Abnormality detection system, abnormality detection method, and abnormality detection program

Abstract

To provide an abnormality detection system, an abnormality detection method, and an abnormality detection program which enable automatically detecting an abnormality in business data to make early detection / treatment.SOLUTION: An abnormality detection system comprises: detection means to divide the population of past subject data by designated specific data, calculate an approximate curve on a lapse rate and a subject progress rate for the subject data for each of different populations, calculate a predicted value of the subject progress rate by substituting the lapse rate for an approximate curve function, calculate upper / lower limits on the basis of the approximate curve and the predicted value for the subject progress rate, and detect an abnormality in comparison between the subject data to be detected and the upper / lower limits; and display control means to display analysis data about the subject data detecting the abnormality on an analysis screen.SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] The present invention relates to an anomaly detection system, an anomaly detection method, and an anomaly detection program. [Background technology]

[0002] For example, the construction industry is an industry where anomalies often occur in construction-related data. Since it is necessary to check for anomalies from a huge amount of data, the range of data that can be checked manually is becoming more than what can be done by humans. For example, Patent Document 1 discloses a system for detecting anomalies in business data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-95122 Summary of the Invention [Problem to be solved by the invention]

[0004] However, Patent Document 1 does not describe anything about automatically detecting anomalies in case (business) data and taking early detection and action.

[0005] In view of the above, the present invention aims to provide an anomaly detection system, an anomaly detection method, and an anomaly detection program that can automatically detect anomalies in case data and enable early discovery and handling. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the object, the present invention provides an anomaly detection system equipped with a control unit, the control unit being configured to be able to access case data including one or more specific conditions, progress rates, and case progress rates, and is characterized by comprising: detection means for dividing a population of past case data according to specified specific conditions, calculating an approximate curve for the progress rate and case progress rate of the case data for each population, calculating a predicted value of the case progress rate by substituting the progress rate into a function of the approximate curve, calculating upper and lower limit values ​​based on the approximate curve and the predicted value of the case progress rate, and detecting an anomaly by comparing the case data to be detected with the upper and lower limit values; and display control means for displaying analysis data on an analysis screen for case data in which an anomaly has been detected.

[0007] According to one aspect of the present invention, the project data may include construction data, the specific conditions may include the type of work, construction period, or public / private classification, the progress rate may include the construction period progress rate calculated by the number of months elapsed since construction divided by the planned construction period, and the project progress rate may include the cumulative progress rate calculated by the current incurred costs divided by (budgeted cost + current incurred costs), and the construction data may include the work, organization, one or more specific conditions, fiscal year and month, sales amount, construction period progress rate, and cumulative progress rate.

[0008] Furthermore, according to one aspect of the present invention, the display control means may display a message in a predetermined area of ​​the analysis screen based on the construction data, including the construction work detected as abnormal, the detection method, and the cumulative progress rate determined to be abnormal.

[0009] Furthermore, according to one aspect of the present invention, the display control means may display, in a predetermined area of ​​the analysis screen based on the construction data, the actual cumulative progress rate of past construction work by population or of all past construction work by construction period progress rate, and its approximate curve, as well as a graph showing the trend in the actual cumulative progress rate linked to construction work in which an abnormality has been detected.

[0010] Furthermore, according to one aspect of the present invention, the display control means may display, in a predetermined area of ​​the analysis screen based on the construction data, the actual cumulative progress rate of past construction work and its approximate curve, by accounting year and month, and a graph showing the trend in the actual cumulative progress rate linked to construction work in which an abnormality has been detected.

[0011] Furthermore, according to one aspect of the present invention, the display control means may display a table showing the trends in actual sales and sales forecasts by organization within a specified accounting period in a specified area of ​​the analysis screen based on the construction data.

[0012] Furthermore, according to one aspect of the present invention, the display control means may display, based on the construction data, a table in a predetermined area of ​​the analysis screen showing the trends in actual sales and sales forecasts for each construction project of the organization in charge of the construction project in which the abnormality occurred.

[0013] Furthermore, in order to solve the above-mentioned problems and achieve the object, the present invention provides an anomaly detection method executed by an information processing device having a control unit, wherein the control unit is configured to be able to access case data including one or more specific conditions, progress rates, and case progress rates, and the method includes a detection process of dividing a population of past case data according to specified specific conditions executed by the control unit, calculating an approximate curve for the progress rate of business data and the case progress rate for each population, calculating a predicted value of the case progress rate by substituting the progress rate into a function of the approximate curve, calculating upper and lower limit values ​​based on the approximate curve and the predicted value of the case progress rate, and comparing the case data to be detected with the upper and lower limit values ​​to detect an anomaly, and a display control process of displaying analysis data on an analysis screen for the case data in which an anomaly has been detected.

[0014] Furthermore, in order to solve the above-mentioned problems and achieve the object, the present invention provides an anomaly detection program to be executed by an information processing device having a control unit, wherein the control unit is configured to be able to access case data including one or more specific conditions, progress rates, and case progress rates, and the control unit executes the following detection steps in the control unit: dividing a population of past case data according to the specified specific conditions, calculating an approximate curve for the progress rate and case progress rate of the case data for each population, calculating a predicted value of the case progress rate by substituting the progress rate into a function of the approximate curve, calculating upper and lower limit values ​​based on the approximate curve and the predicted value of the case progress rate, and comparing the case data to be detected with the upper and lower limit values ​​to detect an anomaly; and display control step of displaying analysis data on an analysis screen for the case data in which an anomaly has been detected. [Effects of the Invention]

[0015] The present invention has the effect of automatically detecting anomalies in case data, enabling early detection and handling of the problem. [Brief explanation of the drawings]

[0016] [Figure 1] FIG. 1 is a diagram showing an example of an illegal system. [Figure 2] FIG. 2 is a diagram showing an example of an image of a good performance system. [Figure 3] FIG. 3 is a diagram for explaining the applicable range (industries in which the present invention can be used) of the present invention. [Figure 4] FIG. 4 is a diagram for explaining the measures and effects for problem (1). [Figure 5] FIG. 5 is a diagram for explaining the measures and effects for problem (2). [Figure 6] FIG. 6 is a diagram for explaining the measures and effects for problem (3). [Figure 7] FIG. 7 is a diagram for explaining the measures and effects for problem (4). [Figure 8] FIG. 8 is a diagram showing a display example of the initial analysis screen. [Figure 9]FIG. 9 is a diagram showing an example of the analysis screen. [Figure 10] FIG. 10 is a block diagram illustrating a hardware configuration of the anomaly detection system according to the embodiment. [Figure 11] FIG. 11 is a flowchart illustrating an outline of the overall processing of the control unit of the anomaly detection system according to this embodiment. [Figure 12] FIG. 12 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 13] FIG. 13 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 14] FIG. 14 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 15] FIG. 15 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 16] FIG. 16 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 17] FIG. 17 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 18A] FIG. 18A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 18B] FIG. 18B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 19A] FIG. 19A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 19B] FIG. 19B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 20A] FIG. 20A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 20B]FIG. 20B is a diagram for explaining a specific example of the process of the control unit of the anomaly detection system according to the present embodiment. [Figure 21] FIG. 21 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 22A] FIG. 22A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 22B] FIG. 22B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 23A] FIG. 23A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 23B] FIG. 23B is a diagram for explaining a specific example of the process of the control unit of the anomaly detection system according to the present embodiment. [Figure 24A] FIG. 24A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 24B] FIG. 24B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 25A] FIG. 25A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 25B] FIG. 25B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 26A] FIG. 26A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 26B] FIG. 26B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 27A] FIG. 27A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 27B]FIG. 27B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 28A] FIG. 28A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 28B] FIG. 28B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 29A] FIG. 29A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 29B] FIG. 29B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 30] FIG. 30 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 31] FIG. 31 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 32] FIG. 32 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 33A] FIG. 33A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 33B] FIG. 33B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 34] FIG. 34 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 35] FIG. 35 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 36] FIG. 36 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 37]FIG. 37 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 38] FIG. 38 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 39] FIG. 39 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 40] FIG. 40 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 41] FIG. 41 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 42A] FIG. 42A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 42B] FIG. 42B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 43] FIG. 43 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 44] FIG. 44 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 45] FIG. 45 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 46A] FIG. 46A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 46B] FIG. 46B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 47A] FIG. 47A is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 47B]FIG. 47B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 48A] FIG. 48A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 48B] FIG. 48B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 49A] FIG. 49A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 49B] FIG. 49B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 50] FIG. 50 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 51] FIG. 51 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 52] FIG. 52 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 53A] FIG. 53A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 53B] FIG. 53B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 54] FIG. 54 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 55] FIG. 55 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 56] FIG. 56 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 57]FIG. 57 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 58A] FIG. 58A is a diagram for explaining a specific example of processing by the control unit of the anomaly detection system according to the present embodiment. [Figure 58B] FIG. 58B is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 59] FIG. 59 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 60] FIG. 60 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 61] FIG. 61 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 62] FIG. 62 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 63] FIG. 63 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 64] FIG. 64 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 65] FIG. 65 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 66] FIG. 66 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 67] FIG. 67 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 68] FIG. 68 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 69] FIG. 69 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. [Figure 70] FIG. 70 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 71] FIG. 71 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to the present embodiment. [Figure 72] FIG. 72 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 73] FIG. 73 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 74] FIG. 74 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system according to this embodiment. [Figure 75] FIG. 75 is a diagram for explaining a specific example of the processing of the control unit of the anomaly detection system in this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0017] An anomaly detection system according to an embodiment of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] [1. Overview] The outline of the present invention will be explained in the following order: (1-1. Background and premise), (1-2. Measures and effects for the issues), and (1-3. Analysis screen).

[0019] (1-1.Background / Premise) The background and premise will be explained with reference to Figures 1 to 3. Traditionally, the construction industry has been an industry where abnormalities have frequently occurred in construction-related data. Since it is necessary to check for abnormalities from a huge amount of data, the range of what can be checked by human beings is becoming more than what can be checked by human beings, and there is a demand for a system that can detect and deal with the abnormalities early by means other than human beings.

[0020] In this embodiment, a system is constructed that automatically detects abnormal patterns periodically by creating a scenario as a measure for early detection and handling of data abnormalities and a measure that does not require human intervention.

[0021] Specifically, in this embodiment, fraudulent business data is automatically detected and reported within transaction data in a business system. The system calculates the approximate progress of construction work managed by the company based on past construction results, and detects construction work that shows a large discrepancy compared to the results of uncompleted construction work as an anomaly. The system also outputs a screen on which the approximate progress curve of construction work and the progress curve of construction work for which an anomaly has been detected can be confirmed. Additionally, because construction anomalies affect the organization's figures, the system outputs a screen on which the results by organization and construction work period can be confirmed.

[0022] According to this embodiment, it is possible to periodically detect fraud from a huge amount of information related to construction work, and to confirm the predicted future progress of unfinished construction work and the degree of workload that may occur in the future. It is also possible to perform detailed analysis of the predicted completion rate for each organization by period and the monthly progress trend of construction work.

[0023] Here, as an example, we assume that the system will be used by companies in the construction industry that implement "percentage-of-completion sales" to manage the progress of each project.

[0024] There are two methods for recording sales for construction work in the construction industry: (1) The percentage-of-completion method is a method of recording sales in accordance with the ratio of the accumulated cost amount recorded during construction work to the estimated final cost amount. (2) Sales from completed construction is a method of recording sales when construction is completed.

[0025] Possible patterns of data anomalies include the following: Possible fraudulent activities include misallocation of construction costs, non-recording of costs, and delays in recording sales. -For projects with good results, construction work may be brought forward.

[0026] A feature that applies to both patterns is the "impact on the construction progress rate." The approximation line of the progress rate trends of past construction work = "the progress rate trends that our company basically follows when carrying out construction work," and if there is a large discrepancy between the approximation line of the progress rate trends of past construction work, which is used as a reference value, and the current construction work, it can be seen as an abnormality, as it means that the progress rate is different from the past trend.

[0027] Figure 1 is a diagram showing an example of fraudulent activity. Figure 1 shows a graph of the progress curve, with the horizontal axis showing the progress rate (%) for the construction period and the vertical axis showing the cumulative progress rate (%). An approximation curve of the progress rate trends for past construction work and the progress rate of the construction work are plotted. In this figure, the progress rate is abnormally low compared to the approximation curve, which may indicate fraudulent activity.

[0028] Figure 2 is a diagram showing an example of a good performance type. Figure 2 shows a graph of the progress curve, with the horizontal axis showing the progress rate (%) of the construction period and the vertical axis showing the cumulative progress rate (%). An approximation curve of the progress rate trends of past construction work and the progress rate of the construction work are plotted. In this figure, the progress rate of the construction work has increased abnormally compared to the approximation curve, which may be in the good performance type.

[0029] Here, we will explain the progress rate of the construction period, the completed volume, and the progress rate of the construction work (cumulative progress rate). The completion rate for the construction period refers to the progress rate in months as of the current fiscal year / month for the construction period. For example, if there is a construction project with a 12-month construction period, which begins in January and is completed in December, and the current month is May, the completion rate for the construction period = 5 / 12 ≒ 42% (decimals are rounded off). - In the case of percentage-of-completion sales, which generally refers to the sales amount of the construction work, the sales amount of the construction work = the sales amount of the construction work adjusted to the cumulative progress rate. The construction progress rate (cumulative progress rate) refers to the actual progress rate for each construction project. The progress rate generally refers to the degree to which the current cost has accumulated compared to the total of the current accumulated costs and the future forecast (the estimated cost amount to be incurred until the construction is completed). For example, if the final cost budget is 1,000 yen and the current incurred costs are 500 yen, the cumulative progress rate = 500 / (500+1000) ≒ 33%.

[0030] From here on, the explanation will mainly be based on graphs, so to make the chronological explanation easier to understand, we will use the term cumulative progress rate.

[0031] FIG. 3 is a diagram for explaining the applicable range (applicable industries) of the present invention. In this specification, an embodiment for the construction industry will be described as an example. The present invention is not limited to the construction industry, It can be applied to other companies and industries that manage projects. Figure 3 shows the connections between the concepts by terminology in this specification when considering expansion to industries other than the construction industry (software development, advertising and media, etc.).

[0032] "Project (work)" includes projects, which further include construction work, development tasks, and content creation. "Specific conditions" include project type / period, which include work type / construction period / public / private classification, system type / development period, content medium / production period, etc. "Project progress" includes project progress, which includes completed volume, completed man-hours, and completed amount, etc.

[0033] In this way, the anomaly detection system of the present invention is not limited to the construction industry, but can also be applied to other industries that manage projects (cases), for example.

[0034] (1-2. Measures and effects for the issues) The measures and effects for the issues (1) to (4) will be explained with reference to FIGS. 4 to 7.

[0035] (Task (1)) Figure 4 is a diagram for explaining the measures and effects for problem (1). The measures and effects for problem (1) will be explained with reference to Figure 4.

[0036] When the progress rate changes for specific units such as construction period or type of work, it is almost impossible to analyze the progress rate status and abnormalities for each unit. When calculating the progress rate under specific conditions, the following issues arise: - Because it is necessary to define specific conditions and aggregate data from a huge amount of data, there is a risk of calculation errors and data reference errors, which are computationally expensive. In few cases, the "specific conditions that are known to cause different characteristics of output" are known in advance, so it is necessary to separate into multiple units and check the aggregated data to identify the characteristics. Therefore, there are cases where the benefits obtained are low compared to the work costs incurred.

[0037] As a countermeasure to this, the volume of production per specific condition is calculated by automatic execution, and by simply changing the specification of the specific condition, it is possible to check whether the specified condition is a "specific condition that is known to have different production characteristics."

[0038] This makes it easier to analyze which units have different characteristics when it comes to the volume of work completed by a company, and allows the company to quickly narrow down its review to projects with similar volumes, making it possible to more accurately identify abnormalities in volume.

[0039] (Task (2)) Figure 5 is a diagram for explaining the measures and effects for problem (2). The measures and effects for problem (2) will be explained with reference to Figure 5.

[0040] It is not realistic, and is virtually impossible, to regularly check the progress trends of all past construction projects. When trying to refer to the progress records of all construction projects, the following issues arise: - It is necessary to extract information about production volume from a huge amount of data, which is costly and prone to errors. Calculating volume trends requires specialized skills, so in addition to understanding data structures, it is also necessary to calculate volume over time.

[0041] Therefore, regular checks are required to understand trends, and the above two risks arise each time.

[0042] To address this issue, the system periodically checks the progress of all past construction work, and performs calculations on the system side to visualize the progress of all past construction work.In addition, a graph is provided on the analysis screen to visualize the progress of progress.

[0043] This allows you to see the progress of past construction work with just one graph, reducing the risk of work costs and mistakes, and eliminating the need for specialized skills to visualize progress.

[0044] (Task (3)) 6 is a diagram for explaining the measures and effects for problem (3). The measures and effects for problem (3) will be explained with reference to FIG.

[0045] In order to compare the trends in the progress of construction work in progress with the progress trends of all past construction work, it is necessary to confirm the approximate value of the progress during the construction period of all past construction work. When trying to obtain an approximate value from the progress trend of all construction work, the following issues arise.

[0046] - It is necessary to extract information about production volume from a huge amount of data, which is costly and prone to errors. Calculating volume trends requires specialized skills, so in addition to understanding data structures, it is also necessary to calculate volume over time. Calculating an approximate value of volume requires specialized skills, so calculations are required to calculate an approximate value of the data taking into account the characteristics of the data trends.

[0047] As a countermeasure to this, the system's algorithm is used to calculate an approximate value of the progress made from all past construction work, and the approximate value is visualized on an analysis screen.

[0048] This allows you to intuitively grasp the trends in the volume of work completed in past construction projects, making it possible to confirm the approximate trends in the volume of work completed by your company, taking into account the characteristics of all past construction projects.This reduces the risk of work costs and errors, and eliminates the need for specialized skills to obtain approximate values ​​for data.

[0049] (Task (4)) 7 is a diagram for explaining the measures and effects for problem (4). The measures and effects for problem (4) will be explained with reference to FIG.

[0050] In order to notice that the progress of a construction project in progress is abnormal compared to past construction projects, it is necessary to compare each individual progress trend point (each month, 10% progress intervals, etc.) with the approximate value of all past construction projects, which is unrealistic and virtually impossible. When trying to compare each individual progress trend point (each month, 10% progress intervals, etc.) with the approximate value of all past construction projects, the following issues arise:

[0051] - It is necessary to compile data by pattern, such as by tallying the volume of work progress at each point (each month, 10% intervals of progress, etc.), and since the work of compiling by pattern from a huge amount of data is necessary, in addition to the risk of work costs and mistakes, knowledge of pattern compilation is required. Furthermore, since it is necessary to judge as abnormal anything that deviates abnormally from the approximate value when comparing all work in progress, it is almost impossible to do this work manually.

[0052] As a countermeasure, the "progression of progress for construction work in progress" and the "approximate value of progress for all past construction work" are visualized side by side. It is also possible to check the progress rate and month by month comparisons on a single screen. The system also calculates and visualizes a threshold that can be used to determine an abnormality based on the deviation from the approximate value. Furthermore, it visualizes whether the progress rate of construction work in progress is abnormal at a glance.

[0053] This makes it possible to immediately grasp any deviation from past trends at each progress rate point. This reduces work costs and the risk of mistakes, eliminates the need for manual tabulation work, and makes it possible to check and understand at a glance whether the data is abnormal.

[0054] (1-3.Analysis screen) The analysis screen of the anomaly detection system of this embodiment will be described with reference to Figures 8 and 9. The analysis screen outputs "progress rate and cumulative progress rate by construction project," "approximate cumulative progress rate by progress rate for the entire construction project," and "predicted sales amount (approximate value) and actual results by organization and construction project." Information for which an anomaly has been detected is highlighted by changing the color and font size so that the anomaly can be identified at a glance. The anomaly detection data that can be checked targets the cumulative progress rate by progress rate for construction projects in progress, and detects construction projects that deviate significantly from the trend of past cumulative progress rates.

[0055] The analysis screen is composed of an initial analysis screen as shown in Fig. 8 and an analysis screen as shown in Fig. 9. The analysis screen switches from Fig. 8 to Fig. 9 in this order.

[0056] The initial analysis screen in Figure 8 displays a list of messages resulting from detected anomalies, and outputs summary messages of detected anomalies.

[0057] Area A1 displays messages related to the anomaly detection process, showing the timing of the anomaly detection, the construction work, and the difference between the cumulative progress rate for the month in which the anomaly was detected and an approximate value for the progress rate of past construction work. Since the output is on an overview basis, you must select the message and switch screens to see the detailed detection method. If there are multiple pieces of information detected as anomalies, they will be displayed in a vertical row.

[0058] The analysis screen in Figure 9 displays the graphs necessary for analyzing the message resulting from the detection of an anomaly. This screen changes when a message is selected on the initial analysis screen in Figure 8. The graphs required for analysis are displayed, and the message outputs detailed information related to detection. The analysis screen in Figure 9 has multiple display areas, Area A1 to Area A6.

[0059] Area A1 is an area that displays messages related to the anomaly detection process. It displays the "detection method used to detect the anomaly" and "key information for the data that detected the anomaly." If multiple pieces of information are detected as an anomaly, a scroll bar (not shown) is displayed in area A1, and different messages can be displayed by scrolling, even for the number of messages that are not displayed. Clicking on a displayed message will highlight the portion of the graph in areas A2 to A5 that is related to the anomaly detection information that was clicked on.

[0060] Area A2 is an area that displays graphs that output "Actual cumulative progress rates of past construction projects and their approximate curves" and "Actual cumulative progress rates linked to construction projects for which an abnormality was detected." It outputs a graph that allows you to check the "trend in cumulative progress rates by construction period progress rate." You can check the plot of cumulative progress rates by construction period progress rate for all past construction projects. You can check the approximate curve that passes through the approximate value calculated from the cumulative progress rate for each construction period progress rate for all past construction projects (this can be considered as the trend in progress rates for your company's construction projects in general). You can check how construction projects for which an abnormality was detected are progressing abnormally far from the approximate curve.

[0061] Area A3 is an area that displays graphs that output the "approximate curve of cumulative progress rate results for past construction projects" and the "cumulative progress rate results linked to construction projects for which abnormalities were detected" on a monthly basis. It outputs a graph that allows you to check the "trend in cumulative progress rate occurrence by fiscal year and month." By checking the deviation from the approximate curve for each month, you can analyze which month an abnormal trend is beginning to appear.

[0062] Area A4 is an area that displays a table that outputs monthly construction results and forecast results by organization. The forecast values ​​that are output in the table that allows you to check "current construction results and forecast results by organization" are displayed using approximate values ​​calculated from past construction results. Using a prediction that "approximately, it will end up at about this level," you can check the deviation from the current situation. If progress is poor, it is expected that a trend toward poor performance will be visualized on an organization-by-organization basis.

[0063] Area A5 is an area that displays a table that outputs monthly construction results and forecast results for each project. It outputs a table that allows you to check "current construction results for each project and forecast results expected to occur by the end of the period." Forecast values ​​are displayed using approximate values ​​calculated from past construction results, and a prediction that "approximately, it will end up being around this amount" is used to check for deviations from the current situation. If progress is poor, it is expected that a trend toward poor performance on a project-by-project basis will be visualized. Note that if performance is poor on an organizational level when checked in area A4, it is possible that poor performance across multiple projects will be visualized here.

[0064] Area A6 is an extraction condition specification area for specifying extraction conditions such as the base date, accounting period, business establishment, department, and data reference unit for the graphs and tables to be output in areas A2 to A5. This section is used when you want to check the data extracted based on conditions from the data output to the graphs and tables in areas A2 to A5. When extracting and analyzing the accounting period to be checked, the graphs and tables are output only for construction work that has occurred within the accounting year and month range within the specified accounting period. When extracting and analyzing output data at the organizational level, specify the business office or department and output the graphs and tables.

[0065] [2. Configuration] Fig. 10 is a block diagram showing an example of the configuration of an anomaly detection system 100 according to this embodiment. In Fig. 10, the anomaly detection system 100 includes a control unit 102, a communication interface unit 104, a storage unit 106, and an input / output interface unit 108. The units included in the anomaly detection system 100 are connected to each other so as to be able to communicate with each other via any communication path.

[0066] The communication interface unit 104 communicatively connects the anomaly detection system 100 to a network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 has a function of communicating data with other devices via the communication line. Here, the network 300 has a function of connecting the anomaly detection system 100 and the server 200 etc. so that they can communicate with each other, and is, for example, the Internet or a LAN (Local Area Network).

[0067] An input device 112 and an output device 114 are connected to the input / output interface unit 108. The output device 114 may be a monitor (including a home television), a speaker, or a printer. The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that functions as a pointing device in cooperation with a mouse. Note that hereinafter, the output device 114 may be referred to as the monitor 114.

[0068] Various databases, tables, files, etc. are stored in the storage unit 106. Computer programs that work in conjunction with an OS (Operating System) to issue commands to a CPU (Central Processing Unit) to perform various processes are recorded in the storage unit 106. The storage unit 106 can be, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, etc.

[0069] The storage unit 106 includes a business database 106a, an abnormality detection execution data table 106b, an abnormality determination result data table 106c, an abnormality determination definition master 106d, and the like.

[0070] The business database 106a is a database for storing business data (also called "project data"), accounting period master data, etc. Construction data will be explained as an example of business data. Other business data includes development task data, production content data, etc.

[0071] The construction data may include the name of the construction, the type of work (specific conditions), the organization (business establishment, department), the month the construction started, the month the construction ended, the construction period (specific conditions), the construction completion category (completed or incomplete), the fiscal year and month, the sales amount, the construction period progress rate, the cumulative progress rate, etc. (see Figure 12(A) etc.).

[0072] The accounting period master can be configured as a table in which accounting periods, accounting years and months, and display orders are associated with each other and registered (see FIG. 23A(D)).

[0073] The abnormality detection execution data table 106b is a table for storing automatic detection execution schedule data, acquisition range condition data for business data (for example, construction data), abnormality determination result registration target period data, and the like.

[0074] The automatic detection execution schedule data may include a detection ID, a schedule ID, an execution condition, and an execution time (see FIG. 13). The detection ID and the schedule ID are key information when referencing the data. The detection unit 102b automatically executes anomaly detection in the construction data in accordance with the automatic detection execution schedule data.

[0075] The acquisition range condition data for business data (e.g., construction data) may include a detection ID, a schedule ID, a condition, and a condition value (see FIG. 14(C)). The detection ID and schedule ID serve as key information when referencing the data. The detection unit 102b divides the population of construction data for each specific condition (e.g., type of work) and automatically performs anomaly detection for each population.

[0076] The data for the period for which anomaly determination results are to be registered may include a detection ID, a period determination item, and an acquisition criterion (see FIG. 13). The detection ID serves as key information when referencing the data. The detection unit 102b sets the construction data for the period determination item (e.g., fiscal year and month) that matches the acquisition criterion as an anomaly determination target and performs an anomaly determination.

[0077] The abnormality determination result data table 106c is a table for storing the determination results of abnormality detection execution, such as abnormality determination result data, abnormality determination result message data, detailed abnormality determination result message data, abnormality determination result attached information data, etc. When the detection unit 102b detects an abnormality in the construction data, it stores the abnormality determination result data, abnormality determination result message data, detailed abnormality determination result message data, and abnormality determination result attached information data in the abnormality determination result data table 106c.

[0078] The abnormality judgment result data may include the detection ID, JOB ID, judgment group CD, message ID, fiscal year and month, business establishment, department, construction project, type of work, abnormality judgment result (if abnormal: True, if not abnormal: False), construction progress rate, cumulative progress rate, predicted progress rate (predicted value of cumulative progress rate), etc. (See Figure 19A (A)). The "judgment group CD" is the key of the judgment group (population). The detection ID, JOB ID, and judgment group CD are key information when referencing the data. If the judgment result determines that there is no abnormality, no judgment result message data record is created.

[0079] The abnormality determination result message data may include a detection ID, a JOB ID, a determination group CD, a message ID, an abnormality level, a definition name, an overview, and a detection target (see FIG. 19A(B)). The detection ID, the JOB ID, and the determination group CD are key information when referencing the data.

[0080] The detailed data of the abnormality determination result message may include a detection ID, a JOB ID, a determination group CD, a message ID, a detection technique, a determination method, a lower limit value, an upper limit value, etc. (See FIG. 19B(C) etc.). The detection ID, the JOB ID, and the determination group CD are key information when referencing the data.

[0081] The anomaly determination result ancillary information data may include the detection ID, JOB ID, determination group CD, line number, progress rate of the construction period, predicted progress rate, lower limit value, upper limit value, etc. (See FIG. 19B(D)). The detection ID, JOB ID, and determination group CD are key information when referencing the data.

[0082] The anomaly determination definition master 106d can be configured as a table or the like that associates and registers anomaly determination definition IDs, anomaly determination definition names, acquisition definitions, algorithms used (e.g., approximation curves), and parameter settings {X-axis items (e.g., progress rate of construction), Y-axis items (e.g., cumulative progress rate), X-axis scale standard items, Y-axis scale standard items, approximate function candidate names (e.g., polynomial functions, logistic functions, Bass model functions), smoothness, significance levels}. The operator can set the data in the anomaly determination definition master 106d. The detection unit 102b refers to the anomaly determination definition master 106d when calculating the approximation curve, the predicted value of the cumulative progress rate, and the upper and lower limits.

[0083] The control unit 102 is a CPU or the like that performs overall control of the anomaly detection system 100. The control unit 102 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, required data, etc., and executes various information processing operations based on these stored programs.

[0084] The control unit 102 is configured to be able to access the business database 106a, the abnormality detection execution data table 106b, the abnormality determination result data table 106c, the abnormality determination definition master 106d, and the like, which are stored in the storage unit 106. Note that the business database 106a, the abnormality detection execution data table 106b, the abnormality determination result data table 106c, and the abnormality determination definition master 106d may be provided in another location (for example, the server 200) as long as they are accessible by the control unit 102.

[0085] The control unit 102 conceptually includes a memory control unit 102a, a detection unit 102b, and a display control unit 102c.

[0086] The storage control unit 102a may, for example, acquire daily business data from a business system (not shown) connected via the network 300 and store it in the business database 106a, or may input daily business data in response to an operator's operation on an input screen (not shown) displayed on the monitor 114 and store it in the business database 106a.

[0087] In addition, the memory control unit 102a sets automatic detection execution schedule data, construction data acquisition range condition data, and abnormality determination result registration period data, for example, in accordance with operator operations on a data setting screen (not shown) displayed on the monitor 114, and stores them in the abnormality detection execution data table 106b.

[0088] The detection unit 102b executes anomaly detection in accordance with the automatic detection execution schedule data, business data acquisition range condition data, and anomaly determination result registration period data stored in the anomaly detection execution data table 106b, divides the population of past business data into specified specific conditions based on the business data stored in the business database 106a, calculates an approximate curve for the progress rate of the business data and the project progress rate for each population, calculates a predicted value of the project progress rate by substituting the progress rate into the approximate curve function, calculates an upper limit value (also referred to as an "upper limit threshold") and a lower limit value (also referred to as a "lower limit threshold") based on the approximate curve and the predicted value of the project progress rate, and detects an anomaly by comparing the business data to be detected with the upper limit value and the lower limit value. If the detection unit 102b detects an anomaly, it stores the anomaly determination result data, the anomaly determination result message data, the anomaly determination result message detail data, and the anomaly determination result attached information data in the anomaly determination result data table 106c.

[0089] The display control unit 102c controls the display of an analysis screen (initial analysis screen, analysis screen) on the monitor 114. Based on the business data stored in the business database 106a, the display control unit 102c displays analysis data (for example, graphs 1 and 2, tables 3 and 4 in FIG. 4, etc.) on the analysis screen for business data in which an abnormality has been detected by the detection unit 102b.

[0090] The business data includes construction data, the specific conditions include the type of work, construction period, or public / private classification, the progress rate includes the construction period progress rate calculated by the number of months elapsed since construction divided by the planned construction period, and the project progress rate includes the cumulative progress rate calculated by the current incurred costs divided by (budgeted cost + current incurred costs), and the construction data may include the construction, organization (business establishment, department), one or more specific conditions, fiscal year and month, sales amount, construction period progress rate, and cumulative progress rate.

[0091] Based on the construction data, the display control unit 102c may display a message in a predetermined area of ​​the analysis screen (for example, area A1 in Figure 9) that includes the construction work that was detected as abnormal, the detection method, and the cumulative progress rate that was determined to be abnormal.

[0092] Based on the construction data, the display control unit 102c may display in a predetermined area of ​​the analysis screen (for example, area A2 in Figure 9) the actual cumulative progress rates of past construction projects by population or all past construction projects, by construction period progress rate, and their approximate curves, as well as a graph showing the trends in the actual cumulative progress rates linked to construction projects in which an abnormality has been detected.

[0093] Based on the construction data, the display control unit 102c may display, in a predetermined area of ​​the analysis screen (for example, area A3 in Figure 9), the actual cumulative progress rate of past construction work by accounting year and month, its approximate curve, and a graph showing the trend in the actual cumulative progress rate linked to construction work in which an abnormality has been detected.

[0094] Based on the construction data, the display control unit 102c may display a table in a predetermined area of ​​the analysis screen (for example, area A4 in Figure 9) showing the trends in construction sales actuals (construction actuals) and sales forecasts (construction forecasts) by organization within a specified accounting period.

[0095] Based on the construction data, the display control unit 102c may display a table in a predetermined area of ​​the analysis screen (for example, area A5 in Figure 9) showing the actual sales performance and sales forecast trends for each construction project of the organization in charge of the construction project in which the abnormality occurred.

[0096] [3. Specific Examples] 10 to 74, specific examples of processing by the control unit 102 of the anomaly detection system 100 in this embodiment will be described in the order of [3-1. Overall processing], [3-2. Sample data], [3-3. Methods for calculating approximate curves, predicted values, and upper and lower limit values], and [3-4. Options for processing past cases].

[0097] [3-1. Overall processing] FIG. 11 is a flowchart illustrating an outline of the overall processing of the control unit 102 of the anomaly detection system according to this embodiment.

[0098] An overview of the overall processing of the control unit 102 of the anomaly detection system 100 according to this embodiment will be described with reference to Fig. 11. In Fig. 11, the detection unit 102b executes an anomaly detection process (step S1). Specifically, in the anomaly detection process, the detection unit 102b executes anomaly detection for construction data in accordance with the automatic detection execution schedule data, business data acquisition range condition data, and anomaly determination result registration period data stored in the anomaly detection execution data table 106b. Based on the construction data stored in the business database 106a, the detection unit 102b divides a population of past construction data into specified specific conditions, calculates an approximate curve for the construction data progress rate and cumulative progress rate for each population, calculates a predicted value for the cumulative progress rate by substituting the construction data progress rate into the approximate curve function, calculates upper and lower limit values ​​based on the approximate curve and the predicted value for the cumulative progress rate, and detects an anomaly by comparing the construction data to be detected with the upper and lower limit values. When detecting an abnormality, the detecting unit 102b stores the abnormality determination result data, the abnormality determination result message data, the abnormality determination result message detailed data, and the abnormality determination result attached information data in the abnormality determination result data table 106c.

[0099] The display control unit 102c executes an analysis screen display process (step S2). Specifically, in the analysis screen display process, the display control unit 102c controls the display of an analysis screen (initial analysis screen, analysis screen) on the monitor 114. Based on the construction data stored in the business database 106a, the display control unit 102c displays analysis data (e.g., messages, graphs 1 and 2, tables 3 and 4 in FIG. 9, etc.) on the analysis screen for the construction work for which an abnormality has been detected by the detection unit 102b.

[0100] In addition, the display control unit 102c may display a message in a predetermined area of ​​the analysis screen (for example, area A1 in Figure 9) based on the construction data, including the construction work detected as abnormal, the detection method, and the cumulative progress rate at which the abnormality was determined.

[0101] Furthermore, based on the construction data, the display control unit 102c may display in a predetermined area of ​​the analysis screen (for example, area A2 in Figure 9) the actual cumulative progress rates of past construction projects by population or all past construction projects, and their approximate curves, as well as a graph showing the trends in the actual cumulative progress rates linked to construction projects in which an abnormality has been detected, by construction period progress rate.

[0102] Furthermore, the display control unit 102c may display, in a predetermined area of ​​the analysis screen (for example, area A3 in FIG. 9) based on the construction data, the actual cumulative progress rate of past construction work by accounting year and month, its approximate curve, and a graph showing the trend in the actual cumulative progress rate linked to construction work in which an abnormality has been detected.

[0103] In addition, the display control unit 102c may display a table showing the actual sales figures and forecasted sales figures for construction work by organization within a specified accounting period in a predetermined area of ​​the analysis screen (for example, area A4 in Figure 9) based on the construction work data.

[0104] Furthermore, the display control unit 102c may display, based on the construction data, a table in a predetermined area of ​​the analysis screen (for example, area A5 in FIG. 9) showing the trends in actual sales and sales forecasts for each construction project of the organization in charge of the construction project in which the abnormality occurred.

[0105] [3-2. Sample Data] 12 to 51 are diagrams showing sample data for explaining a specific example of the processing by control unit 102 of anomaly detection system 100 according to this embodiment. A specific example of the processing by control unit 102 of anomaly detection system 100 according to this embodiment will be described with reference to FIGS. 12 to 51.

[0106] (S1: Abnormality detection process) 12 to 19, a specific example of anomaly detection processing will be described. The detection unit 102b executes anomaly detection for construction data in accordance with the automatic detection execution schedule data, business data acquisition range condition data, and anomaly determination result registration period data stored in the anomaly detection execution data table 106b, divides a population of past construction data for each specified specific condition based on the construction data stored in the business database 106a, calculates an approximate curve for the construction data progress rate and cumulative progress rate for each population, calculates a predicted value for the cumulative progress rate by substituting the construction data progress rate for the approximate curve function, calculates upper and lower limit values ​​based on the approximate curve and the predicted value of the cumulative progress rate, and detects anomalies by comparing the construction data to be detected with the upper and lower limit values.

[0107] (Terminology) The progress rate for the construction period refers to the progress rate for the construction period as of the current accounting month and year in months. For example, if there is a construction project with a 12-month construction period, which begins in January and is completed in December, and the current month is May, the progress rate for the construction period = 5 / 12 ≒ 42% (decimals are rounded off). - Cumulative progress rate refers to the actual progress rate for each construction project. The progress rate generally refers to the degree to which current costs have accumulated compared to the total of the current accumulated costs and the future forecast (the estimated cost amount to be incurred until the construction is completed). For example, if the final cost budget is 1,000 yen and the current incurred costs are 500 yen, the cumulative progress rate = 500 / (500+1000) ≒ 33%. The predicted progress rate is a predicted value of the progress rate at an approximate position based on the past construction results, as determined by the abnormality detection process of the detection unit 102b.

[0108] The method for calculating the approximate curve, forecast value, upper and lower limit values ​​from the construction progress rate and cumulative progress rate of the construction data is explained in detail in (3-3. Method for calculating the approximate curve, forecast value, upper and lower limit values), and only an outline will be explained here.

[0109] 1. A process of calculating a predicted construction progress rate for all managed construction projects from construction data in the business database 106a, and detecting construction projects that are progressing abnormally.

[0110] Figure 12 is a diagram for explaining construction data. The construction data is used to hold information about the construction work to be carried out (organization, construction period, start time, completion time, planned man-hours, order amount, cumulative progress rate, etc.), and as shown in Figure 12(A), it includes items such as construction name, business establishment, department, construction start month, construction end month, construction period, construction completion category, accounting year and month, sales amount, construction period progress rate, cumulative progress rate, etc.

[0111] In the construction industry, companies that use percentage-of-completion sales often already manage the progress rate and cumulative progress rate in their business data. Therefore, this specification will be explained assuming that the progress rate and cumulative progress rate already exist in the business data. In the construction industry, companies that use sales based on completed work or companies that manage projects in other industries may not manage the progress rate, so in those cases, a separate calculation is required.

[0112] The "progress rate" is calculated using the following formula: "Construction progress rate" = (number of months elapsed since construction began ÷ total number of months required for construction × 100 = (Number of months from "Construction start month" to "Fiscal year and month" + 1 ÷ Number of months in "Construction period") x 100

[0113] The table in Figure 12(B) shows the fiscal years and months in which the above construction works A, B, and C were carried out. The numbers in the colored cells represent the "construction progress rate."

[0114] (1) Pre-settings (1-1) The storage control unit 102a stores information necessary for anomaly detection in advance in the anomaly detection execution data table 106b (data provided in advance). Specifically, automatic detection execution schedule data and anomaly determination result registration target period data are set in the anomaly detection execution data table 106b.

[0115] FIG. 13(A) is a diagram showing an example of automatic detection execution schedule data. The automatic detection execution schedule data has fields for detection ID, schedule ID, execution condition, and execution time. In the example shown in the figure, the detection ID is "AB001," the schedule ID is "SH001," the execution condition is "5th business day of every month," and the execution time is "23:00." In this example, automatic detection is executed at "23:00" on the 5th business day of every month. The 5th business day is assumed to be the timing when the monthly closing process for the previous month has been finalized.

[0116] Figure 13(B) shows an example of data for the period for which anomaly determination results are registered. The data for the period for which anomaly determination results are registered includes the following items: detection ID, period determination item, and acquisition criteria. In the example shown in the figure, the detection ID is "AB001," the period determination item is "fiscal year and month," and the acquisition criteria item is "the month preceding the month in which the startup date falls." In this example, the acquisition criteria is "the month preceding the month in which the startup date falls," and if the system is started on, for example, 2022 / 10 / 5, anomaly determination is performed on the data for 2022 / 9. Detailed internal setting items are not directly related to this application, so they will be omitted.

[0117] (2) The detection unit 102b automatically executes anomaly detection. (2-1) First, acquire timing information for detecting an abnormality. Specifically, acquire automatic detection execution schedule data and abnormality determination result registration target period data set in the abnormality detection execution data table 106b in (1-1).

[0118] (2-2) Determine whether the timing of automatic execution is the timing to detect an abnormality. Figure 14(A) is a diagram for explaining the automatic execution timing determination process. In the example shown in the figure, the start timing is "2022 / 11 / 07", which is the fifth business day, and the automatic detection execution schedule data is "the fifth business day of every month", so the execution determination result is "execute."

[0119] Depending on the execution determination result, the process branches to whether or not to perform subsequent processing. If the execution determination result is "execute," the process from (2-3) onwards is executed. If the execution determination result is "do not execute," the process ends.

[0120] The method of determining whether a day is a business day or a holiday is to refer to a calendar master (not shown) stored in the business database 106a. The calendar master is based on the sales business calendar and is always updated.

[0121] (2-3) Obtain the range conditions for the data to detect anomalies. Specifically, for example, the detection ID "AB001" and schedule ID "SH001" of the automatic detection execution schedule data shown in FIG. 14(B) are used as parameters (keys) to acquire the acquisition range condition data for construction data set in the anomaly detection execution data table 106b, as shown in FIG. 14(C). The acquisition range condition data for construction data has items for detection ID, schedule ID, condition, and condition value. In the example shown in the same figure, the items are detection ID "AB001", schedule ID "SH001", condition "data reference unit", and condition value "type of construction".

[0122] The "condition value" can be set in a general way depending on the company using the service. In many cases, the shape of the progress rate change will differ depending on the construction characteristics, such as the type of work. Construction characteristics that similarly indicate the progress rate change include construction period, type of work, and public / private classification (public or private construction). Here, as an example, we will explain the pattern set for the type of work. Note that all of the above characteristics are assumed to be information contained in the construction data, just like the type of work. It is also possible to set the condition value as empty. If empty, it is possible to analyze the progress rate change for all construction projects as a single unit.

[0123] (2-4) The construction data in the business database 106a is referenced to calculate the predicted progress rate for each construction project and each construction period progress rate. (2-4-1) Obtain construction data from the business database 106a. Data is obtained by setting the values ​​in the acquisition range condition data of the construction data obtained in (2-3) as parameters. For example, using parameters (data reference unit, work type) as shown in Figure 15(A) as keys, the construction data in the business database 106a is referenced and populations are divided and obtained by data reference unit. For example, construction data for population A: work type (construction) and population B: work type (demolition) as shown in Figures 15(B) and (C) is obtained. An approximate value is calculated for each population.

[0124] (2-4-2) Using the construction data obtained in (2-4-1), calculate the cumulative progress rate (approximate value) for each population by construction progress rate. Identify abnormal construction work based on the upper and lower limits considered to be normal ranges obtained when calculating the approximate values. The "approximate value" is a reference value that will be used to compare with actual results, and will be referred to as the "predicted value" from here on.

[0125] Figure 16(A) shows the acquired construction data, and Figure 16(B) plots each construction project in the construction data, with the horizontal axis showing the construction progress rate (%) and the vertical axis showing the cumulative progress rate (%).

[0126] The "construction period progress rate" and "cumulative progress rate" in the construction data are used to calculate the predicted value of the cumulative progress rate by construction period progress rate. The method for calculating the predicted value is to use an anomaly detection algorithm: approximation curve. The data used in the approximation curve is only for construction data whose "construction completion category" is completed. In addition, the predicted progress rate by month and construction work is calculated using the approximation formula of the approximation curve that was used to calculate the predicted value.

[0127] Figure 17 is a diagram for explaining the calculation of the approximation curve F(x), predicted progress rate, and upper and lower limits. As shown in Figure 17(A), the completed construction work is used as the population, and the approximation curve F(x) of the cumulative progress rate by construction period progress rate of the population is calculated. In addition, the standard deviation function S(x) is calculated by focusing on the tendency of data dispersion within the population. Then, as shown in Figure 17(B), the construction period progress rate is substituted into the approximation curve F(x) and the standard deviation function S(x), respectively, to calculate the predicted progress rate and upper and lower limits of the uncompleted construction work. In this way, the predicted progress rate is calculated using F(x), and the upper and lower limits are calculated using S(x).

[0128] The key to this invention is using the results calculated by this process, so the specific calculation process is explained in detail in [3-3. Calculation method for approximation curves, forecast values, and upper and lower limits], and only an outline will be explained here. This explanation targets companies with percentage-of-sales, which already have the construction progress rate and cumulative progress rate in the construction data, so there is no need to scale the approximation curve or process the data.

[0129] For example, the predicted value and upper and lower limit values ​​are calculated for each work type based on the approximate curve parameters (X-axis item: construction progress rate, Y-axis item: cumulative progress rate, approximate function candidates [polynomial function, logistic function, Bass model function], etc.) as shown in FIG. 18A(A) set in the anomaly judgment definition master 106d. Note that since the explanation this time will use values ​​that have already been scaled, an explanation of scale adjustment will be omitted. After calculating F(x), the dispersion of the data is calculated to calculate the standard deviation function S(x). The construction progress rate is substituted for x in F(x) and S(x), and the predicted value and the upper and lower limit values ​​of S(x) are calculated, respectively. The same function is used to calculate the predicted value and upper and lower limit values ​​for each work type and construction project.

[0130] Figure 18A(B) shows an example of approximate data of cumulative progress rate calculated by construction period progress rate for construction and demolition, which are calculated by population, and includes items such as construction, type of work, construction period progress rate, cumulative progress rate, predicted value (predicted progress rate, predicted value of cumulative progress rate), lower limit, and upper limit.

[0131] FIG. 18B(C) shows the construction data after the predicted value and upper and lower limit values ​​have been calculated, and in addition to the predicted value (predicted progress rate), lower limit value, and upper limit value, information on the construction type, which is the data reference unit, is also stored.

[0132] According to the "acquisition criteria" of the data for the period for which anomaly determination results are registered acquired in (1-1) shown in Figure 18B(D), a determination is made as to whether there are any anomalies in the data for the month before the month to which the anomaly determination execution date belongs. In this case, the execution date is 2022 / 11 / 07, so a determination is made as to whether there are any anomalies on 2022 / 10. In this example, the record on the fourth line in Figure 18B(C) has a cumulative progress rate of "30%," a predicted progress rate of "65%," a lower limit of "60%," and an upper limit of "75%." Because the cumulative progress rate of "30%" is below the lower limit of "60%, an anomaly is detected.

[0133] (2-4-3) The construction work determined to be abnormal in (2-4-2) is saved in the abnormality determination result data table 106c. At the same time, a message to be displayed on the analysis screen is saved. Figures 19A and 19B (A) to (D) show examples of abnormality determination result data, abnormality determination result message data, abnormality determination result message detailed data, and abnormality determination result attached information data. Note that the abnormality determination result attached information data holds display data for each population obtained. For results that are determined not to be abnormal as a result of the determination, no determination result message record is created.

[0134] (S2: Analysis screen display processing) The analysis screen display process will be described in detail with reference to Figures 20 to 51. The display control unit 102c displays analysis data (e.g., messages, graphs 1 and 2, tables 3 and 4 in Figure 9, etc.) on the analysis screen for construction work for which an abnormality has been detected by the detection unit 102b, based on the construction work data stored in the business database 106a.

[0135] 2. Execute the process to display the abnormal data detected in 1 and data related to the abnormal data on the initial analysis screen. (1) Information automatically detected as an anomaly from the construction data in the business database 106a is output to the initial screen for analysis. (1-1). First, obtain the result data and result message of the detected abnormality. Specifically, as shown in FIG. 20A(A), using the JOBID "output forecast alert" and the abnormality determination result "True" as parameters (keys), the abnormality determination result data as shown in FIG. 20A(B), the abnormality determination result message data as shown in FIG. 20B(C), and the detailed abnormality determination result message data as shown in FIG. 20B(D) are obtained from the abnormality determination result data table 106c. Note that in order to display the detected information in a list, data is obtained without the detection ID being set. On the screen that will be displayed from now on, only data for which the abnormality determination result has been determined to be abnormal will be checked. Therefore, only data for which the value of the "Abnormal result or not" column in the abnormality determination result data is "True" will be referenced.

[0136] (1-2) When an abnormality is detected, a message is displayed on the screen. Specifically, the abnormality level, definition name, summary, and detection target of the abnormality determination result message data in FIG. 20B(C) are extracted and displayed in area A1 of the initial analysis screen as shown in FIG.

[0137] (1-3) Obtain the date when the initial analysis screen is launched and set it as the base date for the extraction conditions of the initial analysis screen. Specifically, as shown in Figure 21, set the base date "2022 / 11 / 1" as the base date for the extraction conditions of the initial analysis screen.

[0138] (2) Select the result message for the detected abnormality on the initial analysis screen to launch the analysis screen. The following process is performed by referring to the abnormality judgment result data, abnormality judgment result message data, and abnormality judgment result message detailed data selected from the list in (1).

[0139] (2-1) Switch the message to detailed information display to secure the output area for graphs and tables. 1. Switch the message to detailed display. Specifically, for the detailed data of the abnormality determination result message shown in Fig. 22A(A), the detection method, determination method, and lower limit value are referenced, and the detailed message is displayed as shown in Fig. 22A(B) (the detection method and determination reason are added).

[0140] 2. Allocate an output area for graphs and tables on the analysis screen. In this system, for example, as shown in Figure 22B(C), figures 1 to 4 (two graphs and two tables) are output. Therefore, an output area for two graphs and two tables is allocated.

[0141] (2-2). Set the extraction conditions. 1. Set extraction conditions for the accounting year and month of the abnormality judgment result data linked to the abnormality judgment result message data.

[0142] (i) From the accounting year and month of the target data, obtain the accounting period that includes that accounting year and month from the accounting period master, and set the beginning and ending accounting years and months linked to the accounting period as the extraction conditions for the analysis screen.

[0143] Figure 23A(D) shows an example of accounting period master data, which has fields for accounting period, accounting year and month, and display order. The accounting period master manages the accounting years and months included in each accounting period. For example, using the accounting year and month of "2022 / 10" in the anomaly determination result data shown in Figure 23A(C) as a key, the accounting period "50" that includes "2022 / 10" is obtained from the accounting period master shown in Figure 23A(D). Furthermore, the accounting years and months of the beginning and end of the period linked to accounting period "50," "2022 / 04 to 2023 / 03," are obtained and set as the initial values ​​in the extraction conditions, as shown in Figure 23A(A).

[0144] (ii) The accounting year and month of the beginning and end of the accounting period obtained from the accounting period master are obtained as the setting values ​​for the extraction conditions and set as the extraction conditions for the analysis data acquisition range conditions.In addition, the construction work that is the source of the selected abnormality is also obtained and set as the setting value for the extraction conditions.Figure 23A(B) shows an example of the analysis data acquisition range conditions, with the extraction condition column being "Accounting Year and Month", the FROM condition being "2022 / 04", the TO condition being "2023 / 03", and the target construction work being "Construction A".

[0145] Set the extraction conditions with the information in 2.1 set as the initial display values. Specifically, as shown in Figure 23B(A), set the initial extraction conditions as follows: base date "2022 / 11 / 1" (timing when the analysis screen is launched), start of period "2022 / 04" (FROM condition of analysis data acquisition range conditions), end of period "2023 / 03" (TO condition of analysis data acquisition range conditions), and target construction "Construction A" (target of analysis data acquisition).

[0146] As shown in Figures 23B(B) and (C), when switching to graph display mode, filter items for extracting data for graph output are displayed. Items are output as fixed items. If a judgment group CD and a judgment group key are set in the abnormality judgment result data, an additional extraction item is displayed so that data can be extracted by selecting the judgment group key. In this example, the construction type "construction" is additionally displayed.

[0147] (2-3) The cumulative progress rate transition_actual data is acquired from the construction data in the business database 106a. The cumulative progress rate transition_actual data is acquired based on the conditions initially set in the extraction conditions, and is acquired as data for display in Charts 1 and 2.

[0148] Specifically, the construction data shown in Figure 24B(D) is extracted based on the parameters (keys) of the start period "2022 / 04," end period "2023 / 03," construction "Construction A," and construction type "Construction" as shown in Figure 24A(A), and the cumulative progress rate trend_actual data shown in Figure 24B(C) is obtained. The cumulative progress rate trend_actual data includes items such as construction name, business establishment, department, construction type, construction completion category, accounting year and month, sales amount, construction period progress rate, and cumulative progress rate.

[0149] In this case, if there is even one record that contains the "fiscal year and month" within the period from the start of the period to the end of the period, that construction project will be eligible for acquisition. For example, as shown in Figure 24A(B), in this example, construction projects A and B will be eligible for acquisition, but construction project C will not be eligible for acquisition.

[0150] (2-4) "Cumulative progress rate transition_approximate curve data" is acquired from the abnormality determination result data, abnormality determination result message data, and abnormality determination result attached information data in the abnormality determination result data table 106c.

[0151] Figure 25B(C) shows an example of abnormality determination result data, Figure 25B(D) shows an example of abnormality determination result message data, Figure 25B(E) shows an example of abnormality determination result ancillary information data, and Figure 25B(F) shows an example of cumulative progress rate trend_approximate curve data (construction).

[0152] Using the start period "2022 / 04", end period "2023 / 03", work "Work A", and work type "Construction" as parameters (keys) as shown in Figure 25A(B), the cumulative progress rate trend_approximate curve data (construction) shown in Figure 25B(F) is obtained as data for additional display in Figures 1 and 2 from the abnormality determination result data shown in Figure 25B(C), the abnormality determination result message data shown in Figure 25B(D), and the abnormality determination result ancillary information data shown in Figure 25B(E).

[0153] The cumulative progress rate transition_approximate curve data (construction) includes items such as project name, construction progress rate, predicted progress rate, upper limit of progress rate, and lower limit of progress rate threshold.

[0154] (2-5) "Sales amount trend data by organization" is acquired from the cumulative progress rate trend_actual data acquired in (2-3) and the abnormality determination result data in the abnormality determination result data table 106c.

[0155] Figure 26B(D) shows an example of the cumulative progress rate trend_actual data acquired in (2-3), Figure 26B(E) shows an example of abnormality judgment result data, and Figure 26A(C) shows an example of sales amount trend data by organization.

[0156] Using the start period "2022 / 04," end period "2023 / 03," and construction type "construction" as parameters (keys) as shown in Figure 26A(A), the cumulative progress rate transition_actual data shown in Figure 26B(D) and the abnormality determination result data shown in Figure 26B(E) are used to obtain the sales amount transition data by organization shown in Figure 26A(C) as data for display in Chart 3. Chart 3 checks only the construction results that occurred within the relevant accounting period, and obtains and displays only the data for the results within the frame shown in Figure 26A(B).

[0157] For cumulative progress rate trend_actual data, if there are results from the previous period, obtain the results excluding the previous period (obtain only the results that can be counted as for the current period). For example, for the cumulative progress rate of construction A, 1% for 2022 / 03 is the previous period's results, 2% for 2022 / 04 but 1% for the current period, 4% for 2022 / 05 but 3% for the current period, etc.

[0158] As shown in Figure 26A(C), the sales amount trend data by organization has the following fields: business establishment, department, accounting year / month, type of work, sales amount (actual), and sales amount (forecast). "Sales amount (actual)" and "sales amount (forecast)" are the actual and forecast values ​​of the cumulative recorded amount of "sales amount," respectively, and are calculated using the following formula.

[0159] Sales amount (actual) = Sales amount × (cumulative progress rate ÷ 100) Sales amount (forecast) = Sales amount × (forecast progress rate ÷ 100)

[0160] Create records for all fiscal years and months in the period range (for graph drawing). For fiscal years and months with no actual or forecast results, create records with 0 yen. Obtain records (1) aggregated by business establishment and department, and records (2) aggregated by business establishment.

[0161] In the example for the fiscal year and month "2022 / 04", the results are as follows: Department A: Actual results "400", forecast "600", Department B: Actual results "12,000", forecast "12,400" (in this explanation, this record will not be retrieved due to the work type extraction condition "Construction", but for the sake of explaining the image of the summary calculation, we will assume that it was retrieved), Business Office A (Department A + Department B): Actual results "12,400", forecast "13,000".

[0162] (2-6) From the "Cumulative progress rate trend_actual data" acquired in (2-3) and the abnormality judgment result data in the abnormality detection result data table, the sales amount trend data by construction project is acquired as the display data for Figure 4.

[0163] Figure 27A(C) shows an example of data on the trend in sales amount by construction project, Figure 27B(D) shows an example of data on the trend in cumulative progress rate_actual data obtained in (2-3), and Figure 27B(E) shows an example of data on the abnormality judgment result data.

[0164] Using the start period "2022 / 04," end period "2023 / 03," work "Work A," and work type "Construction" as parameters (keys) as shown in Figure 27A(A), the sales amount trend data by work shown in Figure 27A(C) is obtained as data for display in Figure 4 from the cumulative progress rate trend_actual data shown in Figure 27B(D) and the abnormality determination result data shown in Figure 27B(E). If there is even one record that includes the "fiscal year / month" during the above period, that work will be subject to acquisition. For example, in the table shown in Figure 27A(A), works A and B will be subject to acquisition, but work C will not be subject to acquisition.

[0165] Here, extraction is not performed by construction number. Data is extracted and obtained by the business office linked by the construction number. For example, if Construction 1 is a construction managed by the Tokyo business office, the actual data for the Tokyo business office is obtained. The determination of who is managing it is made by whether or not there is actual data in "Cumulative progress rate trend_Actual data."

[0166] As shown in Figure 27A(C), the sales amount trend data by construction project has the following fields: construction project name, business establishment, department, accounting year and month, construction type, sales amount (actual), sales amount (forecast), and forecast progress rate. "Sales amount (actual)" and "Sales amount (forecast)" are the actual and forecast values ​​of the cumulative recorded amount of "sales amount," respectively, and are calculated using the following formula.

[0167] Sales amount (actual) = Sales amount × (cumulative progress rate ÷ 100) Sales amount (forecast) = Sales amount × (forecast progress rate ÷ 100)

[0168] For the accounting year and month "End of previous period," records before the relevant accounting period will be aggregated as the end of the previous period. If the beginning accounting year and month is 2022 / 04, records before 2022 / 03 will be displayed as the end of the previous period. For the accounting year and month "Next period and onwards," records after the relevant accounting period will be aggregated as the next period and onwards. If the ending accounting year and month is 2023 / 03, records after 2023 / 04 will be displayed as the next period and onwards. Records will be created even if there is no data to be aggregated as the end of the previous period or the next period and onwards. In this case, records will be created with actual and forecast values ​​of 0 yen.

[0169] (2-7) Bind the cumulative progress rate transition_actual data, cumulative progress rate transition_approximate curve data, sales amount transition data by organization, and sales amount transition data by construction to the corresponding charts 1 to 4, for a total of four charts.

[0170] 1. Bind the cumulative progress rate trend_actual data and cumulative progress rate trend_approximate curve data to Figure 1. For example, in accordance with the binding information (X axis, Y axis, legend, title) shown in Fig. 28A(A) and the data used for each legend shown in Fig. 28A(B), the cumulative progress rate transition_actual data shown in Fig. 28A(C) and the cumulative progress rate transition_approximate curve data shown in Fig. 28A(D) are bound to Chart 1 as shown in Fig. 28B(E). In Fig. 28A(B), it appears that the same plot is used for past performance by construction and performance of construction work determined to be abnormal, but the data is color-coded in the subsequent graph coloring process so that the data can be distinguished.

[0171] In Figure 1, the title is Comparison of forecast / actual volume curves (construction progress rate), the X axis is the construction progress rate, the Y axis is the cumulative progress rate, and the legend is the forecast volume approximation curve, the volume of uncompleted work, the normal data threshold, past performance by construction project, and the performance of construction projects judged to be abnormal.

[0172] 2. Bind the cumulative progress rate trend_actual data and cumulative progress rate trend_approximate curve data to Figure 2. For example, according to the binding information (X axis, Y axis, legend, title) shown in FIG. 29A(A) and the data used for each legend shown in FIG. 29A(B), the cumulative progress rate transition_actual data shown in FIG. 29A(C) and the cumulative progress rate transition_approximate curve data shown in FIG. 29A(D) are bound to Chart 2 as shown in FIG. 29B(E).

[0173] In Figure 2, the title is Forecast / Actual Volume Comparison (Fiscal Year / Month), the X axis is the fiscal year / month, the Y axis is the cumulative progress rate, and the legend is Forecast Volume Approximation Curve, Uncompleted Work Volume Line, Normal Data Threshold, and Work A.

[0174] 3. Bind the sales amount trend data by organization to Figure 3. For example, in accordance with the bind information (header, display data, title) shown in FIG. 30(A), the sales amount transition data by organization shown in FIG. 30(B) is bound to Chart 3 as shown in FIG. 30(C).

[0175] In Figure 3, the title is Forecast and Actual Results by Organization (50th Period: 2022 / 04~2023 / 03), the row items are business establishment and department, and the column items are fiscal year and month, sales amount (forecast), and sales amount (actual).

[0176] 4. Bind the data on sales revenue trends by construction project to Figure 4. For example, in accordance with the binding information (header, display data, title) shown in FIG. 31(A), the sales amount transition data by construction work shown in FIG. 31(B) is bound to Chart 4 as shown in FIG. 31(C).

[0177] In Figure 4, the title is "Forecast and actual results by project (projects managed by business A - 50th period: 2022 / 04~2023 / 03)," the row items are the project name, and the column items are the accounting year and month, sales amount (forecast), sales amount (actual), and forecast progress rate.

[0178] 5. Output Figures 1 to 4 to the analysis screen The bound charts 1 to 4 are output to areas A2 to A5 of the analysis screen, respectively, as shown in FIG.

[0179] 6. Highlight the data for abnormalities based on the construction project name information contained in the "abnormality determination result data" linked to the message. Figure 33A(A) shows an example of graph coloring parameters, Figure 33A(B) shows an example of abnormality judgment result data, Figure 33A(C) shows an example of cumulative progress rate trend_actual data (data bound to graphs 1 and 2), Figure 33A(D) shows an example of cumulative progress rate trend_approximation curve data, and Figure 33B(E) shows an example of the analysis screen display.

[0180] According to the graph coloring parameters, the plots, lines, and legends are highlighted in red in Graphs 1 and 2. As shown in Figure 33A(A), the graph coloring parameters include the legend abnormality flag (True or False), reference information flag (True or False), legend color, and line color. The reference information is information used to confirm how abnormal the object being judged to be abnormal (construction work in this job) is.

[0181] If the construction name in the legend is the same as the construction name for which an abnormality was detected, the color will be applied when the abnormality flag in the legend is True. For constructions other than those for which an abnormality was detected, the color will be applied when the abnormality flag in the legend is False.

[0182] In both graphs, the normal data threshold is set as a dashed line. For graph 2, the past performance data does not show plots because the fiscal years and months of construction start dates vary.

[0183] The points to note about the initial display of the analysis screen are as follows: (1) A message is displayed in area A1 of the analysis screen. It outputs detailed information including the detected anomaly, the detection method, and the standard value used to determine the anomaly. For the user checking the information, analysis can be performed from a state where the construction work that may have an anomaly is known. (2) Areas A2 to A5 of the analysis screen display graphs 1 and 2 and tables 3 and 4. Graph 1 makes it possible to check the cumulative progress rate transition of an approximate construction project based on past construction work. It is possible to assume that future construction work will also basically follow this approximate cumulative progress rate transition. It is also possible to check how much the cumulative progress rate of a construction project for which an abnormality has been detected deviates from the predicted cumulative progress rate approximation curve. Construction projects for which an abnormality has been detected are considered to be construction projects where the following issues may occur, rather than "unfinished construction projects." -High-intensity work towards the end of the construction period · Delayed completion of construction work Check the cumulative progress rate for each construction period progress rate to see the increasing trend. In this example, we can see that the cumulative progress rate of the construction work where an abnormality was detected is lower than that of other construction work.

[0184] Graph 2 shows the trend in the cumulative progress rate of construction work for which an abnormality was detected, by fiscal year and month. It is possible to see how there is a deviation from the cumulative progress rate approximation curve calculated from past construction work, by month. It is highly likely that the deviation is caused by work carried out in months with a large deviation. In this example, we can see that from fiscal year and month 2022 / 05 onwards, the actual results have exceeded the normal range (within the lower progress rate threshold) from the approximate value.

[0185] Table 3 allows you to check the actual results of construction work by organization that occurred within a specified fiscal period. The actual results of the organization managing the construction work for which an abnormality was detected may deviate significantly from the forecast. Furthermore, since the sales results for construction work that occurred only within the fiscal period can be checked, they can be used to analyze the current period's sales results. Since the forecast values ​​can be used as a reference for potential sales projections, they can also be used to forecast the actual sales results for the current period. Figure 34 is a diagram that explains how to check discrepancies in Table 3. For example, in this figure, if the difference between the forecast and actual results in August is 10,000 and the difference between the forecast and actual results in November is 74,800, it can be seen that the discrepancy amount has increased abnormally in November.

[0186] In Table 4, you can check the performance of each managed project at the business establishment that manages the project where an abnormality was detected. If the cause of the abnormality in the project where an abnormality was detected lies within the organization, there is a possibility that an abnormality has also occurred in another project that the business establishment manages. Figure 35 is a diagram to explain the image of checking the discrepancy amount in Table 4. In the figure, for example, assume that an abnormality was detected in project A this time. It can be confirmed that the actual performance of project E also deviates significantly from the predicted value.

[0187] (Points to check) An example will be explained in which the trend in the completed volume (cumulative progress rate by construction progress rate) is analyzed by switching to each construction type. Figure 36 is a diagram for explaining an image of analysis using Graph 1, where (A) shows the case of construction type, and (B) shows the case of demolition type.

[0188] There are many cases where the progress rate trends differ depending on the characteristics of the construction work, such as the type of work. The following types of construction work have the following characteristics that show similar progress rate trends:

[0189] ·Construction period condition specification image: Divide into one-month units and calculate forecast values. For example, 6-month construction period, 7-month construction period, 8-month construction period, 24-month construction period (2-year construction period), etc. Divide into several-month units and calculate forecast values. For example, 6-12-month construction period (6-month span), 12-24-month construction period (1-year span), etc. - Image of specifying work type conditions: Explained in detail in this example. · Image of specifying conditions for public / private division (public works or private works): Calculate forecast values ​​for public works, calculate forecast values ​​for private works, etc.

[0190] Few companies know in advance which features to limit their analysis to in order to detect abnormalities or whether they are appropriate for progress rate analysis, so in many cases analysis is required to identify these features. By performing the process explained in Processing Flow 2(3-2)1·2 on a feature-by-feature basis to analyze how your company's features appear, you can analyze where they appear.

[0191] For example, if you want to calculate a forecast value by dividing the construction period into one-month or several-month units, you can do so by adding a row to the construction data acquisition range condition data and registering, for example, the condition "Determination range" and the condition value "1" as shown in Figure 37. If this column has been added, additional information is passed to the parameters used when acquiring data for anomaly detection, and data acquisition is performed.

[0192] (3) Switch the display of the analysis graph and perform the analysis (if no specific conditions are specified) (3-1) When calculating the predicted value at the time of an abnormality detection, an analysis is performed on cases where the characteristics of progress trends appear when targeting all construction work of the executing company without specifying specific conditions (construction period, type of work, etc.). The predicted value is calculated without specifying the "data reference unit" of the [construction data acquisition range condition data] used when determining an abnormality. Basically, the same processing is performed as in 1: Anomaly detection and 2: Initial screen display processing in this processing flow. The differences in processing are described below.

[0193] 1. Anomaly detection 1-1. Obtain the range conditions for the data to detect anomalies. The set construction data acquisition range condition data and abnormality determination result registration target period data are acquired. Figure 38 is a diagram showing an example of construction data acquisition range condition data. In this figure, no condition value is set. Here, we will explain a pattern in which no condition value is set (analyzing the progress rate transition for all construction projects).

[0194] 1-2. The construction data in the business database 106a is referenced to calculate the predicted progress rate for each construction project and construction period progress rate. The data is acquired by setting the parameters to values ​​within the acquisition range condition data for the construction data acquired in 1-1. Using the parameters (data reference unit unspecified) as shown in Figure 39(A) as keys, the construction data in the business database 106a is referenced and the construction data as shown in Figure 39(B) is acquired.

[0195] From the construction data obtained in 1-3.1-2, since the data reference unit is not specified this time, the data for all construction work is used as the population, and the cumulative progress rate (approximate value) by construction period progress rate is calculated based on this population.

[0196] Figure 40(A) shows the acquired construction data, and Figure 40(B) plots each construction project in the construction data, with the horizontal axis showing the construction progress rate (%) and the vertical axis showing the cumulative progress rate (%).

[0197] Figure 41 is a diagram for explaining the calculation of the approximation curve F(x), predicted progress rate, and upper and lower limits. As shown in Figure 41(A), the completed (past) construction work is used as the population, and the approximation curve F(x) of the cumulative progress rate by construction period progress rate of the population is calculated. In addition, the standard deviation function S(x) is calculated by focusing on the tendency of data dispersion within the population. Then, as shown in Figure 41(B), the construction period progress rate is substituted into the approximation curve F(x) and the standard deviation function S(x), respectively, to calculate the predicted progress rate and upper and lower limits of the uncompleted construction work. In this way, the predicted progress rate is calculated using F(x), and the upper and lower limits are calculated using S(x).

[0198] For example, the predicted value and upper and lower limits are calculated using the parameters shown in Figure 42A(A) (X-axis item: construction progress rate, Y-axis item: cumulative progress rate, candidate approximate function [polynomial function, logistic function, Bass model function], etc.). Figure 42A(B) shows an example of approximate data for the cumulative progress rate calculated by construction progress rate, and includes the items construction progress rate, forecast value (forecasted value of cumulative progress rate), lower limit, and upper limit. Note that this explanation uses values ​​that have already been scaled, so an explanation of scale adjustment will be omitted. After calculating F(x), the data dispersion is calculated to calculate the standard deviation function S(x). The construction progress rate is substituted for x in F(x) and S(x), and F(x) → predicted value and S(x) → upper and lower limits are calculated, respectively. The same function is used to calculate the predicted value and upper and lower limits for each work type and project.

[0199] FIG. 42B(D) shows the construction data after the predicted value and upper and lower limit values ​​have been calculated, and in addition to the predicted value (predicted progress rate), lower limit value, and upper limit value, information on the type of work is also stored.

[0200] According to the "acquisition criteria" of the anomaly judgment result registration target period data acquired in 2-1 shown in Figure 42B(C), a determination is made as to whether there are any anomalies in the data for the month before the month to which the anomaly judgment execution date belongs. In this case, the execution date is 2022 / 11 / 07, so a determination is made as to whether there are any anomalies in 2022 / 10.

[0201] 2-4. Obtain data for graph display. In step 2 of this process flow, the work type in the parameters (2-3) to (2-5) is left blank (unspecified) when data is acquired. Since there is no difference other than the addition of data extraction conditions, we will omit the explanation here.

[0202] 2-5. Bind the data for graph display to the graph, color the graph, and complete the display process. This is the same process as step 2 in this processing flow, so a detailed explanation will be omitted. Figure 43 shows the final image that will be drawn.

[0203] (Points to check) It is assumed that it will be used in many cases when the specific conditions for capturing the trend of completed work are not known in advance. First, check the company-wide trend of completed work, and if the results are not useful, specify the characteristics that are likely to show a trend and check them again to grasp the trend of your own company. Depending on the company, there are cases where the possible completed work trend of the construction can be grasped once the company-wide trend of completed work is confirmed.

[0204] Figure 44 is a diagram to explain an example of checking company-wide trends. There are no plot features such as inclination or concentration, and the company's trends are not apparent, so it is not possible to grasp the trends in construction volume.

[0205] Figure 45 is a diagram to explain an example of extraction and confirmation using specific conditions. As shown in Figure 45(A), approximate cumulative progress values ​​that can occur for each construction period can be confirmed. A trend that appears to be an upward trend in the volume of work being completed emerges. Figure 45(B) is a diagram showing the state of this job when it is actually used; there are also cases where such a trend can be identified when checking the entire company without narrowing down the specific conditions.

[0206] (3-2) Check and analyze the actual volume and forecasts that occurred in the past accounting periods. As shown in Figure 46A(A), you can specify the accounting period item in the extraction conditions to switch the data to be output for analysis. If the current period is set to 50 (2022 / 04 to 2023 / 03), switch to 49 (2021 / 04 to 2022 / 03) (the previous period).

[0207] 46B(B) is a diagram showing an example of the display on the analysis screen after switching the accounting period. For graphs affected by the accounting period extraction items, the graph display will be switched according to the specified value as follows. Graph 1: Only the construction work that occurred during the specified period is output to the graph. Graph 2: If the construction work judged as abnormal has been performed during the specified period, the graph is output. If not, the graph is not output. Table 3: Information displayed during the period Switch information for the previous period / construction period according to the specified accounting period Table 4: Information displayed during the period Switch information for the previous period / construction period according to the specified accounting period

[0208] The following describes the process from data acquisition to screen display, but since the basics are the same as the process in (2), only the different parts of the process will be explained.

[0209] 1. The cumulative progress rate transition_actual data is acquired from the construction data in the business database 106a. The cumulative progress rate transition_actual data is acquired based on the conditions initially set in the extraction conditions, and is acquired as data for displaying Charts 1 and 2.

[0210] The parameters shown in Figure 47A(A) do not specify the type of work. Based on the parameters (keys) of the start period "2021 / 04," the end period "2022 / 03," and the work "Work A," the work data shown in Figure 47B(D) is extracted, and the cumulative progress rate transition_actual data shown in Figure 47B(C) is obtained.

[0211] In this case, if there is even one record that contains the "fiscal year and month" within the period from the start of the period to the end of the period, that construction will be acquired. For example, as shown in Figure 47B(B), in this example, constructions A, B, and C will all be acquired.

[0212] 2. The "sales amount trend data by organization" is obtained from the cumulative progress rate trend_actual data and the abnormality determination result data in the abnormality determination result data table 106c obtained in 1.

[0213] Figure 48B(D) shows an example of cumulative progress rate trend_actual data acquired in (2-3), Figure 48B(E) shows an example of abnormality judgment result data, and Figure 48A(C) shows an example of sales amount trend data by organization.

[0214] Using the start period "2021 / 04" and end period "2022 / 03" as parameters (keys) as shown in Figure 48A(A), the cumulative progress rate trend_actual data shown in Figure 48B(D) and the abnormality determination result data shown in Figure 48B(E) are used to obtain the sales amount trend data by organization shown in Figure 48A(C) as data for display in Chart 3. Chart 3 checks only the construction results that occurred within the relevant accounting period, and obtains and displays only the data for the results within the frame shown in Figure 48A(B).

[0215] In the example for the fiscal year and month "2022 / 03", the results are: Department A: Actual results "200", forecast "600", Department B: Actual results "9,000", forecast "9,200", Business location A (Department A + Department B): Actual results "9,200", forecast "9,800".

[0216] The sales amount trend data by construction project is obtained as the display data for Chart 4 from the cumulative progress rate trend_actual data obtained in 3.1 and the abnormality determination result data in the abnormality determination result data table 106c.

[0217] Figure 49A(C) shows an example of data on the trend in sales amount by construction project, Figure 49B(D) shows an example of data on the trend in cumulative progress rate_actual data obtained in (2-3), and Figure 49B(E) shows an example of data on the abnormality judgment result data.

[0218] Using the start period "2021 / 04," end period "2022 / 03," and project "Project A" as parameters (keys) as shown in Figure 49A(A), the sales amount trend data by project shown in Figure 49A(C) is obtained as data for display in Figure 4 from the cumulative progress rate trend_actual data shown in Figure 49B(D) and the abnormality determination result data shown in Figure 49B(E). If there is even one record in the above period that includes the "fiscal year / month," that project will be subject to acquisition. For example, in the table of Figure 49A(B), projects A, B, and C will be subject to acquisition.

[0219] 4. A graph of the acquired data is output to the analysis screen through the process in (2) and processes 1 to 3. Figure 50 shows an example of the display on the analysis screen.

[0220] (Points to check) FIG. 51 is a diagram for explaining the points to check focusing on Tables 3 and 4. FIG. 51(A) is a diagram for explaining the points to check in Table 3. In this diagram, it is possible to check the actual sales performance of construction work that occurred within a specified accounting period. When construction work for which an abnormality has been detected spans multiple periods, it is possible to check the actual situation based on actual performance by checking the information for the period in which a large deviation from the predicted value occurs.

[0221] Figure 51(B) is a diagram to explain the points to check in Table 4. In this figure, it is possible to check the progress of construction work as of the specified accounting period. For construction work in which an abnormality has been detected, it is possible to analyze by period when the deviation from the predicted value began to widen.

[0222] [3-3. Calculation method for approximate curve, forecast value, upper limit and lower limit] 52 to 75, the calculation method used by the detection unit 102b to calculate the approximate curve, the forecast value, and the upper and lower limits will be described in detail. While the above processing targets construction data, the following describes an example in which monthly actual data is processed. While the processing targets are different, the calculation method is similar. The explanation of the calculation method here also applies to the above processing. FIG. 75 illustrates the correspondence between the items (item names used in the explanation of the present invention) used in the explanations of [3-1. Overall Processing] and [3-2. Sample Data] above, which are characteristic features of the present invention, and the items (item names used in the explanation of the approximate curve algorithm) used in the explanation of [3-3. Method for Calculating the Approximate Curve, the Forecast Value, and the Upper and Lower Limits]. The item names "Percentage of Progress of Construction Period," "Cumulative Progress Rate," and "Sales Amount (Actual)" used in the explanation of the present invention correspond to the item names "Percentage of Elapsed Construction Period," "Actual Progress Rate," and "Actual Amount," respectively, used in the explanation of the approximate curve algorithm.

[0223] 52 is a diagram showing an example of the configuration of the abnormality determination definition master 106d. The abnormality determination definition master 106d registers abnormality determination definition IDs, abnormality determination definition names, data acquisition definitions, algorithms used, and parameter settings in association with each other.

[0224] In the example shown in the figure, the anomaly determination definition ID is "JD001," the anomaly determination definition name is "Project progress alert," the algorithm used is "Approximation curve," and the parameter settings are {X-axis item: number of months elapsed, Y-axis item: cumulative actual amount, X-axis scale unified item: planned construction period, Y-axis scale unified item: actual amount, approximate function candidate name: [polynomial function, logistic function, Bass model function], smoothness: 12, significance level: 0.1}.

[0225] (T1: Approximate curve calculation process) The details of the approximate curve calculation process will be described with reference to Figures 53A to 55. In the approximate curve calculation process, the data is not distinguished by case code, but an approximate curve is calculated using approximate components based on all case data of past case monthly performance data. This process has the following features.

[0226] (1) The options for approximation functions include polynomial functions, exponential functions, logarithmic functions, logistic functions, and Bass model functions, and multiple options can be selected. (2) Among the selected functions, the one with the best AIC (Akaike Information Criterion), an index for measuring the accuracy of approximation, will be the approximate curve used in the subsequent processing flow.

[0227] Hereafter, the approximation function deemed to have the best approximation accuracy will be referred to as "Y = F(X)", the name of this approximation function will be called the "performance-centered approximation function", and the curve that this approximation function shows when plotted on a graph will be called the "performance-centered approximation curve". X represents the planned completion rate, Y represents the actual progress rate, and F represents the performance-centered approximation function. By passing X: the planned completion rate to F, the predicted value of Y: the actual progress rate can be calculated.

[0228] The above "bus model function" is used by the Ministry of Land, Infrastructure, Transport and Tourism to estimate the progress rate of construction work. In addition, polynomial functions and logistic functions are also used for comparison with the bus model function (https: / / www.mlit.go.jp / sogoseisaku / jouhouka / content / 001348995.pdf).

[0229] Specifically, the following processing is performed.

[0230] (1) Among the parameter settings registered in the abnormality determination definition master 106d, information on the X-axis item, Y-axis item, and function for each approximate function candidate name is passed to the approximation component to find the optimal coefficient. (2) Calculate the AIC based on the coefficients returned from the approximate parts. (3) The approximation function with the smallest AIC is used as the performance center approximation function.

[0231] Each process will be described in detail below. (1) Among the parameter settings registered in the abnormality determination definition master 106d, information on the X-axis item, Y-axis item, and function for each approximate function candidate name is passed to the approximation component to find the optimal coefficient.

[0232] 53A and 53B, (A) shows an example of data in the anomaly determination definition master 106d. In this example, the anomaly determination definition ID is "JD001," the anomaly determination definition name is "project progress alert," the algorithm used is "approximate curve," and the parameter settings are {X-axis item: number of months elapsed, Y-axis item: cumulative actual amount, approximate function candidate names: [polynomial function, logistic function, Bass model function], ...}. The options for the approximating function correspond to the approximate function candidates in the parameter settings for the data registered in the anomaly determination definition master 106d, and the X and Y values ​​of the data for which an approximate curve is to be drawn correspond to the processed values ​​in the X value item and the Y value item in the parameter settings for the data registered in the anomaly determination definition master 106d.

[0233] (B) shows an example of monthly performance data for a past project after processing. (C) shows the candidate approximation functions, including polynomial functions, logistic functions, and Bass model functions. If a polynomial function is selected, polynomial function expressions of degrees 0 to 6 will be passed. In this example, polynomial functions, logistic functions, and Bass model functions have been selected as candidate approximation functions, so the nine functions above will be passed to the approximation component. Exponential functions and logarithmic functions can also be selected as candidate approximation functions.

[0234] The following process is performed for each candidate approximation function. As an example, we will explain the case where the function to be approximated is y = a / (1 + ebx + c) (logistic function). (D) shows an example of information (1) (data to be passed as an approximate curve) passed to the approximation component. (E) shows an example of information (2) (function to be approximated) passed to the approximation component. (F) shows an example of the information (coefficients) returned from the approximation component, where a = 100.85, b = -0.069, and c = 3.68.

[0235] (2) Calculate the AIC based on the coefficients returned from the approximate parts. For details on calculating the AIC, see http: / / www.radio3.ee.uec.ac.jp / ronbun / TR_YK_048_AIC.pdf.

[0236] In FIG. 54, (A) shows the formula for calculating AIC, which is AIC = (data size) × log (sum of squares of differences from predicted value) / (data size) + 2 × (number of coefficients). Here, the predicted value of Y is calculated by substituting X, the data for which an approximate curve is to be drawn, into the approximation function. The square of the difference from the predicted value of Y is calculated as the square of (Y value - predicted value of Y) of the data for which an approximate curve is to be drawn. The data size is the number of pairs of data (X, Y).

[0237] AIC is characterized by the fact that the smaller the sum of squares of the difference from the predicted value, the smaller the AIC, and the larger the number of coefficients, the larger the AIC. The AIC value itself has no meaning; comparing AIC values ​​is an indicator for determining the superiority of an approximation function. The AIC formula is explained below. The first term in the AIC formula, "(data size) × log(sum of squares of the difference from the predicted value) / (data size)," represents the accuracy of the data for which the approximate curve is to be drawn, and the second term, "2 × (number of coefficients)," represents the penalty for having a large number of coefficients. By determining the superiority of an approximation function based on AIC, a well-balanced approximation function that does not overly fit the data for which the approximate curve is to be drawn and is robust to data changes is selected. The effect of determining the superiority of an approximation function based on AIC is explained below. Even if the actual data from past projects increases over time, an approximation function that fits past data too well is not selected. This has the following two effects: (1) It prevents the type and order of the approximation function from frequently changing each time an anomaly is detected. (2) Even if the number of projects that deviate significantly from the planned progress is drastically reduced due to thorough project progress management, the approximate curve will shift gradually before and after the thorough management.

[0238] (B) shows the data (X, Y) for which you want to draw an approximate curve. (C) shows the predicted value of Y and the sum of squares of the difference between the predicted value of Y and the data.

[0239] When the data size, the sum of squares of the difference between the predicted value of Y, the coefficient, and the number of coefficients (3) are substituted into the AIC calculation formula, the AIC becomes 94.18, as shown in (D).

[0240] When the above process is completed for all approximate function candidates, data such as that shown in Figure 55(A) can be obtained, and the AICs for all approximate function candidates can be obtained. In this figure, actual numerical values ​​are entered in the "...".

[0241] (3) The approximation function with the smallest AIC is used as the performance center approximation function. Assuming that the AIC of all candidate approximation functions was calculated and the AIC of the logistic function was the smallest, we will explain the processing flow. If the performance center approximation function is Y=F(X), in this explanation, the performance center approximation function is expressed as y=100.85 / (1+e -0.069X+3.68 ) This is the function with the smallest AIC, and the coefficients are set to the function you want to approximate. Hereafter, we will refer to this function as F(X).

[0242] (T2: Processing to calculate the dispersion of data from the fitted curve) The process of calculating the dispersion of data from the performance center approximation curve will be described in detail with reference to Figures 56 and 57. The process of calculating the dispersion of data from the performance center approximation curve involves the following steps.

[0243] (1) Calculate the predicted value of the actual progress rate for each scheduled progress rate of the monthly actual data of past projects. (2) Calculate the difference (residual) between the predicted value and the actual data of the progress rate. (3) Calculate the square of the residual (square of the residual).

[0244] A specific calculation will be described with reference to FIG. (1) Calculate the predicted value of the actual progress rate as predicted value = F (progress rate of construction period). (2) The residual, which is the difference between the actual progress rate and the predicted value, is calculated as follows: Residual = Actual progress rate - Predicted value. (3) Square the residual, Square of residual = (residual) 2 Calculate as follows.

[0245] Figure 56(A) shows the planned construction period elapsed rate [%] and the actual progress rate [%] of the actual data of past projects. Figure 56(B) shows the newly added columns of predicted value [%], residual [%], and square of residual [%]. 2 ]. The forecast value [%] is calculated by passing the planned completion progress rate to the actual performance centered approximation function F(X). Because the residuals are squared, all residual squares are greater than or equal to 0. By squaring the residuals, the magnitude of the residual square can be expressed as the degree to which the past performance data deviates from the approximation curve.

[0246] Figure 57(A) is a graph showing the predicted value vs. approximate curve of past project data, with the horizontal axis showing the planned progress rate [%] and the vertical axis showing the actual progress rate [%]. Past actual data is plotted, and the predicted value is plotted on the approximate curve. The actual progress rate - predicted value shows the residual.

[0247] FIG. 57(B) is a graph showing the residuals, with the horizontal axis representing the progress rate [%] over the scheduled construction period and the vertical axis representing the residuals [%].

[0248] Figure 57(C) is a graph showing the square of the residual, where the horizontal axis is the progress rate [%] and the vertical axis is the square of the residual [% 2 ] is shown.

[0249] (T3: Processing to calculate the dispersion trend of data) The process of calculating the tendency of data dispersion will be described in detail with reference to Figures 58A to 60. The process of calculating the tendency of data dispersion involves the following steps.

[0250] (1) Draw an approximate curve for the square of the residual, name this approximate function the residual square approximation function, and define this residual square approximation function as σ 2 =V(X). The residual square approximation function is (polynomial function) 2 By passing X (the percentage progress of the planned completion period) to the residual square approximation function, the predicted value of the residual square at that percentage progress of the planned completion period is returned. (2) Take the square root of the residual square approximation function, name this function the standard deviation function, and let this standard deviation function be σ = S(X). By passing X: the planned completion period progress rate to the standard deviation function, the standard deviation (the degree of dispersion of the actual progress rate) for that planned completion period progress rate is returned.

[0251] By taking the square root of the residual square approximation function, we can create a function that returns the standard deviation. The specific processing will be explained below.

[0252] (1) Draw an approximate curve using approximate parts for the square of the residual (this curve is called the residual square approximate curve). In FIG. 58A and FIG. 58B, (A) shows the planned construction period progress rate [%], actual progress rate [%], forecast value [%], residual [%], square of residual [%] of the actual data of past projects. 2 ]. (B) shows candidates for the residual square approximation function.

[0253] (Polynomial functions) 2 The reason why the function is specified as the function to be approximated is as follows. 1. Because we are not approximating the progress rate of a project, there is no need to specify special functions such as bus model functions that are said to represent the progress of a project, so we specify functions that are as simple as possible. 2. When approximating the actual progress rate, the actual progress rate increases as the planned progress rate progresses, but the squared residual may increase or decrease as the planned progress rate progresses. Therefore, in order to capture the increase or decrease in the squared residual, a polynomial function is used. 2 We have adopted the following. 3. (Polynomial functions) 2 This allows us to later use the square root of the residual square approximation function (a polynomial function) as the standard deviation function.

[0254] The following process is performed for each candidate residual squared approximation function. For example, let the function to be approximated be y=(ax 2 +bx+c) 2In Figures 58A and 58B, (C) shows information (1) (data for which an approximate curve is to be drawn) passed to the approximating part, and (D) shows information (2) (function to be approximated) passed to the approximating part. (E) shows the information returned from the approximating part, with coefficients a = -0.0028, b = 0.3209, and c = 2.1336.

[0255] Next, the AIC is calculated. Since the same processing as (T1: Approximate curve calculation processing) in the processing flow is performed, detailed explanation will be omitted.

[0256] When the above process is completed for all candidates for the function that approximates the square of the residual, data such as that shown in FIG. 59(A) is obtained.

[0257] In this example, the function with the smallest AIC and the coefficients set to the function to be approximated is the residual square approximation function y=(-0.0028x 2 +0.3209x+2.1336) 2 From now on, this (quadratic function) 2 The following processing flow will be explained under the assumption that the AIC of the function of the form is the smallest. 2 =V(X). In this example, V(X)=(-0.0028X 2 +0.3209X+2.1336) 2 This becomes:

[0258] Figure 59(C) shows a graph in which the residual square approximation curve V(X) is plotted against the square of the residual. The horizontal axis is the planned completion period progress rate [%], and the vertical axis is the square of the residual [% 2 In this way, by drawing an approximate curve, it is possible to take into account the variance in the data according to the progress rate of the construction period, such as the deviation from the approximate curve being small from the start of the project to the early and later stages, and the deviation being large in the middle stages.

[0259] (2) Calculate the standard deviation function. When the expected completion time progress rate is passed to X of the residual square approximation function obtained in (1), the output value is in [%2 ], so we take the square root and convert it into a standard deviation function with units of [%]. The standard deviation function S(X) is calculated as S(X) = √V(X). In this processing flow example, V(X) = (-0.0028X 2 +0.3209X+2.1336) 2 Therefore, S(X)=-0.0028X 2 The result is +0.3209X+2.1336.

[0260] Figure 60(A) shows the progress rate, progress rate, forecast value, residual, square of residual, and the absolute value of residual of the actual data of past projects. Figure 60(B) shows a graph that displays the standard deviation function V(X) as a standard deviation curve, with the horizontal axis representing progress rate [%] and the vertical axis representing the absolute value of residual [%]. 2 ]. The absolute value of the residual indicates the distance of deviation between the performance data of past projects and the performance center approximation curve. Note that in actual calculations, the absolute value of the residual is not calculated from the residual between the performance data of past projects and the predicted value using the performance center approximation function, but it is provided here as a reference when displaying the standard deviation curve on a graph.

[0261] (T4: Calculation of predicted values ​​and thresholds for cases subject to abnormality detection) The calculation process for the predicted value and threshold for an abnormality detection target case will be explained in detail with reference to Figures 61 to 64. The actual result center approximation function F(X) and standard deviation function S(X) for finding the predicted value of the actual progress rate have been prepared through the processing up to this point. In the calculation process for the predicted value and threshold for an abnormality detection target case, past case data is not used, and the actual data of the abnormality detection target case is used for the calculation. In the calculation process for the predicted value and threshold for an abnormality detection target case, the following processing is performed on the processed actual data of the abnormality detection target case.

[0262] (1) The predicted value is calculated using the estimated progress rate and actual results center approximation function F(X). (2) Calculate the standard deviation (the predicted value of the distance of deviation from the approximate curve) using the scheduled completion period progress rate / standard deviation function S(X). (3) The upper and lower thresholds are set by multiplying the standard deviation by a constant value calculated based on the significance level, leaving a range above and below the predicted value.

[0263] (1) Calculate the predicted value. Calculate the predicted value of the actual progress rate for the scheduled progress rate of the project to be determined as abnormal.

[0264] Figure 61(A) shows the processed actual data of the project to be judged as abnormal, with columns for predicted values ​​added to the project code, fiscal year and month, progress rate, and actual progress rate. The predicted values ​​are calculated by using the actual results center approximation function F(X) where x = progress rate.

[0265] FIG. 61(B) is a graph of the predicted values ​​of FIG. 61(A), with the horizontal axis representing the planned progress rate [%] and the vertical axis representing the actual progress rate [%].

[0266] (2) Calculate the standard deviation. Calculate the standard deviation of the progress rate of the planned completion period for the processed projects to be judged as abnormal.

[0267] Figure 62(A) shows the processed actual data for a project to be judged as abnormal, with the standard deviation column added to the project code, fiscal year and month, progress rate, actual progress rate, and forecast value. The standard deviation is calculated by using the standard deviation function S(X) where x = progress rate. The calculated standard deviation means that there is a 68% chance that the actual data will fall within a range that is the standard deviation away from the center performance approximation curve.

[0268] Figure 62(B) is a graph of the standard deviation of Figure 62(A), where the horizontal axis is the progress rate [%] and the vertical axis is the standard deviation [% 2 ] is shown.

[0269] (3) Determine the threshold value. Figure 63(A) shows the processed monthly performance data for projects subject to abnormality detection, with columns for upper threshold [%] and lower threshold [%] added to the project code, accounting year and month, progress rate during planned completion period, progress rate, forecast value, and standard deviation.

[0270] The threshold is calculated as shown in Figure 63(B): Upper threshold = predicted value + Z α / 2 × (standard deviation), lower threshold = predicted value - Z α / 2 Calculate by multiplying the standard deviation by Z. α / 2 means the value of the upper α / 2% point of the standard normal distribution. For this α, the value of the parameter setting (significance level) registered in the abnormality determination definition master 106d in premise (1) is used.

[0271] FIG. 64(A) is a diagram showing an example of data in the abnormality determination definition master 106d, in which a parameter of {significance level: 0.1} is set for the abnormality determination definition ID "JD001."

[0272] For example, if the significance level α is 0.1, then α / 2 = 0.05 (5%), and Z α / 2 = 1.645. In this case, in Figure 64(B), for the record of 40% progress rate for project B001 that is the target for abnormality judgment, the upper threshold = 28.678 + 1.645 x 10.415 and the lower threshold = 28.678 - 1.645 x 10.415. This can be interpreted as meaning that there is about a 90% probability that the actual progress rate will be above the lower threshold and below the upper threshold. Conversely, if this range is exceeded, it will be judged to be abnormal.

[0273] Figure 64(C) is a graph of the predicted values, upper threshold, and lower threshold of Figure 63(B), with the horizontal axis representing the planned progress rate [%] and the vertical axis representing the actual progress rate [%]. Note that the graphs of the actual progress center approximation curve and the upper and lower thresholds are shown, but they are only shown for reference.

[0274] Figure 64(D) is a diagram to explain the significance level and percentile (https: / / ai-trend.jp / basic-study / normal-distribution / normal-distribution / ).

[0275] (T5: Abnormality determination process) The abnormality determination process will be described in detail with reference to Figures 65 and 66. In the abnormality determination process, the following processes are performed.

[0276] (1) Abnormality determination is performed based on the monthly performance data of the case to be determined and the threshold value obtained in the previous process. (2) The data of the determination result is stored in a table (not shown) in the storage unit 206.

[0277] The abnormality determination process will be specifically described below. (1) For cases subject to abnormality detection, an abnormality is detected by comparing the actual progress rate of the case subject to abnormality detection with the threshold value determined in the previous process.

[0278] FIG. 65(A) shows the case code, accounting year and month, progress rate after scheduled completion, progress rate, forecast value, standard deviation, upper threshold, and lower threshold of the performance data of the case to be judged as abnormal.

[0279] Figure 65(B) shows a graph of the anomaly determination in Figure 65(A), with the horizontal axis representing the planned completion period elapsed rate [%] and the vertical axis representing the actual progress rate [%], and plotting the actual performance center approximation curve, upper threshold, lower threshold, B001, and B002. In the figure, B001's planned completion period elapsed rate of "40" and actual progress rate of "60" are determined to be abnormal because they exceed the upper threshold. Also, B002's planned completion period elapsed rate of "57.143" and actual progress rate of "20" are determined to be abnormal because they fall below the lower-upper threshold. For example, if the upper threshold is exceeded, the possibility of fraudulent overstating of actual performance is detected. Also, if the lower threshold is exceeded, the possibility of a delay in progress is detected.

[0280] (2) The judgment result data is stored in the judgment result table. Figure 66 is a diagram showing an example of judgment result data. The judgment result data may include the case code, fiscal year and month, progress rate for planned completion period, actual progress rate, forecast value, standard deviation, upper threshold, lower threshold, and judgment result (FALSE or TRUE). For data judged to be abnormal, the judgment result is "TRUE", and for other data, it is "FALSE".

[0281] (T6: Calculation processing of display data) The calculation process for the display data will be explained in detail with reference to Figures 67 to 69. In the process up to this point, the performance center approximation function F(X) and the standard deviation function S(X) based on past cases have been calculated. In the calculation process for the display data, data for display (for displaying the approximation curve on the screen) is created and the results are stored in a table for auxiliary information.

[0282] The display data calculation process involves the following steps. (1) Calculate the progress rate of the construction period to display the data for display. (2) The values ​​on the approximation curve of the data to be displayed, the upper and lower threshold values ​​are calculated. (3) The results of the approximation curve algorithm are stored in the attached information table.

[0283] The specific processing contents will be explained below. (1) Calculate the progress rate of the construction period to be displayed in the data for display. The progress rate of the construction period of the displayed data is the minimum and maximum value of the progress rate of the construction period of the past project data divided by the value of the parameter called "smoothness." Figure 67 shows a graph of the actual data of past projects, with the horizontal axis representing the planned progress rate [%] and the vertical axis representing the actual progress rate [%], and A001, A002, and A003 are plotted.

[0284] The range of this planned progress rate from 0 to 120% is divided into 12 equal parts with a smoothness of 12 to prepare the progress rate of the progress of the project period for display. For the smoothness, the parameter setting (smoothness) value registered in the abnormality judgment definition master 106d in premise (1) is used.

[0285] 67(B) shows an example of data setting in the abnormality determination definition master 106d. In the example shown in the figure, the parameter "smoothness: 12" is set for the abnormality determination definition ID "JD001".

[0286] When the display data is displayed on the screen, the predicted value, upper threshold, and lower threshold may be displayed as a line graph, so the larger the "smoothness" value, the smoother the line graph will be, closer to a curve.

[0287] By dividing 0 to 120% into 12 equal parts, the construction progress rate of the display data shown in Figure 67(C) is prepared. In this example, the interval between each construction progress rate is 10%, which is 0 to 120% divided into 12 equal parts, and the number of points in the display data is 13.

[0288] (2) The values ​​on the approximation curve of the data to be displayed, the upper and lower threshold values ​​are calculated. Using F(X) and S(X) obtained when processing the actual data of past projects, the values ​​on the approximate curve for the progress rate of the construction period of the data to be displayed and the values ​​of the upper and lower thresholds are calculated. Since this is exactly the same process as that used to calculate the predicted values ​​and thresholds for projects subject to abnormality detection, a detailed explanation of the calculation process will be omitted.

[0289] After the calculation process, the display data will be as shown in Figure 68(A), with columns for predicted value, upper threshold, and lower threshold added to the planned completion period elapsed rate. Figure 68(B) is a graph (line graph) of the display data in Figure 68(A), with the horizontal axis representing the planned completion period elapsed rate [%] and the vertical axis representing the actual progress rate [%], and the predicted value, upper threshold, and lower threshold are plotted. The values ​​of the white dots on the graph are calculated.

[0290] As can be seen from the graph, the width between the upper and lower thresholds and the performance center approximation curve is calculated using a standard deviation function, so the thresholds reflect the following two characteristics:

[0291] 1. In the early and later stages of a project, there is little chance of deviation from the approximate curve. Therefore, even a slight deviation from the approximate curve is highly abnormal, so the threshold range is narrow. 2. There is a high possibility of deviation from the approximate curve in the middle stage of a project. As a result, even if something is normal, there is a possibility of deviation from the approximate curve to some extent, and even if there is a slight deviation, it is not considered abnormal, so the range of the threshold is wide.

[0292] (3) The approximate curve result is stored in the attached information table. The following display data is stored in the approximate curve algorithm result attached information table (hereinafter referred to as the "approximate curve result attached information table"). The approximate curve result attached information table stores data to be displayed as supplementary information when the anomaly judgment results are displayed as a graph on the screen. Unlike the data of the anomaly judgment target case itself, data to be compared with the data of the anomaly judgment target case is stored. The stored data is calculated based only on data from past cases.

[0293] 69(A) shows an example of display data, and FIG. 69(B) shows an example of approximate curve result attached information stored in approximate curve result attached information table 106c based on the display data. The approximate curve result attached information has items such as execution history ID, line number, X, Y, upper threshold, and lower threshold.

[0294] Here, the execution history ID is an ID automatically assigned for each execution of anomaly determination, the row number is a consecutive number assigned to each row of the display data starting from 0, X corresponds to the X-axis value of the display data, Y corresponds to the Y-axis value of the display data, the upper threshold is the upper threshold of the display data, and the lower threshold is the lower threshold of the display data.

[0295] As described above, if an abnormality is detected using the judgment result data (see Figure 66) and the information attached to the approximate curve result (see Figure 69(B)), a message indicating the abnormality detection and a graph (e.g., a line graph) of the abnormality judgment result are displayed.

[0296] (Options for processing past projects) 70 to 74, options for processing monthly performance data of past cases will be described. As described above, the anomaly determination definition master 106d is configured to allow setting of scale unification items.

[0297] Figure 70 is a diagram showing a setting example of the anomaly determination definition master 106d. The assumed item set in the X-axis scale unified item is the planned construction period or the implementation period, and the assumed item set in the Y-axis scale unified item is the budget or the actual amount. The setting example of the anomaly determination definition master 106d shown in Figure 70 is an example for the construction industry, but it is also assumed that it can be used in other industries.

[0298] The X-axis item, Y-axis item, X-axis scale unification item, and Y-axis scale unification item can be freely set according to the industry of the user of this anomaly detection system.

[0299] 71(A) is a diagram showing an example of monthly performance data of a past case, and FIG. 71(B) shows an example of schedule and performance data of a past case.

[0300] Four methods for processing data of past cases will be described with reference to Fig. 72. Data is classified into four patterns, A, B, C, and D, according to the parameter settings of the abnormality determination definition master 106d.

[0301] AX-axis scale unified item: implementation period, Y-axis scale unified item: actual amount BX-axis scale unified item: planned construction period, Y-axis scale unified item: actual amount In the case of a unified scale item on the C-axis: implementation period, and a unified scale item on the Y-axis: budget DX axis scale unified item: planned construction period, Y axis scale unified item: budget

[0302] The patterns ABCD are named as follows: Pattern A is "strict scheduled construction period and budget type", Pattern B is "flexible scheduled construction period and strict budget type", Pattern C is "strict scheduled construction period and flexible budget type", and Pattern D is "flexible scheduled construction period and budget type".

[0303] "Strict" and "Flexible" mean that when a project is completed, strict and flexible checks are performed to ensure that it was completed within the scheduled timeframe and budget. An example of Pattern B is explained below.

[0304] (Pattern B. Flexible construction period and strict budget (X-axis scale unified item: planned construction period, Y-axis scale unified item: actual amount)) Pattern B, flexible scheduled construction period / strict budget type, will be explained with reference to Figures 73 and 74. An example of processing A003 will be explained below. Figure 73(A) shows an example of monthly actual data for A003, and Figure 73(B) shows an example of planned / actual data for A003. When the monthly actual data for A003 is processed and the planned construction period progress rate is calculated using the number of months elapsed / planned construction period, and the actual progress rate is calculated using the cumulative actual amount / actual amount, the result is shown in Figure 73(C).

[0305] When the monthly performance data for all past cases is processed, it will look like Figure 74(A). Figure 74(B) is a graph of the monthly performance data for past cases in Figure 39(A), and Figure 74(C) shows the graph after calculation processing of the display data.

[0306] Since the maximum value of the X-axis for past projects is 120%, the threshold value calculated in subsequent processing will converge to an X value of around 120%. This makes it possible to determine that a project to be judged as abnormal is within the normal range even if it is completed with a slight delay. Also, since the maximum value of the Y-axis for past projects is all 100%, the threshold value calculated in subsequent processing will converge to a Y value of around 100%. This makes it possible to strictly check that projects to be judged as abnormal are completed according to the budget before they started.

[0307] As described above, according to this embodiment, the system is provided with a detection unit 102b that divides a population of past case data according to specified specific conditions, calculates an approximate curve for the progress rate of the case data and the case progress rate for each population, calculates a predicted value of the progress rate by substituting the progress rate into the function of the approximate curve, calculates upper and lower limit values ​​based on the approximate curve and the predicted value of the case progress rate, and compares the case data to be detected with the upper and lower limit values ​​to detect anomalies, and a display control unit 102c that displays analysis data for case data in which an anomaly has been detected on an analysis screen, thereby making it possible to automatically detect anomalies in case data and to discover and deal with them early.

[0308] [4. Contribution to the United Nations-led Sustainable Development Goals (SDGs)] This embodiment can contribute to improving business efficiency and promoting appropriate management decisions by companies, thereby contributing to the achievement of SDGs Goals 8 and 9.

[0309] Furthermore, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, thereby contributing to the achievement of SDGs Goals 12, 13, and 15.

[0310] Furthermore, this embodiment can contribute to strengthening control and governance, which can contribute to Goal 16 of the SDGs.

[0311] 5. Other Embodiments The present invention may be implemented in various different embodiments other than those described above within the scope of the technical concept set forth in the claims.

[0312] For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

[0313] Furthermore, the processing procedures, control procedures, specific names, information including parameters such as registered data and search conditions for each process, screen examples, and database configurations shown in this specification and drawings can be changed as desired unless otherwise specified.

[0314] Furthermore, with regard to the anomaly detection system 100, the components shown in the figures are functional concepts, and do not necessarily have to be physically configured as shown in the figures.

[0315] For example, all or any part of the processing functions of the anomaly detection system 100, particularly the processing functions performed by the control unit, may be implemented by a CPU and a program interpreted and executed by the CPU, or may be implemented as hardware using wired logic. The program is recorded on a non-transitory computer-readable recording medium containing programmed instructions for causing an information processing device to execute the processes described in this embodiment, and is mechanically read by the anomaly detection system 100 as needed. That is, a computer program for providing instructions to the CPU in cooperation with an OS and performing various processes is recorded in a storage unit such as a ROM or HDD (Hard Disk Drive). The computer program is executed by being loaded into RAM, and cooperates with the CPU to form the control unit.

[0316] In addition, this computer program may be stored in an application program server connected to the anomaly detection system 100 via any network, and all or part of it may be downloaded as needed.

[0317] Furthermore, the program for executing the processes described in this embodiment may be stored in a non-transitory computer-readable recording medium or configured as a program product. Here, the term "recording medium" includes any "portable physical medium" such as a memory card, a Universal Serial Bus (USB) memory, a Secure Digital (SD) card, a flexible disk, a magneto-optical disk, a ROM, an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable and Programmable Read Only Memory (EEPROM (registered trademark)), a Compact Disk Read Only Memory (CD-ROM), a Magneto-Optical disk (MO), a Digital Versatile Disk (DVD), and a Blu-ray (registered trademark) disc.

[0318] Furthermore, a "program" is a data processing method written in any language or description method, regardless of the format, such as source code or binary code. Note that a "program" is not necessarily limited to a single structure, but also includes a structure that is distributed as multiple modules or libraries, or a structure that achieves its function by cooperating with a separate program, such as an OS. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, can use well-known configurations and procedures.

[0319] The various databases stored in the memory unit are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and providing websites.

[0320] The anomaly detection system 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device to which any peripheral device is connected. The anomaly detection system 100 may also be realized by installing software (including programs, data, etc.) that causes the device to perform the processing described in this embodiment.

[0321] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be configured by functionally or physically distributing and integrating them in any unit depending on various additions or functional loads. In other words, the above-described embodiments can be implemented in any combination, or embodiments can be implemented selectively. [Explanation of symbols]

[0322] 100 Anomaly Detection System 102 Control section 102a Memory control unit 102b Detection unit 102c Display control unit 104 Communication interface unit 106 Storage section 106a Business Database 106b Data table for anomaly detection execution 106c Abnormality judgment result data table 106d Abnormality Judgment Definition Master 108 Input / Output Interface Section 112 Input Device 114 Output Device 300 Network

Claims

1. An anomaly detection system equipped with a control unit, The control unit, It is configured to provide access to project data including one or more specific conditions, progress rates, and project progress rates. A detection means that divides the population of past project data according to specified conditions, calculates an approximation curve for the elapsed time and project progress rate of the project data for each population, calculates a predicted value for the project progress rate by substituting the elapsed time into the function of the approximation curve, calculates upper and lower limits based on the approximation curve and the predicted value for the project progress rate, and detects anomalies by comparing the project data to be detected with the upper and lower limits. A display control means for displaying analysis data on an analysis screen for project data in which an anomaly has been detected, Equipped with, The aforementioned specific conditions include project type, project duration, or public / private sector classification; the aforementioned progress rate includes the project duration progress rate calculated as project duration months / planned project duration; and the aforementioned project progress rate includes the cumulative progress rate calculated as incurred costs at the present time / (cost budget + incurred costs at the present time). An anomaly detection system characterized in that the project data includes a project, an organization, one or more specific conditions, accounting year and month, sales amount, project progress rate, and cumulative progress rate.

2. The project is a development task, The project type is a system type, The aforementioned project period is the development period. The aforementioned project progress refers to the completed man-hours. An anomaly detection system according to claim 1, characterized by the following:

3. The project is a production content, The aforementioned project type is a content medium, The aforementioned project period is the production period. The aforementioned project progress is defined as the completed amount. An anomaly detection system according to claim 1, characterized by the following:

4. An anomaly detection method performed by an information processing device equipped with a control unit, The control unit, It is configured to provide access to project data including one or more specific conditions, progress rates, and project progress rates. The control unit executes: The detection process involves dividing the population of past project data according to specified conditions, calculating approximation curves for the elapsed time and project progress rate for each population, substituting the elapsed time into the function of the approximation curve to calculate a predicted value for the project progress rate, calculating upper and lower limits based on the approximation curve and the predicted value for the project progress rate, and detecting anomalies by comparing the project data to be detected with the upper and lower limits. For project data in which anomalies have been detected, a display control process is performed to display the data for analysis on the analysis screen, Includes, The aforementioned specific conditions include project type, project duration, or public / private sector classification; the aforementioned progress rate includes the project duration progress rate calculated as project duration months / planned project duration; and the aforementioned project progress rate includes the cumulative progress rate calculated as incurred costs at the present time / (cost budget + incurred costs at the present time). An anomaly detection method characterized in that the project data includes a project, an organization, one or more specific conditions, accounting year and month, sales amount, project progress rate, and cumulative progress rate.

5. An anomaly detection program to be executed by an information processing device equipped with a control unit, The control unit, It is configured to provide access to project data including one or more specific conditions, progress rates, and project progress rates. The control unit, The detection process involves dividing the population of past project data according to specified conditions, calculating approximation curves for the elapsed time and project progress rate for each population, substituting the elapsed time into the function of the approximation curve to calculate a predicted value for the project progress rate, calculating upper and lower limits based on the approximation curve and the predicted value for the project progress rate, and detecting anomalies by comparing the project data to be detected with the upper and lower limits. For project data in which anomalies have been detected, a display control process is performed to display the data for analysis on the analysis screen, This is an anomaly detection program to execute, The aforementioned specific conditions include project type, project duration, or public / private sector classification; the aforementioned progress rate includes the project duration progress rate calculated as project duration months / planned project duration; and the aforementioned project progress rate includes the cumulative progress rate calculated as incurred costs at the present time / (cost budget + incurred costs at the present time). An anomaly detection program characterized in that the project data includes a project, an organization, one or more specific conditions, accounting year and month, sales amount, project progress rate, and cumulative progress rate.

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