Digital railway organization collaboration method, device, equipment, medium and program

Abstract

The invention relates to a digital railway organization collaboration method, device, equipment, medium and program, and the method comprises the steps: collecting the department information of a plurality of preset departments and the personnel information of employees in the departments, and constructing an information base according to the department information and the employee information; obtaining task information of a plurality of target tasks, selecting one piece of task information as target information, and calculating a matching degree between the target information and the information of each department; obtaining the total number of existing department tasks and the total number of department employees of each department, determining a target department according to the matching degree, and calculating the similarity between the target task and the information of each person in the target department; obtaining the existing task load of each person in the target department, and calculating the task number index of each person in the target department according to the existing task load and the similarity; distributing the target task to the corresponding employee according to the task number index; and tracking the task progress of all employees in real time and carrying out task reminding.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing and data analysis, and in particular to a digital railway agency collaboration method, device, equipment, medium and program. Background Art

[0002] Heavy-haul railway production agencies refer to the organizations and management agencies responsible for heavy-haul railway transportation production activities. Due to the increasing demand for large-scale freight transportation, in order to ensure the safe, efficient and stable operation of heavy-haul railways, a digital railway agency collaboration method can track task progress in real time, monitor task status, and ensure efficient collaboration of heavy-haul railway production agencies.

[0003] The existing heavy-haul railway production agency operation management system is based on the traditional manual management model. However, in actual work, there are many departments in the heavy-haul railway production agency, and the work tasks involve multiple departments. The traditional management model cannot achieve balanced distribution, resulting in employees being overloaded or too idle, and low efficiency in completing tasks. At the same time, it is impossible to monitor the progress of tasks in real time and feedback the task status, resulting in poor real-time performance when working in the heavy-haul railway production agency. Summary of the invention

[0004] The present invention discloses a digital railway agency collaboration method, device, equipment, medium and program, the main purpose of which is to solve the problems of unbalanced task allocation and poor real-time performance of task allocation in heavy-duty railway production agencies.

[0005] In a first aspect, the present disclosure provides a digital railway agency collaboration method, comprising:

[0006] Collecting department information of multiple preset departments and collecting personnel information of employees in each preset department, and building an information database based on the department information and the employee information;

[0007] Acquire task information of multiple target tasks, select one of the task information as target information one by one, and calculate the matching degree between the target information and each department information in the information database;

[0008] Obtain the total number of existing department tasks and the total number of department employees of each department, confirm the target department according to the total number of existing department tasks, the total number of department employees and the matching degree, and calculate the similarity between the task information and the information of each person in the target department one by one;

[0009] Obtaining the existing task load of each person in the target department, and calculating the task number index of each person in the target department according to the existing task load and the similarity;

[0010] Allocate tasks corresponding to the target information to corresponding employees according to the task number indicator;

[0011] Track the task progress of all employees in real time, and provide task reminders to departments and employees based on the task progress.

[0012] In some embodiments, the building of an information database according to the department information and the employee information includes:

[0013] Extracting department keywords from the department information of each preset department one by one, and generating department searches of each preset department in a preset database according to the department keywords;

[0014] Extracting the personnel skill keywords of each employee information one by one, and generating the personnel search of each employee in the preset database according to the personnel skill keywords;

[0015] The personnel search is associated with the department search of each employee's department and stored to obtain an information database.

[0016] In the embodiment of the present invention, extracting department keywords from the department information of each preset department one by one includes:

[0017] One of the multiple preset departments is selected one by one as the department to be processed, and the department information of the department to be processed is segmented to obtain the department information segmentation;

[0018] Select one of the department information segmentations one by one as the segmentation to be processed;

[0019] Counting the number of local occurrences of the to-be-processed segmentation word in the department information of the to-be-processed department, and counting the number of global occurrences of the to-be-processed segmentation word in the department information of all preset departments;

[0020] Dividing the local number of occurrences by the global number of occurrences to obtain the criticality of the word to be processed;

[0021] The to-be-processed sub-words whose criticality is greater than a preset threshold are collected as keywords of the department information of the to-be-processed department.

[0022] In the embodiment of the present invention, the calculating the matching degree between the target information and each department information in the information database includes:

[0023] Converting the target information into a target vector;

[0024] Converting the department retrieval corresponding to each department information in the information database into a department vector;

[0025] The matching degree between the target information and each department information in the information base is calculated according to the target vector and the department vector.

[0026] In the embodiment of the present invention, the determining the target department according to the total number of tasks of the existing department, the total number of employees of the department and the matching degree includes:

[0027] Calculate the average number of tasks for employees in each department based on the total number of existing department tasks and the total number of employees in the department;

[0028] The degree of fit between each department and the target task corresponding to the target information is calculated according to the matching degree and the average number of tasks, and the department with the greatest degree of fit is determined as the target department.

[0029] In the embodiment of the present invention, the step of calculating the similarity between the task information and the information of each person in the target department one by one includes:

[0030] Converting the task information into a task vector;

[0031] Converting the personnel retrieval corresponding to each personnel information in the target department into a personnel vector;

[0032] The similarity between the task information and the personnel information in the target department is calculated using the following formula:

[0033] W=|xy i ∣

[0034] Among them, x is the task vector obtained by transforming the task information, y i is the personnel vector corresponding to the i-th personnel information in the target department, and W is the similarity.

[0035] In the embodiment of the present invention, the obtaining of the existing task load of each person in the target department includes:

[0036] Selecting one employee in the target department as a target employee one by one;

[0037] Obtaining the task weight of the target employee's existing tasks and the efficiency of completing the existing tasks;

[0038] Calculate the maximum efficiency of the target employee according to the task weight of the existing task and the efficiency of completing the existing task;

[0039] The existing task load of the target employee is calculated according to the average number of tasks and the maximum efficiency.

[0040] In a second aspect, the present disclosure provides a digital railway agency collaboration device, including:

[0041] An information collection module is used to collect department information of multiple preset departments and personnel information of employees in each preset department, and to build an information database based on the department information and the employee information;

[0042] A matching degree calculation module obtains task information of multiple target tasks, selects one of the task information as target information one by one, and calculates the matching degree between the target information and each department information in the information database;

[0043] A similarity calculation module is used to obtain the total number of existing department tasks and the total number of department employees of each department, confirm the target department according to the total number of existing department tasks, the total number of department employees and the matching degree, and calculate the similarity between the task information and the information of each person in the target department one by one;

[0044] A task number index calculation module is used to obtain the existing task load of each person in the target department, and calculate the task number index of each person in the target department according to the existing task load and the similarity;

[0045] A task allocation module allocates tasks corresponding to the target information to corresponding employees according to the task number indicator;

[0046] The task monitoring module tracks the task progress of all employees in real time and issues task reminders to departments and employees based on the task progress.

[0047] In a third aspect, the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above aspects.

[0048] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described in the above aspects when executed by a processor.

[0049] In a fifth aspect, the present disclosure provides a computer program product, including a computer program, which implements the steps of the method described in the above aspects when executed by a processor.

[0050] The present invention collects department information of multiple preset departments and personnel information of employees in each department to build an information database, which can facilitate data reading, reduce the amount of data, and thus improve the efficiency of subsequent data processing. By acquiring task information of multiple target tasks, selecting one of the task information as the target information one by one, and calculating the matching degree between the target information and each department information in the information database, data vectorization and data dimension reduction can be achieved, thereby facilitating subsequent matching degree calculation. By selecting the department with the largest matching degree as the target department, and calculating the similarity between the task information and each personnel information in the target department one by one, the best matching personnel information can be obtained, which is convenient for subsequent task allocation and provides another layer of guarantee for balanced task allocation. By acquiring the existing task load of each person in the target department and combining the similarity, the task number index of each person in the target department is obtained. According to the task number index, the tasks corresponding to the target information are allocated to the corresponding employees, which can achieve balanced task allocation, improve work quality and work efficiency, track the task progress of all employees in real time, and remind the departments and employees of tasks that reach the preset threshold, which can reduce work accumulation and improve real-time performance. Therefore, the digital railway agency collaboration method, device, equipment, medium and program proposed in the present invention can solve the problems of unbalanced task allocation and poor real-time task monitoring of production agencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0052] Figure 1 A flowchart of a digital railway agency collaboration method provided in an embodiment of the present disclosure.

[0053] Figure 2 A structural schematic diagram of a digital railway agency collaboration device provided in an embodiment of the present disclosure.

[0054] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, and to fully understand and implement how the present disclosure applies technical means to solve technical problems and achieve the corresponding technical effects, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. The embodiments of the present disclosure and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the scope of protection of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0056] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0057] The embodiment of the present application provides a digital railway agency collaboration method. The execution subject of the digital railway agency collaboration method includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the system provided by the embodiment of the present application. In other words, the digital railway agency collaboration method can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0058] Reference Figure 1 The figure is a flow chart of a digital railway agency collaboration method provided by one embodiment of the present invention.

[0059] In this embodiment, the digital railway agency collaboration method includes:

[0060] S1. Collect department information of multiple preset departments and collect personnel information of employees in each preset department, and build an information database based on the department information and the personnel information.

[0061] In an embodiment of the present invention, the department information includes functional information of different departments within an institution, company, or enterprise, such as main business, management scope, etc.; the personnel information refers to the personal work information of each employee within each department, such as the employee's professional skills, business that he or she is good at handling, etc.

[0062] In the embodiment of the present invention, programming languages ​​such as python and Java can be used to connect to the preset department through the HTTP protocol, and HTTP requests can be sent to obtain data, so as to collect the department information and personnel information.

[0063] In detail, the information database refers to a database that stores a large amount of data and information documents, including department information of the multiple preset departments and personnel information of employees in each department. In order to more clearly and intuitively understand the department information and employee information in each department, it is necessary to build an information database to improve the relevance of the task with the department and the employees in the department.

[0064] In the existing technology, when constructing an information database, the department information and the employee information are usually simply stored. When the data volume of the department information and the employee information is large, this method often causes data confusion and redundancy, resulting in low efficiency of data analysis.

[0065] In an embodiment of the present invention, an information database with a multi-level structure can be established based on the department information and the employee information, so that the department information and the employee information have a higher structure when stored, thereby improving the efficiency of subsequent analysis of the department information and the employee information.

[0066] In detail, the building of the information database according to the department information and the employee information includes:

[0067] Extracting department keywords from the department information of each preset department one by one, and generating department searches of each preset department in a preset database according to the department keywords;

[0068] Extracting the personnel skill keywords of each employee information one by one, and generating the personnel search of each employee in the preset database according to the personnel skill keywords;

[0069] The personnel search is associated with the department search of each employee's department and stored to obtain an information database.

[0070] In an embodiment of the present invention, since the data volume of the department information and the personnel information is large, in order to realize quick processing of the department information and the personnel information, the department keywords in each department information can be extracted one by one, and the personnel skill keywords in each employee information can be extracted one by one, thereby realizing a reduction in the amount of data and reducing the complexity of the constructed information database.

[0071] In detail, extracting department keywords from the department information of each preset department one by one includes:

[0072] One of the multiple preset departments is selected one by one as the department to be processed, and the department information of the department to be processed is segmented to obtain the department information segmentation;

[0073] Select one of the department information segmentations one by one as the segmentation to be processed;

[0074] Counting the number of local occurrences of the to-be-processed segmentation word in the department information of the to-be-processed department, and counting the number of global occurrences of the to-be-processed segmentation word in the department information of all preset departments;

[0075] Dividing the local number of occurrences by the global number of occurrences to obtain the criticality of the word to be processed;

[0076] The to-be-processed sub-words whose criticality is greater than a preset threshold are collected as keywords of the department information of the to-be-processed department.

[0077] In the embodiment of the present invention, the step of extracting the personnel skill keywords of each employee information one by one is consistent with the step of extracting the department keywords in the department information of each preset department one by one, and will not be described in detail here.

[0078] In detail, the department keywords and the personnel skill keywords may be converted into word vectors, and the converted word vectors may be used as the department search and the personnel search.

[0079] In the embodiment of the present invention, by constructing an information database through department keywords and personnel skill keywords, data dimensionality reduction of the department information and the personnel information can be achieved, thereby improving the construction efficiency of the information database.

[0080] In the embodiment of the present invention, the department information and the personnel information are optimized and stored by constructing an information database, so that the department information and the personnel information are more structured, which can improve the efficiency of subsequent analysis of the department information and the employee information.

[0081] In this embodiment, the department information and the employee information are collected, and an information database is constructed according to the department information and the employee information to obtain an information database, thereby improving the efficiency of subsequent data processing.

[0082] S2, obtaining task information of multiple target tasks, selecting one of the task information as target information one by one, and calculating the matching degree between the target information and each department information in the information database;

[0083] In the embodiment of the present invention, the task information includes task content, task time, task scope, etc.

[0084] In the embodiment of the present invention, the information of the task is obtained by calling a specific API interface, such as GetPackageJob, GetAsyncTask, etc.

[0085] Alternatively, an information aggregation application such as Feedly may be used to aggregate the target tasks from different sources into a unified preset platform, so as to facilitate convenient and fast task information acquisition.

[0086] Specifically, in order to maximize efficiency, it is necessary to preferentially screen out the task-related departments with the highest matching degree with the target tasks from the information database to ensure the best matching between the departments and the target tasks, thereby facilitating subsequent task allocation.

[0087] In the embodiment of the present invention, the calculating the matching degree between the target information and each department information in the information database includes:

[0088] Converting the target information into a target vector;

[0089] Converting the department retrieval corresponding to each department information in the information database into a department vector;

[0090] The matching degree between the target information and each department information in the information base is calculated according to the target vector and the department vector.

[0091] In detail, the calculating the matching degree between the target information and each department information in the information base according to the target vector and the department vector includes:

[0092] The following formula is used to calculate the matching degree between the target information and each department information in the information database:

[0093]

[0094] Among them, x is the target vector obtained by transforming the target information, y i is the department vector corresponding to the i-th department information in the information database, and F is the matching degree.

[0095] In detail, word embedding methods such as one-hot model, bag-of-words model, etc. can be used to convert the target information and the department retrieval corresponding to each department information in the information database into vector form to facilitate subsequent matching degree calculation.

[0096] In the embodiment of the present invention, the target information and the information of each department in the information database can be used to achieve data dimension reduction and compression. By converting the data into the vector form, the interpretability of the data can be enhanced, which is convenient for subsequent matching degree calculation.

[0097] In an embodiment of the present invention, resource management of target information and department information is optimized by calculating the matching degree, making the target information and the department information more vectorial and computable, which can improve the efficiency of subsequent analysis of the target information and the department information, thereby improving the balance of subsequent task allocation.

[0098] S3, obtaining the total number of existing department tasks and the total number of department employees of each department, confirming the target department according to the total number of existing department tasks, the total number of department employees and the matching degree, and calculating the similarity between the task information and the information of each person in the target department one by one;

[0099] In an embodiment of the present invention, the total number of existing department tasks is collected by calling the COUNTIFS function in Excel, and the total number of department employees is obtained by calling a specific API interface, such as POST, access_token, etc., to collect the total number of department employees.

[0100] In the embodiment of the present invention, the determining of the target department according to the total number of tasks of the existing department, the total number of employees of the department and the matching degree includes:

[0101] Calculate the average number of tasks for employees in each department based on the total number of existing department tasks and the total number of employees in the department;

[0102] The degree of fit between each department and the target task corresponding to the target information is calculated according to the matching degree and the average number of tasks, and the department with the greatest degree of fit is determined as the target department.

[0103] The average number of tasks is calculated using two indicators: the total number of existing department tasks and the total number of department employees.

[0104] The average number of tasks is calculated using the following formula:

[0105] Average number of tasks = T / ∑(Mi)

[0106] Wherein, Mi represents the total number of department employees, and T represents the total number of existing department tasks. In the embodiment of the present invention, the target department is determined by calculating the average number of tasks and the matching degree. By calculating the average number of tasks, the average trend of the total number of existing department tasks and the total number of department employees can be reflected, and an intuitive and practical expression of the department task information and the personnel information can be provided, which is convenient for the calculation of subsequent task loads.

[0107] Specifically, according to the average number of tasks and the matching degree, an Excel table is pulled and the maximum value is found by using the VLOOKUP function in conjunction with the max function, thereby achieving efficient and fast selection of the maximum matching degree and determining the department with the highest matching degree as the target department.

[0108] In an embodiment of the present invention, in order to realize the subsequent allocation of personnel and tasks, the similarity between the task information and the personnel information in the target department is calculated one by one to ensure the maximum similarity between the task information and the personnel information, thereby improving the analysis efficiency of subsequent data processing and facilitating subsequent task allocation.

[0109] In the embodiment of the present invention, the step of calculating the similarity between the task information and the information of each person in the target department one by one includes:

[0110] Converting the task information into a task vector;

[0111] Converting the personnel retrieval corresponding to each personnel information in the target department into a personnel vector;

[0112] The similarity between the task information and the personnel information in the target department is calculated using the following formula:

[0113] W=|xy i ∣

[0114] Among them, x is the task vector obtained by transforming the task information, y i is the personnel vector corresponding to the i-th personnel information in the target department, and W is the similarity.

[0115] In detail, the task information and the personnel retrieval corresponding to the personnel information in the target department may be converted into a vector form by using term frequency-inverse document frequency or N-gram model, so as to facilitate subsequent similarity calculation.

[0116] In the embodiment of the present invention, the data dimension of the information database can be reduced by using the task information and the information of each person in the target department, thereby reducing the complexity. The computability of the data can be enhanced by converting it into the vector form.

[0117] In an embodiment of the present invention, data compression is performed on the task information and the personnel information by calculating similarity, so that they are converted into the task vector and the personnel vector, thereby improving the balance of subsequent task allocation and the subsequent analysis efficiency of data processing, and eliminating the differences in subsequent task allocation.

[0118] S4, obtaining the existing task load of each person in the target department, and calculating the task number index of each person in the target department according to the existing task load and the similarity;

[0119] In the embodiment of the present invention, the existing task load includes two indicators: the average number of tasks and the maximum efficiency. The maximum efficiency refers to maximizing the quality of completing the target task per unit time. The existing task load is used to further determine the task volume of each person in the target department and the maximum amount of tasks that can be completed in the future.

[0120] In the embodiment of the present invention, the obtaining of the existing task load of each person in the target department includes:

[0121] Selecting one employee in the target department as a target employee one by one;

[0122] Obtaining the task weight of the target employee's existing tasks and the efficiency of completing the existing tasks;

[0123] Calculate the maximum efficiency of the target employee according to the task weight of the existing task and the efficiency of completing the existing task;

[0124] The existing task load of the target employee is calculated according to the average number of tasks and the maximum efficiency.

[0125] In detail, the task weight is obtained by retrieving the task information, or the task weight is determined by using the AHP analytic hierarchy process. The AHP analytic hierarchy process is widely used in the calculation weight design, which represents a complex problem as a hierarchical structure, determines the relationship between the tasks, and analyzes the importance of the relationship between the tasks to determine the task weight.

[0126] In the embodiment of the present invention, the step of calculating the maximum efficiency of the target employee according to the task weight of the existing task and the efficiency of completing the existing task includes:

[0127] Converting the task weight into a weight vector;

[0128] Converting the efficiency of completing the existing task into an efficiency vector;

[0129] The maximum efficiency of the existing task is calculated according to the weight vector and the efficiency vector.

[0130] The maximum efficiency is calculated using the following formula:

[0131]

[0132] Among them, w i is the weight vector obtained by transforming the weight of the i-th task, c i is the efficiency vector corresponding to the efficiency of completing the i-th existing task, E is the maximum efficiency, and n represents the sequence number, that is, the task number.

[0133] In the embodiment of the present invention, the average number of tasks and the maximum efficiency are used as two major indicators to obtain the corresponding task load, and the task number indicator of each person in the target department is calculated by calculating the existing task load and the similarity, thereby reducing the complexity of data and accelerating data processing efficiency. The task number indicator refers to the maximum amount of tasks that can be completed by each person in the target department when the efficiency is at the maximum value.

[0134] In the embodiment of the present invention, the step of calculating the task number index of each person in the target department according to the existing task load and the similarity includes:

[0135] The absolute value of the difference obtained by subtracting the existing task load from the similarity is used as the task number indicator.

[0136] In the embodiment of the present invention, the task number indicator provides specific and measurable business needs more clearly and intuitively, making the analysis and interpretation of data more intuitive and effective, thereby optimizing subsequent task allocation, thereby improving the participation and effectiveness of the departments and personnel.

[0137] In the embodiment of the present invention, by calculating the task number index, the target tasks can be better allocated to the corresponding personnel, the target tasks can be evenly distributed, and the efficiency and quality of task completion can be improved. Specifically, polling, random allocation, and more complex allocation strategies based on employee capabilities and availability can be used to make the task load of the department personnel close to the average number of tasks, thereby maintaining load balance.

[0138] S5. Allocate tasks corresponding to the target information to corresponding employees according to the task number indicator;

[0139] In the embodiment of the present invention, by calculating the task number index, the tasks are allocated to corresponding employees according to the task number index, which plays a role of balanced distribution and improves the work quality. At the same time, the combination of work and rest improves work efficiency and ensures the quality controllability of the tasks.

[0140] For example, in the target department, Xiao Wang's task number index is 10%, and Xiao Li's task number index is 20%. By comparing the task number indexes of Xiao Wang and Xiao Li, it is found that Xiao Wang's task number index is smaller, so Xiao Wang is more suitable for the target task, and the target task is assigned to Xiao Wang.

[0141] In an embodiment of the present invention, the tasks corresponding to the target information are allocated to corresponding employees according to the task number indicator. Through the task allocation, the completion status of the target tasks and the work efficiency of the employees are continuously monitored, and through the load balancing, it is ensured that the department personnel are not overloaded or idle, thereby improving the overall work efficiency.

[0142] S6. Track the task progress of all employees in real time, and provide task reminders to departments and employees based on the task progress.

[0143] In this embodiment, the task progress refers to a detailed description of the progress of the task, including the number of completed tasks and the total number of tasks. The task progress helps the department and employees understand the latest status of the task, and timely reminds the department and employees of the task according to the task progress, thereby reducing forgetfulness, reducing work backlog, and improving real-time performance.

[0144] In detail, the task progress calculation formula is:

[0145] S = completed tasks / total tasks * 100%

[0146] The preset task progress threshold is 50%, and S is the task progress.

[0147] In the embodiment of the present invention, the real-time tracking of the task progress of all employees can provide real-time feedback at each stage, so that the administrator can understand the real-time progress of each task.

[0148] In the embodiment of the present invention, the task reminder to the department and the employee according to the task progress includes:

[0149] Determining whether the task progress exceeds the preset threshold;

[0150] If the preset threshold is exceeded, no task reminder will be given;

[0151] If the preset threshold is not exceeded, task reminders are given to the departments and employees who have not exceeded the preset threshold.

[0152] In the embodiment of the present invention, the task progress of all employees is tracked in real time, and task reminders are given to departments and employees based on the task progress to prevent omissions and delays in progress, thereby improving personal efficiency and work quality, improving task completion efficiency, and enhancing real-time performance.

[0153] In this embodiment, the notification process is automated, so the conditional probability is 1, indicating that the notification process is a deterministic behavior. When the task progress is lower than the task progress threshold, task reminders are given to departments and employees based on the task progress to prevent omissions and delays in progress. When the departments work together, the task progress will be pushed to the multiple departments to remind them to work together, thereby maintaining smooth communication and coordination between teams, and greatly improving real-time performance. Through the automated notification process, human forgetfulness and delayed notifications are reduced, thereby improving work efficiency and response speed. The task progress calculation formula improves work efficiency and quality and optimizes resource management.

[0154] The present invention collects department information of multiple preset departments and personnel information of employees in each department to build an information database, which can facilitate data reading, reduce the amount of data, and thus improve the efficiency of subsequent data processing. By acquiring task information of multiple target tasks, selecting one of the task information as the target information one by one, and calculating the matching degree between the target information and each department information in the information database, data vectorization and data dimension reduction can be achieved, thereby facilitating subsequent matching degree calculation. By selecting the department with the largest matching degree as the target department, and calculating the similarity between the task information and each personnel information in the target department one by one, the best matching personnel information can be obtained, which is convenient for subsequent task allocation and provides another layer of guarantee for balanced task allocation. By acquiring the existing task load of each person in the target department and combining the similarity, the task number index of each person in the target department is obtained. According to the task number index, the tasks corresponding to the target information are allocated to the corresponding employees, which can achieve balanced task allocation, improve work quality and work efficiency, track the task progress of all employees in real time, and remind the departments and employees of tasks that reach the preset threshold, which can reduce work accumulation and improve real-time performance. Therefore, the digital railway agency collaboration method, device, equipment, medium and program proposed in the present invention can solve the problems of unbalanced task allocation and poor real-time task monitoring of production agencies.

[0155] In some implementations of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0156] In some implementations of this embodiment, a computer program product is provided, including a computer program / instructions, characterized in that when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0157] The processor may include, but is not limited to, one or more processors or microprocessors, etc. Each processor may be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute the methods in the above embodiments.

[0158] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and the computer-readable storage medium may include but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0159] The computer-readable storage medium may also store at least one computer executable program / instruction, which may be, for example, a computer-readable instruction. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The computer-readable storage medium may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0160] In addition, the computer device may also include (but not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.), etc.

[0161] The processor may communicate with external devices via an I / O bus via a wired or wireless network.

[0162] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product / computer program product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0163] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes 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 box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0164] It should be noted that in the present disclosure, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element limited by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0165] Although the embodiments disclosed in the present disclosure are as above, the above contents are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the technical field to which the present disclosure belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A digital railway agency collaboration method, characterized in that: The method comprises: Collecting department information of multiple preset departments and collecting personnel information of employees in each preset department, and building an information database based on the department information and the employee information; Acquire task information of multiple target tasks, select one of the task information as target information one by one, and calculate the matching degree between the target information and each department information in the information database; Obtain the total number of existing department tasks and the total number of department employees of each department, confirm the target department according to the total number of existing department tasks, the total number of department employees and the matching degree, and calculate the similarity between the task information and the information of each person in the target department one by one; Obtaining the existing task load of each person in the target department, and calculating the task number index of each person in the target department according to the existing task load and the similarity; Allocate tasks corresponding to the target information to corresponding employees according to the task number indicator; Track the task progress of all employees in real time, and provide task reminders to departments and employees based on the task progress.

2. The method according to claim 1, characterized in that: The step of constructing an information database according to the department information and the employee information includes: Extracting department keywords from the department information of each preset department one by one, and generating department searches of each preset department in a preset database according to the department keywords; Extracting the personnel skill keywords of each employee information one by one, and generating the personnel search of each employee in the preset database according to the personnel skill keywords; The personnel search is associated with the department search of each employee's department and stored to obtain an information database.

3. The method according to claim 2, characterized in that The step of extracting department keywords from the department information of each preset department one by one includes: One of the multiple preset departments is selected one by one as the department to be processed, and the department information of the department to be processed is segmented to obtain the department information segmentation; Select one of the department information segmentations one by one as the segmentation to be processed; Counting the number of local occurrences of the to-be-processed segmentation word in the department information of the to-be-processed department, and counting the number of global occurrences of the to-be-processed segmentation word in the department information of all preset departments; Dividing the local number of occurrences by the global number of occurrences to obtain the criticality of the word to be processed; The to-be-processed sub-words whose criticality is greater than a preset threshold are collected as keywords of the department information of the to-be-processed department.

4. The method according to claim 1, characterized in that: The calculating the matching degree between the target information and each department information in the information database includes: Converting the target information into a target vector; Converting the department retrieval corresponding to each department information in the information database into a department vector; The matching degree between the target information and each department information in the information base is calculated according to the target vector and the department vector.

5. The method according to claim 1, characterized in that The calculating the similarity between the task information and the information of each person in the target department one by one includes: Converting the task information into a task vector; Converting the personnel retrieval corresponding to each personnel information in the target department into a personnel vector; The similarity between the task information and the personnel information in the target department is calculated using the following formula: W=∣x-y i ∣ Among them, x is the task vector obtained by transforming the task information, y i is the personnel vector corresponding to the i-th personnel information in the target department, and W is the similarity.

6. The method according to claim 1, characterized in that The obtaining of the existing task load of each person in the target department includes: Selecting one employee in the target department as a target employee one by one; Obtaining the task weight of the target employee's existing tasks and the efficiency of completing the existing tasks; Calculate the maximum efficiency of the target employee according to the task weight of the existing task and the efficiency of completing the existing task; The existing task load of the target employee is calculated according to the average number of tasks and the maximum efficiency.

7. A digital railway agency collaboration device, characterized in that: include: An information collection module is used to collect department information of multiple preset departments and personnel information of employees in each preset department, and to build an information database based on the department information and the employee information; A matching degree calculation module obtains task information of multiple target tasks, selects one of the task information as target information one by one, and calculates the matching degree between the target information and each department information in the information database; A similarity calculation module is used to obtain the total number of existing department tasks and the total number of department employees of each department, confirm the target department according to the total number of existing department tasks, the total number of department employees and the matching degree, and calculate the similarity between the task information and the information of each person in the target department one by one; A task number index calculation module is used to obtain the existing task load of each person in the target department, and calculate the task number index of each person in the target department according to the existing task load and the similarity; A task allocation module allocates tasks corresponding to the target information to corresponding employees according to the task number indicator; The task monitoring module tracks the task progress of all employees in real time and issues task reminders to departments and employees based on the task progress.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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