CN117726304A - Project progress prediction and project resource allocation recommendation method - Google Patents
Project progress prediction and project resource allocation recommendation method Download PDFInfo
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Abstract
The invention relates to the technical field of project resource optimization, in particular to a project progress prediction and project resource allocation recommendation method. And analyzing the rationality of man-machine coordination according to the probability vector, and clearly referring to the data chain according to the rationality of coordination, extracting the data segment from the data chain by utilizing the probability vector to finish prediction. Because the embodiment of the invention obtains the sufficient probability vectors of people or equipment based on probability analysis, and the vectors contain the characteristics of sporadic factors, the historical progress found from the data chain through the probability vectors has the characteristics of man-machine coordination and sporadic factors, and is matched with the current characteristics, so that the accuracy of project progress prediction is improved.
Description
Technical Field
The invention relates to the technical field of project resource optimization, in particular to a project progress prediction and project resource allocation recommendation method.
Background
Project progress management refers to determining a progress target by a scientific method, making a progress plan and a resource supply plan, performing progress control, and realizing a construction period target on the basis of resource target coordination. Project progress management is an important aspect of project management, and is one of important measures for ensuring project completion as expected and reasonably arranging resource supply and reducing resource consumption.
For better understanding of the construction period, for better controlling project progress, various progress prediction methods are provided in the related art, wherein the most common method is to predict future progress according to the progress.
The progress prediction according to the progress has a certain limitation, as known, the progress of project development is influenced by various factors, such as weather, personnel flow and the like, and the prediction method lacks consideration of the accident factors, and finally results in inaccurate progress prediction results.
Based on the above, a project progress prediction method needs to be developed and designed.
Disclosure of Invention
The embodiment of the invention provides a project progress prediction and project resource allocation recommendation method, which is used for solving the problem of inaccurate project progress prediction results in the prior art.
In a first aspect, an embodiment of the present invention provides a project progress prediction method, including:
obtaining a plurality of average single-person workload and a plurality of average single-machine workload;
determining human-machine coordination rationality and probability vectors representing influence of sporadic factors according to the plurality of average single-person workload, the plurality of average single-machine workload, the average single-person workload probability and the average single-machine workload probability, wherein the human-machine coordination rationality represents rationality of coordination of project human resources and equipment resources;
selecting a historical progress data chain according to the man-machine coordination rationality, and determining a target progress data segment from the historical progress data chain according to a matching result of the probability vector and a plurality of database indexes, wherein the historical progress data chain is identified through the plurality of database indexes;
and determining the progress of the project according to the current progress of the project and the target progress data segment.
In one possible implementation manner, the determining a human-machine coordination rationality and a probability vector for representing influence of sporadic factors according to the plurality of average single workload, average single workload probability and average single workload probability includes:
Acquiring a single saturated operation amount judgment value, a single saturated operation amount probability, a conditional single saturated operation amount prior probability, a single saturated operation amount probability, a condition full and equipment full probability, a condition full and manpower full probability and a conditional single saturated operation amount prior probability, wherein the conditional single saturated operation amount prior probability is the single saturated operation amount probability when the equipment condition is full and no accidental factor, the conditional single saturated operation amount prior probability is the single saturated operation amount probability when the personnel is full and no accidental factor, the condition full and equipment full probability is the probability that no accidental factor and equipment resource is full, and the condition full and manpower full probability is the probability that no accidental factor and manpower resource is full;
determining a first saturated queue and a second saturated queue according to the single saturated workload judgment value, the plurality of average single workload and the plurality of average single workload, wherein the first saturated queue is a queue constructed according to whether the plurality of average single workload exceeds the single saturated workload judgment value, and the second saturated queue is a queue constructed according to whether the plurality of average single workload exceeds the single saturated workload judgment value;
Constructing a first probability vector and a second probability vector according to the first saturation queue, the second saturation queue, the single saturation workload probability, the conditional single saturation workload prior probability, the single saturation workload probability, the condition full and equipment full probability, the condition full and manpower full probability and the conditional single saturation workload prior probability, wherein the first probability vector represents the probability of sufficient equipment conditions and no accidental factors on the premise of the first saturation queue, and the second probability vector represents the probability of sufficient manpower resource conditions and no accidental factors on the premise of the second saturation queue;
and determining the man-machine coordination rationality according to the first probability vector and the second probability vector.
In one possible implementation manner, the constructing a first probability vector and a second probability vector according to the first saturation queue, the second saturation queue, a single saturation workload probability, a conditional single saturation workload prior probability, a single saturation workload probability, a condition sufficient and equipment sufficient probability, a condition sufficient and manpower sufficient probability, and a conditional single saturation workload prior probability includes:
Constructing a first probability vector according to a first formula, the first saturation queue, single saturation workload probability, sufficient conditions, sufficient equipment probability and conditional single saturation workload prior probability, wherein the first formula is as follows:
in the method, in the process of the invention,is the +.o of the first probability vector>Element(s)>For the prior probability of the saturated workload of the conditional single person, +.>For a sufficient condition and a sufficient probability of device, +.>For the single person saturation workload probability, < >>Is the first saturation queue +>Data;
constructing a second probability vector according to a second formula, the second saturation queue, the single machine saturation workload probability, the condition-sufficient and manpower-sufficient probability and the conditional single machine saturation workload prior probability, wherein the second formula is as follows:
in the method, in the process of the invention,is the +.>Element(s)>Is the prior probability of the saturated workload of the conditional single machine, +.>For the condition of sufficient and manpower sufficient probability, +.>For the single machine saturation workload probability, < >>Is the +.>Data.
In one possible implementation manner, the determining the human-machine coordination rationality according to the first probability vector and the second probability vector includes:
determining a first average probability value and a second average probability value according to the first probability vector and the second probability vector, wherein the first average probability value is an average value of a plurality of elements of the first probability vector, and the second average probability value is an average value of a plurality of elements of the second probability vector;
If the first average probability value is greater than or equal to the condition sufficient and equipment sufficient probability and the second average probability value is less than the condition sufficient and manpower sufficient probability, the manpower resources are sufficient;
if the first average probability value is smaller than the condition sufficient and equipment sufficient probability and the second average probability value is greater than or equal to the condition sufficient and manpower sufficient probability, equipment resources are sufficient;
and if the first average probability value is greater than or equal to the condition sufficient and equipment sufficient probability and the second average probability value is greater than or equal to the condition sufficient and manpower sufficient probability, the man-machine cooperation is reasonable.
In one possible implementation manner, the selecting a historical progress data chain according to the human-computer coordination rationality, and determining a target progress data segment from the historical progress data chain according to the matching result of the probability vector and a plurality of database indexes includes:
if the human resources are sufficient, a first historical progress data chain and the first probability vector are used as a target historical progress data chain and a target probability vector, otherwise, a second historical progress data chain and the second probability vector are used as a target historical progress data chain and a target probability vector, wherein the first historical progress data chain is a data chain constructed by a plurality of historical average single-person workload, and the second historical progress data chain is a data chain constructed by a plurality of historical average single-person workload;
Selecting a plurality of target indexes from a plurality of database indexes of the target historical progress data chain in a matching and translation mode according to the target probability vector;
and selecting a target progress data segment from the target history progress data chain according to the target indexes.
In one possible implementation manner, the selecting, by matching and translating, a plurality of target indexes from a plurality of database indexes of the target historical progress data chain according to the target probability vector includes:
calculating a unit vector of the target probability vector, and taking the unit vector of the target probability vector as a query vector;
selecting database indexes with the same element number as the target probability vector from preset positions of a plurality of database indexes of the target historical progress data chain as a plurality of indexes to be processed;
determining a matching coefficient according to a third formula, the plurality of indexes to be processed and the query vector, wherein the third formula is as follows:
in the method, in the process of the invention,is the%>Element(s)>Is->Index to be processed, ++>For matching coefficients +.>For the total number of query vector elements;
adding the matching coefficient into a coefficient array;
If the plurality of database indexes of the target historical progress data chain are not traversed, adjusting the preset positions, and jumping to the step of selecting the database indexes with the same number as the elements of the target probability vector from the preset positions of the plurality of database indexes of the target historical progress data chain as a plurality of indexes to be processed;
otherwise, selecting the coefficient with the largest median value of the coefficient array as a target coefficient, and taking a plurality of indexes to be processed corresponding to the target coefficient as the plurality of target indexes.
In a second aspect, an embodiment of the present invention provides a project resource allocation recommendation method, where the project resource allocation recommendation method includes:
if the first average probability value is greater than or equal to the condition sufficient and equipment sufficient probability and the second average probability value is less than the condition sufficient and manpower sufficient probability, recommending to increase human resources;
if the first average probability value is smaller than the condition sufficient and equipment sufficient probability and the second average probability value is greater than or equal to the condition sufficient and manpower sufficient probability, recommending to increase equipment resources;
if the first average probability value is less than the condition sufficient and equipment sufficient probability and the second average probability value is less than the condition sufficient and human sufficient probability, then measures should be taken to correct the impact of sporadic factors including at least one of: material factors, process factors, and environmental factors.
In a third aspect, an embodiment of the present invention provides a project progress prediction apparatus, configured to implement the project progress prediction method according to the first aspect or any one of the possible implementation manners of the first aspect, where the project progress prediction apparatus includes:
the work load acquisition module is used for acquiring a plurality of average single work loads and a plurality of average single work loads;
the probability vector construction module is used for determining the human-machine coordination rationality and the probability vector for representing the influence of sporadic factors according to the plurality of average single-person workload, the plurality of average single-machine workload, the average single-person workload probability and the average single-machine workload probability, wherein the human-machine coordination rationality represents the rationality of the coordination of project human resources and equipment resources;
the historical progress extraction module is used for selecting a historical progress data chain according to the man-machine coordination rationality and determining a target progress data segment from the historical progress data chain according to a matching result of the probability vector and a plurality of database indexes, wherein the historical progress data chain is identified through the plurality of database indexes;
the method comprises the steps of,
and the progress prediction module is used for determining the progress of the project according to the current progress of the project and the target progress data segment.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program executable on the processor, and where the processor executes the computer program to implement the steps of the method as described above in any one of the possible implementations of the first aspect, the second aspect, or any one of the possible implementations of the second aspect.
In a fifth aspect, an embodiment of the present invention provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above for the first aspect, any one of the possible implementations of the first aspect, the second aspect, or any one of the possible implementations of the second aspect.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
the embodiment of the invention discloses a project progress prediction method, which comprises the steps of firstly obtaining a plurality of average single-person work volumes and a plurality of average single-machine work volumes; then determining the man-machine coordination rationality and probability vectors representing influence of sporadic factors according to the plurality of average single-person workload, the average single-person workload probability and the average single-person workload probability, wherein the man-machine coordination rationality represents the rationality of coordination of project human resources and equipment resources; then, selecting a historical progress data chain according to the man-machine coordination rationality, and determining a target progress data segment from the historical progress data chain according to a matching result of the probability vector and a plurality of database indexes, wherein the historical progress data chain is identified through the plurality of database indexes; and finally, determining the progress of the project according to the current progress of the project and the target progress data segment. According to the embodiment of the invention, the probabilities of sufficient equipment, sufficient manpower and sporadic factors are analyzed according to a plurality of average single-person workload and a plurality of average single-machine workload, and probability vectors are constructed. And analyzing the rationality of man-machine coordination according to the probability vector, and clearly referring to the data chain according to the rationality of coordination, extracting the data segment from the data chain by utilizing the probability vector to finish prediction. Because the embodiment of the invention obtains the sufficient probability vectors of people or equipment based on probability analysis, and the vectors contain the characteristics of sporadic factors, the historical progress found from the data chain through the probability vectors has the characteristics of man-machine coordination and sporadic factors, and is matched with the current characteristics, so that the accuracy of project progress prediction is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a project progress prediction method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a process for matching a target probability vector to a plurality of database indexes according to an embodiment of the present invention;
FIG. 3 is a functional block diagram of a project progress predicting apparatus provided by an embodiment of the present invention;
fig. 4 is a functional block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made with reference to the accompanying drawings.
The following describes in detail the embodiments of the present invention, and the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation procedure are given, but the protection scope of the present invention is not limited to the following embodiments.
Fig. 1 is a flowchart of a project progress prediction method according to an embodiment of the present invention.
As shown in fig. 1, a flowchart for implementing the project progress prediction method provided by the embodiment of the present invention is shown, and the details are as follows:
in step 101, a plurality of average single-person workload and a plurality of average single-person workload are acquired.
In step 102, according to the plurality of average single-person workload, the average single-person workload probability and the average single-person workload probability, a human-machine coordination rationality and a probability vector representing influence of sporadic factors are determined, wherein the human-machine coordination rationality represents rationality of coordination of project human resources and equipment resources.
In some embodiments, the step 102 includes:
acquiring a single saturated operation amount judgment value, a single saturated operation amount probability, a conditional single saturated operation amount prior probability, a single saturated operation amount probability, a condition full and equipment full probability, a condition full and manpower full probability and a conditional single saturated operation amount prior probability, wherein the conditional single saturated operation amount prior probability is the single saturated operation amount probability when the equipment condition is full and no accidental factor, the conditional single saturated operation amount prior probability is the single saturated operation amount probability when the personnel is full and no accidental factor, the condition full and equipment full probability is the probability that no accidental factor and equipment resource is full, and the condition full and manpower full probability is the probability that no accidental factor and manpower resource is full;
Determining a first saturated queue and a second saturated queue according to the single saturated workload judgment value, the plurality of average single workload and the plurality of average single workload, wherein the first saturated queue is a queue constructed according to whether the plurality of average single workload exceeds the single saturated workload judgment value, and the second saturated queue is a queue constructed according to whether the plurality of average single workload exceeds the single saturated workload judgment value;
constructing a first probability vector and a second probability vector according to the first saturation queue, the second saturation queue, the single saturation workload probability, the conditional single saturation workload prior probability, the single saturation workload probability, the condition full and equipment full probability, the condition full and manpower full probability and the conditional single saturation workload prior probability, wherein the first probability vector represents the probability of sufficient equipment conditions and no accidental factors on the premise of the first saturation queue, and the second probability vector represents the probability of sufficient manpower resource conditions and no accidental factors on the premise of the second saturation queue;
And determining the man-machine coordination rationality according to the first probability vector and the second probability vector.
In some embodiments, the constructing a first probability vector and a second probability vector according to the first saturation queue, the second saturation queue, a single saturation workload probability, a conditional single saturation workload prior probability, a single saturation workload probability, a conditional sufficient and equipment sufficient probability, a conditional sufficient and manpower sufficient probability, and a conditional single saturation workload prior probability includes:
constructing a first probability vector according to a first formula, the first saturation queue, single saturation workload probability, sufficient conditions, sufficient equipment probability and conditional single saturation workload prior probability, wherein the first formula is as follows:
in the method, in the process of the invention,is the +.o of the first probability vector>Element(s)>For the prior probability of the saturated workload of the conditional single person, +.>For a sufficient condition and a sufficient probability of device, +.>For the single person saturation workload probability, < >>Is the first saturation queue +>Data;
constructing a second probability vector according to a second formula, the second saturation queue, the single machine saturation workload probability, the condition-sufficient and manpower-sufficient probability and the conditional single machine saturation workload prior probability, wherein the second formula is as follows:
In the method, in the process of the invention,is the +.>Element(s)>Is the prior probability of the saturated workload of the conditional single machine, +.>For the condition of sufficient and manpower sufficient probability, +.>For the single machine saturation workload probability, < >>Is the +.>Data.
In some embodiments, the determining the human-machine fit rationality according to the first probability vector and the second probability vector includes:
determining a first average probability value and a second average probability value according to the first probability vector and the second probability vector, wherein the first average probability value is an average value of a plurality of elements of the first probability vector, and the second average probability value is an average value of a plurality of elements of the second probability vector;
if the first average probability value is greater than or equal to the condition sufficient and equipment sufficient probability and the second average probability value is less than the condition sufficient and manpower sufficient probability, the manpower resources are sufficient;
if the first average probability value is smaller than the condition sufficient and equipment sufficient probability and the second average probability value is greater than or equal to the condition sufficient and manpower sufficient probability, equipment resources are sufficient;
and if the first average probability value is greater than or equal to the condition sufficient and equipment sufficient probability and the second average probability value is greater than or equal to the condition sufficient and manpower sufficient probability, the man-machine cooperation is reasonable.
In the development of projects, the progress of the projects is influenced by various factors, and can be divided into a person aspect, a machine aspect, a material aspect, a method aspect, a ring aspect and a measuring aspect from the large aspects, wherein the matching of the person and equipment is the most critical, the man-machine matching is optimized, the influence of sporadic factors is eliminated, and the progress of controlling the projects is critical for optimizing the utilization rate of resources.
According to the embodiment of the invention, the average single-person operation amount and the average single-person operation amount are counted, so that the problem of man-machine coordination can be clarified according to probability analysis, and the trend of sporadic factor development can be clarified. In some embodiments, the average single workload is calculated on a daily basis, and the average single workload is continuously counted for several days to form a plurality of average single workload. In response to this, the average work load per unit of operation is continuously counted for a plurality of days for the equipment resource. For example, for a certain project, the daily project work amount is counted, the daily project work amount is divided by the number of workers to obtain the daily average single work amount, and the daily project work amount is divided by the number of workers to obtain the daily average single work amount. The relationship between the two is often the relationship between the two, reflects the matching relationship of the man and the machine, and can achieve the best man-machine matching effect only when the average value of the two reaches a higher level.
In the aspect of analyzing man-machine coordination, the embodiment of the invention analyzes the probability of sufficient equipment and no accidental event according to the single-person workload, in other words, the probability that the equipment meets the requirement of the man operation. And the probability that the human resources are sufficient and no accidental event occurs is analyzed according to the single-machine workload, and the probability that the human resources meet the equipment requirements is the same. These contingent events may be material factors, process factors, weather factors.
In practice, to obtain these probabilities we need to know some known probabilities so that a speculation or calculation is made of the probabilities.
Before probability calculation or estimation is performed, the average single-person work amount and the average single-person work amount are first divided into saturated work and unsaturated work according to the division classification value, the saturated work amount is generally represented by 1, the unsaturated work amount is represented by 0, and thus a first saturated queue and a second saturated queue corresponding to the average single-person work amounts and the average single-person work amounts are constructed.
According to the first saturation queue and the second saturation queue, a first probability vector can be constructed by applying a first formula, a single saturation workload probability, a probability that no contingent factors occur and the equipment is sufficient, a conditional saturation workload probability (a probability that no contingent factors occur and the equipment is sufficient and the workload is saturated):
Constructing a first probability vector according to a first formula, a first saturation queue, a single saturation workload probability, a condition full and equipment full probability and a conditional single saturation workload prior probability, wherein the first formula is as follows:
in the method, in the process of the invention,is the +.o of the first probability vector>Element(s)>For the prior probability of the saturated workload of the conditional single person, +.>For a sufficient condition and a sufficient probability of device, +.>For the single person saturation workload probability, < >>Is the first saturation queue +>Data.
Similarly, a second probability vector is constructed according to a second formula, a second saturation queue, a single machine saturation workload probability, a condition-sufficient and manpower-sufficient probability and a conditional single machine saturation workload prior probability, wherein the second formula is as follows:
in the method, in the process of the invention,is the +.>Element(s)>Is the prior probability of the saturated workload of the conditional single machine, +.>For the condition of sufficient and manpower sufficient probability, +.>For the single machine saturation workload probability, < >>Is the +.>Data.
Of the two probability vectors of the above formula, the meaning of the element of the first probability vector is the probability that the device is sufficient and no sporadic event occurs when knowing that the current single person has been working in saturation or in unsaturation. The meaning of the elements of the second probability vector is that there is sufficient personnel and no probability of sporadic events occurring when knowing that the current stand-alone has been working in saturation or not.
We can make speculations based on two queues, such as speculating the rationality of human coordination. For example, we first calculate the average of two probability vector elements, if the average of two first probability vectors is larger and the average of the second probability vector is smaller, then this means that the human resources are sufficient and the equipment resources are deficient; if the average value of the two first probability vectors is smaller and the average value of the second probability vector is larger, the device resources are sufficient, the human resources are deficient, and when the average value of the two first probability vectors is smaller, the probability that the current progress is influenced by the contingent factors is larger. And when both values are larger, the current man-machine cooperation is reasonable.
It can be seen that the two vectors can indicate the conditions of man-machine coordination, equipment resource utilization rate and manpower utilization, and can also indicate the influence of sporadic factors.
When we analyze progress, the time length exclamation is that the history is always surprisingly similar, and the influence of accidental factors in the exclamation often has the similar effect intensity and time length. For example, for weather, the effect of rainfall may be two consecutive days, while the effect of material may be one week, and the effect of process may take longer, from the historical progress, we can find similar historical progress from the probability vectors obtained from the above calculation, and analyze the development of future progress from the historical progress.
In the following step we will describe how to find the history progress based on the probability vector.
In step 103, a historical progress data chain is selected according to the human-computer coordination rationality, and a target progress data segment is determined from the historical progress data chain according to the matching result of the probability vector and a plurality of database indexes, wherein the historical progress data chain is identified through the plurality of database indexes.
In some embodiments, the step 103 includes:
if the human resources are sufficient, a first historical progress data chain and the first probability vector are used as a target historical progress data chain and a target probability vector, otherwise, a second historical progress data chain and the second probability vector are used as a target historical progress data chain and a target probability vector, wherein the first historical progress data chain is a data chain constructed by a plurality of historical average single-person workload, and the second historical progress data chain is a data chain constructed by a plurality of historical average single-person workload;
selecting a plurality of target indexes from a plurality of database indexes of the target historical progress data chain in a matching and translation mode according to the target probability vector;
And selecting a target progress data segment from the target history progress data chain according to the target indexes.
In some embodiments, the selecting a plurality of target indexes from a plurality of database indexes of the target historical progress data chain by means of matching and translation according to the target probability vector includes:
calculating a unit vector of the target probability vector, and taking the unit vector of the target probability vector as a query vector;
selecting database indexes with the same element number as the target probability vector from preset positions of a plurality of database indexes of the target historical progress data chain as a plurality of indexes to be processed;
determining a matching coefficient according to a third formula, the plurality of indexes to be processed and the query vector, wherein the third formula is as follows:
in the method, in the process of the invention,is the%>Element(s)>Is->Index to be processed, ++>For matching coefficients +.>For the total number of query vector elements;
adding the matching coefficient into a coefficient array;
if the plurality of database indexes of the target historical progress data chain are not traversed, adjusting the preset positions, and jumping to the step of selecting the database indexes with the same number as the elements of the target probability vector from the preset positions of the plurality of database indexes of the target historical progress data chain as a plurality of indexes to be processed;
Otherwise, selecting the coefficient with the largest median value of the coefficient array as a target coefficient, and taking a plurality of indexes to be processed corresponding to the target coefficient as the plurality of target indexes.
Illustratively, it should be noted that, according to the result of man-machine coordination, the embodiment of the present invention selects a history progress data chain, and if the man-machine coordination is in full use of human resources and the equipment is surplus, data should be mined from the data chain of average single-person workload. If the resources of the equipment are fully utilized and the human resources are surplus in man-machine cooperation, the data should be mined from the data chain of the average single-machine workload.
The data chain is identified by an index. In practice, the elements of the vector obtained by our previous steps correspond to the average single-person or single-machine work volume. After the foregoing steps, the workload data is added to the data chain and the elements of the probability vector are added to the index as an index, i.e., the origin of the history progress data chain.
Then, a plurality of indexes which are most similar to the probability vector are found according to the probability vector by matching the plurality of indexes-stabilizing and obtaining indexes-re-matching.
Fig. 2 shows a schematic diagram of this process.
The indexes 202 in the figure identify a single workload or single workload that is averaged over a plurality of histories, and a plurality of consecutive indexes 203 are extracted from the single workload or single workload, and are matched with the probability vectors 201 to obtain matching values, and the matching values are put into an array, and then translated until the indexes 202 in the figure are traversed.
At this time, the data segment corresponding to the value with the largest matching value in the array is the target data segment.
In the embodiment of the invention, in the aspect of calculating the matching value, firstly, a unit vector of the probability vector is extracted, and then the matching value is calculated by using the unit vector and a plurality of continuous indexes by adopting a third formula:
in the method, in the process of the invention,is the%>Element(s)>Is->Index to be processed, ++>For matching coefficients +.>Is the total number of query vector elements.
In step 104, the progress of the project is determined according to the current progress of the project and the target progress data segment.
Illustratively, once the target data segment is obtained, the predicted future progress may be analyzed.
Through the steps, the obtained target data segment is similar to the current progress trend, probability of occurrence of accidental events and influence, and the data at the rear part of the target data segment is the trend of progress development in a future period. Therefore, the extraction of the data segment at the rear of the target data segment is a trend of progress development in the future.
For example, a person is working saturated, the equipment has a surplus, and predictions should be made from the foregoing predictions as to the data chain of average single person workload. The extracted target data segments are DATE101-DATE108, the rear data are DATE109-DATE124, and the rear data can be used as the future average single person or average single machine workload. In the project, the operator has 10 persons, the current progress is A, and the progress of the future DATE1-DATE16 is A+DATE109×10, A+DATE110×10, A+DATE111×10 … …, and A+DATE124×10.
It can be seen that the time and the progress of the project can be determined in the future by the above method, and in practice, the length of the target data segment should be increased, and the length of the rear data should be reduced, so as to obtain a more accurate prediction effect. Taking the above example, in the above prediction, the target data segment is DATE101-DATE108, eight data are total, the data behind it is DATE109-DATE124, 16 data are total, the ratio is 1:2, and if the ratio is improved to 3:1, the prediction accuracy will be greatly increased, but this will be at the expense of not easily finding a more matched, continuous target data segment, and increasing the calculation amount in the prediction. We should take this ratio reasonably and increase the data chain length to ensure the effect of the prediction.
The project progress prediction method comprises the steps of firstly, obtaining a plurality of average single-person workload and a plurality of average single-machine workload; then determining the man-machine coordination rationality and probability vectors representing influence of sporadic factors according to the plurality of average single-person workload, the average single-person workload probability and the average single-person workload probability, wherein the man-machine coordination rationality represents the rationality of coordination of project human resources and equipment resources; then, selecting a historical progress data chain according to the man-machine coordination rationality, and determining a target progress data segment from the historical progress data chain according to a matching result of the probability vector and a plurality of database indexes, wherein the historical progress data chain is identified through the plurality of database indexes; and finally, determining the progress of the project according to the current progress of the project and the target progress data segment. According to the embodiment of the invention, the probabilities of sufficient equipment, sufficient manpower and sporadic factors are analyzed according to a plurality of average single-person workload and a plurality of average single-machine workload, and probability vectors are constructed. And analyzing the rationality of man-machine coordination according to the probability vector, and clearly referring to the data chain according to the rationality of coordination, extracting the data segment from the data chain by utilizing the probability vector to finish prediction. Because the embodiment of the invention obtains the sufficient probability vectors of people or equipment based on probability analysis, and the vectors contain the characteristics of sporadic factors, the historical progress found from the data chain through the probability vectors has the characteristics of man-machine coordination and sporadic factors, and is matched with the current characteristics, so that the accuracy of project progress prediction is improved.
The embodiment of the invention also provides a project resource allocation recommendation method, which is applied after the project progress prediction method determines a first average probability value and a second average probability value according to the first probability vector and the second probability vector, and comprises the following steps:
if the first average probability value is greater than or equal to the condition sufficient and equipment sufficient probability and the second average probability value is less than the condition sufficient and manpower sufficient probability, recommending to increase human resources;
if the first average probability value is smaller than the condition sufficient and equipment sufficient probability and the second average probability value is greater than or equal to the condition sufficient and manpower sufficient probability, recommending to increase equipment resources;
if the first average probability value is less than the condition sufficient and equipment sufficient probability and the second average probability value is less than the condition sufficient and human sufficient probability, then measures should be taken to correct the impact of sporadic factors including at least one of: material factors, process factors, and environmental factors.
Illustratively, as previously described, when the average value determined by the probability analysis is large, it is explained that the current object is high in utilization and saturated in work.
Based on the above reasoning, we can know that when the human resource utilization rate is high, the sufficient probability of the equipment condition and the external condition is high, the human resource (the equipment surplus) should be increased. In other words, when the human resource utilization rate is high, the device condition and external condition sufficiency probability is high, the device resource utilization rate is low, and when the device condition and external condition sufficiency probability is low, the human resource (device surplus) should be increased.
In contrast, when the device resource utilization rate is high, the human resource condition and the external condition sufficiency probability are high, the device resource (human surplus) should be increased. In other words, when the device resource utilization rate is high, the device condition and external condition sufficiency probability is high, the human resource utilization rate is low, and when the human condition and external condition sufficiency probability is low, the device resource (human surplus) should be increased
And when both the person and the equipment do not work in a saturated manner, accidental modes such as precipitation, flood, material shortage and the like are indicated. Measures should be taken immediately to analyze the cause of the impact on progress.
When people and equipment work in saturation, the human-computer cooperation is ideal.
The embodiment of the invention can point out the resource utilization condition and the short board of the resource utilization according to the probability analysis, and point out the position to be improved according to the short board, thereby improving the resource utilization rate.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 3 is a functional block diagram of a project progress predicting apparatus according to an embodiment of the present invention, and referring to fig. 3, the project progress predicting apparatus includes: a job volume acquisition module 301, a probability vector construction module 302, a history progress extraction module 303, and a progress prediction module 304, wherein:
a workload acquisition module 301, configured to acquire a plurality of average single workload and a plurality of average single workload;
the probability vector construction module 302 is configured to determine a human-machine coordination rationality and a probability vector representing influence of sporadic factors according to the plurality of average single-person workload, the average single-person workload probability and the average single-person workload probability, where the human-machine coordination rationality represents rationality of coordination of project human resources and equipment resources;
A history progress extraction module 303, configured to select a history progress data chain according to the human-computer coordination rationality, and determine a target progress data segment from the history progress data chain according to a matching result of the probability vector and a plurality of database indexes, where the history progress data chain is identified by the plurality of database indexes;
and the progress prediction module 304 is configured to determine the progress of the project according to the current progress of the project and the target progress data segment.
Fig. 4 is a functional block diagram of an electronic device provided by an embodiment of the present invention. As shown in fig. 4, the electronic apparatus 4 of this embodiment includes: a processor 400 and a memory 401, said memory 401 having stored therein a computer program 402 executable on said processor 400. The processor 400, when executing the computer program 402, implements the steps of the project progress prediction methods and embodiments described above, such as steps 101 through 104 shown in fig. 1.
By way of example, the computer program 402 may be partitioned into one or more modules/units that are stored in the memory 401 and executed by the processor 400 to accomplish the present invention.
The electronic device 4 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The electronic device 4 may include, but is not limited to, a processor 400, a memory 401. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the electronic device 4 and is not meant to be limiting of the electronic device 4, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device 4 may further include input-output devices, network access devices, buses, etc.
The processor 400 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 401 may be an internal storage unit of the electronic device 4, such as a hard disk or a memory of the electronic device 4. The memory 401 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 4. Further, the memory 401 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 401 is used for storing the computer program 402 and other programs and data required by the electronic device 4. The memory 401 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, and will not be described herein again.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and the details or descriptions of other embodiments may be referred to for those parts of an embodiment that are not described in detail or are described in detail.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on this understanding, the present invention may also be implemented by implementing all or part of the procedures in the methods of the above embodiments, or by instructing the relevant hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may be implemented by implementing the steps of the embodiments of the methods and apparatuses described above when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limited thereto; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and they should be included in the protection scope of the present invention.
Claims (10)
1. A project progress prediction method, comprising:
obtaining a plurality of average single-person workload and a plurality of average single-machine workload;
determining human-machine coordination rationality and probability vectors representing influence of sporadic factors according to the plurality of average single-person workload, the plurality of average single-machine workload, the average single-person workload probability and the average single-machine workload probability, wherein the human-machine coordination rationality represents rationality of coordination of project human resources and equipment resources;
selecting a historical progress data chain according to the man-machine coordination rationality, and determining a target progress data segment from the historical progress data chain according to a matching result of the probability vector and a plurality of database indexes, wherein the historical progress data chain is identified through the plurality of database indexes;
And determining the progress of the project according to the current progress of the project and the target progress data segment.
2. The project progress prediction method according to claim 1, wherein the determining of the human-machine coordination rationality and the probability vector representing the influence of the contingency factor based on the plurality of average single-person workload, the average single-person workload probability, and the average single-person workload probability comprises:
acquiring a single saturated operation amount judgment value, a single saturated operation amount probability, a conditional single saturated operation amount prior probability, a single saturated operation amount probability, a condition full and equipment full probability, a condition full and manpower full probability and a conditional single saturated operation amount prior probability, wherein the conditional single saturated operation amount prior probability is the single saturated operation amount probability when the equipment condition is full and no accidental factor, the conditional single saturated operation amount prior probability is the single saturated operation amount probability when the personnel is full and no accidental factor, the condition full and equipment full probability is the probability that no accidental factor and equipment resource is full, and the condition full and manpower full probability is the probability that no accidental factor and manpower resource is full;
Determining a first saturated queue and a second saturated queue according to the single saturated workload judgment value, the plurality of average single workload and the plurality of average single workload, wherein the first saturated queue is a queue constructed according to whether the plurality of average single workload exceeds the single saturated workload judgment value, and the second saturated queue is a queue constructed according to whether the plurality of average single workload exceeds the single saturated workload judgment value;
constructing a first probability vector and a second probability vector according to the first saturation queue, the second saturation queue, the single saturation workload probability, the conditional single saturation workload prior probability, the single saturation workload probability, the condition full and equipment full probability, the condition full and manpower full probability and the conditional single saturation workload prior probability, wherein the first probability vector represents the probability of sufficient equipment conditions and no accidental factors on the premise of the first saturation queue, and the second probability vector represents the probability of sufficient manpower resource conditions and no accidental factors on the premise of the second saturation queue;
And determining the man-machine coordination rationality according to the first probability vector and the second probability vector.
3. The project progress prediction method according to claim 2, wherein the constructing a first probability vector and a second probability vector from the first saturation queue, the second saturation queue, a single saturation workload probability, a conditional single saturation workload prior probability, a single saturation workload probability, a conditional sufficient and equipment sufficient probability, a conditional sufficient and manpower sufficient probability, and a conditional single saturation workload prior probability, comprises:
constructing a first probability vector according to a first formula, the first saturation queue, single saturation workload probability, sufficient conditions, sufficient equipment probability and conditional single saturation workload prior probability, wherein the first formula is as follows:
in the method, in the process of the invention,is the +.o of the first probability vector>Element(s)>For the prior probability of the saturated workload of the conditional single person, +.>For a sufficient condition and a sufficient probability of device, +.>For the single person saturation workload probability, < >>Is the first saturation queue +>Data;
constructing a second probability vector according to a second formula, the second saturation queue, the single machine saturation workload probability, the condition-sufficient and manpower-sufficient probability and the conditional single machine saturation workload prior probability, wherein the second formula is as follows:
In the method, in the process of the invention,is the +.>Element(s)>Is the prior probability of the saturated workload of the conditional single machine, +.>For the condition of sufficient and manpower sufficient probability, +.>For the single machine saturation workload probability, < >>Is the +.>Data.
4. The project progress prediction method according to claim 2, wherein the determining of the human-machine coordination rationality from the first probability vector and the second probability vector comprises:
determining a first average probability value and a second average probability value according to the first probability vector and the second probability vector, wherein the first average probability value is an average value of a plurality of elements of the first probability vector, and the second average probability value is an average value of a plurality of elements of the second probability vector;
if the first average probability value is greater than or equal to the condition sufficient and equipment sufficient probability and the second average probability value is less than the condition sufficient and manpower sufficient probability, the manpower resources are sufficient;
if the first average probability value is smaller than the condition sufficient and equipment sufficient probability and the second average probability value is greater than or equal to the condition sufficient and manpower sufficient probability, equipment resources are sufficient;
And if the first average probability value is greater than or equal to the condition sufficient and equipment sufficient probability and the second average probability value is greater than or equal to the condition sufficient and manpower sufficient probability, the man-machine cooperation is reasonable.
5. The project progress prediction method of any one of claims 2-4, wherein the selecting a historical progress data chain according to the human-machine fit rationality and determining a target progress data segment from the historical progress data chain according to a matching result of the probability vector with a plurality of database indexes comprises:
if the human resources are sufficient, a first historical progress data chain and the first probability vector are used as a target historical progress data chain and a target probability vector, otherwise, a second historical progress data chain and the second probability vector are used as a target historical progress data chain and a target probability vector, wherein the first historical progress data chain is a data chain constructed by a plurality of historical average single-person workload, and the second historical progress data chain is a data chain constructed by a plurality of historical average single-person workload;
selecting a plurality of target indexes from a plurality of database indexes of the target historical progress data chain in a matching and translation mode according to the target probability vector;
And selecting a target progress data segment from the target history progress data chain according to the target indexes.
6. The project progress prediction method according to claim 5, wherein the selecting a plurality of target indexes from a plurality of database indexes of the target history progress data chain by means of matching and panning according to the target probability vector comprises:
calculating a unit vector of the target probability vector, and taking the unit vector of the target probability vector as a query vector;
selecting database indexes with the same element number as the target probability vector from preset positions of a plurality of database indexes of the target historical progress data chain as a plurality of indexes to be processed;
determining a matching coefficient according to a third formula, the plurality of indexes to be processed and the query vector, wherein the third formula is as follows:
in the method, in the process of the invention,is the%>Element(s)>Is->Index to be processed, ++>For matching coefficients +.>For the total number of query vector elements;
adding the matching coefficient into a coefficient array;
if the plurality of database indexes of the target historical progress data chain are not traversed, adjusting the preset positions, and jumping to the step of selecting the database indexes with the same number as the elements of the target probability vector from the preset positions of the plurality of database indexes of the target historical progress data chain as a plurality of indexes to be processed;
Otherwise, selecting the coefficient with the largest median value of the coefficient array as a target coefficient, and taking a plurality of indexes to be processed corresponding to the target coefficient as the plurality of target indexes.
7. A project resource allocation recommending method, which is applied to the project progress predicting method according to claim 4, comprising:
if the first average probability value is greater than or equal to the condition sufficient and equipment sufficient probability and the second average probability value is less than the condition sufficient and manpower sufficient probability, recommending to increase human resources;
if the first average probability value is smaller than the condition sufficient and equipment sufficient probability and the second average probability value is greater than or equal to the condition sufficient and manpower sufficient probability, recommending to increase equipment resources;
if the first average probability value is less than the condition sufficient and equipment sufficient probability and the second average probability value is less than the condition sufficient and human sufficient probability, then measures should be taken to correct the impact of sporadic factors including at least one of: material factors, process factors, and environmental factors.
8. A project progress predicting apparatus for implementing the project progress predicting method according to any one of claims 1 to 6, the project progress predicting apparatus comprising:
The work load acquisition module is used for acquiring a plurality of average single work loads and a plurality of average single work loads;
the probability vector construction module is used for determining the human-machine coordination rationality and the probability vector for representing the influence of sporadic factors according to the plurality of average single-person workload, the plurality of average single-machine workload, the average single-person workload probability and the average single-machine workload probability, wherein the human-machine coordination rationality represents the rationality of the coordination of project human resources and equipment resources;
the historical progress extraction module is used for selecting a historical progress data chain according to the man-machine coordination rationality and determining a target progress data segment from the historical progress data chain according to a matching result of the probability vector and a plurality of database indexes, wherein the historical progress data chain is identified through the plurality of database indexes;
the method comprises the steps of,
and the progress prediction module is used for determining the progress of the project according to the current progress of the project and the target progress data segment.
9. An electronic device comprising a memory and a processor, the memory having stored therein a computer program executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method according to any of the preceding claims 1 to 6.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 6.
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