of the i-th row and i-th column of the automation rate matrix, and set the non-main diagonal elements to 0 to construct the automation rate matrix.

[0042] Where i is a positive integer, and the value of i does not exceed the total number of key links n.

[0043] The automation rate matrix can be represented in the following form:

[0044] As shown above, the automation rate matrix is ​​an n×n matrix. This represents the automation rate corresponding to the first key step; This represents the automation rate corresponding to the second key step, and so on, until... This represents the automation rate corresponding to the nth key step.

[0045] Assuming the work order system processing flow contains n key steps, the specific steps for constructing a diagonal accuracy matrix include: For the i-th key step among the n key steps, calculate the second ratio of the number of accurately processed work orders corresponding to the i-th key step to the total number of work orders corresponding to the i-th key step. Use this second ratio as the element in the i-th row and i-th column of the accuracy matrix, setting the non-main diagonal elements to 0, thus constructing the accuracy matrix. The number of accurately processed work orders refers to the number of work order suggestions corresponding to the i-th key step that match the work order feedback results; for example, in the root cause localization step, if the root cause automatically given by the system matches the final root cause given in the work order feedback, then the work order is considered an accurately processed work order. The accuracy matrix can be represented in the following form:

[0046] As shown above, the accuracy matrix is ​​an n×n matrix. This represents the accuracy rate corresponding to the first key step; This represents the accuracy rate corresponding to the second key step, and so on, until... This represents the accuracy rate corresponding to the nth critical step.

[0047] S102, based on the automation rate matrix and accuracy rate matrix, and combined with the preset automation rate weights and accuracy rate weights, calculates the basic effectiveness vector that characterizes the basic effectiveness of each key link.

[0048] Specifically, the main diagonal elements of the automation rate matrix are extracted to obtain the automation rate vector for each key step, represented as follows:

[0049] Specifically, the diagonal elements of the accuracy matrix are extracted to obtain the accuracy vector for each key step, represented as follows:

[0050] In this application, the automation rate weight is set as follows: Set the accuracy weight to Both the automation rate weight and the accuracy rate weight are positive numbers greater than 0 and less than 1, and + =1.

[0051] The basic validity vector contains the basic validity corresponding to each key step. , i =1,2,3...n.

[0052] in, Calculate using the following formula:

[0053] The basic validity vector can be represented as .

[0054] S103. Based on the preceding and following relationships between key links, a normalized correlation matrix is ​​obtained.

[0055] As one feasible approach, a normalized correlation matrix is ​​obtained based on the preceding and following relationships between the key links, including: constructing a collaborative influence matrix based on the preceding and following relationships between the key links; and performing column normalization on the collaborative influence matrix to obtain the normalized correlation matrix.

[0056] The synergistic influence matrix is ​​constructed based on the sequential and consequential relationships between key stages. This involves: determining the accuracy rate at the j-th key stage as the first accuracy rate if the i-th key stage is handled correctly; determining the accuracy rate at the j-th key stage as the second accuracy rate if the i-th key stage is handled incorrectly; calculating the ratio of the second accuracy rate to the first accuracy rate as the influence factor of the i-th key stage on the j-th key stage (this influence factor can be understood as the impact of the erroneous suggestion in the i-th key stage on the subsequent j-th key stage), and using this influence factor as the element in the i-th row and j-th column of the synergistic influence matrix. The main diagonal elements and lower triangular elements of the synergistic influence matrix are all set to 0, thus constructing the final synergistic influence matrix. Here, i < j, where i and j are positive integers, and their values ​​do not exceed the total number of key stages, n.

[0057] For example, in a work order where a correct suggestion is given in step 1, the accuracy rate of step 2 is 80%. In a work order where a correct suggestion is not given in step 1, the accuracy rate of step 2 is 50%. This indicates that step 1 has a certain influence on step 2, and the influence factor is 50% / 80% = 0.625. The calculation of the influence factor for other steps follows the same principle.

[0058] The synergistic influence matrix can be expressed in the following form:

[0059] The synergistic influence matrix is ​​an n×n matrix. This represents the influence factor of the first key step on the second key step; This represents the influence factor of the first key link on the third key link, and so on.

[0060] After constructing the synergistic influence matrix, column normalization is performed to obtain a normalized correlation matrix. This process includes: for each column of the synergistic influence matrix, calculating the sum of all elements in that column; if the sum is 0 (for example, the sum of the first column of the synergistic influence matrix is ​​0), keeping the elements of that column unchanged; if the sum is not 0, dividing each non-zero element of that column by the sum, keeping the zero elements unchanged, thus obtaining the normalized elements; after processing all columns, the normalized correlation matrix is ​​obtained, which can be denoted as... .

[0061] The above normalization process can also be understood as calculating the normalization factor:

[0062] like Equal to 0, it indicates that there is no pre - influence on the j - th key link, and all elements in this column remain 0 after normalization; if is not equal to 0, then for the elements in this column , after normalization, it is / .

[0063] S104. Based on the basic effectiveness vector, the normalized correlation matrix and the preset damping factor, through iterative calculation, obtain the importance weights corresponding to each key link respectively, and generate the ability evaluation results of each key link.

[0064] Among them, the calculation formula for the importance weight is: w (k+1) =(1 d)×E + d×w (k) ×C′ In the above formula, d represents the damping factor, and the value range of the damping factor is 0.8 - 0.9; w (k+1) represents the importance weights corresponding to each key link obtained in the (k + 1)-th iteration; w (k) represents the importance weights corresponding to each key link obtained in the k - th iteration; E represents the basic effectiveness vector; C′ represents the normalized correlation matrix.

[0065] Among them, when |w(k + 1) w(k)| < T, that is, the absolute value of the vector difference is less than the threshold T, the result converges, stop the iteration, and obtain the final importance weights corresponding to each key link.

[0066] Among them, generating the ability evaluation results of each of the key links includes: based on the importance weights, combining the basic effectiveness, the automation rate and the accuracy rate to generate the ability evaluation results of each of the key links.

[0067] Furthermore, visually display the evaluation - related information corresponding to each key link in a chart; among them, the evaluation - related information includes at least one of the automation rate, the accuracy rate, the importance weight, the basic effectiveness and the ability evaluation result.

[0068] Based on the ability evaluation results, identify the weak links in each key link, and generate targeted optimization suggestions (such as improving the automation rate of the key link, increasing the processing accuracy rate, strengthening the collaborative adaptation between the previous and subsequent links).

[0069] The self-sufficiency assessment method for the work order system proposed in this application achieves a two-dimensional quantitative assessment of the "automation level" and "processing accuracy" of each key link by constructing an automation rate matrix and an accuracy matrix, and calculating a basic effectiveness vector by combining preset weight coefficients. This avoids the one-sidedness of traditional single-indicator assessments. By constructing a collaborative influence matrix and performing column normalization, the transmission effect of the processing quality of the preceding link on the subsequent link is clarified. Then, iterative calculations are completed by combining the basic effectiveness vector to further accurately quantify the importance weight of each link, solving the pain point of existing technologies being unable to measure the collaborative influence between links. By visually displaying core assessment information such as automation rate, accuracy rate, and importance weight, managers can intuitively grasp the overall picture and shortcomings of the system's self-sufficiency. At the same time, based on the assessment results, targeted optimization suggestions are generated to improve the automation rate, increase the accuracy rate, and strengthen link collaboration, effectively reducing the threshold for operation and maintenance management and reducing the cost of manual analysis.

[0070] Figure 3 This is an exemplary schematic diagram of a self-sufficiency assessment method for a work order system shown in this application. The following is in conjunction with... Figure 3 The self-smart capability assessment method of the work order system proposed in this application is illustrated with specific embodiments.

[0071] Suppose a certain work order system has 3 key steps, and the automation rate matrix is ​​as follows:

[0072] The accuracy matrix is ​​as follows:

[0073] Then, by extracting the main diagonal elements from the automation rate matrix, we obtain the automation rate vector for each key step, represented as:

[0074] Then, by extracting the main diagonal elements from the accuracy matrix, we obtain the accuracy vector for each key stage, represented as:

[0075] Assuming an automation rate weight of 0.4 and an accuracy rate weight of 0.6, the basic effectiveness calculations for the three key components are as follows: The fundamental effectiveness of the first key link =0.95 0.6 ×0.8 0.4 ≈0.886.

[0076] The fundamental effectiveness of the second key link =0.92 0.6 ×0.9 0.4 ≈0.91.

[0077] The fundamental effectiveness of the third key link =0.88 0.6 ×0.7 0.4 ≈0.80.

[0078] Finally, the basic validity vector can be represented as .

[0079] Assume the constructed synergistic influence matrix is ​​as follows:

[0080] That is, the influence factor of the first key link on the second key link is 0.6, the influence factor of the first key link on the third key link is 0.3, and the influence factor of the second key link on the third key link is 0.4.

[0081] The second key step is preceded only by the first key step, therefore, after normalization... =0.6 / 0.6=1; the preceding sequence of the third critical step is the first and second critical steps, therefore after normalization... =0.3 / (0.3+0.4) is approximately equal to 0.429. It is approximately equal to 0.571. Therefore, after normalizing the columns of the above synergistic influence matrix, the obtained normalized correlation matrix is... for:

[0082] Assuming the damping factor d is 0.85, the iterative formula is w(k+1)=(1 d)×E+d×w(k)×C′, where k is the iteration number, when w(k+1) w(k)< The iteration stops, and finally w≈[0.133,0.887,0.882].

[0083] The following conclusions can be drawn from the evaluation results: Table 1 Evaluation Conclusion Table

[0084] Figure 4 This is an exemplary schematic diagram of an intelligent capability assessment device for a work order system shown in this application, such as... Figure 4 As shown, the self-sufficiency assessment device 400 of the work order system includes a first construction module 401, a first calculation module 402, a second construction module 403, and a second calculation module 404, wherein: The first construction module 401 is used to determine the key links in the work order system processing flow, and to construct an automation rate matrix and an accuracy matrix in the form of a diagonal matrix for the key links; wherein, the main diagonal elements of the automation rate matrix are the automation rates of each key link, and the main diagonal elements of the accuracy matrix are the accuracy rates of each key link. The first calculation module 402 is used to calculate a basic effectiveness vector that characterizes the basic effectiveness of each key link based on the automation rate matrix and the accuracy rate matrix, combined with preset automation rate weights and accuracy rate weights. The second construction module 403 is used to obtain a normalized correlation matrix based on the pre- and post-order correlation and influence relationships between key links. The second calculation module 404 is used to obtain the importance weights corresponding to each key link through iterative calculation based on the basic validity vector, normalized correlation matrix and preset damping factor, and generate the capability assessment results of each key link.

[0085] This device achieves a two-dimensional quantitative assessment of the "automation level" and "processing accuracy" of each key link by constructing an automation rate matrix and an accuracy matrix, and calculating a basic effectiveness vector based on preset weight coefficients, thus avoiding the one-sidedness of traditional single-indicator assessments. By constructing a synergistic influence matrix and performing column normalization, it clarifies the transmission effect of the processing quality of preceding links on subsequent links. Then, by combining iterative calculations with the basic effectiveness vector, it further accurately quantifies the importance weight of each link, solving the pain point of existing technologies being unable to measure the synergistic influence between links. By visually displaying core assessment information such as automation rate, accuracy rate, and importance weight, it helps managers intuitively grasp the overall picture and shortcomings of the system's self-sufficiency. At the same time, based on the assessment results, it generates targeted optimization suggestions such as improving automation rate, increasing accuracy, and strengthening link synergy, effectively reducing the threshold for operation and maintenance management and reducing manual analysis costs.

[0086] Furthermore, the first construction module 401 is also used to: for the i-th key step, calculate the first ratio of the number of automatically processed work orders corresponding to the i-th key step to the total number of work orders corresponding to the i-th key step, and use the first ratio as the element of the i-th row and i-th column of the automation rate matrix, setting the non-main diagonal elements to 0, and constructing the automation rate matrix; for the i-th key step, calculate the second ratio of the number of accurately processed work orders corresponding to the i-th key step to the total number of work orders corresponding to the i-th key step, and use the second ratio as the element of the i-th row and i-th column of the accuracy matrix, setting the non-main diagonal elements to 0, and constructing the accuracy matrix, where i is a positive integer and the value of i does not exceed the total number of key steps.

[0087] Furthermore, the second construction module 403 is also used to: construct a collaborative influence matrix based on the preceding and following relationships between key links; and perform column normalization on the collaborative influence matrix to obtain a normalized correlation matrix.

[0088] Furthermore, the second construction module 403 is also used to: determine, if the i-th key link is handled correctly, take the accuracy rate at the j-th key link as the first accuracy rate, where i < j, and i and j are both positive integers, and the values ​​of i and j do not exceed the total number of key links; determine, if the i-th key link is handled incorrectly, take the accuracy rate at the j-th key link as the second accuracy rate; calculate the ratio of the second accuracy rate to the first accuracy rate as the influence factor of the i-th key link on the j-th key link, and take the influence factor as the element in the i-th row and j-th column of the collaborative influence matrix, set the main diagonal elements and lower triangular elements of the collaborative influence matrix to 0, and construct the final collaborative influence matrix.

[0089] Furthermore, the second construction module 403 is also used to: calculate the sum of all elements in each column of the synergistic influence matrix; if the sum is 0, the elements in the column remain 0; if the sum is not 0, divide each non-zero element in the column by the sum, and keep the zero elements in the column unchanged, to obtain the normalized elements; after all columns have been processed, the normalized correlation matrix is ​​obtained.

[0090] Furthermore, the sum of the automation rate weight and the accuracy rate weight is 1, and both are positive numbers greater than 0 and less than 1; the damping factor ranges from 0.8 to 0.9.

[0091] Furthermore, the second calculation module 404 is also used to: generate capability assessment results for each key link based on importance weights, combined with basic effectiveness, automation rate and accuracy.

[0092] Furthermore, the second calculation module 404 is also used to: visualize the evaluation-related information corresponding to each key link in charts; wherein the evaluation-related information includes at least one of automation rate, accuracy rate, importance weight, basic effectiveness and capability assessment results; based on the capability assessment results, identify the weak links in each key link and generate targeted optimization suggestions.

[0093] To implement the above embodiments, this application also proposes an electronic device 500, such as... Figure 5 As shown, the electronic device 500 includes a processor 501 and a memory 502 communicatively connected to the processor. The memory 502 stores instructions that can be executed by at least one processor. The instructions are executed by at least one processor 501 to implement the self-smart capability assessment method of the work order system as shown in the above embodiment.

[0094] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the self-intelligent capability assessment method of the work order system as shown in the above embodiments.

[0095] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the self-sufficiency assessment method of the work order system as shown in the above embodiments.

[0096] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0097] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0098] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0099] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating self-intelligent capability of a work order system, characterized in that, include: The key steps in the work order system processing flow are identified, and a diagonal matrix of automation rate and accuracy rate is constructed for each key step. The main diagonal elements of the automation rate matrix represent the automation rate of each key step, and the main diagonal elements of the accuracy rate matrix represent the accuracy rate of each key step. Based on the automation rate matrix and the accuracy rate matrix, and combined with the preset automation rate weights and accuracy rate weights, a basic effectiveness vector characterizing the basic effectiveness of each key link is calculated. Based on the preceding and following relationships between the key links, a normalized correlation matrix is ​​obtained. Based on the basic validity vector, the normalized correlation matrix, and the preset damping factor, the importance weights corresponding to each key link are obtained through iterative calculation, and the capability assessment results of each key link are generated.

2. The method of claim 1, wherein, The construction of the automation rate matrix and accuracy matrix in diagonal matrix form for the key processes includes: For the i-th key step, calculate the first ratio of the number of automatically processed work orders corresponding to the i-th key step to the total number of work orders corresponding to the i-th key step. Use the first ratio as the element of the i-th row and i-th column of the automation rate matrix, and set the non-main diagonal elements to 0 to construct the automation rate matrix, where i is a positive integer and the value of i does not exceed the total number of key steps. For the i-th critical step, calculate the second ratio of the number of accurately processed work orders corresponding to the i-th critical step to the total number of work orders corresponding to the i-th critical step. Use the second ratio as the element in the i-th row and i-th column of the accuracy matrix, and set the non-main diagonal elements to 0 to construct the accuracy matrix.

3. The method of claim 2, wherein, The normalized correlation matrix is ​​obtained based on the preceding and following relationships between the key links, including: Based on the preceding and following relationships between the key links, a collaborative influence matrix is ​​constructed. The column normalization process is performed on the synergistic influence matrix to obtain the normalized correlation matrix.

4. The method according to claim 3, characterized in that, The construction of a synergistic influence matrix based on the preceding and following relationships between the key links includes: If the i-th critical step is handled correctly, the accuracy rate at the j-th critical step is taken as the first accuracy rate, where i < j, i and j are both positive integers, and the values ​​of i and j do not exceed the total number of critical steps. If an error is found in the i-th critical step, the accuracy rate at the j-th critical step is taken as the second accuracy rate. The ratio of the second accuracy rate to the first accuracy rate is calculated as the influence factor of the i-th key link on the j-th key link, and the influence factor is used as the element in the i-th row and j-th column of the collaborative influence matrix. The main diagonal elements and the lower triangular elements of the collaborative influence matrix are all set to 0 to construct the final collaborative influence matrix.

5. The method according to claim 4, characterized in that, The column normalization process of the synergistic influence matrix to obtain the normalized correlation matrix includes: For each column in the synergistic influence matrix, calculate the sum of all elements in that column; If the sum is 0, the elements in this column remain 0. If the sum is not 0, divide each non-zero element in the column by the sum, and leave the zero elements in the column unchanged to obtain the normalized elements; After all columns have been processed, the normalized correlation matrix is ​​obtained.

6. The method according to any one of claims 1-5, characterized in that, in: The sum of the automation rate weight and the accuracy rate weight is 1, and both are positive numbers greater than 0 and less than 1; The damping factor has a value range of 0.8 to 0.

9.

7. The method according to claim 1, characterized in that, The capability assessment results for generating each of the key components include: Based on the importance weights, the capability assessment results for each of the key links are generated by combining the basic effectiveness, the automation rate, and the accuracy rate.

8. The method according to claim 7, characterized in that, The method further includes: The evaluation-related information corresponding to each of the key links is displayed in a visual chart; wherein, the evaluation-related information includes at least one of the automation rate, the accuracy rate, the importance weight, the basic effectiveness, and the capability evaluation result; Based on the capability assessment results, weak links in each key process are identified, and targeted optimization suggestions are generated.

9. A self-sustaining intelligence assessment device for a work order system, characterized in that, include: The first construction module is used to identify the key links in the work order system processing flow, and construct an automation rate matrix and an accuracy matrix in the form of a diagonal matrix for the key links; wherein, the main diagonal elements of the automation rate matrix are the automation rates of each key link, and the main diagonal elements of the accuracy matrix are the accuracy rates of each key link. The first calculation module is used to calculate a basic effectiveness vector that characterizes the basic effectiveness of each key link based on the automation rate matrix and the accuracy rate matrix, combined with preset automation rate weights and accuracy rate weights. The second construction module is used to obtain a normalized correlation matrix based on the pre- and post-order correlation and influence relationships between the key links. The second calculation module is used to obtain the importance weights corresponding to each of the key links through iterative calculation based on the basic validity vector, the normalized correlation matrix and the preset damping factor, and generate the capability assessment results of each of the key links.

10. An electronic device, comprising: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-8.

12. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-8.

Images