Workflow management method and device, equipment, storage medium and product

Abstract

The invention discloses a workflow management method and device, equipment, a storage medium and a product, and relates to the technical field of office automation. The method comprises the following steps: determining historical workflow data corresponding to each historical time interval; determining a next candidate working link matched with the current working link of the current working process according to a working link statistical result of the historical working process data of each historical time interval; in response to a selection operation on the candidate working links, determining a next target working link; determining a candidate execution subject matched with the next target working link according to an execution subject statistical result of the historical workflow data of each historical time interval; and in response to a selection operation on each candidate execution main body, determining a target execution main body of the target working link. According to the invention, automatic intelligent recommendation of the working links and the execution main bodies is realized, and the efficiency, accuracy and flexibility of workflow management are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of office automation technology, and in particular to a workflow management method, device, equipment, storage medium and product. Background Art

[0002] Office automation aims to improve work efficiency and reduce human resource consumption through electronic means. Among them, workflow management is an important part of improving work efficiency.

[0003] In the existing office automation system, when the workflow flows from one link to the next, it is necessary to select the next link and the execution subject of the next link. The selection process of the work link and its execution subject usually relies on manual execution, which is inefficient and inaccurate. Especially for complex or large-scale workflows, the difficulty and complexity of selecting the work link and its execution subject are further increased. However, if a fixed workflow is set, it will not be able to adapt to dynamic work needs and has poor flexibility. Summary of the invention

[0004] Embodiments of the present invention provide a workflow management method, apparatus, device, storage medium and product to solve the problems of low efficiency and poor accuracy in manually selecting work links and their execution entities, as well as the problem of poor flexibility in setting fixed workflows.

[0005] In a first aspect, an embodiment of the present invention provides a workflow management method, the method comprising:

[0006] Determining historical workflow data corresponding to at least two historical time intervals;

[0007] Determine the next candidate work link that matches the current work link of the current work link according to the work link statistics of the historical work flow data at each historical time interval, and provide the determined next candidate work links to the user interaction interface for display;

[0008] In response to the selection operation of each next candidate work link on the user interaction interface, determining the next target work link;

[0009] Determine candidate execution entities that match the next target work link based on at least one execution entity statistical result of the historical workflow data at each historical time interval, and provide the determined candidate execution entities to the user interaction interface for display;

[0010] In response to the selection operation of each candidate execution subject on the user interaction interface, the target execution subject corresponding to the next target work link is determined.

[0011] In a second aspect, an embodiment of the present invention further provides a workflow management device, the device comprising:

[0012] A historical workflow data determination module, used to determine historical workflow data corresponding to at least two historical time intervals;

[0013] A candidate work link determination module is used to determine the next candidate work link that matches the current work link of the current workflow according to the work link statistics of the historical workflow data at each historical time interval, and provide the determined next candidate work links to the user interaction interface for display;

[0014] A target work link determination module, used to determine the next target work link in response to the selection operation of each next candidate work link on the user interaction interface;

[0015] A candidate execution subject determination module is used to determine a candidate execution subject that matches the next target work link based on at least one execution subject statistical result of the historical workflow data at each historical time interval, and provide each determined candidate execution subject to a user interaction interface for display;

[0016] The target execution subject determination module is used to determine the target execution subject corresponding to the next target work link in response to the selection operation of each candidate execution subject on the user interaction interface.

[0017] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a workflow management method as described in any one of the embodiments of the present invention is implemented.

[0018] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a workflow management method as described in any one of the embodiments of the present invention.

[0019] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements a workflow management method as described in any one of the embodiments of the present invention.

[0020] In the embodiment of the present invention, by statistically analyzing the historical workflow data corresponding to at least two historical time intervals, the next candidate work link matching the current work link of the current workflow is determined according to the work link statistical results, and after the candidate work link is selected to determine the next target work link, the candidate execution subject matching the next target work link is determined according to the execution subject statistical results, and the candidate execution subject is selected to determine the target execution subject corresponding to the next target work link. The technical solution of the embodiment of the present invention realizes the automatic intelligent recommendation of work links and execution subjects, and improves the efficiency, accuracy and flexibility of workflow management.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of a workflow management method provided by an embodiment of the present invention;

[0024] Figure 2 A flowchart of a workflow management method provided for a specific applicable scenario of the present invention;

[0025] Figure 3 A schematic diagram of the structure of a workflow management device provided by an embodiment of the present invention;

[0026] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0028] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. The acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of national laws and regulations. It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. The purpose is only to illustrate the feasibility of the implementation of the technical solution of this application, but it does not mean that the applicant has or must use the relevant content of the solution.

[0029] In an office automation system, if a fixed workflow is established, it will not be able to adapt to complex and dynamic work requirements. However, if the current execution subject selects the next work link and the next execution subject in the office automation system, it is not only time-consuming and inefficient, but also prone to errors when the office automation system is large in size and has a large number of work links and execution subjects. This embodiment provides a technical solution for automatically recommending the next work link and the next execution subject based on historical workflow data analysis, aiming to solve the problems of inefficient workflow management and poor user experience in existing office automation systems, and provide a more efficient, intelligent and personalized workflow management solution.

[0030] Figure 1 A flowchart of a workflow management method provided by an embodiment of the present invention is applicable to workflow management in an office automation system, especially for recommending work links and execution entities. The embodiment of the present invention can be implemented by a workflow management device, which can be implemented in the form of hardware and / or software, and can be deployed in an electronic device or a server.

[0031] like Figure 1 As shown, the workflow management method in the embodiment of the present invention includes:

[0032] S110: Determine historical workflow data corresponding to at least two historical time intervals.

[0033] The historical time interval refers to the time interval before the current moment, which is used to represent the minimum granularity for statistical analysis of historical workflow data. In this embodiment, a timer task can be set to perform statistics on historical workflow data at a preset time every time interval. If the time difference between the current moment and the preset moment when the historical workflow data was last statistically analyzed does not reach the time interval, the time period between the preset moment when the historical workflow data was last statistically analyzed and the current moment can be used as a historical time interval, and the historical workflow data within the historical time interval can be statistically analyzed.

[0034] Exemplarily, the time interval may be 24 hours, and the historical workflow data statistics may be performed once every 24 hours at 0:00. If the current time is 10:00, 0:00-10:00 is also taken as a historical time interval for performing historical workflow data statistics.

[0035] In this embodiment, the number of historical time intervals is not limited, and the number of historical time intervals may be a preset fixed value, such as 7. The number of historical time intervals may also be determined in response to a selection of the number of historical time intervals on the user interaction interface.

[0036] The historical workflow data includes each workflow, each work link, the execution subject of each work link, and the work organization to which each execution subject belongs within the historical time interval. A workflow includes at least two work links. A work link can be executed by one or more execution subjects. Different execution subjects may belong to the same work organization or different work organizations.

[0037] In this embodiment, statistical analysis is performed based on historical workflow data within multiple historical time intervals to achieve automated intelligent recommendations for work links and execution entities.

[0038] Furthermore, S110 may include:

[0039] S1. Determine a preset time period, wherein the preset time period includes at least two historical time intervals;

[0040] S2. Determine the historical workflow data corresponding to each historical time interval within a preset time period before the current moment.

[0041] Among them, the preset time period is used to indicate the number of historical time intervals for pulling historical workflow data. For example, taking the historical time interval as 24 hours, the preset time period can be 180 days, that is, statistical analysis is performed on the historical workflow data within 180 days before and including the current date.

[0042] In this embodiment, the purpose of determining the preset time period is to, on the one hand, determine the scope of statistical analysis of historical workflow data, and on the other hand, determine the retention time of historical workflow data. Historical workflow data within the preset time period before the current date is saved, and historical workflow data that exceeds the preset time period is deleted to save storage space.

[0043] S120. Determine the next candidate work link that matches the current work link of the current workflow based on the work link statistics of the historical workflow data at each historical time interval, and provide the determined next candidate work links to the user interaction interface for display.

[0044] The work link statistics results of the historical workflow data at each historical time interval may include the statistics results of the next work link corresponding to each work link in different workflows within each historical time interval.

[0045] In this embodiment, work link statistics are performed on the historical workflow data of each historical time interval. This can be performed at a preset time for each time interval, or a work link statistics cycle can be set. Work link statistics of the historical workflow data of each historical time interval within this cycle are performed every other work link statistics cycle. This embodiment does not impose any restrictions on this.

[0046] In an optional embodiment, the statistical result of the work link can be represented by the number of times the work link is selected, that is, the number of times the work link is selected as the next work link when the workflow and the current work link are the same.

[0047] In another optional embodiment, the statistical results of the work link can also be expressed by the probability of the work link being selected, that is, the probability of the work link being selected in all the next work links with the same work flow and the same current work link is calculated as the statistical result of the work link.

[0048] Taking the work link statistics results represented by the number of times the work link is selected as an example, the work link statistics results are illustrated, as shown in Table 1, which are the work link statistics results of the historical workflow data in the historical time interval corresponding to xx / xx / xx.

[0049] Table 1

[0050]

[0051]

[0052] The next candidate work link that matches the current work link of the current workflow refers to the next work link that may correspond to the current work link of the current workflow that is screened out in the work link statistics results of the historical workflow data at each historical time interval. The number of candidate work links may be one or more, and the number of candidate work links may be a preset fixed value, such as 3, or may be determined in response to the selection of the number of candidate work links on the user interaction interface, and this embodiment does not limit this.

[0053] In this embodiment, based on the work link statistics of historical workflow data at multiple historical time intervals, the next work link of the current work link of the current workflow is screened and recommended, which is more in line with the actual needs of the workflow and improves the relevance and accuracy of the work link recommendation.

[0054] Furthermore, S120 may include:

[0055] A1. For each historical time interval, determine the number of selections corresponding to at least one next historical work link of the current work link when the current work link, the current work link, and the previous work link of the current work link are used as indexes according to the historical work flow data of the current historical time interval;

[0056] A2. Determine the score of each next historical work link according to the number of selections of each next historical work link corresponding to each historical time interval;

[0057] A3. According to the scores of each next historical work link, determine the next candidate work link that matches the current work link of the current workflow from each next historical work link.

[0058] In this embodiment, a time interval is used as the minimum statistical granularity to perform work link statistics on historical workflow data within a historical time interval. The current workflow, the current work link, and the previous work link of the current work link are used as indexes because the same work link may appear multiple times in the same workflow. Therefore, in addition to the current workflow and the current work link, adding the previous work link of the current work link to the index can improve the accuracy of work link recommendation.

[0059] For example, taking Table 1 as an example, if the current workflow is process A, the current work link is work link 1, and the previous work link is work link 0, it can be known that within the historical time interval corresponding to xx / xx / xx, the next historical work link includes work link 2 and work link 3, and work link 2 is selected 1 times, and work link 3 is selected 3 times.

[0060] For each historical time interval, the same method as A1 is used to count the number of selections for each next historical work link.

[0061] In an optional embodiment, A2 may include: under the premise of taking the current workflow, the current work link and the previous work link of the current work link as indexes, the ratio of the sum of the number of selections of a certain next historical work link corresponding to each historical time interval to the sum of the number of selections of all next historical work links corresponding to each historical time interval is used as the score of the next historical work link.

[0062] For example, still taking the current workflow as process A, the current work link as work link 1, and the previous work link as work link 0 as an example, if there are two historical time intervals, the work link statistics corresponding to one historical time interval are shown in Table 1, the next historical work link includes work link 2 and work link 3, the number of times work link 2 is selected is 1, and the number of times work link 3 is selected is 3, and the work link statistics corresponding to the other historical time interval are: the next historical work link includes work link 2 and work link 3, the number of times work link 2 is selected is 0, and the number of times work link 3 is selected is 1. Then the sum of the number of times work link 2 is selected is 1, the sum of the number of times work link 3 is selected is 4, and the sum of the number of times each next historical work link is selected is 5, then it can be calculated that the score of work link 2 is 0.2, and the score of work link 3 is 0.8.

[0063] In another optional embodiment, A2 may further include:

[0064] A21. Determine the time decay coefficient for each historical time interval;

[0065] A22. Determine the score of each next historical work link according to the number of selections of each next historical work link corresponding to each historical time interval and the time decay coefficient of each historical time interval.

[0066] The time decay coefficient refers to the weight of each historical time interval. As the time difference between the historical time interval and the current moment increases, its weight decreases, so its weight is called the time decay coefficient. This embodiment does not limit the specific setting method of the time decay coefficient and the specific value of each time decay coefficient.

[0067] In an optional embodiment, the time decay coefficient of each historical time interval is determined. Each historical time interval can be assigned a time decay coefficient that decreases from 1 to 0 in order from the small to the large time difference between the historical time interval and the current moment. This embodiment does not limit the specific value of each time decay coefficient.

[0068] Exemplarily, if there are five historical time intervals in total, the time decay coefficient of each historical time interval may be set to be 1, 0.8, 0.6, 0.4, and 0.2, respectively.

[0069] In another optional embodiment, determining the time decay coefficient of each historical time interval may further include: determining the time decay coefficient of each historical time interval by the following formula: Among them, w i represents the time decay coefficient of the i-th historical time interval starting from the current moment, λ is the decay coefficient, t is the time interval of the current moment, t i is the i-th historical time interval starting from the current moment.

[0070] For example, if there are five historical time intervals in total and λ is 0.3, the time decay coefficients of the historical time intervals are 1.8221, 1.4918, 1.2214, 1, and 0.8187, respectively.

[0071] In this embodiment, the score of each next historical work link is determined according to the number of selections of each next historical work link corresponding to each historical time interval and the time decay coefficient of each historical time interval. Specifically, for a certain next historical work link, a weighted sum is performed according to the number of selections within each historical time interval and the time decay coefficient of each historical time interval to obtain the weighted number of selections corresponding to the next historical work link; a weighted sum is performed according to the number of selections of each next historical work link within each historical time interval and the time decay coefficient of each historical time interval to obtain the weighted number of selections corresponding to each next historical work link; the ratio of the weighted number of selections corresponding to the next historical work link to the weighted number of selections corresponding to each next historical work link is used as the score of the next historical work link.

[0072] For example, still taking the current workflow as process A, the current work link as work link 1, and the previous work link as work link 0 as an example, if there are three historical time intervals in total, their time attenuation coefficients are 1, 0.8, and 0.6, respectively. The statistical results of the work links corresponding to the first historical time interval are shown in Table 1. The next historical work link includes work link 2 and work link 3, the number of times work link 2 is selected is 1, and the number of times work link 3 is selected is 3. The statistical results of the work links corresponding to the second historical time interval are: the next historical work link includes work link 2 and work link 3, the number of times work link 2 is selected is 0, and the number of times work link 3 is selected is 1. The statistical results of the work links corresponding to the third historical time interval are: the next historical work link includes work link 2 and work link 3, the number of times work link 2 is selected is 2, and the number of times work link 3 is selected is 3. The score of work link 2 is: 1×1+0+2×0.6 / 4×1+1×0.8+5×0.6=0.282, and the score of work link 3 is: 3×1+1×0.8+3×0.6 / 4×1+1×0.8+5×0.6=0.718.

[0073] Furthermore, determining the score of each next historical work link based on the number of selections of each next historical work link corresponding to each historical time interval and the time decay coefficient of each historical time interval may include: in each historical time interval, determining a target historical time interval whose time decay coefficient is greater than or equal to a preset coefficient threshold; determining the score of each next historical work link based on the number of selections of each next historical work link corresponding to each target historical time interval and the time decay coefficient of each target historical time interval.

[0074] It is understandable that when the preset time period is long, that is, when the number of historical time intervals is large, there may sometimes be a historical time interval with a large time difference from the current moment, and its corresponding time decay coefficient is very small, resulting in the statistical results of the work link of the historical time interval having a negligible impact on the calculation of the final work link score. At this time, the final work link score can be determined only based on the statistical results of the work link of the historical time interval whose time decay coefficient is greater than or equal to the preset coefficient threshold.

[0075] For example, the above Determine the time decay coefficient, taking λ as 3 as an example, the coefficient threshold can be set to 0.05, then it can be seen that the time decay coefficient corresponding to the 7th historical time interval is 0.4279. At this time, the 7th and subsequent historical time intervals can be ignored, and the final work link score can be determined only based on the work link statistics corresponding to the first 6 historical time intervals.

[0076] In this embodiment, when performing statistical analysis of historical work links, a higher weight can be given to recent work link selections based on the time decay coefficient. This dynamic configuration mechanism can improve the flexibility and accuracy of work link recommendations and ensure the real-time and relevance of recommendation results.

[0077] In this embodiment, a threshold is set for the time decay coefficient and a screening is performed, which can reduce the consumption of computing resources and improve the efficiency of work link recommendation while ensuring the accuracy of the recommendation results.

[0078] In an optional embodiment, A3 may further include: sorting the scores of each next historical work link in descending order, and selecting the first certain number of next historical work links as candidate work links. The number of the selected next historical work links may be a preset fixed value, such as 3. It may also be a number calculated based on the total number of each next historical work link and a preset ratio, but a minimum number should be set in this case, such as 2. When the calculated number is greater than or equal to the minimum value, the calculated number shall prevail; when the calculated number is less than the minimum value, the minimum value shall prevail.

[0079] In another optional embodiment, A3 may further include: selecting the next historical work link with a score greater than or equal to a preset score threshold as a candidate work link. The score threshold may be 0.3, etc., and this embodiment does not limit the specific value of the score threshold.

[0080] In this embodiment, based on the work link statistics results of the historical workflow data of each historical time interval, the next historical work link that may correspond to the same situation of the current workflow, the current work link and the previous work link is screened out as a candidate work link, and the candidate work link is provided to the user interaction interface for display.

[0081] S130 , in response to the selection operation of each next candidate work link on the user interaction interface, determining the next target work link.

[0082] In this embodiment, in response to a selection operation on the user interaction interface, the next candidate work link corresponding to the selection operation is determined as the next target work link, and a recommendation is made to the execution subject corresponding to the subsequent next target work link.

[0083] It should be noted that there may be a situation where the work link corresponding to the selection operation is a work link outside the candidate work link. At this time, there may be a change in the work process link, etc. When recommending candidate work links for the same work process, the same current work link, and the same previous work link in the future, in addition to recommending candidate work links based on the work link statistics, the work link corresponding to the above selection operation can also be added. Alternatively, when recommending candidate work links for the same work process, the same current work link, and the same previous work link in the future, a higher weight is given to the adjacent historical time intervals, so that the candidate work links determined according to the work link statistics can include the work link corresponding to the above selection operation.

[0084] S140. Determine candidate execution entities that match the next target work link based on at least one execution entity statistical result of the historical workflow data at each historical time interval, and provide the determined candidate execution entities to the user interaction interface for display.

[0085] The statistical results of the execution subjects of the historical workflow data at each historical time interval may include the statistical results of the execution subjects corresponding to each work link in different workflows within each historical time interval. The statistical results of the execution subjects may include one or more items, and different statistical results of the execution subjects correspond to different statistical dimensions.

[0086] Similarly, the execution subject statistics of the historical workflow data at each historical time interval may be performed at a preset time for each time interval, or an execution subject statistics cycle may be set to perform the execution subject statistics of the historical workflow data at each historical time interval within the cycle for each execution subject statistics cycle. This embodiment does not impose any restrictions on this.

[0087] In an optional embodiment, the statistical result of the execution subject can be represented by the number of times the execution subject is selected, that is, the number of times the execution subject is selected under the same workflow and the same work links.

[0088] In another optional embodiment, the statistical result of the execution subject can also be expressed by the probability of the execution subject being selected, that is, the probability of the execution subject being selected in the same workflow and the same work link is calculated as the statistical result of the execution subject.

[0089] Taking the execution subject statistical results represented by the number of times the execution subject is selected as an example, the execution subject statistical results are illustrated, as shown in Table 2, which are the execution subject statistical results of the historical workflow data within the historical time interval corresponding to xx / xx / xx.

[0090] Table 2

[0091]

[0092] The candidate execution subject that matches the next target work link refers to the execution subject that may correspond to the target work link of the current workflow that is screened out from the statistical results of the execution subjects of the historical workflow data at each historical time interval. The number of candidate execution subjects may be one or more, and the number of candidate execution subjects may be a preset fixed value, such as 3, or may be determined in response to the selection of the number of candidate execution subjects in the user interaction interface, which is not limited in this embodiment.

[0093] In this embodiment, based on the statistical results of the execution entities of the historical workflow data at multiple historical time intervals, the execution entity of the next target work link of the current workflow is screened and recommended, which is more in line with the actual needs of the workflow and improves the relevance and accuracy of the execution entity recommendation.

[0094] Furthermore, S140 may include:

[0095] B1. For each historical time interval, according to the historical workflow data of the current historical time interval, determine the number of selections of each historical execution subject matching the next target work link under at least one index;

[0096] B2. Determine the score of each historical execution subject according to the number of selections of each historical execution subject corresponding to each historical time interval;

[0097] B3. According to the scores of each historical execution subject, determine the candidate execution subject that matches the next target work link from among the historical execution subjects.

[0098] In this embodiment, a time interval is used as the minimum statistical granularity to perform statistics on the execution subject of the historical workflow data within a historical time interval. Based on the statistical results of the execution subject of the historical workflow data of multiple historical time intervals, the execution subject of the next target work link is screened and recommended, which is more in line with the actual needs of the workflow and the current execution subject, and improves the relevance and accuracy of the execution subject recommendation.

[0099] Similar to the process of A1-A3 in the above embodiment, the specific process of B1-B3 in this embodiment will not be repeated.

[0100] Furthermore, B2 may include:

[0101] B21. Determine the time decay coefficient for each historical time interval;

[0102] B22. Determine the score of each historical execution subject according to the number of selections of each historical execution subject corresponding to each historical time interval and the time attenuation coefficient of each historical time interval.

[0103] The specific process of determining the time decay coefficient of each historical time interval has been described in the above embodiment, and will not be repeated in this embodiment. When performing statistical analysis of historical execution subjects, a higher weight can be given to the recent execution subject selection according to the time decay coefficient. This dynamic configuration mechanism can improve the flexibility and accuracy of the execution subject recommendation and ensure the real-time and relevance of the recommendation results.

[0104] Furthermore, B21 may further include: determining the time decay coefficient of each historical time interval by the following formula: Among them, w i represents the time decay coefficient of the i-th historical time interval starting from the current moment, λ is the decay coefficient, t is the time interval of the current moment, t i is the i-th historical time interval starting from the current moment.

[0105] Similarly, the specific process of determining the score of each historical execution subject based on the number of selections of each historical execution subject corresponding to each historical time interval and the time attenuation coefficient of each historical time interval is similar to the process A22 in the above embodiment, and will not be repeated in this embodiment.

[0106] Furthermore, the index in B1 may include at least one of the following: indexed by the current workflow, the current execution subject, and the next target work link; indexed by the current workflow, the work organization to which the current execution subject belongs, and the next target work link; indexed by the current workflow and the next target work link.

[0107] In this embodiment, when determining the candidate execution subject, the index may be one or more items, and the corresponding index item may be determined in response to a selection operation of the index item on the user interaction interface.

[0108] In this embodiment, the current executor is added to the index so that the recommendation of the next target work link executor can analyze the selection habits of the current executor, thereby predicting the next target work link executor that meets the selection habits of the current executor. At the same time, the work unit to which the current executor belongs is added to the index, combining the work unit to which the current executor belongs and the historical data of the entire workflow, and achieving more accurate and comprehensive executor recommendation through statistical analysis at different levels.

[0109] It is understandable that when the current executive subject of the current work link is not familiar with the workflow, the index of the current workflow and the next target work link, as well as the index of the current workflow, the work unit to which the current executive subject belongs, and the next target work link, can ensure that the current executive subject can obtain a relatively accurate recommendation of the executive subject of the target work link. At the same time, as the number of operations of the current executive subject in the office automation system increases, personalized recommendations that are closer to the habits of the current executive subject are achieved through the index of the current workflow, the current executive subject, and the next target work link. Therefore, in this embodiment, the index of multiple dimensions is set, and the index items when the executive subject is determined can be customized. The benefit is that the recommendation of the executive subject has higher adaptability and flexibility, can adapt to different scales and needs, and ensures the relevance and accuracy of the executive subject recommendation results. At the same time, with the continuous use of the office automation system and the continuous accumulation of historical workflow data, the executive subject recommendation will continue to learn and optimize, further improving the accuracy of the recommendation, making the workflow management more efficient and intelligent over time.

[0110] Correspondingly, when the index includes at least two items, B2 may include:

[0111] B210. For each index, determine the score of each historical execution subject under the current index according to the number of selections of each historical execution subject corresponding to each historical time interval under the current index;

[0112] B220. Determine the score of each historical execution subject according to the score of each historical execution subject under each index and the weight of each index.

[0113] For example, taking Table 2 as an example, under the index of the current workflow, the current execution subject, and the next target work link, in the historical time interval corresponding to xx / xx / xx, the current workflow is process A, the current execution subject is X, the next target work link is link 2, the corresponding historical execution subject is Y, and the number of selections is 3. According to the number of selections of each historical execution subject in each historical time interval, the score of each historical execution subject is calculated. Among them, the specific process of calculating the score of each historical execution subject has been described in the above embodiment B2, and this embodiment will not be repeated here.

[0114] For each index, the same method as B210 is used to calculate the scores of each historical execution subject under different indexes. According to the scores of each historical execution subject under different indexes and the weights of each index, a weighted sum is performed to obtain the scores of each historical execution subject under the comprehensive index. Finally, the candidate execution subject is determined according to the scores of each historical execution subject under the comprehensive index.

[0115] Exemplarily, the weight of each index can be set as follows: the weight of the current workflow, the current execution subject, and the next target work link as the index is 0.7, the weight of the current workflow, the work organization to which the current execution subject belongs, and the next target work link as the index is 0.2, and the weight of the current workflow and the target work link as the index is 0.1. That is, the behavioral habits of the current execution subject are greater than the behavioral habits of the work organization to which the current execution subject belongs, and the behavioral habits of the work organization to which the current execution subject belongs are greater than the overall behavioral habits in the workflow. The advantage of such a setting is that it can not only ensure that when the current execution subject uses the office automation system less frequently, it can also provide the current execution subject with accurate execution subject recommendation results, but also ensure that as the current execution subject continues to use the office automation system, the execution subject recommendation results are more accurate and comprehensive, meeting the personalized needs of the current execution subject.

[0116] In a specific example, if the scores calculated for a certain execution subject under three different indexes are 0.8, 0.7, and 0.7 respectively, then the comprehensive score of the execution subject is 0.8×0.7+0.7×0.2+0.7×0.1=0.77.

[0117] S150 . In response to the selection operation of each candidate execution subject on the user interaction interface, determine the target execution subject corresponding to the next target work link.

[0118] In this embodiment, in response to the selection operation of the user interaction interface, the candidate execution subject corresponding to the selection operation is determined as the target execution subject corresponding to the next target work link. After obtaining the next target work link and its corresponding execution subject, the flow of the workflow can be realized.

[0119] Similarly, there may be a situation where the execution subject corresponding to the selection operation is an execution subject other than the candidate execution subject, in which case there may be a change in the execution subject, etc. When recommending candidate execution subjects for the target work link later, in addition to recommending candidate execution subjects based on the execution subject statistics, the execution subject corresponding to the above selection operation may also be added. Alternatively, when recommending candidate execution subjects for the target work link later, a higher weight is given to adjacent historical time intervals so that the candidate execution subjects determined based on the execution subject statistics can include the execution subject corresponding to the above selection operation.

[0120] Figure 2 A flowchart of a workflow management method provided for a specific applicable scenario of the present invention, such as Figure 2As shown, the review duration refers to a preset time period, for example, 180 days, and the historical workflow data within the review duration is retained and counted. For each historical time interval, the time decay coefficient is calculated. A scheduled task is configured to perform statistical analysis of work links and statistical analysis of execution subjects. According to the statistical results of the work links of the historical workflow data of each historical time interval within the review duration, the candidate work links are determined and recommended to the user. In response to the user's selection operation, the target work link is determined. Further recommendation of the candidate execution subject of the target work link and selection of the target execution subject are performed. According to the statistical results of the execution subject of the historical workflow data of each historical time interval within the review duration, three dimensions of candidate execution subject recommendation are provided: recommendation of candidate execution subjects with the same workflow, the same work link, and the same current execution subject; recommendation of candidate execution subjects with the same workflow, the same work link, and the same work organization to which the current execution subject belongs; recommendation of candidate execution subjects with the same workflow and the same work link. The recommendation results of the candidate execution subjects in the three dimensions and the weights of the three different dimensions are combined to determine the recommendation results of the total candidate execution subject. Finally, in response to the user's selection operation, the target execution subject of the target work link is determined.

[0121] Figure 3 A schematic diagram of a workflow management device provided in an embodiment is shown in FIG. Figure 3 As shown, the workflow management device includes:

[0122] A historical workflow data determination module 310, configured to determine historical workflow data corresponding to at least two historical time intervals;

[0123] The candidate work link determination module 320 is used to determine the next candidate work link that matches the current work link of the current work flow according to the work link statistics of the historical work flow data at each historical time interval, and provide the determined next candidate work links to the user interaction interface for display;

[0124] The target work link determination module 330 is used to determine the next target work link in response to the selection operation of each next candidate work link on the user interaction interface;

[0125] The candidate execution subject determination module 340 is used to determine the candidate execution subject matching the next target work link according to the statistical result of at least one execution subject of the historical workflow data in each historical time interval, and provide the determined candidate execution subjects to the user interaction interface for display;

[0126] The target execution subject determination module 350 is used to determine the target execution subject corresponding to the next target work link in response to the selection operation of each candidate execution subject on the user interaction interface.

[0127] In the embodiment of the present invention, by statistically analyzing the historical workflow data corresponding to at least two historical time intervals, the next candidate work link matching the current work link of the current workflow is determined according to the work link statistical results, and after the candidate work link is selected to determine the next target work link, the candidate execution subject matching the next target work link is determined according to the execution subject statistical results, and the candidate execution subject is selected to determine the target execution subject corresponding to the next target work link. The technical solution of the embodiment of the present invention realizes the automatic intelligent recommendation of work links and execution subjects, and improves the efficiency, accuracy and flexibility of workflow management.

[0128] Based on the above embodiment, optionally, the candidate work link determination module includes:

[0129] A next historical work link selection number determination unit is used to determine, for each historical time interval, the number of selections corresponding to at least one next historical work link of the current work link when the current workflow, the current work link, and the previous work link of the current work link are used as indexes according to the historical workflow data of the current historical time interval;

[0130] A next historical work link score determination unit, used to determine the score of each next historical work link according to the number of selections of each next historical work link corresponding to each historical time interval;

[0131] The candidate work link determination unit is used to determine the next candidate work link that matches the current work link of the current workflow from each next historical work link according to the score of each next historical work link.

[0132] Based on the above embodiment, optionally, the next historical work link score determination unit includes:

[0133] A time decay coefficient determination subunit, used to determine the time decay coefficient of each historical time interval;

[0134] The next work link score determination subunit is used to determine the score of each next historical work link according to the number of selections of each next historical work link corresponding to each historical time interval and the time decay coefficient of each historical time interval.

[0135] Based on the above embodiment, optionally, the candidate execution subject determination module includes:

[0136] A historical execution subject selection number determination unit is used to determine, for each historical time interval, the selection number of each historical execution subject matching the next target work link under at least one index according to the historical workflow data of the current historical time interval;

[0137] A historical execution subject score determination unit, used to determine the score of each historical execution subject according to the number of selections of each historical execution subject corresponding to each historical time interval;

[0138] The candidate execution subject determination unit is used to determine a candidate execution subject that matches the next target work link from among the historical execution subjects according to the scores of the historical execution subjects.

[0139] Based on the above embodiment, optionally, the historical execution subject score determination unit includes:

[0140] A time decay coefficient determination subunit, used to determine the time decay coefficient of each historical time interval;

[0141] The execution subject score determination subunit is used to determine the score of each historical execution subject according to the number of selections of each historical execution subject corresponding to each historical time interval and the time decay coefficient of each historical time interval.

[0142] Based on the above embodiment, optionally, the index includes at least one of the following:

[0143] The current workflow, the current execution subject, and the next target work link are used as indexes;

[0144] The current workflow, the work organization to which the current execution subject belongs, and the next target work link are used as indexes;

[0145] The current workflow and the next target workflow are used as indexes.

[0146] Based on the above embodiment, optionally, when the index includes at least two items, the historical execution subject score determination unit is specifically configured to:

[0147] For each index, according to the number of selections of each historical execution subject corresponding to each historical time interval under the current index, determine the score of each historical execution subject under the current index;

[0148] The score of each historical execution subject is determined according to the score of each historical execution subject under each index and the weight of each index.

[0149] Based on the above embodiment, optionally, the historical workflow data determination module 310 includes:

[0150] A preset time period determination unit, used to determine a preset time period, wherein the preset time period includes at least two historical time intervals;

[0151] The historical workflow data determination unit is used to determine the historical workflow data corresponding to each historical time interval within a preset time period before the current moment.

[0152] Based on the above embodiment, optionally, the time attenuation coefficient determination subunit is specifically used to:

[0153] The time decay coefficient of each historical time interval is determined by the following formula: i =e -λ×(t-ti) ;

[0154] Among them, w i represents the time decay coefficient of the i-th historical time interval starting from the current moment, λ is the decay coefficient, t is the time interval of the current moment, t i is the i-th historical time interval starting from the current moment.

[0155] The workflow management device provided in the embodiment of the present invention can execute the workflow management method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0156] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0157] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0158] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0159] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a workflow management method.

[0160] In some embodiments, the workflow management method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the workflow management method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the workflow management method in any other appropriate manner (e.g., by means of firmware).

[0161] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0162] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0163] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0164] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0165] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0166] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0167] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0168] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the workflow management method provided in any embodiment of the present application.

[0169] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0170] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A workflow management method, characterized in that: include: Determining historical workflow data corresponding to at least two historical time intervals; Determine the next candidate work link that matches the current work link of the current work link according to the work link statistics of the historical work flow data at each historical time interval, and provide the determined next candidate work links to the user interaction interface for display; In response to the selection operation of each next candidate work link on the user interaction interface, determining the next target work link; Determine candidate execution entities that match the next target work link based on at least one execution entity statistical result of the historical workflow data at each historical time interval, and provide the determined candidate execution entities to the user interaction interface for display; In response to the selection operation of each candidate execution subject on the user interaction interface, the target execution subject corresponding to the next target work link is determined.

2. The method according to claim 1, characterized in that According to the work link statistics results of the historical workflow data at each historical time interval, the next candidate work link matching the current work link of the current workflow is determined, including: For each historical time interval, based on the historical workflow data of the current historical time interval, determine the number of selections corresponding to at least one next historical work link of the current work link when the current workflow, the current work link, and the previous work link of the current work link are used as indexes; Determine the score of each next historical work link according to the number of selections of each next historical work link corresponding to each historical time interval; According to the scores of each next historical work link, a next candidate work link matching the current work link of the current workflow is determined from each next historical work link.

3. The method according to claim 2, characterized in that According to the number of selections of each next historical work link corresponding to each historical time interval, the score of each next historical work link is determined, including: Determine the time decay coefficient for each historical time interval; The score of each next historical work link is determined according to the number of selections of each next historical work link corresponding to each historical time interval and the time decay coefficient of each historical time interval.

4. The method according to claim 1, characterized in that Determine a candidate execution subject matching the next target work link according to at least one execution subject statistical result of the historical workflow data at each historical time interval, including: For each historical time interval, according to the historical workflow data of the current historical time interval, determine the number of selections of each historical execution subject matching the next target work link under at least one index; Determine the score of each historical execution subject according to the number of selections of each historical execution subject corresponding to each historical time interval; According to the scores of each historical execution subject, a candidate execution subject matching the next target work link is determined from among the historical execution subjects.

5. The method according to claim 4, characterized in that The score of each historical execution subject is determined according to the number of selections of each historical execution subject corresponding to each historical time interval, including: Determine the time decay coefficient for each historical time interval; The score of each historical execution subject is determined according to the number of selections of each historical execution subject corresponding to each historical time interval and the time decay coefficient of each historical time interval.

6. The method according to claim 4, characterized in that The index includes at least one of the following: The current workflow, the current execution subject, and the next target work link are used as indexes; The current workflow, the work organization to which the current execution subject belongs, and the next target work link are used as indexes; The current workflow and the next target workflow are used as indexes.

7. The method according to claim 6, characterized in that When the index includes at least two items, the score of each historical execution subject is determined according to the number of selections of each historical execution subject corresponding to each historical time interval, including: For each index, according to the number of selections of each historical execution subject corresponding to each historical time interval under the current index, determine the score of each historical execution subject under the current index; The score of each historical execution subject is determined according to the score of each historical execution subject under each index and the weight of each index.

8. The method according to claim 1, characterized in that Determine historical workflow data corresponding to at least two historical time intervals, including: Determining a preset time period, wherein the preset time period includes at least two historical time intervals; Determine the historical workflow data corresponding to each historical time interval within a preset time period before the current moment.

9. The method according to claim 3 or 5, characterized in that: Determine the time decay coefficient for each historical time interval, including: The time decay coefficient of each historical time interval is determined by the following formula: Among them, w i represents the time decay coefficient of the i-th historical time interval starting from the current moment, λ is the decay coefficient, t is the time interval of the current moment, t i is the i-th historical time interval starting from the current moment.

10. A workflow management device, characterized in that: include: A historical workflow data determination module, used to determine historical workflow data corresponding to at least two historical time intervals; A candidate work link determination module is used to determine the next candidate work link that matches the current work link of the current workflow according to the work link statistics of the historical workflow data at each historical time interval, and provide the determined next candidate work links to the user interaction interface for display; A target work link determination module, used to determine the next target work link in response to the selection operation of each next candidate work link on the user interaction interface; A candidate execution subject determination module is used to determine a candidate execution subject that matches the next target work link based on at least one execution subject statistical result of the historical workflow data at each historical time interval, and provide each determined candidate execution subject to a user interaction interface for display; The target execution subject determination module is used to determine the target execution subject corresponding to the next target work link in response to the selection operation of each candidate execution subject on the user interaction interface.

11. The device according to claim 10, characterized in that The candidate work link determination module includes: A next historical work link selection number determination unit is used to determine, for each historical time interval, the number of selections corresponding to at least one next historical work link of the current work link when the current workflow, the current work link, and the previous work link of the current work link are used as indexes according to the historical workflow data of the current historical time interval; A next historical work link score determination unit, used to determine the score of each next historical work link according to the number of selections of each next historical work link corresponding to each historical time interval; The candidate work link determination unit is used to determine the next candidate work link that matches the current work link of the current workflow from each next historical work link according to the score of each next historical work link.

12. The device according to claim 10, characterized in that The candidate execution subject determination module includes: A historical execution subject selection number determination unit is used to determine, for each historical time interval, the selection number of each historical execution subject matching the next target work link under at least one index according to the historical workflow data of the current historical time interval; A historical execution subject score determination unit, used to determine the score of each historical execution subject according to the number of selections of each historical execution subject corresponding to each historical time interval; The candidate execution subject determination unit is used to determine a candidate execution subject that matches the next target work link from among the historical execution subjects according to the scores of the historical execution subjects.

13. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the workflow management method as described in any one of claims 1 to 9 when executing the computer program.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the workflow management method as described in any one of claims 1 to 9 is implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the workflow management method according to any one of claims 1 to 9.

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