Life cycle evaluation system and method for construction device

Abstract

The invention relates to the technical field of life prediction, and discloses a life cycle evaluation system and method for a construction device, and the system comprises a historical database module; the acquisition module is used for calculating the predicted residual life cycle of the to-be-monitored construction device based on the analysis result; obtaining a data fluctuation value according to the operation data set; wherein the operation data set comprises the operation data of the to-be-monitored construction device at the current moment and the operation data of the to-be-monitored construction device at the previous moment; the judgment module is configured to compare the data fluctuation value with a fluctuation threshold value and judge abnormal data according to a comparison result; judging whether to adjust the predicted residual life cycle or not according to the life influence factor; and the adjusting module is used for adjusting the predicted residual life cycle according to the adjusting coefficient to obtain an adjusted residual life cycle. Maintenance and replacement of the construction device can be more scientifically and reasonably arranged, accidental shutdown and production loss caused by equipment faults are avoided, and construction efficiency and economic benefits are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of life prediction, and in particular to a life cycle assessment system and method for a construction device. Background Art

[0002] In the field of modern construction, the performance and reliability of construction equipment are crucial to the success of the entire project. However, during use, construction equipment will experience a variety of complex working environments and conditions, which will have a significant impact on its life and performance. Traditional evaluation methods often focus on the initial performance test of the equipment, while ignoring the wear, aging and maintenance of the equipment during actual use. The impact on the life cycle.

[0003] Therefore, it is necessary to design a life cycle assessment system and method for construction equipment to solve the problems existing in the current technology. Summary of the invention

[0004] In view of this, the present invention proposes a life cycle assessment system and method for a construction device, aiming to solve the problem that the current technology focuses on the initial performance test of the device, but ignores the impact of wear, aging and maintenance of the device during actual use on the life cycle.

[0005] In one aspect, the present invention provides a life cycle assessment system for a construction device, comprising:

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

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

[0008] It is also configured to obtain real-time operation data of the construction device to be monitored, establish an operation data set, and 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 moment and the operation data of the construction device to be monitored at the previous moment;

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

[0010] The adjustment module is configured to calculate the correlation between the abnormal data when it is determined to adjust the predicted remaining life cycle, 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.

[0011] Furthermore, 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, including:

[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 the first operation quantity of each type of historical abnormal operation data;

[0014] Classifying the historical safety operation data, and counting the number of second operations of each type of historical safety operation data;

[0015] Construct a historical abnormal operation time data set according to the historical abnormal operation data; determine a historical safe operation time data set according to the historical safe operation data; and determine 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] The predicted remaining life cycle of the construction device to be monitored is calculated 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, it includes:

[0018] Calculate the historical abnormal average running time according to the historical abnormal running time data set;

[0019] Calculate the historical safety average running time according to the historical safety running time data set;

[0020] Calculate the average running time difference between the historical abnormal average running time and the historical safe average running time;

[0021] The historical abnormal running time corresponding to each of the historical abnormal running time data is subtracted from the average running time difference to obtain an abnormal deviation value.

[0022] Further, when calculating the predicted remaining life cycle of the construction device to be monitored according to the abnormal deviation value corresponding to each historical abnormal operation data, it includes:

[0023] The predicted remaining life cycle 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 life cycle, Ti represents the initial life cycle, Davg represents the deviation average of historical abnormal operation data, Tsafe represents the sum of historical safe operation time, Rerr,i represents the operation times of the i-th category of historical abnormal operation data, Terr,i represents the operation time corresponding to the i-th category of historical abnormal operation data, Tavg,err represents the average operation time of historical abnormal operation data, Tavg,safe represents the average operation time of historical safe operation data, Tsafe,j represents the safe operation time corresponding to the j-th category of historical safety data, n represents the number of categories of historical abnormal operation data, and m represents the number of categories of historical safety operation data.

[0027] Furthermore, the data fluctuation value is compared with the fluctuation threshold, and when abnormal data is determined according to the comparison result, it includes:

[0028] Filter out the data whose fluctuation value is greater than the fluctuation threshold and retain it, and remove other data;

[0029] Calculate the outlier value of each filtered data based on the isolation forest model;

[0030]

[0031] Where s(a) represents the outlier 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 used to standardize the path length;

[0032] When the outlier value of the screened 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 abnormal data of the same type is stored separately.

[0033] Furthermore, when determining the lifespan influencing factor according to the abnormal data, it includes:

[0034] The lifespan impact factor is obtained by the following formula:

[0035]

[0036] Among them, LIF represents the life impact factor, sa represents the outlier value of the i-th outlier data, ωa represents the weight of the a-th outlier data, and g represents the total number of all identified outlier data.

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

[0038] Comparing the life impact factor with the impact factor threshold, and determining whether to adjust the predicted remaining life cycle according to the comparison result;

[0039] When the life impact factor is greater than or equal to the impact factor threshold, determining to adjust the predicted remaining life cycle;

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

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

[0042]

[0043] Wherein, R represents the correlation between 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.

[0044] Further, determining an adjustment coefficient of the predicted remaining life cycle according to the correlation, and adjusting the predicted remaining life cycle according to the adjustment coefficient to obtain the adjusted remaining life cycle includes:

[0045] Setting an adjustment coefficient interval, wherein the adjustment coefficient interval includes a first adjustment coefficient, a second adjustment coefficient, and a third adjustment coefficient;

[0046] Calculating a correlation ratio between the correlation and a correlation threshold, and determining an adjustment coefficient 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 the product of the first adjustment coefficient and the predicted remaining life cycle is used 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 the product of the second adjustment coefficient and the predicted remaining life cycle is used as the adjusted remaining life cycle;

[0049] 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 used as the adjusted remaining life cycle;

[0050] The first adjustment coefficient is smaller than the second adjustment coefficient, and the second adjustment coefficient is larger than the third adjustment coefficient.

[0051] Compared with the prior art, the beneficial effect of the present invention is that the life cycle assessment system of the construction device provided by the present invention can effectively assess 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 life cycle of the construction device based on historical data, but also monitor the operating status of the device in real time, detect abnormal fluctuations in time, and adjust the prediction results accordingly, thereby providing a more accurate life cycle assessment. In this way, the maintenance and replacement of the construction device can be arranged more scientifically and reasonably, avoiding unexpected downtime and production losses caused by equipment failure, and improving construction efficiency and economic benefits.

[0052] In another aspect, the present invention also provides a life cycle assessment method for a construction device, comprising the following steps:

[0053] S100: storing historical operation data of the construction device to be monitored;

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

[0055] S300: Acquire real-time operation data of the construction device to be monitored, establish an operation data set, and 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 moment and the operation data of the construction device to be monitored at the previous moment;

[0056] S400: comparing the data fluctuation value with the fluctuation threshold, and judging abnormal data according to the comparison result; and further configured to determine a life impact factor according to the abnormal data, and judging whether to adjust the predicted remaining life cycle according to the life impact factor;

[0057] S500: When it is determined to adjust the predicted remaining life cycle, the correlation between the abnormal data is calculated, an 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 to obtain an adjusted remaining life cycle.

[0058] It is understandable that the life cycle assessment system and method of the above-mentioned construction equipment have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0060] Figure 1 A structural block diagram of a life cycle assessment system for a construction device provided by an embodiment of the present invention;

[0061] Figure 2 A flow chart of a life cycle assessment method for a construction device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0063] See also Figure 1 As shown, in some embodiments of the present application, this embodiment provides a life cycle assessment system for a construction device, including:

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

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

[0066] It is also configured to obtain real-time operation data of the construction device to be monitored, establish an operation data set, and 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 moment and the operation data of the construction device to be monitored at the previous moment;

[0067] A judgment module is configured to compare the data fluctuation value with the fluctuation threshold value, and judge abnormal data according to the comparison result; and is also configured to determine a life impact factor according to the abnormal data, and judge whether to adjust the predicted remaining life cycle according to the life impact factor;

[0068] The adjustment module is configured to calculate the correlation between the abnormal data when it is determined to adjust the predicted remaining life cycle, 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.

[0069] In this embodiment, the construction equipment to be monitored includes various types of construction machinery.

[0070] In the embodiments of this city, historical operation data refers to the operation records of the construction equipment to be monitored, that is, the operation records involved in the entire process from turning on the construction equipment to be monitored to turning off the construction equipment to be monitored, including but not limited to operating time, number of operations, fault records, maintenance records, etc.

[0071] It can be seen that the historical database module is used to store the historical operating data of the construction equipment to be monitored, so as to facilitate subsequent data analysis and calculation of the predicted remaining life cycle; the acquisition module is used to collect real-time operating data and compare and analyze it with the historical operating data to ensure the continuity and accuracy of the data; the judgment module can detect data anomalies in time by analyzing the data fluctuation value and the preset fluctuation threshold, so as to quickly respond to potential equipment failures or performance degradation; when abnormal data is identified, the system will analyze the data to determine the key factors affecting the life of the construction equipment, and then make necessary adjustments to the predicted remaining life cycle; the adjustment module is used to calculate the adjustment coefficient based on the correlation of the abnormal data, to ensure the accuracy of the prediction results, and provide a scientific basis for the maintenance and replacement of the construction equipment.

[0072] It is understandable that the life cycle assessment system for construction equipment provided in this embodiment can effectively assess the life cycle of construction equipment 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 life cycle of the construction equipment based on historical data, but also monitor the operating status of the equipment in real time, detect abnormal fluctuations in time, and adjust the prediction results accordingly, thereby providing a more accurate life cycle assessment. In this way, the maintenance and replacement of construction equipment can be arranged more scientifically and reasonably, avoiding unexpected downtime and production losses caused by equipment failure, and improving construction efficiency and economic benefits.

[0073] Specifically, 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, including:

[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 the first operation quantity of each type of historical abnormal operation data;

[0076] Classifying the historical safety operation data, and counting the number of second operations of each type of historical safety operation data;

[0077] Construct a historical abnormal operation time data set according to the historical abnormal operation data; determine a historical safe operation time data set according to the historical safe operation data; and determine 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] The predicted remaining life cycle of the construction device to be monitored is calculated according to the abnormal deviation value corresponding to each historical abnormal operation data.

[0079] In this embodiment, historical abnormal operation data refers to data records that do not meet normal operation standards due to various reasons during the operation of the construction device. These data records include, but are not limited to, abnormal operation conditions caused by equipment failure, operating errors, environmental factors or other unforeseen factors. 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 comparative analysis of historical abnormal operation time data sets and historical safe operation time data sets, the system can identify the specific time periods and conditions that lead to abnormal operation. For example, if a certain type of abnormal operation data appears frequently under specific environmental conditions, the system can infer that the environmental conditions may be one of the causes of the abnormality. The calculation of abnormal deviation values ​​can help evaluate the specific impact of specific abnormalities on the life of construction equipment, thereby providing data support for the formulation of maintenance plans and replacement strategies. Ultimately, through this comprehensive analysis method, the life cycle assessment system of construction equipment can provide more comprehensive and accurate life cycle predictions, providing strong technical support for the management decisions of construction equipment.

[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] Calculate the historical abnormal average running time according to the historical abnormal running time data set;

[0083] Calculate the historical safety average running time according to the historical safety running time data set;

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

[0085] The historical abnormal running time corresponding to each of the historical abnormal running time data is subtracted from the average running time difference to obtain an abnormal deviation value.

[0086] It can be seen that the abnormal deviation value is obtained by calculating the difference between the historical abnormal operation data and the historical safe operation data. This calculation method can reveal the potential risks of construction equipment under specific conditions and provide a quantitative reference for maintenance and replacement plans. In this way, managers of construction equipment can more accurately predict the remaining service life of the equipment, thereby reasonably arranging maintenance work, avoiding unnecessary downtime, and ensuring the smooth progress of construction projects.

[0087] Specifically, when calculating the predicted remaining life cycle of the construction device to be monitored according to the abnormal deviation value corresponding to each historical abnormal operation data, it includes:

[0088] The predicted remaining life cycle of the construction device to be monitored is calculated 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 deviation average of historical abnormal operation data, Tsafe represents the sum of historical safe operation time, Rerr,i represents the operation times of the i-th category of historical abnormal operation data, Terr,i represents the operation time corresponding to the i-th category of historical abnormal operation data, Tavg,err represents the average operation time of historical abnormal operation data, Tavg,safe represents the average operation time of historical safe operation data, Tsafe,j represents the safe operation time corresponding to the j-th category of historical safety data, n represents the number of categories of historical abnormal operation data, and m represents the number of categories of historical safety operation data.

[0092] It can be seen that the calculation method for predicting the remaining life cycle can comprehensively consider the impact of historical abnormal operation data and historical safe operation data, and provide a scientific basis for the maintenance and replacement of construction equipment. In this way, managers of construction equipment can more accurately predict the remaining service life of equipment, thereby reasonably arranging maintenance work, avoiding unnecessary downtime, and ensuring the smooth progress of construction projects.

[0093] Specifically, the data fluctuation value is compared with the fluctuation threshold, and when abnormal data is determined according to the comparison result, it includes:

[0094] Filter out the data whose fluctuation value is greater than the fluctuation threshold and retain it, and remove other data;

[0095] Calculate the outlier value of each filtered data based on the isolation forest model;

[0096]

[0097] Where s(a) represents the outlier 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 used to standardize the path length;

[0098] When the outlier value of the screened 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 abnormal data of the same type is stored separately.

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

[0100] Specifically, when determining the lifespan influencing factors based on the abnormal data, it includes:

[0101] The lifespan impact factor is obtained by the following formula:

[0102]

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

[0104] It can be seen that the calculation of the life impact factor (LIF) takes into account the outlier value and corresponding weight of each abnormal data, which reflects the relative importance of the impact of different abnormal data on the equipment life. In this way, the impact of abnormal data on the life cycle of construction equipment 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. Ultimately, managers of construction equipment can optimize maintenance plans based on the life impact factor, give priority to those abnormal situations that have a greater impact on the equipment life, 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 includes:

[0106] Comparing the life impact factor with the impact factor threshold, and determining 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, determining to adjust the predicted remaining life cycle;

[0108] When the life impact factor is less than the impact factor threshold, it is determined not to adjust the predicted remaining life cycle.

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

[0110] Specifically, when it is determined to adjust the predicted remaining life cycle, the correlation between the abnormal data is calculated, including:

[0111]

[0112] Wherein, R represents the correlation between 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 construction equipment 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, determining an adjustment coefficient of the predicted remaining life cycle according to the correlation, and adjusting the predicted remaining life cycle according to the adjustment coefficient to obtain the adjusted remaining life cycle includes:

[0115] Setting an adjustment coefficient interval, wherein the adjustment coefficient interval includes a first adjustment coefficient, a second adjustment coefficient, and a third adjustment coefficient;

[0116] Calculating a correlation ratio between the correlation and a correlation threshold, and determining an adjustment coefficient 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 of the first adjustment coefficient and the predicted remaining life cycle is used 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 of the second adjustment coefficient and the predicted remaining life cycle is used 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 used as the adjusted remaining life cycle;

[0120] The first adjustment coefficient is smaller than the second adjustment coefficient, and the second adjustment coefficient is larger 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 the abnormal data. The first adjustment coefficient is applicable to the case of a low correlation ratio, which means that the correlation between the abnormal data is weak and may be caused by different factors, so the adjustment range is small. The second adjustment coefficient is applicable to a moderate degree of correlation, indicating that the abnormal data may be partially caused by the same cause and need to be adjusted moderately. The third adjustment coefficient is used for highly correlated situations, indicating that the abnormal data is likely to be caused by the same root cause and requires a larger adjustment to reflect potential systemic problems. In this way, the life cycle assessment system of the construction device can take corresponding maintenance measures according to the correlation of the abnormal data to ensure the stable operation of the equipment and extend its service life. Finally, 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] See also Figure 2 As shown, in some embodiments of the present application, this embodiment provides a life cycle assessment method for a construction device, comprising the following steps:

[0123] S100: storing historical operation data of the construction device to be monitored;

[0124] S200: collecting the historical operation data, analyzing the historical operation data, and calculating the predicted remaining life cycle of the construction device to be monitored based on the analysis result;

[0125] S300: Acquire real-time operation data of the construction device to be monitored, establish an operation data set, and 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 moment and the operation data of the construction device to be monitored at the previous moment;

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

[0127] S500: When it is determined to adjust the predicted remaining life cycle, the correlation between the abnormal data is calculated, an 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 to obtain an adjusted remaining life cycle.

[0128] It can be seen that the life cycle assessment method of the construction device can realize real-time monitoring and evaluation of the operating status of the construction device, thereby providing a scientific basis for the 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 the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0130] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, 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 device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A life cycle assessment system for a construction device, characterized in that: include: A historical database module is configured to store historical operation data of the construction device to be monitored; A collection module is configured to collect the historical operation data, analyze the historical operation data, and calculate the predicted remaining life cycle of the construction device to be monitored based on the analysis result; It is also configured to obtain real-time operation data of the construction device to be monitored, establish an operation data set, and 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 moment and the operation data of the construction device to be monitored at the previous moment; A judgment module is configured to compare the data fluctuation value with the fluctuation threshold value, and judge abnormal data according to the comparison result; and is also configured to determine a life impact factor according to the abnormal data, and judge whether to adjust the predicted remaining life cycle according to the life impact factor; The adjustment module is configured to calculate the correlation between the abnormal data when it is determined to adjust the predicted remaining life cycle, 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.

2. The life cycle assessment system for construction equipment according to claim 1, characterized in that: Analyzing the historical operation data and calculating the predicted remaining life cycle of the construction device to be monitored based on the analysis result includes: 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 the first operation quantity of each type of historical abnormal operation data; Classifying the historical safety operation data, and counting the number of second operations of each type of historical safety operation data; Construct a historical abnormal operation time data set according to the historical abnormal operation data; determine a historical safe operation time data set according to the historical safe operation data; and determine 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; The predicted remaining life cycle of the construction device to be monitored is calculated according to the abnormal deviation value corresponding to each historical abnormal operation data.

3. The life cycle assessment system for construction equipment 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, it includes: Calculate the historical abnormal average running time according to the historical abnormal running time data set; Calculate the historical safety average running time according to the historical safety running time data set; Calculate the average running time difference between the historical abnormal average running time and the historical safe average running time; The historical abnormal running time corresponding to each of the historical abnormal running time data is subtracted from the average running time difference to obtain an abnormal deviation value.

4. The life cycle assessment system for construction equipment according to claim 2, characterized in that: When calculating the predicted remaining life cycle of the construction device to be monitored according to the abnormal deviation value corresponding to each historical abnormal operation data, it includes: The predicted remaining life cycle of the construction device to be monitored is calculated according to the following formula: T r =T i -(D avg +T safe ); Wherein, Tr represents the predicted remaining life cycle, Ti represents the initial life cycle, Davg represents the deviation average of historical abnormal operation data, Tsafe represents the sum of historical safe operation time, Rerr,i represents the operation times of the i-th category of historical abnormal operation data, Terr,i represents the operation time corresponding to the i-th category of historical abnormal operation data, Tavg,err represents the average operation time of historical abnormal operation data, Tavg,safe represents the average operation time of historical safe operation data, Tsafe,j represents the safe operation time corresponding to the j-th category of historical safety data, n represents the number of categories of historical abnormal operation data, and m represents the number of categories of historical safety operation data.

5. The life cycle assessment system for construction equipment according to claim 1, characterized in that: The data fluctuation value is compared with the fluctuation threshold, and abnormal data is determined according to the comparison result, including: Filter out the data whose fluctuation value is greater than the fluctuation threshold and retain it, and remove other data; Calculate the outlier value of each filtered data based on the isolation forest model; Where s(a) represents the outlier 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 used to standardize the path length; When the outlier value of the screened 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 abnormal data of the same type is stored separately.

6. The life cycle assessment system for construction equipment according to claim 1, characterized in that: When determining the lifespan influencing factors based on the abnormal data, it includes: The lifespan impact factor is obtained by the following formula: Among them, LIF represents the life impact factor, sa represents the outlier value of the i-th outlier data, ωa represents the weight of the a-th outlier data, and g represents the total number of all identified outlier data.

7. The life cycle assessment system for construction equipment according to claim 1, characterized in that: Determining whether to adjust the predicted remaining life cycle according to the life impact factor includes: Comparing the life impact factor with the impact factor threshold, and determining whether to adjust the predicted remaining life cycle according to the comparison result; When the life impact factor is greater than or equal to the impact factor threshold, determining to adjust the predicted remaining life cycle; When the life impact factor is less than the impact factor threshold, it is determined not to adjust the predicted remaining life cycle.

8. The life cycle assessment system for construction equipment according to claim 1, characterized in that: When it is determined to adjust the predicted remaining life cycle, the correlation between the abnormal data is calculated, including: Wherein, R represents the correlation between 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.

9. The life cycle assessment system for construction equipment according to claim 1, characterized in that: Determining an adjustment coefficient of the predicted remaining life cycle according to the correlation, and adjusting the predicted remaining life cycle according to the adjustment coefficient to obtain the adjusted remaining life cycle, including: Setting an adjustment coefficient interval, wherein the adjustment coefficient interval includes a first adjustment coefficient, a second adjustment coefficient, and a third adjustment coefficient; Calculating a correlation ratio between the correlation and a correlation threshold, and determining an adjustment coefficient according to the correlation ratio; 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 of the first adjustment coefficient and the predicted remaining life cycle is used as the adjusted remaining life cycle; 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 of the second adjustment coefficient and the predicted remaining life cycle is used as the adjusted remaining life cycle; 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 used as the adjusted remaining life cycle; The first adjustment coefficient is smaller than the second adjustment coefficient, and the second adjustment coefficient is larger than the third adjustment coefficient.

10. A method for life cycle assessment of a construction device, applied to a life cycle assessment system of a construction device as claimed in any one of claims 1 to 9, characterized in that: include: Storing historical operation data of the construction equipment to be monitored; Collecting the historical operation data, analyzing the historical operation data, and calculating the predicted remaining life cycle of the construction device to be monitored based on the analysis results; Acquire real-time operation data of the construction device to be monitored, establish an operation data set, and 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 moment and the operation data of the construction device to be monitored at the previous moment; Comparing the data fluctuation value with the fluctuation threshold, and determining abnormal data according to the comparison result; It is also configured to determine a life impact factor according to the abnormal data, and determine whether to adjust the predicted remaining life cycle according to the life impact factor; When it is determined to adjust the predicted remaining life cycle, the correlation between the abnormal data is calculated, 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 to obtain the adjusted remaining life cycle.

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