Monitoring method and system for enterprise project management

Abstract

The invention is suitable for the technical field of enterprise project management and equipment maintenance optimization, and provides a monitoring method and system for enterprise project management, and the method comprises the steps: obtaining an initial maintenance period of target equipment in a specified construction project, and the load data of a plurality of key monitoring regions of the target equipment in the initial maintenance period; according to the method, intelligent analysis is carried out on a plurality of key monitoring areas of the target equipment in the initial maintenance period, and the initial maintenance period is dynamically adjusted according to the time dimension difference between the abnormal load curve and the standard load curve of the target equipment. According to the method, potential abnormal conditions in equipment operation can be accurately identified, and the maintenance period can be timely optimized according to the actual load change, so that the phenomenon of excessive maintenance or delayed maintenance is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise project management and equipment maintenance optimization, and in particular relates to a monitoring method and system for enterprise project management. Background Art

[0002] As enterprise project management becomes increasingly complex, the accuracy and efficiency of equipment maintenance have become important factors in improving project management effectiveness. Traditional equipment maintenance cycles rely on fixed time intervals. Regardless of whether the equipment is in optimal operating condition, it is inspected and maintained according to the preset cycle. This fixed-cycle management method may lead to excessive or delayed maintenance of equipment in some cases, affecting the operating stability and service life of the equipment. Moreover, the working conditions of equipment vary under different construction environments and loads, and traditional methods fail to fully consider the impact of these dynamic changes on equipment maintenance.

[0003] In the prior art, although there are methods to evaluate the operating status of equipment by monitoring the load data and working status of the equipment, most methods still fail to achieve dynamic adjustment of the maintenance cycle. Most equipment maintenance plans still set the cycle based on historical data, experience or predetermined maintenance standards, and cannot make timely adjustments based on the current working status of the equipment. The limitations of these methods are that they fail to fully combine the real-time data generated by the equipment during actual operation, making it difficult to flexibly respond to the needs of equipment performance fluctuations and load changes.

[0004] Therefore, the existing technology has the following defects: first, the setting of the equipment maintenance cycle lacks flexibility and fails to be dynamically adjusted according to the actual working status of the equipment; second, the traditional method fails to effectively utilize the real-time monitoring data of the equipment to evaluate and optimize the maintenance cycle, resulting in the maintenance cycle being too long or too short, and unable to accurately match the actual needs of the equipment; finally, the existing technology generally ignores the importance of equipment load changes to the optimization of the maintenance cycle, and fails to adjust the maintenance plan in time according to load fluctuations. Summary of the invention

[0005] The purpose of the present invention is to provide a monitoring method and system for enterprise project management, aiming to solve the problems raised in the background technology.

[0006] The present invention is implemented in a monitoring method for enterprise project management, the method comprising: Obtain the initial maintenance cycle of the target equipment in the specified construction project, as well as the load data of several key monitoring areas of the target equipment during the initial maintenance cycle; Intelligently analyze load data and filter out all abnormal load data for each key monitoring area when carrying items within the specified load range during the initial maintenance cycle; For the abnormal load data of each key monitoring area, generate a curve of abnormal load value change over time; Retrieve the preset reference standard model, obtain the standard load value change curve of each key monitoring area when carrying items within the specified load range, compare and analyze the abnormal load value change curve of each key monitoring area with the standard load value change curve, and calculate the deviation degree value of the average slope between the two; According to the calculated deviation degree value, a correction factor is generated, and based on the correction factor, the initial maintenance cycle is dynamically adjusted to obtain an optimized maintenance cycle.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the step of intelligently parsing the load data and screening out all abnormal load data of each key monitoring area when carrying items within a specified load range during the initial maintenance period includes: Receive and store the load data of the target equipment during the initial maintenance cycle, and perform noise filtering and preprocessing on the load data; According to the preset load range standard, analyze the load data of each key monitoring area and filter out abnormal load data that exceeds the preset load range when carrying items within the specified load range; The screened abnormal load data are organized into a data table, which includes the abnormal load occurrence time and load value of each key monitoring area.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, for the abnormal load data of each key monitoring area, the step of generating a time-varying abnormal load value change curve includes: Extract the abnormal load occurrence time and load value of each key monitoring area from the abnormal load data and sort them in chronological order; Correlate the abnormal load occurrence time and load value of each key monitoring area to generate the corresponding load value change curve; Abnormal load data is smoothed to reduce fluctuations and ensure a smoother load value change curve.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the reference standard model refers to a reference model established based on historical operating data, equipment usage specifications and industry standards, for different key monitoring areas of the target equipment, under normal operating conditions, when carrying items in different load ranges, and the load change law that changes over time during the initial maintenance cycle.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the steps of calling a preset reference standard model, obtaining a standard load value change curve of each key monitoring area when carrying items within a specified load range, comparing and analyzing the abnormal load value change curve of each key monitoring area with the standard load value change curve, and calculating the deviation degree value of the average slope of the two include: Retrieve the preset reference standard model and generate the corresponding standard load value change curve according to the load change law of each key monitoring area over time during the initial maintenance cycle; Compare the abnormal load value change curve with the standard load value change curve of each key monitoring area to ensure that the time axis is consistent and the load values ​​are comparable; The average slope of the abnormal load value change curve and the standard load value change curve is calculated to determine the degree of deviation between the two.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the steps of generating a correction factor according to the calculated deviation degree value, and dynamically adjusting the initial maintenance cycle based on the correction factor to obtain the optimized maintenance cycle include: Calculate the average value of the deviation degree corresponding to all key monitoring areas and set it as the correction factor; Retrieve the preset maintenance cycle optimization formula, and dynamically adjust the initial maintenance cycle in combination with the correction factor to obtain the optimized maintenance cycle; Apply the optimized maintenance cycle to the maintenance of target equipment in designated construction projects.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the maintenance cycle optimization formula is: , where T optimized Refers to the optimized maintenance cycle, T initial Refers to the initial maintenance cycle, F adjust refers to the correction factor, and K refers to the adjustment coefficient of the correction factor; In the maintenance cycle optimization formula, , where n refers to the total number of key monitoring areas, A i It refers to the average slope of the abnormal load value change curve of the i-th key monitoring area, B i Refers to the average slope of the standard load value change curve of the i-th key monitoring area, It refers to the deviation degree of the average slope between the abnormal load value change curve and the standard load value change curve of the i-th key monitoring area.

[0013] A monitoring system for enterprise project management, the system comprises: a data acquisition module, a load data screening module, an abnormal curve generation module, a curve comparison module and a maintenance cycle optimization module, wherein: A data acquisition module is used to acquire the initial maintenance cycle of the target equipment in the specified construction project, and the load data of several key monitoring areas of the target equipment during the initial maintenance cycle; The load data screening module is used to intelligently analyze the load data and screen out all abnormal load data when each key monitoring area carries items within the specified load range during the initial maintenance cycle; An abnormal curve generation module is used to generate an abnormal load value change curve that changes over time for abnormal load data in each key monitoring area; The curve comparison module is used to call the preset reference standard model, obtain the standard load value change curve of each key monitoring area when carrying items within the specified load range, compare and analyze the abnormal load value change curve of each key monitoring area with the standard load value change curve, and calculate the deviation degree value of the average slope between the two; The reference standard model refers to a reference model established based on historical operating data, equipment usage specifications and industry standards, for different key monitoring areas of the target equipment, under normal operating conditions, when carrying items of different load ranges, and the load variation law that changes over time during the initial maintenance cycle; The maintenance cycle optimization module is used to generate a correction factor according to the calculated deviation degree value, and dynamically adjust the initial maintenance cycle based on the correction factor to obtain an optimized maintenance cycle.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the load data screening module specifically includes: A load data preprocessing unit, used for receiving and storing the load data of the target device in the initial maintenance cycle, and performing noise filtering and preprocessing on the load data; An abnormal load data screening unit is used to analyze the load data of each key monitoring area according to a preset load range standard, and screen out abnormal load data that exceeds the preset load range when carrying items within a specified load range; The abnormal load data sorting unit is used to sort the screened abnormal load data into a data table, which includes the abnormal load occurrence time and load value of each key monitoring area.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the abnormal curve generation module specifically includes: An abnormal load data extraction unit is used to extract the abnormal load occurrence time and load value of each key monitoring area from the abnormal load data and sort them in chronological order; A load value change curve generating unit, used for associating the abnormal load occurrence time of each key monitoring area with the load value, and generating a corresponding load value change curve; The curve smoothing processing unit is used to smooth abnormal load data to reduce fluctuations and ensure that the load value change curve is more stable.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention intelligently analyzes multiple key monitoring areas of the target equipment during the initial maintenance cycle, and dynamically adjusts the initial maintenance cycle based on the time dimension difference between its abnormal load curve and the standard load curve. This method can not only accurately identify potential abnormal conditions in equipment operation, but also timely optimize the maintenance cycle according to actual load changes, thereby avoiding excessive maintenance or delayed maintenance.

[0017] By calculating the degree of deviation in each key monitoring area and combining it with the preset maintenance cycle optimization formula, it is possible to ensure the consistency of the working state with the standard state while maintaining the normal operation of the equipment, thereby maximizing the efficiency and scientificity of equipment maintenance. In addition, the optimized maintenance cycle has strong flexibility and can better adapt to changes in equipment load and the actual needs of construction projects, providing more forward-looking and effective support for project management, and significantly improving resource utilization and equipment operation stability during the construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a method provided by an embodiment of the present invention; Figure 2 A flow chart of preprocessing and screening load data in the method provided in an embodiment of the present invention; Figure 3 A flow chart of generating an abnormal load value change curve of a key monitoring area in the method provided in an embodiment of the present invention; Figure 4 A flow chart of comparing and analyzing an abnormal load value change curve of a key monitoring area with a standard load value change curve in a method provided in an embodiment of the present invention; Figure 5 A flow chart for optimizing the initial maintenance cycle in the method provided in an embodiment of the present invention; Figure 6 An application architecture diagram of a system provided by an embodiment of the present invention; Figure 7 A structural block diagram of a load data screening module in a system provided by an embodiment of the present invention; Figure 8 This is a structural block diagram of an abnormal curve generation module in the system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0021] Specifically, a monitoring method for enterprise project management includes the following steps: Step S100, obtaining an initial maintenance period of a target device in a specified construction project, and load data of several key monitoring areas of the target device during the initial maintenance period.

[0022] In the embodiments of the present invention, the target equipment generally refers to mechanical equipment used to perform the main tasks in the construction project, such as cranes, excavators, bulldozers, concrete mixers, drilling rigs, etc. These equipment play a key role in the entire construction cycle and require regular maintenance and upkeep. The preferred target equipment is those with large load capacity, long-term operation and involving complex mechanical load changes, which can effectively reflect the use and load characteristics of the equipment.

[0023] Designated construction projects usually refer to specific construction projects, such as large-scale infrastructure construction, road paving, bridge construction, mining and other projects. These projects are characterized by long cycles, complex tasks and high dependence on equipment. Dynamic optimization management of maintenance cycles for target equipment in these construction projects helps improve equipment efficiency, reduce failure risks and reduce maintenance costs.

[0024] The initial maintenance cycle is set based on the equipment's usage specifications, historical data, and industry standards. The initial maintenance cycle is usually set based on the instructions and maintenance cycles recommended by the equipment manufacturer, and is appropriately adjusted in combination with the equipment's operating conditions in the current construction project. For example, if the equipment is under certain high-load and frequent use conditions, its initial maintenance cycle may be shorter, while under low-load and light use conditions, the initial maintenance cycle can be appropriately extended. The initial maintenance cycle is generally set by the equipment management department based on the specific conditions of the equipment and the needs of the construction project before the equipment is put into construction.

[0025] Several key monitoring areas of the target equipment can be those key parts of the equipment that bear heavy loads during equipment operation and are susceptible to wear or damage. For cranes, key monitoring areas may include booms, hooks, hydraulic systems, engine loads, etc.; for excavators, key monitoring areas may include buckets, crawlers, engines, hydraulic systems, etc. These key monitoring areas are important guarantees for equipment performance and safety, so they need to be monitored intensively.

[0026] Load data comes from the sensors and monitoring systems of the target equipment. The equipment will collect various load data in real time during operation. For example, sensors can monitor the equipment's working load, operating speed, hydraulic pressure, current, voltage and other data. These data can reflect the load status of each key monitoring area of ​​the equipment, especially its working performance under high load conditions. The specific content may include the working time, load value, load change trend, etc. of each monitoring area of ​​the equipment.

[0027] Furthermore, the monitoring method for enterprise project management also includes the following steps: Step S200, intelligently analyzing the load data to filter out all abnormal load data when each key monitoring area carries items within a specified load range during the initial maintenance cycle.

[0028] Specifically, Figure 2 A flow chart for preprocessing and screening load data is shown.

[0029] Among them, intelligently analyzing the load data and screening out all abnormal load data of each key monitoring area when carrying items within the specified load range during the initial maintenance cycle specifically includes the following steps: Step S201, receiving and storing the load data of the target device in the initial maintenance cycle, and performing noise filtering and preprocessing on the load data; Step S202, analyzing the load data of each key monitoring area according to the preset load range standard, and screening out abnormal load data that exceeds the preset load range when carrying items within the specified load range; Step S203, arranging the screened abnormal load data into a data table, including the abnormal load occurrence time and load value of each key monitoring area.

[0030] In an embodiment of the present invention, the purpose of noise filtering and preprocessing the load data is to remove irrelevant or interfering signals in the original data to ensure that the subsequent analysis results are more accurate and reliable. Noise may come from many aspects, such as sensor failure, external environmental interference, or errors in the data acquisition process. Therefore, when processing load data, it is first necessary to denoise the data, such as using a filtering algorithm to remove high-frequency noise, or smoothing data points through an algorithm to extract the true change trend of the load data. The preprocessing process also includes normalization and standardization of the data to eliminate the differences in data between different devices or sensors, so that the data can be compared and analyzed under a unified standard. The significance of preprocessing is to reduce data errors and improve analysis accuracy, thereby better supporting subsequent fault prediction and maintenance cycle optimization.

[0031] Carrying a specified load range refers to the items or tasks that the target equipment needs to carry during operation, and its load value is within a preset range. This range is usually set according to the design capacity and usage requirements of the target equipment. For example, when a crane is lifting cargo, the maximum load is the designed load capacity of the equipment, and the minimum load range is the lowest load of the equipment when it is unloaded. This range should be representative and match the load conditions faced by the equipment in actual construction. Setting a reasonable load range can ensure that the equipment operates within its performance range during use, thereby avoiding inefficiency caused by overload or underload.

[0032] Data outside the preset load range is considered abnormal load data because these load conditions represent the equipment operating under certain abnormal conditions. Exceeding the load range may mean that the equipment is facing too high a workload during operation, or the load is too low, resulting in the equipment not being able to operate efficiently. The appearance of abnormal load data is usually a sign of equipment failure, operating error, or abnormal working environment. If the equipment continues to operate in an overloaded or underloaded state, it may cause equipment damage, failure, or even safety accidents, so these abnormal conditions must be identified and handled in a timely manner.

[0033] Furthermore, the monitoring method for enterprise project management also includes the following steps: Step S300: generating a time-varying abnormal load value variation curve for abnormal load data of each key monitoring area.

[0034] Specifically, Figure 3 A flow chart for generating an abnormal load value variation curve for a key monitoring area is shown.

[0035] Among them, for the abnormal load data of each key monitoring area, generating an abnormal load value change curve that changes over time specifically includes the following steps: Step S301, extracting the abnormal load occurrence time and load value of each key monitoring area from the abnormal load data, and arranging them in chronological order; Step S302, associating the abnormal load occurrence time of each key monitoring area with the load value, and generating a corresponding load value change curve; Step S303, smoothing the abnormal load data to reduce fluctuations and ensure that the load value change curve is more stable.

[0036] In an embodiment of the present invention, in the process of generating an abnormal load value change curve, it is first necessary to extract the abnormal load occurrence time and load value of each key monitoring area from the abnormal load data. These data are usually obtained in real time from the equipment monitoring system, identifying the load exceeding the standard at a specific time node. The abnormal load events in each key monitoring area need to be sorted in chronological order to ensure the temporal consistency of the data in subsequent analysis. The purpose of this step is to ensure that the relevant abnormal load information is screened out from the original data and arranged according to the time axis to facilitate the analysis of its changing trend.

[0037] Next, the time of occurrence of abnormal load in each key monitoring area needs to be associated with the load value to generate the corresponding load value change curve. This process can not only reflect the absolute value of the load change, but also show the trend of the load change. The load value change curve is generated by plotting the extracted abnormal load data on the time axis, and each point on the curve represents the load condition at a certain moment. These curves reflect the fluctuations in the load of the equipment in different time periods, and by comparing them with the standard load value curve, the deviation of abnormal fluctuations in the equipment load from normal operation can be identified.

[0038] In order to make the load value change curve smoother and reduce fluctuations in the data, further smoothing is essential. Abnormal load data may contain short-term and drastic fluctuations due to factors such as sensor errors and external environmental interference. These fluctuations will affect the smoothness of the curve, making it difficult to accurately reflect the load change trend of the equipment. Therefore, by using smoothing algorithms (such as sliding average method, weighted average method, etc.) to process the data, unnecessary short-term fluctuations can be effectively removed, thereby obtaining a smoother and continuous load value change curve. The smoothed curve can more clearly show the long-term change trend of the equipment load, help operation and maintenance personnel better identify potential load anomalies of the equipment, and provide a basis for supporting further fault diagnosis and maintenance decisions.

[0039] Furthermore, the monitoring method for enterprise project management also includes the following steps: Step S400, call up the preset reference standard model, obtain the standard load value change curve of each key monitoring area when carrying items within the specified load range, compare and analyze the abnormal load value change curve of each key monitoring area with the standard load value change curve, and calculate the deviation degree value of the average slope between the two.

[0040] Specifically, Figure 4 A flow chart is shown for comparing and analyzing the abnormal load value change curve and the standard load value change curve of the key monitoring area.

[0041] The preset reference standard model is retrieved to obtain the standard load value change curve of each key monitoring area when carrying items within a specified load range, and the abnormal load value change curve of each key monitoring area is compared and analyzed with the standard load value change curve to calculate the deviation degree value of the average slope of the two. Specifically, the following steps are included: Step S401, calling a preset reference standard model, and generating a corresponding standard load value change curve according to the load change law of each key monitoring area over time during the initial maintenance cycle; Step S402, comparing the abnormal load value change curve of each key monitoring area with the standard load value change curve to ensure that the time axis is consistent and the load values ​​are comparable; Step S403, calculating the average slope of the abnormal load value change curve and the standard load value change curve, and determining the deviation degree value between the two.

[0042] The reference standard model refers to a reference model established based on historical operating data, equipment usage specifications and industry standards, for different key monitoring areas of the target equipment, under normal operating conditions, when carrying items of different load ranges, and the load change law that changes over time during the initial maintenance cycle.

[0043] In an embodiment of the present invention, the process of generating a standard load value change curve is to first extract the load change law of each key monitoring area from the reference standard model. This reference standard model is established based on the operating data, equipment usage specifications and industry standards accumulated by the target equipment during historical operation, and is intended to reflect the load change characteristics of the equipment under normal operating conditions and when carrying items in different load ranges. During the initial maintenance cycle, the load changes of the target equipment should conform to certain regularities. By analyzing these historical data, the standard load value change curves within the specified load range can be inferred. These curves describe the normal trend of load changes over time under predetermined operating conditions. In this way, the standard load value change curve becomes the basis for comparison with the actual load value changes of the equipment.

[0044] Comparing the abnormal load value change curve with the standard load value change curve of each key monitoring area is intended to find out the degree to which the equipment deviates from the normal operating law during actual operation. The abnormal load value change curve reflects the abnormal conditions that occurred during the initial maintenance cycle of the equipment. These abnormalities may be caused by factors such as equipment failure, changes in operating conditions or external interference. The standard load value change curve is the expected operating trajectory of the equipment when there are no abnormal conditions. By comparing these two curves, it is possible to clearly identify which load changes deviate from the normal trajectory, thereby providing a basis for equipment maintenance and adjustment.

[0045] The significance of calculating the average slope of the abnormal load value change curve and the standard load value change curve and determining the deviation degree value is that it quantifies the deviation of the equipment in the load change trend. The slope reflects the rate of load change, that is, the speed of increase or decrease of the equipment load over time. If the load change slope of the equipment deviates from the slope of the standard model, it means that the equipment may be in an abnormal operating state. Load changes that are too fast or too slow may indicate potential failures or equipment instability. By calculating these deviations, the operating health of the equipment can be evaluated, providing an important quantitative indicator for judging whether the equipment needs to adjust the maintenance cycle or perform troubleshooting. This deviation degree value provides a quantitative basis for the subsequent dynamic adjustment of the maintenance cycle, thereby optimizing the management and maintenance strategy of the equipment.

[0046] Furthermore, the monitoring method for enterprise project management also includes the following steps: Step S500, generating a correction factor according to the calculated deviation value, and dynamically adjusting the initial maintenance cycle based on the correction factor to obtain an optimized maintenance cycle.

[0047] Specifically, Figure 5 A flow chart for optimizing the initial maintenance cycle is shown.

[0048] Among them, according to the calculated deviation degree value, a correction factor is generated, and based on the correction factor, the initial maintenance cycle is dynamically adjusted to obtain the optimized maintenance cycle, which specifically includes the following steps: Step S501, calculating the average value of the deviation degree values ​​corresponding to all key monitoring areas, and setting it as a correction factor; Step S502, calling a preset maintenance cycle optimization formula, and dynamically adjusting the initial maintenance cycle in combination with a correction factor to obtain an optimized maintenance cycle; Step S503: Apply the optimized maintenance cycle to the maintenance of the target equipment in the specified construction project.

[0049] The maintenance cycle optimization formula is: , where T optimizedRefers to the optimized maintenance cycle, T initial Refers to the initial maintenance cycle, F adjust refers to the correction factor, and K refers to the adjustment coefficient of the correction factor; In the maintenance cycle optimization formula, , where n refers to the total number of key monitoring areas, A i It refers to the average slope of the abnormal load value change curve of the i-th key monitoring area, B i Refers to the average slope of the standard load value change curve of the i-th key monitoring area, It refers to the deviation degree of the average slope between the abnormal load value change curve and the standard load value change curve of the i-th key monitoring area.

[0050] In an embodiment of the present invention, the average value of the degree of deviation of all key monitoring areas is calculated and set as a correction factor, mainly for the purpose of comprehensively evaluating the overall operating status of the equipment. The load change of each key monitoring area may be affected by multiple factors. By calculating the average value of the degree of deviation of all areas, occasional anomalies in a single monitoring area can be eliminated, and a more objective and comprehensive reflection of the equipment operating status can be obtained. In this way, the correction factor can not only take into account the overall operating status of the equipment, but also effectively adjust the initial maintenance cycle that is closely related to the health status of the target equipment, avoid the abnormality of a single area affecting the adjustment of the overall cycle, and make the maintenance cycle more in line with the actual needs of the equipment.

[0051] By optimizing and shortening the initial maintenance cycle in this way, the operating efficiency and maintenance benefits of the equipment can be significantly improved. Traditional equipment maintenance cycles are often fixed and do not take into account load changes and abnormal conditions that may occur during the actual operation of the equipment. This fixed cycle may lead to over-maintenance or under-maintenance of the equipment at certain stages, resulting in waste of resources or risk of equipment failure. Dynamic adjustment of the maintenance cycle can be adjusted according to the load deviation and state changes of the equipment in actual operation, making maintenance work more flexible and efficient, avoiding excessive or insufficient maintenance work, and ultimately improving the overall operating stability of the equipment and extending its service life. In addition, this adjustment can also reduce maintenance costs and improve the economic efficiency of equipment operation.

[0052] Let's take a detailed example: suppose that in a construction project, the initial maintenance cycle of the target equipment is 15 days. During this period, after intelligent analysis by the monitoring system, the deviation values ​​of multiple key monitoring areas are 0.15, -0.05, 0.10 and 0.03 respectively. Based on these deviation values, we first calculate the average deviation value of all key monitoring areas. This average value is 0.08, which is set as the correction factor.

[0053] Next, call the preset maintenance cycle optimization formula. Assuming that the adjustment coefficient set in the formula is 1.2, then multiply the correction factor by the adjustment coefficient to get 1.08 (the product of the correction factor and the coefficient). Then divide 15 days by 1.08 to get 13.89 days. For the convenience of actual operation, this result can be rounded to 14 days.

[0054] Therefore, the optimized maintenance cycle is 14 days, which reduces the maintenance cycle by 1 day compared to the initial 15 days.

[0055] Through this adjustment, the maintenance cycle of the target equipment has been dynamically optimized, avoiding the problem of excessive work or untimely maintenance of the equipment caused by too long maintenance intervals. At the same time, it can also effectively improve the operating efficiency of the equipment and reduce unnecessary downtime and maintenance costs in the construction project. The optimized cycle can ensure that the equipment remains in the best working condition, thereby improving the overall operating efficiency and reliability of the construction project.

[0056] This optimization method makes up for the defects of the traditional fixed maintenance cycle, making the maintenance work more accurate and flexible, and can make real-time adjustments according to the actual use of the equipment, thereby ensuring the long-term efficient operation of the equipment and meeting the dynamically changing construction needs. This can also effectively solve the problem of the cycle being too rigid in traditional technology, and improve the management level of construction projects and the intelligence level of equipment operation and maintenance.

[0057] Furthermore, Figure 6 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0058] Among them, in another preferred embodiment provided by the present invention, a monitoring system for enterprise project management includes: The data acquisition module 100 is used to acquire the initial maintenance period of the target equipment in the specified construction project, and the load data of several key monitoring areas of the target equipment during the initial maintenance period.

[0059] In the embodiments of the present invention, the target equipment generally refers to mechanical equipment used to perform the main tasks in the construction project, such as cranes, excavators, bulldozers, concrete mixers, drilling rigs, etc. These equipment play a key role in the entire construction cycle and require regular maintenance and upkeep. The preferred target equipment is those with large load capacity, long-term operation and involving complex mechanical load changes, which can effectively reflect the use and load characteristics of the equipment.

[0060] Designated construction projects usually refer to specific construction projects, such as large-scale infrastructure construction, road paving, bridge construction, mining and other projects. These projects are characterized by long cycles, complex tasks and high dependence on equipment. Dynamic optimization management of maintenance cycles for target equipment in these construction projects helps improve equipment efficiency, reduce failure risks and reduce maintenance costs.

[0061] The initial maintenance cycle is set based on the equipment's usage specifications, historical data, and industry standards. The initial maintenance cycle is usually set based on the instructions and maintenance cycles recommended by the equipment manufacturer, and is appropriately adjusted in combination with the equipment's operating conditions in the current construction project. For example, if the equipment is under certain high-load and frequent use conditions, its initial maintenance cycle may be shorter, while under low-load and light use conditions, the initial maintenance cycle can be appropriately extended. The initial maintenance cycle is generally set by the equipment management department based on the specific conditions of the equipment and the needs of the construction project before the equipment is put into construction.

[0062] Several key monitoring areas of the target equipment can be those key parts of the equipment that bear heavy loads during equipment operation and are susceptible to wear or damage. For cranes, key monitoring areas may include booms, hooks, hydraulic systems, engine loads, etc.; for excavators, key monitoring areas may include buckets, crawlers, engines, hydraulic systems, etc. These key monitoring areas are important guarantees for equipment performance and safety, so they need to be monitored intensively.

[0063] Load data comes from the sensors and monitoring systems of the target equipment. The equipment will collect various load data in real time during operation. For example, sensors can monitor the equipment's working load, operating speed, hydraulic pressure, current, voltage and other data. These data can reflect the load status of each key monitoring area of ​​the equipment, especially its working performance under high load conditions. The specific content may include the working time, load value, load change trend, etc. of each monitoring area of ​​the equipment.

[0064] Furthermore, the monitoring system for enterprise project management also includes: The load data screening module 200 is used to intelligently analyze the load data and screen out all abnormal load data when each key monitoring area carries items within a specified load range during the initial maintenance cycle.

[0065] Specifically, Figure 7 It shows a structural block diagram of the load data screening module 200 in the system provided by the embodiment of the present invention.

[0066] Among them, in the preferred implementation mode provided by the present invention, the load data screening module 200 specifically includes: The load data preprocessing unit 201 is used to receive and store the load data of the target device in the initial maintenance cycle, and perform noise filtering and preprocessing on the load data; The abnormal load data screening unit 202 is used to analyze the load data of each key monitoring area according to the preset load range standard, and screen out the abnormal load data that exceeds the preset load range when carrying items within the specified load range; The abnormal load data sorting unit 203 is used to sort the screened abnormal load data into a data table, which includes the abnormal load occurrence time and load value of each key monitoring area.

[0067] In an embodiment of the present invention, the purpose of noise filtering and preprocessing the load data is to remove irrelevant or interfering signals in the original data to ensure that the subsequent analysis results are more accurate and reliable. Noise may come from many aspects, such as sensor failure, external environmental interference, or errors in the data acquisition process. Therefore, when processing load data, it is first necessary to denoise the data, such as using a filtering algorithm to remove high-frequency noise, or smoothing data points through an algorithm to extract the true change trend of the load data. The preprocessing process also includes normalization and standardization of the data to eliminate the differences in data between different devices or sensors, so that the data can be compared and analyzed under a unified standard. The significance of preprocessing is to reduce data errors and improve analysis accuracy, thereby better supporting subsequent fault prediction and maintenance cycle optimization.

[0068] Carrying a specified load range refers to the items or tasks that the target equipment needs to carry during operation, and its load value is within a preset range. This range is usually set according to the design capacity and usage requirements of the target equipment. For example, when a crane is lifting cargo, the maximum load is the designed load capacity of the equipment, and the minimum load range is the lowest load of the equipment when it is unloaded. This range should be representative and match the load conditions faced by the equipment in actual construction. Setting a reasonable load range can ensure that the equipment operates within its performance range during use, thereby avoiding inefficiency caused by overload or underload.

[0069] Data outside the preset load range is considered abnormal load data because these load conditions represent the equipment operating under certain abnormal conditions. Exceeding the load range may mean that the equipment is facing too high a workload during operation, or the load is too low, resulting in the equipment not being able to operate efficiently. The appearance of abnormal load data is usually a sign of equipment failure, operating error, or abnormal working environment. If the equipment continues to operate in an overloaded or underloaded state, it may cause equipment damage, failure, or even safety accidents, so these abnormal conditions must be identified and handled in a timely manner.

[0070] Furthermore, the monitoring system for enterprise project management also includes: The abnormal curve generating module 300 is used to generate an abnormal load value variation curve that varies with time for the abnormal load data of each key monitoring area.

[0071] Specifically, Figure 8 It shows a structural block diagram of the abnormal curve generating module 300 in the system provided by the embodiment of the present invention.

[0072] Among them, in the preferred embodiment provided by the present invention, the abnormal curve generating module 300 specifically includes: The abnormal load data extraction unit 301 is used to extract the abnormal load occurrence time and load value of each key monitoring area from the abnormal load data, and arrange them in chronological order; A load value variation curve generating unit 302 is used to associate the abnormal load occurrence time of each key monitoring area with the load value and generate a corresponding load value variation curve; The curve smoothing processing unit 303 is used to smooth the abnormal load data to reduce fluctuations and ensure that the load value change curve is more stable.

[0073] In an embodiment of the present invention, in the process of generating an abnormal load value change curve, it is first necessary to extract the abnormal load occurrence time and load value of each key monitoring area from the abnormal load data. These data are usually obtained in real time from the equipment monitoring system, identifying the load exceeding the standard at a specific time node. The abnormal load events in each key monitoring area need to be sorted in chronological order to ensure the temporal consistency of the data in subsequent analysis. The purpose of this step is to ensure that the relevant abnormal load information is screened out from the original data and arranged according to the time axis to facilitate the analysis of its changing trend.

[0074] Next, the time of occurrence of abnormal load in each key monitoring area needs to be associated with the load value to generate the corresponding load value change curve. This process can not only reflect the absolute value of the load change, but also show the trend of the load change. The load value change curve is generated by plotting the extracted abnormal load data on the time axis, and each point on the curve represents the load condition at a certain moment. These curves reflect the fluctuations in the load of the equipment in different time periods, and by comparing them with the standard load value curve, the deviation of abnormal fluctuations in the equipment load from normal operation can be identified.

[0075] In order to make the load value change curve smoother and reduce fluctuations in the data, further smoothing is essential. Abnormal load data may contain short-term and drastic fluctuations due to factors such as sensor errors and external environmental interference. These fluctuations will affect the smoothness of the curve, making it difficult to accurately reflect the load change trend of the equipment. Therefore, by using smoothing algorithms (such as sliding average method, weighted average method, etc.) to process the data, unnecessary short-term fluctuations can be effectively removed, thereby obtaining a smoother and continuous load value change curve. The smoothed curve can more clearly show the long-term change trend of the equipment load, help operation and maintenance personnel better identify potential load anomalies of the equipment, and provide a basis for supporting further fault diagnosis and maintenance decisions.

[0076] Furthermore, the monitoring system for enterprise project management also includes: The curve comparison module 400 is used to call the preset reference standard model, obtain the standard load value change curve of each key monitoring area when carrying items within a specified load range, compare and analyze the abnormal load value change curve of each key monitoring area with the standard load value change curve, and calculate the deviation degree value of the average slope between the two.

[0077] The reference standard model refers to a reference model established based on historical operating data, equipment usage specifications and industry standards, for different key monitoring areas of the target equipment, under normal operating conditions, when carrying items of different load ranges, and the load change law that changes over time during the initial maintenance cycle.

[0078] In an embodiment of the present invention, the process of generating a standard load value change curve is to first extract the load change law of each key monitoring area from the reference standard model. This reference standard model is established based on the operating data, equipment usage specifications and industry standards accumulated by the target equipment during historical operation, and is intended to reflect the load change characteristics of the equipment under normal operating conditions and when carrying items in different load ranges. During the initial maintenance cycle, the load changes of the target equipment should conform to certain regularities. By analyzing these historical data, the standard load value change curves within the specified load range can be inferred. These curves describe the normal trend of load changes over time under predetermined operating conditions. In this way, the standard load value change curve becomes the basis for comparison with the actual load value changes of the equipment.

[0079] Comparing the abnormal load value change curve with the standard load value change curve of each key monitoring area is intended to find out the degree to which the equipment deviates from the normal operating law during actual operation. The abnormal load value change curve reflects the abnormal conditions that occurred during the initial maintenance cycle of the equipment. These abnormalities may be caused by factors such as equipment failure, changes in operating conditions or external interference. The standard load value change curve is the expected operating trajectory of the equipment when there are no abnormal conditions. By comparing these two curves, it is possible to clearly identify which load changes deviate from the normal trajectory, thereby providing a basis for equipment maintenance and adjustment.

[0080] The significance of calculating the average slope of the abnormal load value change curve and the standard load value change curve and determining the deviation degree value is that it quantifies the deviation of the equipment in the load change trend. The slope reflects the rate of load change, that is, the speed of increase or decrease of the equipment load over time. If the load change slope of the equipment deviates from the slope of the standard model, it means that the equipment may be in an abnormal operating state. Load changes that are too fast or too slow may indicate potential failures or equipment instability. By calculating these deviations, the operating health of the equipment can be evaluated, providing an important quantitative indicator for judging whether the equipment needs to adjust the maintenance cycle or perform troubleshooting. This deviation degree value provides a quantitative basis for the subsequent dynamic adjustment of the maintenance cycle, thereby optimizing the management and maintenance strategy of the equipment.

[0081] Furthermore, the monitoring system for enterprise project management also includes: The maintenance cycle optimization module 500 is used to generate a correction factor according to the calculated deviation degree value, and dynamically adjust the initial maintenance cycle based on the correction factor to obtain an optimized maintenance cycle.

[0082] In an embodiment of the present invention, the average value of the degree of deviation of all key monitoring areas is calculated and set as a correction factor, mainly for the purpose of comprehensively evaluating the overall operating status of the equipment. The load change of each key monitoring area may be affected by multiple factors. By calculating the average value of the degree of deviation of all areas, occasional anomalies in a single monitoring area can be eliminated, and a more objective and comprehensive reflection of the equipment operating status can be obtained. In this way, the correction factor can not only take into account the overall operating status of the equipment, but also effectively adjust the initial maintenance cycle that is closely related to the health status of the target equipment, avoid the abnormality of a single area affecting the adjustment of the overall cycle, and make the maintenance cycle more in line with the actual needs of the equipment.

[0083] By optimizing and shortening the initial maintenance cycle in this way, the operating efficiency and maintenance benefits of the equipment can be significantly improved. Traditional equipment maintenance cycles are often fixed and do not take into account load changes and abnormal conditions that may occur during the actual operation of the equipment. This fixed cycle may lead to over-maintenance or under-maintenance of the equipment at certain stages, resulting in waste of resources or risk of equipment failure. Dynamic adjustment of the maintenance cycle can be adjusted according to the load deviation and state changes of the equipment in actual operation, making maintenance work more flexible and efficient, avoiding excessive or insufficient maintenance work, and ultimately improving the overall operating stability of the equipment and extending its service life. In addition, this adjustment can also reduce maintenance costs and improve the economic efficiency of equipment operation.

[0084] Let's take a detailed example: suppose that in a construction project, the initial maintenance cycle of the target equipment is 15 days. During this period, after intelligent analysis by the monitoring system, the deviation values ​​of multiple key monitoring areas are 0.15, -0.05, 0.10 and 0.03 respectively. Based on these deviation values, we first calculate the average deviation value of all key monitoring areas. This average value is 0.08, which is set as the correction factor.

[0085] Next, call the preset maintenance cycle optimization formula. Assuming that the adjustment coefficient set in the formula is 1.2, then multiply the correction factor by the adjustment coefficient to get 1.08 (the product of the correction factor and the coefficient). Then divide 15 days by 1.08 to get 13.89 days. For the convenience of actual operation, this result can be rounded to 14 days.

[0086] Therefore, the optimized maintenance cycle is 14 days, which reduces the maintenance cycle by 1 day compared to the initial 15 days.

[0087] Through this adjustment, the maintenance cycle of the target equipment has been dynamically optimized, avoiding the problem of excessive work or untimely maintenance of the equipment caused by too long maintenance intervals. At the same time, it can also effectively improve the operating efficiency of the equipment and reduce unnecessary downtime and maintenance costs in the construction project. The optimized cycle can ensure that the equipment remains in the best working condition, thereby improving the overall operating efficiency and reliability of the construction project.

[0088] This optimization method makes up for the defects of the traditional fixed maintenance cycle, making the maintenance work more accurate and flexible, and can make real-time adjustments according to the actual use of the equipment, thereby ensuring the long-term efficient operation of the equipment and meeting the dynamically changing construction needs. This can also effectively solve the problem of the cycle being too rigid in traditional technology, and improve the management level of construction projects and the intelligence level of equipment operation and maintenance.

[0089] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0090] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0091] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A monitoring method for enterprise project management, characterized in that: The method comprises: Obtain the initial maintenance cycle of the target equipment in the specified construction project, as well as the load data of several key monitoring areas of the target equipment during the initial maintenance cycle; Intelligently analyze load data and filter out all abnormal load data for each key monitoring area when carrying items within the specified load range during the initial maintenance cycle; For the abnormal load data of each key monitoring area, generate a curve of abnormal load value change over time; Retrieve the preset reference standard model, obtain the standard load value change curve of each key monitoring area when carrying items within the specified load range, compare and analyze the abnormal load value change curve of each key monitoring area with the standard load value change curve, and calculate the deviation degree value of the average slope between the two; According to the calculated deviation degree value, a correction factor is generated, and based on the correction factor, the initial maintenance cycle is dynamically adjusted to obtain an optimized maintenance cycle.

2. The monitoring method for enterprise project management according to claim 1, characterized in that: The steps of intelligently parsing the load data and screening out all abnormal load data of each key monitoring area when carrying items within the specified load range during the initial maintenance period include: Receive and store the load data of the target equipment during the initial maintenance cycle, and perform noise filtering and preprocessing on the load data; According to the preset load range standard, analyze the load data of each key monitoring area and filter out abnormal load data that exceeds the preset load range when carrying items within the specified load range; The screened abnormal load data are organized into a data table, which includes the abnormal load occurrence time and load value of each key monitoring area.

3. The monitoring method for enterprise project management according to claim 2, characterized in that: For the abnormal load data of each key monitoring area, the steps of generating a time-varying abnormal load value change curve include: Extract the abnormal load occurrence time and load value of each key monitoring area from the abnormal load data and sort them in chronological order; Correlate the abnormal load occurrence time and load value of each key monitoring area to generate the corresponding load value change curve; Abnormal load data is smoothed to reduce fluctuations and ensure a smoother load value change curve.

4. The monitoring method for enterprise project management according to claim 1, characterized in that: The reference standard model refers to a reference model established based on historical operating data, equipment usage specifications and industry standards, for different key monitoring areas of the target equipment, under normal operating conditions, when carrying items of different load ranges, and the load change law that changes over time during the initial maintenance cycle.

5. The monitoring method for enterprise project management according to claim 4, characterized in that: The steps of calling a preset reference standard model, obtaining a standard load value change curve of each key monitoring area when carrying items within a specified load range, comparing and analyzing the abnormal load value change curve of each key monitoring area with the standard load value change curve, and calculating the deviation degree value of the average slope of the two include: Retrieve the preset reference standard model and generate the corresponding standard load value change curve according to the load change law of each key monitoring area over time during the initial maintenance cycle; Compare the abnormal load value change curve with the standard load value change curve of each key monitoring area to ensure that the time axis is consistent and the load values ​​are comparable; The average slope of the abnormal load value change curve and the standard load value change curve is calculated to determine the degree of deviation between the two.

6. The monitoring method for enterprise project management according to claim 5, characterized in that: According to the calculated deviation value, a correction factor is generated, and based on the correction factor, the initial maintenance cycle is dynamically adjusted to obtain the optimized maintenance cycle. The steps include: Calculate the average value of the deviation degree corresponding to all key monitoring areas and set it as the correction factor; Retrieve the preset maintenance cycle optimization formula, and dynamically adjust the initial maintenance cycle in combination with the correction factor to obtain the optimized maintenance cycle; Apply the optimized maintenance cycle to the maintenance of target equipment in designated construction projects.

7. The monitoring method for enterprise project management according to claim 6, characterized in that: The maintenance cycle optimization formula is: , where T optimized Refers to the optimized maintenance cycle, T initial Refers to the initial maintenance cycle, F adjust refers to the correction factor, and K refers to the adjustment coefficient of the correction factor; In the maintenance cycle optimization formula, , where n refers to the total number of key monitoring areas, A i It refers to the average slope of the abnormal load value change curve of the i-th key monitoring area, B i Refers to the average slope of the standard load value change curve of the i-th key monitoring area, It refers to the deviation degree of the average slope between the abnormal load value change curve and the standard load value change curve of the i-th key monitoring area.

8. A monitoring system for enterprise project management, characterized in that: The system includes: a data acquisition module, a load data screening module, an abnormal curve generation module, a curve comparison module and a maintenance cycle optimization module, wherein: A data acquisition module is used to acquire the initial maintenance cycle of the target equipment in the specified construction project, and the load data of several key monitoring areas of the target equipment during the initial maintenance cycle; The load data screening module is used to intelligently analyze the load data and screen out all abnormal load data when each key monitoring area carries items within the specified load range during the initial maintenance cycle; An abnormal curve generation module is used to generate an abnormal load value change curve that changes over time for abnormal load data in each key monitoring area; The curve comparison module is used to call the preset reference standard model, obtain the standard load value change curve of each key monitoring area when carrying items within the specified load range, compare and analyze the abnormal load value change curve of each key monitoring area with the standard load value change curve, and calculate the deviation degree value of the average slope between the two; The reference standard model refers to a reference model established based on historical operating data, equipment usage specifications and industry standards, for different key monitoring areas of the target equipment, under normal operating conditions, when carrying items of different load ranges, and the load variation law that changes over time during the initial maintenance cycle; The maintenance cycle optimization module is used to generate a correction factor according to the calculated deviation degree value, and dynamically adjust the initial maintenance cycle based on the correction factor to obtain an optimized maintenance cycle.

9. The monitoring system for enterprise project management according to claim 8, characterized in that: The load data screening module specifically includes: A load data preprocessing unit, used for receiving and storing the load data of the target device in the initial maintenance cycle, and performing noise filtering and preprocessing on the load data; An abnormal load data screening unit is used to analyze the load data of each key monitoring area according to a preset load range standard, and screen out abnormal load data that exceeds the preset load range when carrying items within a specified load range; The abnormal load data sorting unit is used to sort the screened abnormal load data into a data table, which includes the abnormal load occurrence time and load value of each key monitoring area.

10. The monitoring system for enterprise project management according to claim 9, characterized in that: The abnormal curve generation module specifically includes: An abnormal load data extraction unit is used to extract the abnormal load occurrence time and load value of each key monitoring area from the abnormal load data and sort them in chronological order; A load value change curve generating unit, used for associating the abnormal load occurrence time of each key monitoring area with the load value, and generating a corresponding load value change curve; The curve smoothing processing unit is used to smooth abnormal load data to reduce fluctuations and ensure that the load value change curve is more stable.

Images