A system and method for life cycle assessment of a construction device

Abstract

The application relates to the technical field of life prediction, and discloses a construction device life cycle evaluation system and method, which comprises a historical database module, a collection module, a judgment module and an adjustment module.The collection module is used for calculating the predicted residual life cycle of a to-be-monitored construction device based on analysis results, and obtaining a data fluctuation value according to a running data set; the running data set comprises current-time to-be-monitored construction device running data and previous-time to-be-monitored construction device running data; the judgment module is configured to compare the data fluctuation value with a fluctuation threshold value, judge abnormal data according to a comparison result, and judge whether the predicted residual life cycle needs to be adjusted according to a life influence factor; and the adjustment module is used for adjusting the predicted residual life cycle according to an adjustment coefficient to obtain an adjusted residual life cycle.The maintenance and replacement of the construction device can be more scientifically and reasonably arranged, unexpected shutdown and production loss caused by equipment failure are avoided, and construction efficiency and economic benefits are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of life prediction, in particular to a construction device life cycle evaluation system and method. BACKGROUND

[0002] In the field of modern construction, the performance and reliability of construction devices are crucial for the success of the entire project. However, construction devices undergo various complex working environments and conditions during use, which will have a significant impact on their life and performance. Traditional evaluation methods often focus on initial performance testing of the device, ignoring the impact of wear and tear, aging, and maintenance during actual use on the life cycle.

[0003] Therefore, it is necessary to design a construction device life cycle evaluation system and method to solve the problems existing in the current technology. SUMMARY

[0004] In view of this, the present application proposes a construction device life cycle evaluation system and method, aiming to solve the problem that the current technology focuses on initial performance testing of the device, ignoring the impact of wear and tear, aging, and maintenance during actual use on the life cycle.

[0005] In one aspect, the present application proposes a construction device life cycle evaluation system, comprising:

[0006] a historical database module configured to store historical operation data of a construction device to be monitored;

[0007] a collection module configured to collect the historical operation data and analyze the historical operation data, and calculate the predicted remaining life cycle of the construction device to be monitored based on the analysis results;

[0008] and is further configured to obtain real-time operation data of the construction device to be monitored and establish an operation data set to obtain a data fluctuation value according to the operation data set; wherein the operation data set includes the operation data of the construction device to be monitored at the current time and the operation data of the construction device to be monitored at the previous time;

[0009] a judgment module configured to compare the data fluctuation value with a fluctuation threshold value and determine abnormal data according to the comparison result; and is further configured to determine a life influence factor according to the abnormal data and determine whether to adjust the predicted remaining life cycle according to the life influence factor;

[0010] The adjusting module is configured to calculate the correlation between the abnormal data, determine an adjustment coefficient of the predicted remaining service life according to the correlation, adjust the predicted remaining service life according to the adjustment coefficient, and obtain an adjusted remaining service life when it is determined to adjust the predicted remaining service life.

[0011] Further, when calculating the predicted remaining service life of the to-be-monitored construction device based on the analysis result, the method comprises:

[0012] dividing the historical operation data into historical abnormal operation data and historical safe operation data based on the analysis result;

[0013] classifying the historical abnormal operation data and counting a first operation quantity of each type of historical abnormal operation data;

[0014] classifying the historical safe operation data and counting a second operation quantity of each type of historical safe operation data;

[0015] constructing a historical abnormal operation time data set according to the historical abnormal operation data, determining a historical safe operation time data set according to the historical safe operation data, and determining an abnormal deviation value corresponding to each historical abnormal operation data according to the historical abnormal operation time data set and the historical safe operation time data set;

[0016] calculating the predicted remaining service life of the to-be-monitored construction device according to the abnormal deviation value corresponding to each historical abnormal operation data.

[0017] Further, when determining the abnormal deviation value corresponding to each historical abnormal operation data according to the historical abnormal operation time data set and the historical safe operation time data set, the method comprises:

[0018] calculating a historical abnormal average operation time according to the historical abnormal operation time data set;

[0019] calculating a historical safe average operation time according to the historical safe operation time data set;

[0020] calculating an average operation time difference between the historical abnormal average operation time and the historical safe average operation time;

[0021] obtaining an abnormal deviation value by subtracting the average operation time difference from the historical abnormal operation time corresponding to each historical abnormal operation time data.

[0022] Further, when calculating the predicted remaining service life of the to-be-monitored construction device according to the abnormal deviation value corresponding to each historical abnormal operation data, the method comprises:

[0023] A predicted remaining service life of the construction device to be monitored is calculated according to the following formula:

[0024] T r = T i - (D avg + T safe );

[0025]

[0026] wherein Tr represents the predicted remaining service life, Ti represents the initial service life, Davg represents a deviation average value of historical abnormal operation data, Tsafe represents a total sum of historical safe operation time, Rerr,i represents a number of operation times of the i-th type of historical abnormal operation data, Terr,i represents an operation time corresponding to the i-th type of historical abnormal operation data, Tavg,err represents an average operation time of historical abnormal operation data, Tavg,safe represents an average operation time of historical safe operation data, Tsafe,j represents a safe operation time corresponding to the j-th type of historical safe data, n represents a number of types of historical abnormal operation data, and m represents a number of types of historical safe operation data.

[0027] Further, the data fluctuation value is compared with a fluctuation threshold value, and when the comparison result is used to determine abnormal data, the method comprises:

[0028] data whose data fluctuation value is greater than the fluctuation threshold value is screened out and retained, and other data is excluded;

[0029] an abnormal value of each screened data is calculated based on an isolation forest model;

[0030]

[0031] wherein s(a) represents an abnormal value of data X, E(h((a)) represents an average path length of data X in all isolated trees in the isolation forest, and c(n) represents an average path length used for normalizing the path length;

[0032] when the abnormal value of the screened data is greater than 0.75, the data is determined to be abnormal data, and a type of the abnormal data is identified, and the same type of abnormal data is stored separately.

[0033] Further, when a life influencing factor is determined according to the abnormal data, the method comprises:

[0034] the life influencing factor is obtained by the following formula:

[0035]

[0036] Wherein, LIF represents a life influence factor, sa represents an abnormal value of the i-th abnormal data, ωa represents a weight of the a-th abnormal data, and g represents a total number of all identified abnormal data.

[0037] Further, judging whether to adjust the predicted remaining life cycle according to the life influence factor comprises:

[0038] Comparing the life influence factor with an influence factor threshold value, and judging whether to adjust the predicted remaining life cycle according to a comparison result;

[0039] When the life influence factor is greater than or equal to the influence factor threshold value, it is determined to adjust the predicted remaining life cycle;

[0040] When the life influence factor is less than the influence factor threshold value, it is determined not to adjust the predicted remaining life cycle.

[0041] Further, when it is determined to adjust the predicted remaining life cycle, calculating the correlation between the abnormal data comprises:

[0042]

[0043] Wherein, R represents the correlation between the abnormal data, sx and sy represent abnormal values of any two abnormal data, and y > x, sp represents an average abnormal value of all abnormal data, and z represents a total number of identified abnormal data.

[0044] Further, according to the correlation, a adjustment coefficient of the predicted remaining life cycle is determined, the predicted remaining life cycle is adjusted according to the adjustment coefficient, and the adjusted remaining life cycle is obtained, comprising:

[0045] An adjustment coefficient interval is set, and the adjustment coefficient interval comprises a first adjustment coefficient, a second adjustment coefficient, and a third adjustment coefficient;

[0046] A correlation ratio of the correlation and a correlation threshold value is calculated, and an adjustment coefficient is determined according to the correlation ratio;

[0047] When the correlation ratio is greater than 1.1 and less than or equal to 1.25, the first adjustment coefficient is selected to adjust the predicted remaining life cycle, and a product value of the first adjustment coefficient and the predicted remaining life cycle is taken as the adjusted remaining life cycle;

[0048] When the correlation ratio is greater than 1.25 and less than or equal to 1.4, the second adjustment coefficient is selected to adjust the predicted remaining life cycle, and a product value of the second adjustment coefficient and the predicted remaining life cycle is taken as the adjusted remaining life cycle;

[0049] when the correlation ratio is greater than 1.4, selecting the third adjustment coefficient to adjust the predicted remaining service life, and taking the product value of the third adjustment coefficient and the predicted remaining service life as the adjusted remaining service life;

[0050] wherein the first adjustment coefficient is less than the second adjustment coefficient, and the second adjustment coefficient is greater than the third adjustment coefficient.

[0051] Compared with the prior art, the construction device life cycle evaluation system provided by the application can effectively evaluate the life cycle of the construction device through the collaborative work of the historical database module, the acquisition module, the judgment module and the adjustment module. The system can not only predict the remaining service life of the construction device based on historical data, but also can monitor the running state of the device in real time, find abnormal fluctuations in time, and adjust the prediction results accordingly, so as to provide more accurate life cycle evaluation. In this way, the maintenance and replacement of the construction device can be more scientifically and reasonably arranged, avoiding unexpected downtime and production loss caused by equipment failure, and improving construction efficiency and economic benefits.

[0052] In another aspect, the application also provides a construction device life cycle evaluation method, comprising the following steps:

[0053] S100: storing historical running data of a construction device to be monitored;

[0054] S200: acquiring the historical running data and analyzing the historical running data, calculating the predicted remaining service life of the construction device to be monitored based on the analysis result;

[0055] S300: acquiring real-time running data of the construction device to be monitored, establishing a running data set, and obtaining a data fluctuation value according to the running data set; wherein the running data set includes the running data of the construction device to be monitored at the current time and the running data of the construction device to be monitored at the previous time;

[0056] S400: comparing the data fluctuation value with a fluctuation threshold value, judging abnormal data according to the comparison result, determining a life influence factor according to the abnormal data, and judging whether to adjust the predicted remaining service life according to the life influence factor;

[0057] S500: when it is determined to adjust the predicted remaining service life, calculating the correlation between the abnormal data, determining the adjustment coefficient of the predicted remaining service life according to the correlation, adjusting the predicted remaining service life according to the adjustment coefficient, and obtaining the adjusted remaining service life.

[0058] It can be understood that the construction device life cycle assessment system and method have the same beneficial effects, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the application. Moreover, the same reference numerals are intended to denote the same components throughout the accompanying drawings. In the drawings:

[0060] Figure 1 The structural block diagram of the construction device life cycle assessment system provided for the embodiments of the present application is shown in the figure;

[0061] Figure 2 The flow chart of the construction device life cycle assessment method provided for the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0062] Exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0063] Referring to Figure 1 As shown in the figure, in some embodiments of the present application, the present embodiment provides a construction device life cycle assessment system, comprising:

[0064] a historical database module configured to store historical operation data of a construction device to be monitored;

[0065] a collection module configured to collect the historical operation data and analyze the historical operation data, and calculate a predicted remaining life cycle of the construction device to be monitored based on the analysis result;

[0066] is also configured to obtain real-time operation data of the construction device to be monitored, and establish an operation data set to obtain a data fluctuation value according to the operation data set; wherein the operation data set includes current operation data of the construction device to be monitored and previous operation data of the construction device to be monitored;

[0067] The judging module is configured to compare the data fluctuation value with a fluctuation threshold value, judge abnormal data according to a comparison result, determine a life influencing factor according to the abnormal data, and judge whether to adjust the predicted remaining life cycle according to the life influencing factor.

[0068] The adjusting module is configured to calculate a correlation between the abnormal data when it is determined to adjust the predicted remaining life cycle, determine an adjustment coefficient of the predicted remaining life cycle according to the correlation, adjust the predicted remaining life cycle according to the adjustment coefficient, and obtain an adjusted remaining life cycle.

[0069] In the embodiment, the construction device to be monitored includes multiple types of construction machines.

[0070] In the embodiment, the historical operation data refers to an operation record of the construction device to be monitored, that is, an operation record involved in the whole process from starting the construction device to be monitored to stopping the construction device, including but not limited to running time, operation times, fault records, maintenance records, etc.

[0071] It can be seen that the historical database module is used to store the historical operation data of the construction device to be monitored, so as to facilitate subsequent data analysis and calculation of the predicted remaining life cycle; the collecting module is used to collect real-time operation data and compare and analyze the real-time operation data with the historical operation data, so as to ensure the continuity and accuracy of the data; the judging module can timely find data abnormalities by analyzing the data fluctuation value and the preset fluctuation threshold value, so as to quickly respond to potential equipment failure or performance decline; when the abnormal data are identified, the system will analyze these data to determine key factors influencing the life of the construction device, and then make necessary adjustment to the predicted remaining life cycle; and the adjusting module is used to calculate an adjustment coefficient according to the correlation of the abnormal data, so as to ensure the accuracy of the prediction result and provide a scientific basis for the maintenance and replacement of the construction device.

[0072] It can be understood that the life cycle evaluation system of the construction device provided in the embodiment can effectively evaluate the life cycle of the construction device through the cooperative work of the historical database module, the collecting module, the judging module and the adjusting module. The system can not only predict the remaining life cycle of the construction device based on historical data, but also can monitor the running state of the device in real time, timely find abnormal fluctuations, and adjust the prediction result accordingly, so as to provide more accurate life cycle evaluation. In this way, the maintenance and replacement of the construction device can be more scientifically and reasonably arranged, avoiding unexpected downtime and production loss caused by equipment failure, and improving construction efficiency and economic benefits.

[0073] Specifically, when the historical operation data is analyzed and the predicted remaining life cycle of the construction device to be monitored is calculated based on the analysis result, the following steps are included:

[0074] dividing the historical operation data into historical abnormal operation data and historical safe operation data based on the analysis result;

[0075] classifying the historical abnormal operation data and counting a first operation quantity of each type of historical abnormal operation data;

[0076] classifying the historical safe operation data and counting a second operation quantity of each type of historical safe operation data;

[0077] constructing a historical abnormal operation time data set according to the historical abnormal operation data, determining a historical safe operation time data set according to the historical safe operation data, and determining an abnormal deviation value corresponding to each historical abnormal operation data according to the historical abnormal operation time data set and the historical safe operation time data set;

[0078] calculating a predicted remaining service life of the to-be-monitored construction device according to the abnormal deviation value corresponding to each historical abnormal operation data.

[0079] In this embodiment, the historical abnormal operation data refers to data records that do not meet the normal operation standards during the operation of the construction device due to various reasons. These data records include but are not limited to abnormal operation conditions caused by equipment failure, operation error, environmental factors or other unpredictable factors. The historical safe operation data refers to the operation data records of the construction device under normal operating conditions, which reflect the stable operation state of the device without abnormal conditions.

[0080] It can be understood that through the comparative analysis of the historical abnormal operation time data set and the historical safe operation time data set, the system can identify the specific time period and conditions that lead to abnormal operation. For example, if a certain type of abnormal operation data frequently occurs under specific environmental conditions, the system can infer that the environmental conditions may be one of the reasons for the abnormality. The calculation of the abnormal deviation value can help to evaluate the specific impact of a particular abnormality on the service life of the construction device, thereby providing data support for the development of maintenance plans and replacement strategies. Ultimately, through this comprehensive analysis method, the service life evaluation system of the construction device can provide more comprehensive and accurate service life prediction, providing strong technical support for the management decision of the construction device.

[0081] Specifically, when determining the abnormal deviation value corresponding to each historical abnormal operation data according to the historical abnormal operation time data set and the historical safe operation time data set, it includes:

[0082] calculating a historical abnormal average operation time according to the historical abnormal operation time data set;

[0083] calculating a historical safe average running time according to the historical safe running time dataset;

[0084] calculating an average running time difference between the historical abnormal average running time and the historical safe average running time;

[0085] obtaining an abnormal deviation value by subtracting the average running time difference from the historical abnormal running time corresponding to each historical abnormal running time data.

[0086] It can be seen that the abnormal deviation value is obtained by calculating the difference between the historical abnormal running data and the historical safe running data. This calculation method can reveal the potential risks of the construction device under certain conditions, and provide a quantitative reference basis for maintenance and replacement planning. In this way, the manager of the construction device can more accurately predict the remaining service life of the equipment, so as to reasonably arrange the maintenance work, avoid unnecessary downtime, and ensure the smooth progress of the construction project.

[0087] Specifically, when calculating the predicted remaining life cycle of the to-be-monitored construction device according to the abnormal deviation value corresponding to each historical abnormal running data, the method comprises:

[0088] calculating the predicted remaining life cycle of the to-be-monitored construction device according to the following formula:

[0089] T r = T i - (D avg + T safe );

[0090]

[0091] wherein, Tr represents the predicted remaining life cycle, Ti represents the initial life cycle, Davg represents the average deviation value of the historical abnormal running data, Tsafe represents the total of the historical safe running time, Rerr,i represents the running times of the i-th type of historical abnormal running data, Terr,i represents the running time corresponding to the i-th type of historical abnormal running data, Tavg,err represents the average running time of the historical abnormal running data, Tavg,safe represents the average running time of the historical safe running data, Tsafe,j represents the safe running time corresponding to the j-th type of historical safe data, n represents the number of types of the historical abnormal running data, and m represents the number of types of the historical safe running data.

[0092] It can be seen that the calculation method of the predicted remaining service life can comprehensively consider the influence of historical abnormal operation data and historical safe operation data, and provide a scientific basis for the maintenance and replacement of the construction device. In this way, the manager of the construction device can more accurately predict the remaining useful life of the equipment, so as to reasonably arrange the maintenance work, avoid unnecessary downtime, and ensure the smooth progress of the construction project.

[0093] Specifically, when comparing the data fluctuation value with the fluctuation threshold value and judging abnormal data according to the comparison result, the method comprises:

[0094] Filtering and retaining data with a data fluctuation value greater than the fluctuation threshold value, and excluding other data;

[0095] Calculating the abnormal value of each filtered data based on the Isolation Forest model;

[0096]

[0097] Where s(a) represents the abnormal value of data X, E(h((a)) represents the average path length of data X in all isolated trees in the Isolation Forest, and c(n) represents the average path length for normalizing the path length;

[0098] When the abnormal value of the filtered data is greater than 0.75, the data is determined to be abnormal data, and the type of the abnormal data is identified, and the same type of abnormal data is stored separately.

[0099] It can be seen that the Isolation Forest model is an anomaly detection algorithm based on tree structure, which evaluates the abnormality of data points by constructing multiple isolated trees. In this embodiment, the Isolation Forest model determines the abnormal value of data points by calculating their average path length in the tree. The shorter the path length, the easier the data point is isolated, and the higher the abnormality. In the calculation formula of the abnormal value s(a), E(h(a)) represents the average path length of the data point in all isolated trees, and c(n) is a constant for normalizing the path length, which is related to the number of trees n. By setting the threshold value of the abnormal value, abnormal data can be effectively identified and distinguished from normal data. Compared with traditional rule-based methods, this model-based anomaly detection method can more flexibly adapt to dynamic changes in data, improve the accuracy and efficiency of anomaly detection. Through this method, the service life evaluation system of the construction device can monitor the equipment state in real time, discover potential abnormal conditions in time, and take preventive measures to prolong the service life of the equipment and reduce maintenance costs.

[0100] Specifically, when determining the life influence factor according to the abnormal data, the method comprises:

[0101] The life influence factor is obtained by the following formula:

[0102]

[0103] wherein LIF represents the life impact factor, sa represents the abnormal value of the i-th abnormal data, ωa represents the weight of the a-th abnormal data, g represents the total number of all identified abnormal data.

[0104] It can be seen that the calculation of the life impact factor (LIF) takes into account the abnormal value and the corresponding weight of each abnormal data, which reflects the relative importance of different abnormal data on the life of the equipment. In this way, the impact of abnormal data on the life cycle of the construction device can be more accurately evaluated, providing a basis for formulating targeted maintenance measures. The determination of the weight ωa is usually based on expert experience or statistical analysis of historical data, ensuring the scientificity and practicality of the evaluation results. Finally, the manager of the construction device can optimize the maintenance plan according to the life impact factor, prioritizing abnormal situations that have a greater impact on the life of the equipment, thereby effectively extending the service life of the equipment, reducing the occurrence of unexpected failures, and improving construction efficiency and safety.

[0105] Specifically, judging whether to adjust the predicted remaining life cycle according to the life impact factor comprises:

[0106] comparing the life impact factor with an impact factor threshold value, and judging whether to adjust the predicted remaining life cycle according to the comparison result;

[0107] when the life impact factor is greater than or equal to the impact factor threshold value, it is determined that the predicted remaining life cycle is adjusted;

[0108] when the life impact factor is less than the impact factor threshold value, it is determined that the predicted remaining life cycle is not adjusted.

[0109] It can be seen that this judgment process can dynamically adjust the predicted remaining life cycle according to the actual operating conditions of the equipment. By real-time monitoring and analysis of abnormal data, the system can timely discover potential life risks of the equipment, so as to take corresponding maintenance measures.

[0110] Specifically, when it is determined that the predicted remaining life cycle is adjusted, the correlation between the abnormal data is calculated, comprising:

[0111]

[0112] wherein R represents the correlation between the abnormal data, sx and sy represent the abnormal values of any two abnormal data, and y > x, sp represents the average abnormal value of all abnormal data, and z represents the total number of identified abnormal data.

[0113] It can be seen that the correlation calculation formula R can evaluate the degree of association between different abnormal data. When the correlation between abnormal data is high, it indicates that these abnormalities may be caused by the same reason, which is of great significance for determining maintenance strategies and preventive measures. By analyzing the correlation of abnormal data, the life cycle assessment system of the construction device can more accurately identify potential systemic problems, so as to take targeted measures to solve the root cause and avoid the recurrence of similar problems.

[0114] Specifically, the adjustment coefficient of the predicted remaining life cycle is determined according to the correlation, and the predicted remaining life cycle is adjusted according to the adjustment coefficient, and when the adjusted remaining life cycle is obtained, it comprises:

[0115] An adjustment coefficient interval is set, and the adjustment coefficient interval includes a first adjustment coefficient, a second adjustment coefficient and a third adjustment coefficient;

[0116] The correlation ratio of the correlation and the correlation threshold value is calculated, and the adjustment coefficient is determined according to the correlation ratio;

[0117] When the correlation ratio is greater than 1.1 and less than or equal to 1.25, the first adjustment coefficient is selected to adjust the predicted remaining life cycle, and the product value of the first adjustment coefficient and the predicted remaining life cycle is taken as the adjusted remaining life cycle;

[0118] When the correlation ratio is greater than 1.25 and less than or equal to 1.4, the second adjustment coefficient is selected to adjust the predicted remaining life cycle, and the product value of the second adjustment coefficient and the predicted remaining life cycle is taken as the adjusted remaining life cycle;

[0119] When the correlation ratio is greater than 1.4, the third adjustment coefficient is selected to adjust the predicted remaining life cycle, and the product value of the third adjustment coefficient and the predicted remaining life cycle is taken as the adjusted remaining life cycle;

[0120] Wherein, the first adjustment coefficient is less than the second adjustment coefficient, and the second adjustment coefficient is greater than the third adjustment coefficient.

[0121] It can be seen that the design of the adjustment coefficient interval allows the system to flexibly adjust the predicted remaining life cycle according to the degree of correlation of abnormal data. The first adjustment coefficient is suitable for cases with low correlation ratio, meaning that the relevance between abnormal data is weak and may be caused by different factors, so the adjustment range is small. The second adjustment coefficient is suitable for moderate correlation, indicating that abnormal data may be partially caused by the same reason, and needs to be adjusted moderately. The third adjustment coefficient is used for highly correlated cases, indicating that abnormal data is likely caused by the same root cause, and needs to be adjusted to reflect potential systemic problems. In this way, the life cycle assessment system of the construction device can take appropriate maintenance measures according to the relevance of abnormal data to ensure stable operation and prolong the service life of the equipment. Ultimately, through real-time monitoring and dynamic adjustment, the system can provide more accurate and personalized life cycle management for the construction device, thereby improving the overall construction efficiency and safety.

[0122] Referring to Figure 2 In some embodiments of the present application, the present embodiment provides a life cycle assessment method for a construction device, comprising the following steps:

[0123] S100: store historical operation data of a construction device to be monitored;

[0124] S200: collect the historical operation data and analyze the historical operation data, calculate the predicted remaining life cycle of the construction device to be monitored based on the analysis results;

[0125] S300: obtain real-time operation data of the construction device to be monitored, and establish an operation data set to obtain a data fluctuation value according to the operation data set; wherein the operation data set includes current operation data of the construction device to be monitored and previous operation data of the construction device to be monitored;

[0126] S400: compare the data fluctuation value with a fluctuation threshold value, determine abnormal data according to the comparison result; further configured to determine a life influence factor according to the abnormal data, and determine whether to adjust the predicted remaining life cycle according to the life influence factor;

[0127] S500: when it is determined to adjust the predicted remaining life cycle, calculate the correlation between the abnormal data, determine the adjustment coefficient of the predicted remaining life cycle according to the correlation, adjust the predicted remaining life cycle according to the adjustment coefficient, and obtain the adjusted remaining life cycle.

[0128] It can be seen that the construction device life cycle evaluation method can realize real-time monitoring and evaluation of the operation state of the construction device, thereby providing a scientific basis for maintenance and management of the construction device. Through this method, equipment failure can be effectively prevented, unexpected downtime can be reduced, and construction efficiency can be improved.

[0129] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems or computer program products. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) having computer usable program code embodied therein.

[0130] The application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart block or blocks.

[0131] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction means, which implement the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart block or blocks.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart block or blocks.

[0133] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it. Although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered in the protection scope of the claims of the present application.

Claims

1. A system for life cycle assessment of a construction device, characterized in that Comprise: A historical database module configured to store historical operation data of a construction device to be monitored; An acquisition module configured to acquire the historical operation data, analyze the historical operation data, and calculate a predicted remaining service life of the construction device to be monitored based on an analysis result; Further configured to obtain real-time operation data of the construction device to be monitored, and establish an operation data set to obtain a data fluctuation value according to the operation data set; wherein the operation data set comprises current operation data of the construction device to be monitored and previous operation data of the construction device to be monitored; A judgment module configured to compare the data fluctuation value with a fluctuation threshold value, determine abnormal data according to a comparison result, further determine a service life influencing factor according to the abnormal data, and determine whether to adjust the predicted remaining service life according to the service life influencing factor; An adjustment module configured to, when it is determined to adjust the predicted remaining service life, calculate a correlation between the abnormal data, determine an adjustment coefficient of the predicted remaining service life according to the correlation, adjust the predicted remaining service life according to the adjustment coefficient, and obtain an adjusted remaining service life; When comparing the data fluctuation value with the fluctuation threshold value and determining abnormal data according to a comparison result, comprising: Filtering and retaining data with a data fluctuation value greater than a fluctuation threshold value, and excluding other data; Calculating an abnormal value of each filtered data based on an isolation forest model; ； Wherein s(a) represents an abnormal value of data X, E(h((a)) represents an average path length of data X in all isolated trees in the isolation forest, and c(n) represents an average path length for normalizing the path length; When the abnormal value of the filtered data is greater than 0.75, the data is determined to be abnormal data, and the type of the abnormal data is identified, and abnormal data of the same type is stored separately; When determining a service life influencing factor according to the abnormal data, comprising: The service life influencing factor is obtained by the following formula: ； Wherein LIF represents a service life influencing factor, sa represents an abnormal value of the i-th abnormal data, ωa represents a weight of the a-th abnormal data, and g represents a total number of all identified abnormal data.

2. The life cycle assessment system of a construction device according to claim 1, characterized in that, When analyzing the historical operation data and calculating a predicted remaining service life of the construction device to be monitored based on an analysis result, comprising: Dividing the historical operation data into historical abnormal operation data and historical safe operation data based on the analysis result; Classifying the historical abnormal operation data, and counting a first operation number of each type of historical abnormal operation data; Classifying the historical safe operation data, and counting a second operation number of each type of historical safe operation data; Constructing a historical abnormal operation time data set according to the historical abnormal operation data, determining a historical safe operation time data set according to the historical safe operation data, and determining an abnormal deviation value corresponding to each historical abnormal operation data according to the historical abnormal operation time data set and the historical safe operation time data set; calculating a predicted remaining service life of the to-be-monitored construction device according to the abnormal deviation value corresponding to each historical abnormal operation data.

3. The life cycle assessment system of a construction plant according to claim 2, characterized in that, When determining the abnormal deviation value corresponding to each historical abnormal operation data according to the historical abnormal operation time data set and the historical safe operation time data set, the method comprises: calculating a historical abnormal average operation time according to the historical abnormal operation time data set; calculating a historical safe average operation time according to the historical safe operation time data set; calculating an average operation time difference between the historical abnormal average operation time and the historical safe average operation time; obtaining an abnormal deviation value by subtracting the average operation time difference from the historical abnormal operation time corresponding to each historical abnormal operation time data.

4. The life cycle assessment system of a construction plant according to claim 2, characterized in that, When calculating a predicted remaining service life of the to-be-monitored construction device according to the abnormal deviation value corresponding to each historical abnormal operation data, the method comprises: calculating the predicted remaining service life of the to-be-monitored construction device according to the following formula: ； ； ； wherein, Tr represents the predicted remaining service life, Ti represents the initial service life, Davg represents the average deviation value of the historical abnormal operation data, Tsafe represents the total of the historical safe operation time, Rerr,i represents the operation times of the i-th type of historical abnormal operation data, Terr,i represents the operation time corresponding to the i-th type of historical abnormal operation data, Tavg,err represents the average operation time of the historical abnormal operation data, Tavg,safe represents the average operation time of the historical safe operation data, Tsafe,j represents the safe operation time corresponding to the j-th type of historical safe data, n represents the number of types of the historical abnormal operation data, and m represents the number of types of the historical safe operation data.

5. The life cycle assessment system of a construction plant according to claim 1, characterized in that, When determining whether to adjust the predicted remaining service life according to the life influence factor, the method comprises: comparing the life influence factor with an influence factor threshold value, and determining whether to adjust the predicted remaining service life according to the comparison result; when the life influence factor is greater than or equal to the influence factor threshold value, determining to adjust the predicted remaining service life; when the life influence factor is less than the influence factor threshold value, determining not to adjust the predicted remaining service life.

6. The life cycle assessment system of a construction plant according to claim 1, characterized in that, When determining to adjust the predicted remaining service life, the method for calculating the correlation between the abnormal data comprises: ； wherein, R represents the correlation between the abnormal data, sx and sy represent the abnormal values of any two abnormal data, and y > x, sp represents the average abnormal value of all abnormal data, and z represents the total number of the identified abnormal data.

7. The life cycle assessment system of a construction plant according to claim 1, characterized in that, When determining the adjustment coefficient of the predicted remaining service life according to the correlation, adjusting the predicted remaining service life according to the adjustment coefficient, and obtaining an adjusted remaining service life, the method comprises: setting an adjustment coefficient interval, wherein the adjustment coefficient interval comprises a first adjustment coefficient, a second adjustment coefficient and a third adjustment coefficient; calculating a correlation ratio of the correlation and a correlation threshold value, and determining the adjustment coefficient according to the correlation ratio. when the correlation ratio is greater than 1.1 and less than or equal to 1.25, a first adjustment coefficient is selected to adjust the predicted remaining service life, and a product value of the first adjustment coefficient and the predicted remaining service life is taken as the adjusted remaining service life; when the correlation ratio is greater than 1.25 and less than or equal to 1.4, a second adjustment coefficient is selected to adjust the predicted remaining service life, and a product value of the second adjustment coefficient and the predicted remaining service life is taken as the adjusted remaining service life; when the correlation ratio is greater than 1.4, a third adjustment coefficient is selected to adjust the predicted remaining service life, and a product value of the third adjustment coefficient and the predicted remaining service life is taken as the adjusted remaining service life; wherein the first adjustment coefficient is less than the second adjustment coefficient, and the second adjustment coefficient is greater than the third adjustment coefficient.

8. A method for life cycle assessment of a construction device, applied in a life cycle assessment system of a construction device according to any one of claims 1-7, characterized in that, comprising: storing historical operation data of a to-be-monitored construction device; acquiring the historical operation data and analyzing the historical operation data, and calculating a predicted remaining service life of the to-be-monitored construction device based on an analysis result; obtaining real-time operation data of the to-be-monitored construction device and establishing an operation data set according to the real-time operation data, and obtaining a data fluctuation value according to the operation data set; wherein the operation data set comprises current-time operation data and previous-time operation data of the to-be-monitored construction device; comparing the data fluctuation value with a fluctuation threshold value, and judging abnormal data according to a comparison result; further configured to determine a life influencing factor according to the abnormal data, and judge whether to adjust the predicted remaining service life according to the life influencing factor; when it is determined to adjust the predicted remaining service life, calculating a correlation between the abnormal data, determining an adjustment coefficient of the predicted remaining service life according to the correlation, adjusting the predicted remaining service life according to the adjustment coefficient, and obtaining an adjusted remaining service life.

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