WO2021057318A1

WO2021057318A1 - Service progress monitoring method, apparatus and system, and computer-readable storage medium

Info

Publication number: WO2021057318A1
Application number: PCT/CN2020/109080
Authority: WO
Inventors: 王忠钊; 王培林; 陈煜�; 周继恩; 尹祥龙; 邓昶; 袁野
Original assignee: 中国银联股份有限公司
Priority date: 2019-09-29
Filing date: 2020-08-14
Publication date: 2021-04-01
Also published as: CN110782129A; CN110782129B

Abstract

Provided are a service progress monitoring method, apparatus and system, and a computer-readable storage medium. The method comprises: determining, from a historical service set, at least one similar historical service of a monitored service by using a similarity model, and calculating predicted progress information of the monitored service according to the at least one similar historical service; obtaining actual progress information of the monitored service by using program tracking; and monitoring the progress of the monitored service by means of comparing the actual progress information with the predicted progress information. By using the method, the predicted progress information of the monitored service to be executed can be predicted according to the historical service, and then whether the actual progress of the monitored service is abnormal can be determined more accurately.

Description

Method, device, system and computer readable storage medium for monitoring business progress

Technical field

The invention belongs to the field of computer technology, and specifically relates to a business progress monitoring method, device, system and computer readable storage medium.

Background technique

This section is intended to provide background or context for the embodiments of the invention stated in the claims. The description here is not admitted to be prior art just because it is included in this section.

In modern enterprises, it is usually necessary to rely on online systems to build a compliant, transparent and efficient business monitoring system. However, in the prior art, the business process of an enterprise usually includes complex business links, so it is difficult to accurately predict the business progress, and it is difficult to judge whether the actual process of the business is abnormal. In the face of complex business implementation scenarios, the existing schedule management methods have the following problems: (1) The formulation of business schedules is usually set by business personnel based on experience, which is highly subjective and lacks an objective standard forecasting system; (2) It is difficult to monitor in real time when there are complex constraint relationships (such as sequential execution and parallel execution) between various links of the business.

Summary of the invention

In view of the above-mentioned problems in the prior art, a business progress monitoring method, device, system, and computer-readable storage medium are proposed. Using this method, device, system, and computer-readable storage medium, the above-mentioned problems can be solved.

The present invention provides the following solutions.

In a first aspect, a method for monitoring business progress is provided, which includes: determining at least one similar historical business of a monitored business from a set of historical businesses using a similarity model, and calculating predicted progress information of the monitored business based on the at least one similar historical business ; Use program burying points to obtain the actual progress information of the monitored business; compare the actual progress information with the predicted progress information to monitor the progress of the monitored business.

In some possible implementations, the method further includes: inputting the multi-dimensional evaluation index of the monitored business and the multi-dimensional evaluation index of each historical business in the historical business set into the similarity model to output the difference between the monitored business and each historical business. The similarity value between the two; at least one similar historical business of the monitored business is determined according to the sorting result of the similarity value.

In some possible implementation manners, the method further includes: obtaining the corresponding multi-dimensional evaluation index by performing keyword matching on the monitored business and each historical business.

In some possible implementation manners, the multi-dimensional evaluation index includes the following multiple dimensions: project nature, project type, procurement method, and project rating.

In some possible implementations, the similarity model uses the following similarity algorithm to calculate the similarity value between the monitored business and each historical business:

Among them, C _{x, y} is the similarity value between the monitored business and each historical business, x _i is the i-th dimension evaluation index of the monitored business, y _i is the i-th dimension evaluation index of each historical business, and i is the dimension The serial number, i takes a value from 1 to M, M is a positive integer, and k _i is a weight parameter corresponding to the i-th dimension evaluation index.

In some possible implementation manners, using the at least one similar historical business to calculate the predicted progress information of the monitored business includes: for each link of the monitored business, determining the historical link duration corresponding to each similar historical business; The historical link duration corresponding to the historical business performs a weighted accumulation calculation to obtain the predicted link duration of each link of the monitored business.

In some possible implementation manners, the method further includes: in the weighted accumulation operation, determining the weight value from the similarity value corresponding to each similar historical business.

In some possible implementation manners, determining the weight value based on the similarity value corresponding to each similar historical business further includes: performing dispersion standardization processing on the similarity value corresponding to each similar historical business, and standardizing the dispersion The processed similarity value is converted into the weight value.

In some possible implementation manners, the use of program burial points to obtain actual progress information of the monitored business includes: the monitored business has a corresponding program burial point in each link, and when the monitored business is executed to the corresponding link of each link When the program is burying points, obtain the actual start time and actual end time of each link.

In some possible implementation manners, the actual progress information is compared with the predicted progress information to monitor the progress of the monitored service, including: according to the start time of the monitored service and the relationship between multiple links included in the monitored service Calculate the predicted start time and predicted end time of each link of the monitored business according to the preset timing relationship and the predicted link duration of each link; through the predicted start time, predicted end time, and actual The start time is compared with the actual end time to monitor the progress of each link in the monitored business.

In some possible implementation manners, the method further includes: if the preset timing relationship indicates that the first link and the second link included in the monitored service are executed sequentially, then the predicted end time of the first link is used as the second link If the preset timing relationship indicates that the first link and the third link included in the monitored service are executed in parallel, the latest predicted end time of the first link and the third link is selected as the prediction for the subsequent links Starting time.

In some possible implementation manners, the method further includes: capturing schedule information, and determining an available working period of the monitored service according to the schedule information; and adjusting the predicted progress information according to the available working period.

In a second aspect, a business progress monitoring device is provided, which includes: a progress prediction module for determining at least one similar historical business of the monitored business using a similarity model, and calculating the predicted progress of the monitored business based on the at least one similar historical business Information; the progress tracking module is used to obtain the actual progress information of the monitored business by using program burying points; the progress monitoring module is used to monitor the progress of the monitored business by comparing the actual progress information with the predicted progress information.

In some possible implementations, the progress prediction module is also used to input the multi-dimensional evaluation index of the monitored business and the multi-dimensional evaluation index of each historical business into the similarity model to output the relationship between the monitored business and each historical business. According to the similarity value of the similarity value; at least one similar historical business of the monitored business is determined according to the sorting result of the similarity value.

In some possible implementation manners, the progress prediction module is also used to obtain the corresponding multi-dimensional evaluation index by performing keyword matching on the monitored business and each historical business.

In some possible implementation manners, the progress prediction module further includes: a weighted accumulation module for determining the historical link duration corresponding to each similar historical business for each link of the monitored business; the historical link corresponding to each similar historical business The link duration performs a weighted accumulation calculation to obtain the predicted link duration of each link of the monitored business.

In some possible implementation manners, the weighted accumulation module further includes: a weight module, configured to determine the weight value from the similarity value corresponding to each similar historical service in the weighted accumulation operation.

In some possible implementation manners, the weight module is further used to: perform dispersion standardization processing on the similarity value corresponding to each similar historical business, and convert the similarity value after the dispersion standardization processing into the weight value.

In some possible implementation manners, the progress tracking module is also used for: the monitored business has a corresponding program embedding point in each link, and when the monitored business is executed to the program embedding point corresponding to each link, obtain The actual start time and actual end time of each link.

In some possible implementation manners, the progress monitoring module is also used to: calculate the monitored duration according to the startup time of the monitored service, the preset timing relationship among multiple links included in the monitored service, and the predicted link duration of each link The predicted start time and predicted end time of each link of the business; compare the predicted start time, predicted end time, actual start time and actual end time of each link of the monitored business to monitor each The progress of the link.

In some possible implementation manners, the progress monitoring module is further configured to: if the preset timing relationship indicates that the first link and the second link included in the monitored service are executed in sequence, then the predicted end time of the first link is used as the first link The predicted start time of the second link; if the preset timing relationship indicates that the first link and the third link included in the monitored business are executed in parallel, the latest predicted end time of the first link and the third link is selected as the follow-up link The forecast start time.

In some possible implementation manners, the progress prediction module is further used to: capture schedule information, and determine the available work period of the monitored service according to the schedule information; and adjust the predicted progress information according to the available work period .

In a third aspect, a service progress monitoring system is provided for monitoring the service progress of at least one monitored service, and the system includes: at least one monitored service, used to store historical service sets of multiple historical services, and as described above The monitoring device of the second aspect.

In a fourth aspect, a business progress monitoring device is provided, including: one or more multi-core processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more multi-core processors When the time, the one or more multi-core processors are made to realize: use the similarity model to determine at least one similar historical business of the monitored business from the historical business set, and calculate the predicted progress information of the monitored business according to the at least one similar historical business; The program embeds points to obtain the actual progress information of the monitored business; compare the actual progress information with the predicted progress information to monitor the progress of the monitored business.

In a fifth aspect, a computer-readable storage medium is provided, and the computer-readable storage medium stores a program, and when the program is executed by a multi-core processor, the multi-core processor is caused to execute the method of the above-mentioned first aspect.

The above-mentioned at least one technical solution adopted in the embodiment of this application can achieve the following beneficial effects: In this embodiment, a credible similarity model is obtained by training with a historical business set as a training sample library, and obtained by referencing the similarity model The similar historical business of the monitored business can predict the progress information of the monitored business based on the similar historical business, thereby providing an objective and standard forecasting system, enabling effective monitoring of the monitored business.

It should be understood that the above description is only an overview of the technical solution of the present invention, so that the technical means of the present invention can be understood more clearly, so that it can be implemented in accordance with the content of the description. In order to make the above and other objectives, features and advantages of the present invention more obvious and understandable, the following examples illustrate the specific embodiments of the present invention.

Description of the drawings

By reading the detailed description of the exemplary embodiments below, those of ordinary skill in the art will understand the advantages and benefits described herein, as well as other advantages and benefits. The drawings are only used for the purpose of illustrating exemplary embodiments, and are not considered as a limitation to the present invention. Moreover, the same reference numerals are used to denote the same components throughout the drawings. In the attached picture:

FIG. 1 is a schematic flowchart of a method for monitoring business progress according to an embodiment of the present invention;

Figure 2 is a schematic structural diagram of a business progress monitoring device according to an embodiment of the present invention;

3 is a schematic structural diagram of a service progress monitoring device according to another embodiment of the present invention;

Fig. 4 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention.

In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

detailed description

Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the drawings show exemplary embodiments of the present disclosure, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

In the present invention, it should be understood that terms such as "including" or "having" are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof disclosed in this specification, and are not intended to exclude one Or the possibility of the existence of multiple other features, numbers, steps, behaviors, components, parts or combinations thereof.

In addition, it should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other if there is no conflict. Hereinafter, the present invention will be described in detail with reference to the drawings and in conjunction with the embodiments.

Fig. 1 schematically shows a flow chart of a business progress monitoring method 100 according to an embodiment of the present invention. Preferably, but not necessarily, the method shown in Fig. 1 can be executed at a cloud server or a server cluster, more specifically, The method shown in Figure 1 can be executed by a specific module set in the UnionPay system. In this embodiment, the cloud server is used as the execution subject for specific explanations. However, it should be understood that the application does not specifically limit the execution subject.

As shown in FIG. 1, the method 100 includes:

Step S101: Use the similarity model to determine at least one similar historical business of the monitored business from the historical business set, and calculate the predicted progress information of the monitored business according to the at least one similar historical business;

Specifically, the historical service set is used to store actual execution data of multiple historical services. The above-mentioned similarity model is used to centrally screen out several historical businesses with high similarity to the monitored business from the historical business as similar historical businesses of the monitored business. Optionally, the multi-dimensional evaluation index carried by each historical business in the historical business set can be trained to obtain the similarity model.

In some possible implementation manners, the multi-dimensional evaluation index may include the following multiple dimensions: project nature, project type, procurement method, and project rating. Specifically, the multi-dimensional evaluation indicators can include: (1) Project nature, marking the procurement nature, such as IT projects, engineering projects, etc. In the similarity model, each procurement nature can be converted into a numerical identification within the range of [1,10] ; (2) Item type, mark the purchase category, such as goods, services, etc. In the similarity model, each purchase type can be converted to a numerical identifier within the range of [1,10]; the larger the value, the implementation The more difficult it is; (3) Procurement methods, mark procurement methods, such as public bidding, single source, inquiry, etc. In the similarity model, each procurement method can be converted into a numerical identification within the range of [1,10]; 4) Project rating, marking the evaluation of the project by the demanding personnel, such as high importance level, low importance level, etc.; the similarity model can be converted into a numerical identification within the range of [1,10]. The above-mentioned numerical identifiers are related to the difficulty of business execution and the length of business execution, and can be determined by using a pre-created evaluation index system. It can be understood that the evaluation indicators of the above dimensions all have an associated relationship with the execution time of the business. Therefore, the weight parameters corresponding to each dimension can be determined in the similarity model by performing model training on the above multi-dimensional evaluation indicators.

In some possible implementation manners, in order to determine at least one similar historical business of the monitored business, step S101 may further include: inputting the multi-dimensional evaluation index of the monitored business and the multi-dimensional evaluation index of each historical business in the historical business set into the similarity model , To output the similarity value between the monitored business and each historical business; at least one similar historical business of the monitored business is determined according to the sorting result of the similarity value.

Specifically, the multi-dimensional evaluation index of the monitored business and each historical business may be obtained by crawling the service attribute information through crawler software. For example, the following content can be obtained by capturing the attribute information of the monitored business: <Project nature: IT project; Project type: Goods, Procurement method: Public bidding, Project rating: High importance level>; further, The captured attribute information can be converted into a multi-dimensional evaluation index through a pre-created evaluation index system. The multi-dimensional evaluation index can be formed into a one-dimensional array format. For example, the multi-dimensional evaluation index of the monitored business can be formed as (x ₁ , x ₂ , x ₃ , x ₄ ), the multi-dimensional evaluation index of each historical business can be formed as (y ₁ , y ₂ , y ₃ , y ₄ ). Further, input the above-mentioned (x ₁ , x ₂ , x ₃ , x ₄ ) and (y ₁ , y ₂ , y ₃ , y ₄ ) into the trained similarity model, and output the difference between the monitored business and each historical business The similarity value C _{x, y} between the two can be further sorted by the similarity value to filter the historical business with higher similarity from the historical business set. For example, it is possible to select a historical business with a similarity value of 10% in the historical business concentration as a similar historical business. It can be understood that by inputting the multi-dimensional evaluation index into the similarity model to obtain the similarity value used to filter the historical business, the historical business with higher similarity can be selected from the historical business set as the similar historical business.

In some possible implementations, the corresponding multi-dimensional evaluation index can be obtained by performing keyword matching on the monitored business and each historical business.

For example, if the multi-dimensional evaluation index is not clearly marked in the log of the monitored business, in this case, you can obtain several target keywords by performing keyword matching on the log of the monitored business, such as <IT item ; Cargo category, public bidding, high importance level>; Further, according to the pre-created evaluation index system, the above keywords are converted into multi-dimensional evaluation indexes of the monitored business.

In some possible implementations, further, the similarity model uses the following similarity algorithm to calculate the similarity value between the monitored business and each historical business;

Among them, C _{x, y} is the similarity value between the monitored business and each historical business, x _i is the i-th dimension evaluation index of the monitored business, y _i is the i-th dimension evaluation index of each historical business, and i is the dimension The serial number is 1 to M, M is a positive integer, and k _i is a weight parameter corresponding to the i-th dimension evaluation index, which can be specifically determined during model training.

For example, the multi-dimensional evaluation index for the monitored business: (x ₁ , x ₂ , x ₃ , x ₄ ), and the multi-dimensional evaluation index for each historical business: (y ₁ , y ₂ , y ₃ , y ₄ ). The similarity can be determined by another M=4. It can be seen that the similarity model in this embodiment adopts a similarity algorithm with simple logic and a small amount of calculation.

In some possible implementations, step S101 may further include: for each link of the monitored business, determining the historical link duration corresponding to each similar historical business; performing a weighted accumulation operation on the historical link duration corresponding to each similar historical business to obtain The forecast link duration of each link of the monitored business.

For example, the conventional corporate procurement process can usually be summarized and divided into 16 links: preparation of plan documents, submission of plan documents, review of plan documents, approval of the procurement committee, release of procurement announcements, organization of procurement reviews, preparation of procurement results reports, and approval of procurement Results, approval by the purchasing committee, announcement of purchasing results, notification of purchasing results, preparation of purchasing contracts, review of purchasing contracts, approval of purchasing contracts, confirmation of contract approval results, signing and handover of purchasing contracts. All links constitute a progress template library. Further, the sponsor in the monitored business can filter the progress links from the progress template library to form the link information table of the business, or can automatically determine the links included based on the attributes of the business.

Further, after determining the multiple links included in the monitored service, the predicted link duration of each link j of the monitored service r can be determined according to the following execution weighted accumulation calculation formula:

Among them: p _q is the weight of similar historical business q, t _{q, j} is the actual execution time of similar historical business q in this link j, q is the serial number of similar historical business, q=1,...,n, variables The value of n depends on the number of similar historical businesses. By using the above scheme, the basic unit of progress monitoring is reduced to links, and progress monitoring can be performed more timely and accurately.

In some possible implementation manners, in the foregoing weighted accumulation operation, it may further include: determining the weight value based on the similarity value corresponding to each similar historical business.

For example, for the monitored service r, the similarity values of the corresponding similar historical services are v ₁ , v ₂ , v ₃ ,..., v _n . Based on this, the multiple similarity values can be used to calculate the above-mentioned weighted accumulation The weight p _i in the calculation. For example, multiple threshold intervals can be set, and similar historical businesses with similarity values within the specified threshold interval can be assigned a specified weight.

In some possible implementation manners, in the above-mentioned weighted accumulation operation, it may further include: performing dispersion standardization processing on the similarity values corresponding to each similar historical business, and converting the similarity values after the dispersion standardization processing into weights. . Specifically, the sum of the weights of all the above-mentioned similar historical services is set to 1, and the weight distribution that is more in line with objective laws can be determined through the above-mentioned deviation standardization processing.

For example, for the monitored service r, the top n historical services with the highest similarity are selected as similar historical services, and the similarity values of the top n+1 historical services with the highest similarity are arranged in ascending order as v ₁ , v ₂ ,v ₃ ,…,v _n+1 . The following formula is used to standardize the value _{of v q in the range of 0-1:}

Among them, the value of _{v max} _{is v n+1} , and the _{value of v min} is 0.

Further, according to v′ ₁ , v′ ₂ , v′ ₃ …, v′ _n , the weight of each similar historical business q among the first n historical businesses with the highest similarity (that is, similar historical business) can be obtained:

As shown in FIG. 1, the method 100 includes:

Step S102: Obtain actual progress information of the monitored business by using the program burying point;

Specifically, the program embedding point refers to the related technology and the implementation process of capturing, processing and sending a specific behavior or event, and the developer can preset the program embedding point strategy as needed. For example, it is necessary to collect actual progress information monitoring service A, can be monitored service A is divided into a plurality of sub-task set time sequence _{_{(a 1, a 2, ...}} , a n), is inserted at the end of each sub-task location buried procedures, to give (a _1, buried 1, a _2, buried 2, ..., a _n, a buried n-point), and further, when monitoring traffic to flow buried each program, and will be the monitoring service call interface The actual progress information of the monitored business can be obtained from the execution information of the monitored business, thereby reducing the manual operation of the user to view the business progress during the execution of the monitored business, reducing the complexity of monitoring and the real-time monitoring.

In some possible implementation manners, step S102 may further include: the monitored business has a corresponding program embedding point in each link, and when the monitored business is executed to the corresponding program embedding point of each link, the information of each link is obtained. Actual start time and actual end time.

Specifically, when the monitored business flows to the program embedding point, the interface is called and the execution information of the monitored business is transmitted, and then the actual start time, actual end time and other data of each link are obtained by analyzing the above execution information, and coexist Into the metadata of each link. Optionally, the above procedure point can be set at the start position or the end position of each link, and the actual start time and actual end time of each link above can be determined directly from the time from business execution to procedure point without analysis. .

As shown in FIG. 1, the method 100 includes:

Step S103: The actual progress information is compared with the predicted progress information to monitor the progress of the monitored service.

Specifically, the comparison result can be displayed on a graphical interface to visually display the difference between the predicted progress information and the actual progress information.

In some possible implementation manners, step S103 may further include: calculating the monitored service's time according to the startup time of the monitored service, the preset timing relationship among multiple links included in the monitored service, and the predicted link duration of each link. The predicted start time and predicted end time of each link; compare the predicted start time, predicted end time, actual start time and actual end time of each link of the monitored business to monitor the performance of each link in the monitored business. schedule.

Specifically, there is a preset timing relationship between the multiple links in the monitored business. For example, the “confirmation of contract approval results” link must be executed after the “approval of procurement contract” link is completed. For example, the link must After the "procurement result announcement" and "procurement result notification" links have been executed, the "preparation of procurement contract" link can be executed. Therefore, the above-mentioned preset timing relationship can indicate that multiple links are executed sequentially or in parallel. carried out.

Further, after obtaining the start time of the monitored service, each link of the monitored service can be traversed based on the above-mentioned preset timing relationship and the predicted link duration of each link, so as to obtain the predicted start time and time of each link. Forecast end time.

In some possible implementations, in the above calculation of the predicted start time and predicted end time of each link of the monitored service, if the preset timing relationship indicates that the first link and the second link included in the monitored service are executed in sequence , Then the predicted end time of the first link is used as the predicted start time of the second link. For the parallel execution link, it can also include: if the preset timing relationship indicates that the first link and the third link included in the monitored service are executed in parallel, then the latest predicted end time of the first link and the third link is selected as the follow-up The predicted start time of the link. In this way, the parallel characteristics of the links are taken into consideration, and the problem of timing confusion in the predicted progress information is avoided.

In some possible implementation manners, it may further include: capturing schedule information, and determining the available working period of the monitored service according to the schedule information; and adjusting the predicted progress information according to the available working period. Specifically, the aforementioned schedule information may include schedule information of the personnel involved in the business, schedule information used to indicate weather factors, and the like.

In this embodiment, a credible similarity model is obtained by training the historical business set as a training sample library, and the similar historical business of the monitored business is obtained by quoting the similarity model, so that the predicted business based on the similar historical business Monitoring the progress information of the business, thereby providing an objective standard forecasting system, based on the forecasting system, can realize the effective monitoring of the monitored business. In addition, by reducing the above-mentioned progress monitoring unit, the progress of the monitored business can be refinedly predicted from the link level, thereby providing a more precise and accurate monitoring effect.

Based on the same technical concept, an embodiment of the present invention also provides a service progress monitoring device, which is used to execute the service progress monitoring method provided in any of the foregoing embodiments. Fig. 2 is a schematic structural diagram of a service progress monitoring apparatus provided by an embodiment of the present invention.

As shown in Figure 2, the business progress monitoring device 200 includes:

The progress prediction module 201 is configured to use the similarity model to determine at least one similar historical business of the monitored business, and calculate the predicted progress information of the monitored business according to the at least one similar historical business;

The progress tracking module 202 is used to obtain the actual progress information of the monitored business by using the program buried point;

The progress monitoring module 203 is configured to monitor the progress of the monitored service by comparing the actual progress information with the predicted progress information.

In some possible implementation manners, the progress prediction module 201 is also used to input the multi-dimensional evaluation index of the monitored business and the multi-dimensional evaluation index of each historical business into the similarity model to output the difference between the monitored business and each historical business. The similarity value between the two; at least one similar historical business of the monitored business is determined according to the sorting result of the similarity value.

In some possible implementation manners, the progress prediction module 201 is further configured to obtain the corresponding multi-dimensional evaluation index by performing keyword matching on the monitored business and each historical business.

Among them, C _x,y is the similarity value between the monitored business and each historical business, x _i is the i-th dimension evaluation index of the monitored business, y _i is the i-th dimension evaluation index of each historical business, and i is the dimension The serial number, the value ranges from 1 to M, N ⁺ , and k _i is the weight parameter corresponding to the i-th dimension evaluation index.

In some possible implementation manners, the progress prediction module 201 further includes: a weighted accumulation module for determining the duration of each historical link corresponding to each similar historical business for each link of the monitored business; The historical link duration performs a weighted accumulation calculation to obtain the predicted link duration of each link of the monitored business.

In some possible implementation manners, the progress tracking module 202 is also used for: the monitored business has a corresponding program embedding point in each link, and when the monitored business is executed to the program embedding point corresponding to each link, Get the actual start time and actual end time of each link.

In some possible implementation manners, the progress monitoring module 203 is also used to calculate the predicted link duration according to the startup time of the monitored service, the preset timing relationship among multiple links included in the monitored service, and the predicted link duration of each link. Monitor the predicted start time and predicted end time of each link of the monitored business; compare the predicted start time, predicted end time, actual start time and actual end time of each link of the monitored business to monitor each link in the monitored business. The progress of each link.

In some possible implementation manners, the progress monitoring module 203 is further configured to: if the preset timing relationship indicates that the first link and the second link included in the monitored service are executed sequentially, then the predicted end time of the first link is used as the The predicted start time of the second link; if the preset timing relationship indicates that the first link and the third link included in the monitored service are executed in parallel, the latest predicted end time of the first link and the third link is selected as the follow-up The predicted start time of the link.

In some possible implementation manners, the progress prediction module 201 is further configured to: capture schedule information, and determine the available work period of the monitored service according to the schedule information; and adjust the predicted progress according to the available work period information.

It should be noted that the service progress monitoring device in the embodiment of the present application can implement each process of the foregoing embodiment of the service progress monitoring method, and achieve the same effects and functions, which will not be repeated here.

Based on the same technical concept, the embodiment of the present invention also provides a service progress monitoring system for monitoring the service progress of at least one monitored service, and the system includes: at least one monitored service for storing multiple historical services Historical business set, and the business progress monitoring device as described above.

Those skilled in the art can understand that various aspects of the present invention can be implemented as a device, a method, or a computer-readable storage medium. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which can be collectively referred to herein as "Circuit", "Module" or "Equipment".

In some possible implementation manners, a service progress monitoring apparatus of the present invention may at least include one or more processors and at least one memory. Wherein, the memory stores a program, and when the program is executed by the processor, the processor is caused to perform the steps shown in FIG. 1:

Step S102: Obtain the actual progress information of the monitored service by using the program burying point;

The service progress monitoring device 3 according to this embodiment of the present invention will be described below with reference to FIG. 3. The device 3 shown in FIG. 3 is only an example, and should not bring any limitation to the function and application scope of the embodiment of the present invention.

As shown in FIG. 3, the apparatus 3 may be in the form of a general-purpose computing device, including but not limited to: at least one processor 10, at least one memory 20, and a bus 60 connecting different device components.

The bus 60 includes a data bus, an address bus, and a control bus.

The memory 20 may include a volatile memory, such as a random access memory (RAM) 21 and/or a cache memory 22, and may further include a read-only memory (ROM) 23.

The memory 20 may also include a program module 24. Such program module 24 includes, but is not limited to, an operating device, one or more application programs, other program modules, and program data. Each of these examples or a certain combination may include a network. The realization of the environment.

The apparatus 3 may also communicate with one or more external devices 2 (for example, a keyboard, a pointing device, a Bluetooth device, etc.), and may also communicate with one or more other devices. This communication can be performed through an input/output (I/O) interface 40 and displayed on the display unit 30. In addition, the device 3 may also communicate with one or more networks (for example, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 50. As shown in the figure, the network adapter 50 communicates with other modules in the device 3 through the bus 60. It should be understood that although not shown in the figure, other hardware and/or software modules can be used in conjunction with the device 3, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape drives And data backup storage devices, etc.

Fig. 4 shows a computer-readable storage medium for executing the method as described above.

In some possible implementation manners, various aspects of the present invention can also be implemented in the form of a computer-readable storage medium, which includes program code. When the program code is executed by a processor, the program code is used for The processor is caused to execute the method described above.

The above-described method includes multiple operations and steps shown and not shown in the above drawings, which will not be repeated here.

The computer-readable storage medium may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, device, or device, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable Type programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

As shown in FIG. 4, a computer-readable storage medium 40 according to an embodiment of the present invention is described. It can adopt a portable compact disk read-only memory (CD-ROM) and include program codes, and can be stored in a terminal device, such as a personal computer. Run on. However, the computer-readable storage medium of the present invention is not limited to this. In this document, the readable storage medium can be any tangible medium that contains or stores a program. The program can be used by or in combination with an instruction execution device, device, or device. .

The program code used to perform the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural styles. Programming language-such as "C" language or similar programming language. The program code may be executed entirely on the user's computing device, partly executed on the user's equipment and partly executed on the remote computing device, or entirely executed on the remote computing device or server. In the case of remote computing devices, the remote computing device can be connected to the user computing device through any kind of network-including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (for example, using Internet services). Provider to connect via the Internet).

In addition, although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that these operations must be performed in the specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and/or one step may be decomposed into multiple steps for execution.

Although the spirit and principle of the present invention have been described with reference to several specific embodiments, it should be understood that the present invention is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined for performance. Benefit, this division is only for the convenience of presentation. The present invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

A method for monitoring business progress, which is characterized in that it comprises:

Using a similarity model to determine at least one similar historical business of the monitored business from a collection of historical services, and calculate the predicted progress information of the monitored business according to the at least one similar historical business;

Obtain the actual progress information of the monitored business by using the program buried point;

The progress of the monitored service is monitored by comparing the actual progress information with the predicted progress information.
The method of claim 1, further comprising:

Input the multi-dimensional evaluation index of the monitored business and the multi-dimensional evaluation index of each historical business in the historical business set into the similarity model to output the similarity value between the monitored business and each historical business ；

Determine at least one similar historical business of the monitored business according to the sorting result of the similarity value.
The method of claim 2, further comprising:

The corresponding multi-dimensional evaluation index is obtained by performing keyword matching on the monitored business and each historical business.
The method according to claim 2, wherein the multi-dimensional evaluation index includes the following multiple dimensions: project nature, project type, procurement method, and project rating.
The method according to claim 2, wherein the similarity model uses the following similarity algorithm to calculate the similarity value between the monitored business and the historical business;

Wherein, C x, y is the similarity value between the monitored business and the historical business, x i is the i-th dimension evaluation index of the monitored business, and y i is the historical business’s The i-dimensional evaluation index, i is the dimension serial number, the value of i is 1 to M, M is a positive integer, and k i is the weight parameter corresponding to the i-th dimension evaluation index.
The method according to claim 1, wherein using the at least one similar historical service to calculate the predicted progress information of the monitored service comprises:

For each link of the monitored business, determine the historical link duration corresponding to each similar historical business;

Perform a weighted accumulation operation on the historical link duration corresponding to each similar historical business to obtain the predicted link duration of each link of the monitored business.
The method according to claim 6, further comprising:

In the weighted accumulation operation, the weight is determined by the similarity value corresponding to each similar historical business.
The method according to claim 7, characterized in that determining the weight value according to the similarity value corresponding to each similar historical business, further comprising:

Performing dispersion standardization processing on the similarity value corresponding to each similar historical business, and converting the similarity value after the dispersion standardization processing into the weight value.
The method according to claim 6, characterized in that said using the program to bury points to obtain the actual progress information of the monitored service comprises:

The monitored business has a corresponding program embedding point in each link. When the monitored service is executed to the program embedding point corresponding to each link, the actual start time and the actual start time of each link are obtained. The actual end time.
The method according to claim 9, wherein the monitoring of the progress of the monitored service by comparing the actual progress information with the predicted progress information comprises:

Calculate the predicted start of each link of the monitored business according to the startup time of the monitored business, the preset timing relationship between the multiple links included in the monitored business, and the predicted link duration of each link Time and forecast ending time;

By comparing the predicted start time, predicted end time, actual start time and actual end time of each link of the monitored service, the progress of each link in the monitored service is monitored.
The method of claim 10, further comprising:

If the preset timing relationship indicates that the first link and the second link included in the monitored service are executed sequentially, then the predicted end time of the first link is used as the predicted start time of the second link;

If the preset timing relationship indicates that the first link and the third link included in the monitored service are executed in parallel, the latest predicted end time of the first link and the third link is selected as the prediction of the subsequent link Starting time.
The method of claim 10, further comprising:

Grab schedule information, and determine the available working period of the monitored service according to the schedule information; and,

Adjust the predicted progress information according to the available working period.
A business progress monitoring device, which is characterized in that it comprises:

A progress prediction module, configured to use a similarity model to determine at least one similar historical business of the monitored business, and calculate the predicted progress information of the monitored business according to the at least one similar historical business;

The progress tracking module is used to obtain the actual progress information of the monitored business by using the program buried point;

The progress monitoring module is used to monitor the progress of the monitored service by comparing the actual progress information with the predicted progress information.
The device according to claim 13, wherein the progress prediction module is further configured to:

Input the multi-dimensional evaluation index of the monitored business and the multi-dimensional evaluation index of each historical business into the similarity model to output the similarity value between the monitored business and each historical business;

Determine at least one similar historical business of the monitored business according to the sorting result of the similarity value.
The device according to claim 14, wherein the progress prediction module is further configured to:

The corresponding multi-dimensional evaluation index is obtained by performing keyword matching on the monitored business and each historical business.
The device according to claim 14, wherein the multi-dimensional evaluation index includes the following multiple dimensions: project nature, project type, procurement method, and project rating.
The device according to claim 14, wherein the similarity model uses the following similarity algorithm to calculate the similarity value between the monitored business and the historical business:

Wherein, C x, y is the similarity value between the monitored business and the historical business, x i is the i-th dimension evaluation index of the monitored business, and y i is the historical business’s The i-dimensional evaluation index, i is the dimension serial number, with a value ranging from 1 to M, M is a positive integer, and k i is a weight parameter corresponding to the i-th dimension evaluation index.
The device according to claim 13, wherein the progress prediction module further comprises:

The weighted accumulation module is used to determine the historical link duration corresponding to each similar historical business for each link of the monitored business; perform a weighted accumulation operation on the historical link duration corresponding to each similar historical business to obtain the monitored business The forecast link duration of each link of the business.
The device of claim 18, wherein the weighted accumulation module further comprises:

The weight module is used to determine the weight value from the similarity value corresponding to each similar historical business in the weighted accumulation operation.
The device according to claim 18, wherein the weight module is further used for:

Performing dispersion standardization processing on the similarity value corresponding to each similar historical business, and converting the similarity value after the dispersion standardization processing into the weight value.
The device according to claim 18, wherein the progress tracking module is further used for:

The monitored business has a corresponding program embedding point in each link. When the monitored service is executed to the program embedding point corresponding to each link, the actual start time and the actual start time of each link are obtained. The actual end time.
The device according to claim 21, wherein the progress monitoring module is further used for:

Calculate the predicted start of each link of the monitored business according to the startup time of the monitored business, the preset timing relationship between the multiple links included in the monitored business, and the predicted link duration of each link Time and forecast ending time;

By comparing the predicted start time, predicted end time, actual start time and actual end time of each link of the monitored service, the progress of each link in the monitored service is monitored.
The device according to claim 22, wherein the progress monitoring module is further used for:

If the preset timing relationship indicates that the first link and the second link included in the monitored service are executed sequentially, then the predicted end time of the first link is used as the predicted start time of the second link;

If the preset timing relationship indicates that the first link and the third link included in the monitored service are executed in parallel, the latest predicted end time of the first link and the third link is selected as the prediction of the subsequent link Starting time.
The device according to claim 14, wherein the progress prediction module is further configured to:

Grab schedule information, and determine the available working period of the monitored service according to the schedule information; and,

Adjust the predicted progress information according to the available working period.
A business progress monitoring system, characterized in that it is used to monitor the business progress of at least one monitored business, and the system includes:

At least one monitored service is used to store historical service sets of multiple historical services, and the monitoring device according to any one of claims 13-24.
A business progress monitoring device, which is characterized in that it comprises:

One or more multi-core processors;

Memory, used to store one or more programs;

When the one or more programs are executed by the one or more multi-core processors, the one or more multi-core processors are caused to realize:

Using a similarity model to determine at least one similar historical business of the monitored business from a collection of historical services, and calculate the predicted progress information of the monitored business according to the at least one similar historical business;

Obtain the actual progress information of the monitored business by using the program buried point;

The progress of the monitored service is monitored by comparing the actual progress information with the predicted progress information.
A computer-readable storage medium, the computer-readable storage medium stores a program, and when the program is executed by a multi-core processor, the multi-core processor is caused to execute any one of claims 1-12 method.