Task planning method and device and computer readable storage medium

Abstract

The invention discloses a task planning method and device and a computer readable storage medium, and relates to the technical field of engineering project management, and the method comprises the steps: carrying out the fuzzy quantification and normalization processing of input information based on the influence degree of the input information of each task on each task, and determining the information total value of each task; performing task sequence optimization on the constructed initialized domain mapping matrix based on an information angle, and determining an optimized domain mapping matrix; and sorting the tasks by using the optimization domain mapping matrix according to the information total value of each task, and determining a task plan. The technical effect of improving the execution efficiency and quality of the project can be achieved.

Description

Technical Field

[0001] The present application relates to the technical field of engineering project management, and in particular to a task planning method, device and computer-readable storage medium. Background Art

[0002] In engineering project management, task planning is a key link to ensure the smooth progress of the project, rational allocation of resources, and improving the success rate of the project.

[0003] Traditional task planning methods, such as the Design Structure Matrix (DSM), mainly focus on the connections and dependencies between tasks, and sort and group tasks according to the logical and physical relationships between tasks. The current algorithm for optimizing the task sequence using the design structure matrix concentrates the task associations on the diagonal as much as possible, and on this basis, manually judges the strength of the associations.

[0004] However, this approach often ignores the importance of information in mission planning, which may lead to the neglect or misuse of some important information in the mission planning process, thus affecting the execution efficiency and quality of the project.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of this application is to provide a task planning method, device and computer-readable storage medium, aiming to solve the technical problem of how to improve the execution efficiency and quality of the project.

[0007] To achieve the above objectives, the present application proposes a task planning method, which includes:

[0008] Based on the influence of the input information of each task on the tasks, the input information is fuzzy quantified and normalized to determine the total information value of each task;

[0009] Based on the information perspective, the constructed initialization domain mapping matrix is ​​optimized in task order to determine the optimized domain mapping matrix;

[0010] The optimization domain mapping matrix is ​​used to sort the tasks according to the total information value of each task to determine the task plan.

[0011] In one embodiment, the step of performing fuzzy quantification and normalization processing on the input information based on the influence degree of the input information of each task on the task to determine the total value of the information of each task includes:

[0012] Determining the influence of the input information of each task on each task based on the fuzzy evaluation of the role played by each task in the project;

[0013] According to the degree of influence, the information is fuzzily quantified to determine the absolute information value of the information;

[0014] Based on the absolute information value, the information is normalized to obtain the total information value of each task.

[0015] In one embodiment, the step of performing the fuzzy quantification on the information according to the degree of influence to determine the absolute information value of the information includes:

[0016] According to the influence of the input information of each task on each task, the influence degree is fuzzy quantified to determine the importance ratio of each task;

[0017] Based on the importance ratio of each task, the single information transmission of the information is fuzzily quantified to determine the absolute information value of the information.

[0018] In one embodiment, the step of performing the normalization processing on the information based on the absolute information value to obtain the total information value of each task includes:

[0019] The total information value of each task is calculated according to the absolute information value and the number of the information in each task.

[0020] In one embodiment, the step of optimizing the task sequence of the constructed initialization domain mapping matrix based on the information perspective and determining the optimized domain mapping matrix comprises:

[0021] Constructing the initialization domain mapping matrix according to the information and the input and output of the corresponding task;

[0022] The task sequence is optimized from an information perspective by using an optimization algorithm to determine the optimization domain mapping matrix.

[0023] In one embodiment, the step of optimizing the task sequence from an information perspective by using an optimization algorithm and determining the optimization domain mapping matrix includes:

[0024] Eliminating rows and corresponding columns having only input or output in the initialization domain mapping matrix to determine a simplified domain mapping matrix;

[0025] Based on the simplified domain mapping matrix, matrix adjustment is performed according to the feedback information sorting to determine the optimized domain mapping matrix.

[0026] In one embodiment, the step of performing matrix adjustment based on the simplified domain mapping matrix and sorting according to feedback information to determine the optimized domain mapping matrix includes:

[0027] Based on the simplified domain mapping matrix, adjusting the output of the first row of tasks to correspond to the frontmost column, and determining a preprocessing domain mapping matrix;

[0028] The task rows in the preprocessing domain mapping matrix are sequentially pre-placed according to the feedback information sorting and the task rows and corresponding columns are eliminated, and a pre-placed domain mapping matrix is ​​obtained according to the elimination order;

[0029] The pre-domain mapping matrix is ​​normalized, and the output of the first row of tasks is adjusted again to correspond to the frontmost column to determine the optimized domain mapping matrix.

[0030] In one embodiment, before the step of sequentially prepending the task rows in the preprocessing domain mapping matrix according to the feedback information sorting and eliminating the task rows and corresponding columns to obtain the prepending domain mapping matrix, the step includes:

[0031] The total value of the information in the preprocessing domain mapping matrix is ​​sorted to determine the feedback information sorting.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a task planning device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the task planning method described above.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the task planning method described above are implemented.

[0034] The present application provides a task planning method. The present application firstly uses fuzzy quantification and normalization processing to accurately evaluate the influence of different input information on respective tasks and determine their relative value scores; optimizes the initialization domain mapping matrix from the information perspective to identify the dependencies between tasks and the information flow path; optimizes the task sequence to reduce information delay and redundancy and improve the overall efficiency of task execution; uses the optimized domain mapping matrix to sort tasks according to their information value to ensure the scientificity and rationality of task planning, thereby further improving the execution efficiency and quality of the project.

[0035] In summary, this application evaluates the impact of different input information on respective tasks through fuzzy quantification and normalization processing, determines their relative value scores, optimizes the task sequence, determines the optimization domain mapping matrix, and uses the optimization domain mapping matrix to sort tasks according to the information value. This overcomes the technical defect of ignoring the importance of information in task planning in the design structure matrix, which leads to the neglect or misuse of important information in the task planning process, and improves the execution efficiency and quality of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0038] Figure 1 A flowchart of the first embodiment of the task planning method of this application is provided;

[0039] Figure 2 A flowchart of the second embodiment of the task planning method of this application is provided;

[0040] Figure 3 A flowchart of the third embodiment of the task planning method of this application is provided;

[0041] Figure 4 A flowchart of the fourth embodiment of the task planning method of this application is provided;

[0042] Figure 5 A fuzzy classification diagram of the degree of impact of each task on the project provided for the task planning method of this application;

[0043] Figure 6 The corresponding relationship diagram between the information and tasks provided for the task planning method of this application

[0044] Figure 7 Fuzzy classification diagram of information acquisition, information transmission and error consequences provided for the task planning method of this application

[0045] Figure 8 Flowchart of the DMM optimization algorithm provided for the task planning method of this application;

[0046] Fig. 9 A schematic diagram of the algorithm operation process provided for the task planning method of this application;

[0047] Fig.10 A flow chart of the overall method provided for the task planning method of this application;

[0048] Fig.11 This is a schematic diagram of the device structure of the hardware operating environment involved in the task planning method in the embodiment of the present application.

[0049] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0051] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0052] The main solution of the embodiment of the present application is: based on the degree of influence of the input information of each task on the tasks, the input information is fuzzy quantified and normalized to determine the relative value score of the input information; based on the information perspective, the task sequence of the constructed initialization domain mapping matrix is ​​optimized to determine the optimized domain mapping matrix; the tasks are sorted according to the information value of the tasks using the optimized domain mapping matrix to determine the task plan.

[0053] At present, traditional task planning methods, such as designing structural matrices, mainly focus on the connections and dependencies between tasks, and sort and group tasks based on the logical and physical relationships between tasks. The current algorithm for optimizing the task order by designing structural matrices concentrates the task associations on the diagonal as much as possible, and then manually judges the strength of the associations. This method often ignores the importance of information in task planning, which may lead to the neglect or misuse of some important information in the task planning process, thus affecting the execution efficiency and quality of the project.

[0054] This application evaluates the impact of different input information on respective tasks through fuzzy quantification and normalization processing, determines their relative value scores, optimizes the task sequence, determines the optimization domain mapping matrix, and uses the optimization domain mapping matrix to sort tasks according to the information value. This overcomes the technical defect of ignoring the importance of information in task planning in the design structure matrix, which leads to the neglect or misuse of important information in the task planning process, and improves the execution efficiency and quality of the project.

[0055] It should be noted that the execution subject of this embodiment can be a task planning device, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a task planning device capable of realizing the above functions, etc., and this embodiment does not specifically limit this. The following takes the task planning device as the execution subject as an example to illustrate this embodiment and the following embodiments.

[0056] Embodiment 1

[0057] Based on this, this application proposes a task planning method of the first embodiment, please refer to Figure 1 , the task planning method comprises:

[0058] Step S110 , based on the influence of the input information of each task on the tasks, fuzzy quantification and normalization processing are performed on the input information to determine the total information value of the tasks.

[0059] In this embodiment, the input information refers to various data required in the process of executing the task. At this time, the input information includes parameters such as air temperature, humidity, cleanliness, wind speed, etc. Fuzzy quantification is a method of converting fuzzy, uncertain or difficult to directly quantify information into specific numerical values. Normalization processing is a method of converting data of different dimensions and units into numerical values ​​under a unified scale; the task is a task in a large construction project, such as the air duct and equipment layout task of an air conditioning air system.

[0060] As an optional implementation method, all input information related to the task is collected, the impact of each input information on the task is evaluated, fuzzy evaluation data is obtained, and the membership function in fuzzy mathematics and other methods are used to convert it into specific information value. The input information after fuzzy quantification processing is normalized, and the total information value of each task is determined based on the normalized data.

[0061] Exemplarily, all input information related to the task is collected, and the degree of influence of the input information on the task is evaluated in terms of the degree of fuzziness of information transmission, the degree of personnel understanding, and the degree of influence through expert scoring and historical data analysis methods. Fuzzy evaluation data is obtained, and the membership function method in fuzzy mathematics is used to convert it into specific values ​​to determine the information value of the input information. The linear transformation method is used to obtain the total information value of the task through weighted average, summation and other methods.

[0062] Step S120 , optimizing the task sequence of the constructed initialization domain mapping matrix based on the information perspective, and determining the optimized domain mapping matrix.

[0063] In this embodiment, the information perspective refers to a method of analyzing and optimizing the task sequence from the perspective of information transmission, flow, dependency and value. The Domain Mapping Matrix (DMM) is a matrix that represents the relationship between tasks, in which each element represents a certain relationship between two tasks. Task sequence optimization refers to determining the best processing order to improve overall efficiency or performance when processing multiple tasks or steps.

[0064] As an optional implementation, the various information involved is presented through DMM, with rows representing information and columns representing tasks. The element table information in the matrix and the input-output relationship of the task are used as the initialization domain mapping matrix. The initialization domain mapping matrix is ​​analyzed, and the task order is rearranged using an optimization algorithm. According to the optimized task order, the domain mapping matrix is ​​updated to determine the optimized domain mapping matrix.

[0065] Step S130: using the optimization domain mapping matrix to sort the tasks according to the total information value of each task, and determining task planning.

[0066] In this embodiment, task sorting is to arrange tasks in a certain order according to the requirements and constraints of the project. Sorting is based on the information value factor of the task. Task planning is to determine the execution order, resource allocation, schedule, etc. of the tasks.

[0067] As an optional implementation, the information value of each task is calculated according to the optimization domain mapping matrix, and all tasks are sorted using an appropriate sorting algorithm according to the information value of the task and the dependency relationship in the optimization domain mapping matrix to create a priority list. According to the results of task sorting, a final task plan is formulated.

[0068] This embodiment provides a task planning method. This embodiment evaluates the influence of different input information on respective tasks through fuzzy quantification and normalization processing, determines their relative value scores, optimizes the task sequence, determines the optimization domain mapping matrix, and uses the optimization domain mapping matrix to sort the tasks according to the information value. This overcomes the technical defect that the design structure matrix ignores the importance of information in task planning, resulting in the neglect or misuse of important information in the task planning process, thereby improving the execution efficiency and quality of the project.

[0069] Based on Example 1, Example 2 of the present application proposes a task planning method, referring to Figure 2 , step S110 includes:

[0070] Step S210 , based on the fuzzy evaluation of the role played by each task in the project, determining the influence degree of the input information of each task on the task.

[0071] In this embodiment, fuzzy evaluation uses fuzzy sets and fuzzy logic to describe and process phenomena with unclear boundaries that are difficult to describe using classical set theory. The degree of influence refers to the importance and influence of input information on the performance of the task. Different input information has different degrees of influence on the task.

[0072] As an optional implementation, according to the role of each task in the project, fuzzy evaluation is performed through fuzzy grade scores. Figure 5 The influence degree of each task on the project is fuzzily classified, and the role of each task in the project is fuzzily evaluated. According to the evaluation results, the influence degree of the input information of each task on each task is determined.

[0073] Exemplarily, indicators for evaluating the role of each task in the project are determined. For each task, the impact of its input information on task execution is analyzed, the degree of impact of each input information on the task is represented by a fuzzy set, and a fuzzy evaluation matrix is ​​established. The elements in the matrix represent the degree of impact of the input information on the task. A weight is assigned to each evaluation indicator, and these weights reflect the importance of each indicator in the overall evaluation. The fuzzy evaluation matrix and the indicator weight are synthesized using the synthesis operation method of fuzzy mathematics. Through the synthesis operation, the comprehensive evaluation result of each task is obtained, that is, the overall impact of the input information on the task.

[0074] Step S220: performing fuzzy quantification on the information according to the degree of influence to determine the absolute information value of the information.

[0075] In this embodiment, the absolute information value is a numerical value obtained based on a fuzzy quantification method and reflecting the degree of influence of the input information.

[0076] As an optional implementation, the impact degree result of the fuzzy evaluation is converted into a specific fuzzy numerical value through the membership function in fuzzy mathematics, and the absolute information value is determined for each input information based on the result after fuzzy quantification.

[0077] Optionally, step S220 includes:

[0078] Step S221 , fuzzy quantification of the influence degree is performed according to the influence degree of the input information of each task on each task, and the importance ratio of each task is determined.

[0079] In this embodiment, the task importance ratio refers to the ratio of the importance or contribution of each task to the entire project in the engineering project.

[0080] As an optional implementation, fuzzy quantification is performed according to the impact of the information on the task, the fuzzy grade score of the task is determined, the proportion of the fuzzy grade score of each task in the total fuzzy grade scores of all tasks is calculated, and the importance proportion of each task is determined.

[0081] Exemplarily, fuzzy mathematics methods are used to perform fuzzy quantitative evaluation of the degree of impact, and a fuzzy grade score is determined for each task. The total score is obtained by adding the fuzzy grade scores of each task, and the proportion of the fuzzy grade score of each task in the total score is calculated to obtain the importance ratio of each task.

[0082] Step S222: Based on the importance ratio of each task, fuzzy quantification is performed on the single information transmission of the information to determine the absolute information value of the information.

[0083] In this embodiment, single information transfer refers to the process of transferring information from one task to another task, and the absolute information value is a numerical value obtained after fuzzy quantitative evaluation of single information transfer, which indicates the degree of influence of the information transfer on task execution.

[0084] As an optional implementation, according to the importance ratio of each task and Figure 6 The corresponding relationship between the input information and the task shown in the figure determines the relative importance of the task corresponding to the information as input information. Figure 7 The fuzzy levels of information acquisition, information transmission and error consequences of the information shown correspond to scores to determine the information value of the information. The absolute information value of the information is determined based on the ratio of the information value of the information and the relative importance of the task.

[0085] Exemplarily, the fuzzy level corresponding score of each task is used as the numerator, and the fuzzy level corresponding score of all tasks is used as the denominator, and the obtained ratio is the relative importance ratio of each task; the information value of the information is determined according to the product of the fuzzy level corresponding score of each information acquisition, the fuzzy level corresponding score of information transmission and the fuzzy level corresponding score of information consequence; the information value of the information is determined according to the importance ratio of each task and Figure 6 The corresponding relationship between the input information and the task is shown in the figure, and the relative importance ratio of the task corresponding to the information as input information is determined. According to the product of the information value of the information and the relative importance ratio of the task corresponding to the information as input information, the absolute information value of the information in the task is determined. The specific calculation formula is as follows:

[0086]

[0087] I a,absolute =I a ×T a,input

[0088] n—the number of tasks in the project;

[0089] k—the number of information in the project;

[0090] T i —The relative importance of the i-th task;

[0091] T i m —The corresponding score of the fuzzy level of the i-th task;

[0092] I a —The information value of the a-th information;

[0093] T a,input —The relative importance of the task corresponding to the a-th information as input information;

[0094] —The corresponding score of the fuzzy level of information acquisition of the a-th information;

[0095] —The corresponding score of the fuzzy level of information transmission of the a-th message;

[0096] —The corresponding score of the fuzzy level of the information consequence of the a-th information;

[0097] I a,absolute —The absolute value score of the a-th information in all tasks.

[0098] Step S230: Based on the absolute information value, the information is normalized to obtain the total information value of each task.

[0099] In this embodiment, the total information value is determined based on the absolute information value of the normalized information, which represents the total value of each task information.

[0100] As an optional implementation, an appropriate normalization method is selected, and the fuzzy grade scores are normalized according to the selected normalization method to determine the total value of the input information of each task.

[0101] Optionally, step S230 includes:

[0102] Step S231, calculating the total information value of each task according to the absolute information value and the number of the information in each task.

[0103] For example, normalization is performed, and the absolute value scores of the information are summed according to the total number of information in the task to determine the total information value of the task. The calculation formula is as follows:

[0104]

[0105] t—the tth task in the task cannot be scheduled;

[0106] h—the total number of information in the part of the task that cannot be planned;

[0107] —The total information value of the tth task in the task cannot be planned;

[0108] —It is impossible to plan the absolute value score of the a-th information in the t-th task in the task.

[0109] This embodiment provides a task planning method. This embodiment fuzzily quantifies the input information according to its impact on the task, effectively converts complex information that is difficult to directly quantify into comparable numerical values. The relative value score obtained through normalization processing clearly shows the relative importance of each task input information in the overall project, and more accurately quantifies the impact of each task input information, thereby improving the accuracy and effectiveness of decision-making.

[0110] Based on Example 1, Example 3 of the present application proposes a task planning method, referring to Figure 3 , step S120 includes:

[0111] Step S310: construct the initialization domain mapping matrix according to the information and the input and output of the corresponding task.

[0112] In this embodiment, in machine learning or related tasks, input generally refers to data provided to an algorithm or model to generate output. Corresponding to the input, the output is the result generated by the algorithm or model after receiving the input. When it is necessary to convert from one data domain to another, a matrix is ​​used to perform the conversion. The matrix composed of a set of values ​​set in the initial state is called the "initialization domain mapping matrix".

[0113] As an optional implementation, all key tasks in the project and their corresponding input and output information are identified, a two-dimensional matrix is ​​created based on the number of tasks, the information flow between tasks is analyzed, and the matrix is ​​filled to mark the existence of input and output relationships to obtain an initialization domain mapping matrix.

[0114] For example, the various information involved in this patent is presented through DMM, referring to Fig. 9 , where the first row represents the information, the first column represents the tasks, the "O" part represents that the information is output by the task, and the "X" part represents that the information will enter the task as input.

[0115] Step S320: Optimize the task sequence from an information perspective using an optimization algorithm to determine the optimization domain mapping matrix.

[0116] In this embodiment, the optimization algorithm is an algorithm that uses mathematical methods or heuristic strategies to find the optimal solution or approximate optimal solution to a problem.

[0117] As an optional implementation, the task sequence is optimized according to the order of the feedback information from an information perspective to determine the optimization domain mapping matrix.

[0118] Optionally, step S320 includes:

[0119] Step S321, eliminating rows and corresponding columns that only have input or output in the initialized domain mapping matrix to determine a simplified domain mapping matrix.

[0120] In this embodiment, the simplified domain mapping matrix is ​​a simplified matrix obtained by deleting rows and columns only related to input or output in the initialization domain mapping matrix.

[0121] As an optional implementation, the initialized domain mapping matrix is ​​traversed, each row and each column is checked, and for the rows with only inputs identified, they are placed in the last row and deleted from the matrix, and all the columns corresponding to these rows are deleted. For the columns with only outputs identified, they are deleted from the matrix, and all the rows corresponding to these columns are deleted. The resulting matrix is ​​the simplified domain mapping matrix.

[0122] For example, according to Figure 8 The algorithm flow chart shown in the figure verifies each row of tasks from the first row of DMM. If all the symbols in a row are "X", it means that the task does not have any output and only needs input. The task is placed at the end, so that all "X" can be placed under "O" as much as possible to reduce any negative feedback of information. After placement, the row is eliminated and the position of the task is recorded as the last. At the same time, each row is verified to have only "O". If a row only has "O", it means that the task only has output. The column corresponding to the "O" in the row is eliminated and the position of the task is recorded. Repeat this operation until there are no rows containing only "X" or "O".

[0123] Step S322: Based on the simplified domain mapping matrix, matrix adjustment is performed according to the feedback information sorting to determine the optimized domain mapping matrix.

[0124] In this embodiment, sorting refers to sorting the elements in the simplified domain mapping matrix according to a certain metric of the feedback information, and matrix adjustment refers to adjusting the elements in the simplified domain mapping matrix based on the sorted result.

[0125] As an optional implementation, the simplified domain mapping matrix is ​​adjusted according to the sorting of the feedback information, the output of the first row of tasks is adjusted to correspond to the frontmost column, the task rows in the preprocessing domain mapping matrix are sorted according to the value in the feedback information, the task rows with the highest priority are selected in turn, and they are prepended to the top of the matrix, and the task rows and their corresponding columns are deleted. This process is repeated until all rows are eliminated, and the elimination order is recorded. After all prepending and elimination operations are completed, the task rows are arranged according to the elimination order, and the output of the first row of tasks is adjusted to correspond to the frontmost column, and the optimized domain mapping matrix is ​​determined.

[0126] This embodiment provides a task planning method. This embodiment optimizes the task sequence of a constructed domain mapping matrix from an information perspective through an optimization algorithm to improve the efficiency of data processing.

[0127] Based on the third embodiment, the fourth embodiment of the present application proposes a task planning method, referring to Figure 4 , step S322 includes:

[0128] Step S410: Based on the simplified domain mapping matrix, the output of the first row of tasks is adjusted to correspond to the frontmost column to determine a preprocessing domain mapping matrix.

[0129] As an optional implementation, the simplified domain mapping matrix is ​​adjusted, and the output of the first row of tasks is adjusted to correspond to the frontmost column to determine the preprocessing domain mapping matrix.

[0130] Exemplarily, the order of the columns is adjusted so that all the "O"s of the first row of tasks correspond to the first column. The order of the columns refers to the order of each piece of information.

[0131] Step S420, the task rows in the preprocessing domain mapping matrix are sequentially pre-placed according to the feedback information sorting and the task rows and corresponding columns are eliminated, and a pre-placed domain mapping matrix is ​​obtained according to the elimination order.

[0132] In this embodiment, in a matrix, the process of moving a row or column to the front is called advancing; advancing usually means moving to the top or left.

[0133] As an optional implementation, the task rows in the preprocessing domain mapping matrix are sorted according to the value in the feedback information. Starting from the sorted preprocessing domain mapping matrix, the task rows with the highest priority are selected in turn and prepended to the top of the matrix, and the task rows and their corresponding columns are deleted. This process is repeated until all rows are eliminated, and the elimination order is recorded. After all prepend and elimination operations are completed, the task rows are arranged according to the elimination order to form a pre-processing domain mapping matrix.

[0134] For example, refer to Fig. 9In the DMM optimization process, after the task is adjusted, the information in the same column as "O" will all be converted into positive feedback information, that is, "O" is above "X", so it does not need to be considered, but "X" in the same row as "O" will all become negative feedback information. Therefore, in order to minimize the negative feedback information, it is necessary to summarize the information value of "X" in the corresponding row of the task. If you want the negative feedback information value to be the lowest, select the task with the lowest total information value in all rows to be placed in front, eliminate the row and column at the same time, record the row order, and rearrange the remaining information in the above way until the last row is eliminated.

[0135] Optionally, before step S420, the following steps are included:

[0136] Step S421, sorting the total value of the information in the preprocessing domain mapping matrix to determine the feedback information sorting.

[0137] In this embodiment, sorting is a process of arranging data items or information elements in a certain order.

[0138] As an optional implementation, the tasks in the preprocessing domain mapping matrix are sorted according to the total information value, the value of the feedback information is determined according to the total information value of the task, and the feedback information sorting is determined.

[0139] Step S430, normalizing the pre-domain mapping matrix, adjusting the output of the first row of tasks to correspond to the frontmost column again, and determining the optimized domain mapping matrix.

[0140] In this embodiment, normalization refers to performing some transformation on a matrix or vector so that it meets certain conditions or standards.

[0141] As an optional implementation, the information order in the front domain mapping matrix is ​​adjusted, the output of the first row of tasks is adjusted to correspond to the frontmost column, and other rows are adjusted in sequence to determine the optimized domain mapping matrix.

[0142] Exemplarily, the pre-domain mapping matrix is ​​normalized, and the order of each piece of information is adjusted, that is, the order of the columns is adjusted so that all the "O"s of the first row of tasks correspond to the first column, and all the subsequent "O"s are adjusted. The adjusted matrix is ​​the optimized domain mapping matrix.

[0143] As an example of the task planning method provided in this application, refer to Fig.10The overall method flow is as follows: first, the DSM optimization matrix is ​​obtained, and the information value of the tasks that cannot be planned by DSM is planned. The role of each task in the entire project is fuzzily evaluated, the fuzzy grade score is determined, and the importance ratio of each task is determined; on this basis, the degree of influence of information on the task is quantified based on the fuzziness of the information transmission of the task, the degree of personnel understanding, and the degree of influence of the input information of each task. According to the corresponding scores of the fuzzy grades of the three aspects, the information value of each information is determined, and then the transmission of a single piece of information is quantified. According to the information value of the information and the relative importance ratio of the task, the absolute information value of the information in the task is determined; according to the absolute information value of the information in the task, the tasks that cannot be planned are normalized to obtain the total information value of each task, and the fuzzy quantified value of each information and the corresponding task are formed into a DMM, where the first row represents each piece of information, the first column represents each task, the "O" part represents that the information is output by the task, and the "X" part represents that the information will enter the task as input. Verify each row of tasks from the first row of DMM. If all the symbols in a row are "X", it means that the task does not have any output and only needs input. The task is placed at the end, so that all "X" can be placed under "O" as much as possible to reduce any negative feedback of information. After placement, the row is eliminated and the position of the task is recorded as the last. At the same time, verify that only "O" exists in each row. If there is only "O" in a row, it means that the task only has output. Eliminate the column corresponding to the "O" in the row and the row, and record the position of the task. Repeat this operation until there is no row containing only "X" or "O". Preprocess the remaining matrix and adjust the order of the columns, that is, the order of each information, so that all "O" of the first row of tasks correspond to the front column, and continue to adjust all the following "O". This adjustment can easily see the way information is transmitted before adjustment. After the task is adjusted, all the information in the same column as "O" will be converted into positive feedback information, that is, "O" is above "X", so it does not need to be considered, but "X" in the same row as "O" will all become negative feedback information. Therefore, in order to minimize the negative feedback information, it is necessary to summarize the information value of "X" in the corresponding row of the task. If you want the negative feedback information value to be the lowest, select the task with the lowest total information value in all rows to be placed in front, and eliminate the row and column at the same time, record the order of the row, and rearrange the remaining information in the above manner until the last row is eliminated. Rearrange the order of the eliminated rows and adjust the order of the columns, that is, the order of each information, so that all "O" of the first row of tasks correspond to the frontmost column, and obtain the optimization domain mapping matrix. Based on the optimization domain mapping matrix, all tasks are sorted according to the total information value of the tasks, and the final task plan is formulated according to the results of the task sorting.

[0144] Optionally, the optimized domain mapping matrix is ​​determined based on matrix adjustment according to the feedback information sorting, and a method of adding an error handling mechanism is used to ensure recovery in case of data inconsistency or algorithm failure.

[0145] Optionally, based on adjusting the order of the columns, a heuristic algorithm is also used to select the best column order, for example, using optimization methods such as genetic algorithms and simulated annealing algorithms to evaluate the pros and cons of different column orders according to the value of feedback information, and selecting the best column order for adjustment.

[0146] This embodiment provides a task planning method. This embodiment is based on a new algorithm for task optimization from the information perspective of DMM. By combining information fuzzy quantification and DMM structure, efficient optimization of task execution is achieved. This algorithm not only improves the efficiency and stability of task execution, but also enhances the adaptability and flexibility of the system, providing an effective solution for the processing of complex tasks.

[0147] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the task planning method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0148] The present application provides a task planning device, which includes: at least one processor; and 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the task planning method in the above-mentioned embodiment one.

[0149] Reference below Fig.11 , which shows a schematic diagram of the structure of a task planning device suitable for implementing an embodiment of the present application. The task planning device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.11 The task planning device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0150] like Fig.11As shown, the task planning device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the xxx device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the mission planning device to communicate wirelessly or wired with other devices to exchange data. Although the mission planning device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0151] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0152] The task planning device provided by the present application adopts the task planning method in the above embodiment, which can solve the technical problem of how to improve the execution efficiency and quality of the project. Compared with the prior art, the beneficial effects of the task planning device provided by the present application are the same as the beneficial effects of the task planning method provided by the above embodiment, and the other technical features in the task planning device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0153] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0154] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0155] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the task planning method in the above-mentioned embodiment.

[0156] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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 above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0157] The computer-readable storage medium may be included in the mission planning device; or may exist independently without being assembled into the mission planning device.

[0158] The computer-readable storage medium carries one or more programs, and when the one or more programs are executed by the task planning device, the task planning device can write a computer program code for performing the operation of the present application in one or more programming languages ​​or a combination thereof, and the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and a conventional procedural programming language, such as "C" language or a similar programming language. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely 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).

[0159] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0160] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0161] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned task planning method, and can solve the technical problem of how to improve the execution efficiency and quality of the project. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the task planning method provided in the above-mentioned embodiment, and will not be repeated here.

[0162] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A task planning method, It is characterized in that The task planning method includes: Based on the influence of the input information of each task on the tasks, the input information is fuzzy quantified and normalized to determine the total information value of each task; Based on the information perspective, the constructed initialization domain mapping matrix is ​​optimized in task order to determine the optimized domain mapping matrix; The optimization domain mapping matrix is ​​used to sort the tasks according to the total information value of each task to determine the task plan.

2. The task planning method according to claim 1, It is characterized in that The step of performing fuzzy quantification and normalization processing on the input information based on the influence degree of the input information of each task on the task to determine the total value of the information of each task includes: Determining the influence of the input information of each task on each task based on the fuzzy evaluation of the role played by each task in the project; According to the degree of influence, the information is fuzzily quantified to determine the absolute information value of the information; Based on the absolute information value, the information is normalized to obtain the total information value of each task.

3. The task planning method according to claim 2, It is characterized in that The step of performing fuzzy quantification on the information according to the degree of influence to determine the absolute information value of the information comprises: According to the influence of the input information of each task on each task, the influence degree is fuzzy quantified to determine the importance ratio of each task; Based on the importance ratio of each task, the single information transmission of the information is fuzzily quantified to determine the absolute information value of the information.

4. The task planning method according to claim 2, It is characterized in that The step of performing the normalization processing on the information based on the absolute information value to obtain the total information value of each task includes: The total information value of each task is calculated according to the absolute information value and the number of the information in each task.

5. The task planning method according to claim 1, It is characterized in that The step of optimizing the task sequence of the constructed initialization domain mapping matrix based on the information perspective and determining the optimized domain mapping matrix comprises: Constructing the initialization domain mapping matrix according to the information and the input and output of the corresponding task; The task sequence is optimized from an information perspective by using an optimization algorithm to determine the optimization domain mapping matrix.

6. The task planning method according to claim 5, It is characterized in that The step of optimizing the task sequence from an information perspective by using an optimization algorithm and determining the optimization domain mapping matrix comprises: Eliminating rows and corresponding columns having only input or output in the initialization domain mapping matrix to determine a simplified domain mapping matrix; Based on the simplified domain mapping matrix, matrix adjustment is performed according to the feedback information sorting to determine the optimized domain mapping matrix.

7. The task planning method according to claim 6, It is characterized in that The step of adjusting the matrix based on the simplified domain mapping matrix and sorting the feedback information to determine the optimized domain mapping matrix comprises: Based on the simplified domain mapping matrix, adjusting the output of the first row of tasks to correspond to the frontmost column, and determining a preprocessing domain mapping matrix; The task rows in the preprocessing domain mapping matrix are sequentially pre-placed according to the feedback information sorting and the task rows and corresponding columns are eliminated, and a pre-placed domain mapping matrix is ​​obtained according to the elimination order; The pre-domain mapping matrix is ​​normalized, and the output of the first row of tasks is adjusted again to correspond to the frontmost column to determine the optimized domain mapping matrix.

8. The task planning method according to claim 7, It is characterized in that Before the step of sequentially prepending the task rows in the preprocessing domain mapping matrix according to the feedback information sorting and eliminating the task rows and corresponding columns to obtain the pre-processing domain mapping matrix, the step includes: The total value of the information in the preprocessing domain mapping matrix is ​​sorted to determine the feedback information sorting.

9. A mission planning device, It is characterized in that The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the task planning method according to any one of claims 1 to 8.

10. A storage medium, It is characterized in that The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the task planning method according to any one of claims 1 to 8 are implemented.

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