An enterprise operation supervision system for enterprise management

Abstract

The present invention discloses an enterprise operation supervision system for enterprise management, which specifically relates to the field of equipment supervision. It collects signal strength data of vehicle-mounted monitoring equipment, uses the signal average and standard deviation to dynamically evaluate the stability of the communication environment, and intelligently determines whether it is suitable for data transmission. In an unstable environment, ensure that data is securely stored locally; in a good environment, extract the data update change index and the fault impact trend index to calculate the pre-evaluation coefficient, and compare it with the early warning analysis threshold to generate a warning signal, so as to timely discover potential problems and improve equipment reliability. By calculating the distance from the maintenance point, the vehicle's motion status can be monitored, and the early warning strategy can be dynamically adjusted to improve the safety, efficiency and controllability of vehicle operations, and more intelligently supervise corporate property.

Description

Technical Field

[0001] The present invention relates to the field of equipment supervision, and more specifically, to an enterprise operation supervision system oriented to enterprise management. Background Art

[0002] Vehicle monitoring equipment plays a key role in the muck truck industry. First, it improves transportation efficiency and safety. By monitoring the location, speed and behavior of muck trucks in real time, it helps optimize route planning and reduce congestion and delays. Second, monitoring equipment can record the driver's behavior, including sudden braking and speeding, to promote good driving habits and reduce the risk of traffic accidents. In addition, the monitoring system can effectively prevent overloading of muck trucks and improve safety and compliance. Most importantly, vehicle monitoring equipment supports companies to obtain hardware and software information of vehicles in real time, so as to evaluate the quality of operations and improve the overall management level.

[0003] However, the operating environment of muck trucks is relatively harsh, which can easily affect the on-board monitoring equipment, causing some faults in the on-board monitoring equipment. These faults are generally discovered through regular maintenance or feedback from drivers, and the maintenance and resolution cycle for these faults is long. If the vehicle is running with faulty on-board monitoring equipment, it is difficult to be supervised by the company's back-end, and the back-end personnel cannot truly detect the real-time location information and speeding of the muck truck, which poses a safety hazard. Therefore, timely identifying the operating hazards of the on-board monitoring equipment and rectifying and resolving them according to the maintenance response time by increasing the hazard level is an effective measure to maintain the long-term reliable operation of the on-board monitoring equipment.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method of collecting signal strength data of the vehicle-mounted monitoring equipment, dynamically evaluating the stability of the communication environment using the signal average and standard deviation, and intelligently judging whether it is suitable for data transmission. In an unstable environment, ensure that data is securely stored locally; in a good environment, extract the data update change index and the fault impact trend index to calculate the pre-evaluation coefficient, and compare it with the early warning analysis threshold to generate a warning signal, so as to timely discover potential problems and improve equipment reliability. By calculating the distance from the maintenance point, the vehicle's motion status can be monitored, and the early warning strategy can be dynamically adjusted to improve the safety, efficiency and controllability of vehicle operations, and more intelligently supervise corporate assets to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: including: data statistics module, cloud platform, environment feature extraction module, software feature extraction module, comprehensive evaluation module, warning signal presentation module, dynamic adjustment module;

[0007] The data statistics module obtains the communication quality level of the vehicle-mounted monitoring equipment, and chooses to store the operation data locally or send the operation data to the cloud platform according to the quality level;

[0008] The cloud platform is used to receive the operating data sent by the data statistics module and to build an operating environment for the environmental feature extraction module, software feature extraction module, comprehensive evaluation module, warning signal presentation module and dynamic adjustment module;

[0009] The environmental feature extraction module identifies the vibration cycle and extracts the timestamp corresponding to the maximum vibration amplitude, analyzes the update time change trend to obtain the update change index, and sends the update change index to the comprehensive evaluation module;

[0010] The software feature extraction module calculates the degree of improvement between different fault types and other events in the operation log of the vehicle monitoring equipment through log mining and association rule analysis, obtains the fault impact trend index, and sends the fault impact trend index to the comprehensive evaluation module;

[0011] The comprehensive evaluation module uses the update change index and the fault impact trend index to perform comprehensive processing to obtain a pre-evaluation coefficient, and sends the pre-evaluation coefficient to the early warning signal presentation module;

[0012] The warning signal presentation module compares the pre-assessment coefficient with the warning analysis threshold, and presents warning signals of different degrees according to the comparison results;

[0013] The dynamic adjustment module uses GPS to calculate the distance between the maintenance point and the vehicle to determine whether to dynamically adjust the warning analysis threshold.

[0014] In a preferred embodiment, the operation process of the data statistics module includes the following contents:

[0015] Collect multiple signal strength data of the vehicle-mounted monitoring equipment in the current unit time, calculate the signal average value and signal standard deviation value, compare the signal average value with the signal strength threshold, and compare the signal standard deviation value with the discrete threshold. If the signal average value is not greater than the signal strength threshold, or the signal standard deviation value is not less than the discrete threshold, the operation data is first stored in the local storage container. Once the signal average value is greater than or equal to the signal strength threshold, and the signal standard deviation value is less than the discrete threshold, the operation data of the vehicle-mounted monitoring equipment is sent to the cloud platform.

[0016] In a preferred embodiment, the operation process of the environmental feature extraction module includes the following contents:

[0017] The vibration data of the vehicle monitoring device is collected at a rated time interval using an acceleration sensor. Each collection forms vibration data at a time point. The vibration peak value is determined by detecting the maximum value in the window using the sliding window technology. The amplitude of the vibration data in each window is calculated.

[0018] Collect all historical maximum vibration amplitude update data, including timestamps, arrange the timestamps in chronological order to form a time series, and calculate the time interval for every two adjacent timestamps to obtain a time series of time intervals;

[0019] Calculate the average of all time intervals to get the historical average update time interval, set a sliding window with a window size equal to the rated number of historical records, slide the window on the time series, calculate the average time interval within each window, and for each sliding window, use the average time interval of the most recent record to calculate the difference with the average time interval of the previous record, and then divide it by the difference in the average time interval of the previous record to get the update change index.

[0020] In a preferred embodiment, the operation process of the software feature extraction module includes the following contents:

[0021] Retrieve the operation log of the vehicle monitoring equipment, including the records of fault events and other related events; classify the log, identify and extract the fault events and other events respectively; use the Spearman association rule analysis method to construct frequent item sets, that is, combinations of events that often appear together;

[0022] Calculate the support of frequent itemsets and the confidence of association rules;

[0023] The lift is obtained by dividing the support of the fault event and other events occurring simultaneously by the product of the support of the fault event occurring alone and the support of other events occurring alone.

[0024] The fault impact trend index is obtained by taking the sum of the lifts of all association rules and dividing it by the number of association rules.

[0025] In a preferred embodiment, the operation process of the comprehensive evaluation module includes the following:

[0026] The update change index and the fault impact trend index are normalized and mapped to a unified interval, and the normalized update change index and the fault impact trend index are weighted and summed according to the weight coefficient to obtain the pre-evaluation coefficient.

[0027] In a preferred embodiment, the operation process of the warning signal presentation module includes the following contents:

[0028] The pre-evaluation coefficient is compared with the early warning analysis threshold. If the pre-evaluation coefficient is greater than or equal to the early warning analysis threshold, a warning signal is generated; otherwise, no signal is generated.

[0029] In a preferred embodiment, the operation process of the dynamic adjustment module includes the following contents:

[0030] Use the GPS module in the vehicle monitoring device to regularly obtain the real-time location data of the vehicle;

[0031] Calculate the distance between the vehicle's current location and the preset maintenance point using GPS coordinate data;

[0032] When the actual distance of the vehicle is greater than or equal to the distance warning threshold, the warning mechanism is triggered;

[0033] Dynamically adjust the warning analysis threshold based on the distance that triggers the warning;

[0034] The dynamically adjusted early warning analysis threshold is used to replace the initial early warning analysis threshold.

[0035] The technical effects and advantages of the enterprise operation supervision system for enterprise management of the present invention are as follows:

[0036] 1. The present invention collects multiple signal strength data of the vehicle-mounted monitoring equipment and calculates the signal average and standard deviation, so that the system can dynamically evaluate the stability of the communication environment. By comparing the signal average with the signal strength threshold, and the signal standard deviation with the discrete threshold, the system can intelligently determine whether the current traffic environment is suitable for data transmission. If the signal average does not meet the requirements or the signal standard deviation is large, the system will store the operating data locally to ensure data security and integrity. Once the communication environment is good, the signal average meets the threshold requirements and the signal standard deviation is lower than the discrete threshold, the system can instantly transmit the operating data of the vehicle-mounted monitoring equipment to the cloud platform to achieve real-time monitoring and data cloud storage. This intelligent communication management strategy effectively avoids data transmission problems in unstable communication environments, improves the reliability and efficiency of data transmission, and effectively reduces excessive reliance on cloud platform resources, making the overall system more robust and adaptable.

[0037] 2. The present invention normalizes the update change index and the fault impact trend index, and uses weight coefficients to perform weighted summation on the two indexes, so as to flexibly adjust the importance of different indexes in the comprehensive evaluation. Such weight distribution can be customized according to actual needs and business priorities, so that the pre-evaluation coefficient is closer to the actual focus. Comparison with the early warning analysis threshold provides an intuitive judgment mechanism. When the pre-evaluation coefficient exceeds the early warning analysis threshold, it indicates that there may be potential problems or hidden dangers in the overall condition of the vehicle-mounted monitoring equipment. The generation of a warning signal helps to attract attention in time and take corresponding countermeasures, thereby reducing the risk of equipment failure and improving the reliability and safety of the equipment.

[0038] 3. The present invention can realize accurate monitoring of the vehicle's motion state by calculating the distance between the vehicle's current position and the preset maintenance point, set the distance warning threshold and trigger the corresponding warning mechanism, and dynamically adjust the warning analysis threshold. The system can flexibly adjust the sensitivity of the warning according to the actual situation, and conveniently lower the signal generation threshold of the pre-evaluation threshold according to the distance between the vehicle and the maintenance point, which helps to achieve early detection of potential problems and can dynamically adjust the warning strategy according to the vehicle's operating conditions, thereby improving the safety, efficiency and controllability of vehicle operations and better, more efficiently and more intelligently supervising corporate assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a structural schematic diagram of an enterprise operation supervision system for enterprise management according to the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example 1

[0042] Figure 1 The present invention provides an enterprise operation supervision system for enterprise management, including: a data statistics module, a cloud platform, an environmental feature extraction module, a software feature extraction module, a comprehensive evaluation module, a warning signal presentation module, and a dynamic adjustment module;

[0043] The data statistics module obtains the communication quality level of the vehicle-mounted monitoring equipment, and chooses to store the operation data locally or send the operation data to the cloud platform according to the quality level;

[0044] The cloud platform is used to receive the operating data sent by the data statistics module and to build an operating environment for the environmental feature extraction module, software feature extraction module, comprehensive evaluation module, warning signal presentation module and dynamic adjustment module;

[0045] The environmental feature extraction module identifies the vibration cycle and extracts the timestamp corresponding to the maximum vibration amplitude, analyzes the update time change trend to obtain the update change index, and sends the update change index to the comprehensive evaluation module;

[0046] The software feature extraction module calculates the degree of improvement between different fault types and other events in the operation log of the vehicle monitoring equipment through log mining and association rule analysis, obtains the fault impact trend index, and sends the fault impact trend index to the comprehensive evaluation module;

[0047] The comprehensive evaluation module uses the update change index and the fault impact trend index to perform comprehensive processing to obtain a pre-evaluation coefficient, and sends the pre-evaluation coefficient to the early warning signal presentation module;

[0048] The warning signal presentation module compares the pre-assessment coefficient with the warning analysis threshold, and presents warning signals of different degrees according to the comparison results;

[0049] The dynamic adjustment module uses GPS to calculate the distance between the maintenance point and the vehicle to determine whether to dynamically adjust the warning analysis threshold.

[0050] The operation process of the data statistics module includes the following:

[0051] Collect multiple signal strength data of the vehicle-mounted monitoring equipment in the current unit time, calculate the signal average value and signal standard deviation value, compare the signal average value with the signal strength threshold, and compare the signal standard deviation value with the discrete threshold. If the signal average value is not greater than the signal strength threshold, or the signal standard deviation value is not less than the discrete threshold, it means that the current traffic environment does not meet the transmission requirements for transport data, indicating that the communication does not meet the data transmission standard. The operation data is first stored in the local storage container. Once the signal average value is greater than or equal to the signal strength threshold and the signal standard deviation value is less than the discrete threshold, the operation data of the vehicle-mounted monitoring equipment is sent to the cloud platform, which optimizes the communication management system so that it can more accurately determine the data processing method according to the signal characteristics, thereby improving the efficiency and reliability of data transmission.

[0052] By collecting multiple signal strength data of the vehicle-mounted monitoring equipment and calculating the signal average and standard deviation, the system can dynamically evaluate the stability of the communication environment. By comparing the signal average with the signal strength threshold, and the signal standard deviation with the discrete threshold, the system can intelligently determine whether the current traffic environment is suitable for data transmission. If the signal average does not meet the requirements or the signal standard deviation is large, the system will store the operating data locally to ensure data security and integrity. Once the communication environment is good, the signal average meets the threshold requirements and the signal standard deviation is lower than the discrete threshold, the system can instantly transmit the operating data of the vehicle-mounted monitoring equipment to the cloud platform to achieve real-time monitoring and data cloud storage. This intelligent communication management strategy effectively avoids data transmission problems in unstable communication environments, improves the reliability and efficiency of data transmission, and effectively reduces excessive reliance on cloud platform resources, making the overall system more robust and adaptable.

[0053] The operation process of the environmental feature extraction module includes the following:

[0054] Understanding the vibration conditions of the vehicle monitoring equipment installation environment is of great significance for analyzing and evaluating the size of equipment hidden dangers. Vibration conditions directly affect the stability and performance of vehicle monitoring equipment, so a comprehensive understanding of it can provide the following benefits:

[0055] First of all, vibration is one of the important indicators for evaluating the working environment of vehicle monitoring equipment. Different installation environments may generate various vibrations, such as shock, impact, or continuous low-frequency vibration. These vibrations may come from vehicle movement, road conditions, or other external factors. By understanding the vibration conditions, the design and working parameters of the equipment can be better adapted to improve the adaptability of the equipment in different environments.

[0056] Secondly, vibration is directly related to the stability and life of the equipment. Frequent or strong vibration may cause wear, looseness or damage to equipment components, thus affecting the normal operation of the equipment. By timely monitoring and evaluating vibration conditions, the life of the equipment can be estimated more accurately, and corresponding preventive maintenance measures can be taken to extend the service life of the equipment.

[0057] In addition, understanding the vibration situation is crucial for analyzing and evaluating the potential risks of vehicle-mounted monitoring equipment. If the vibration situation is abnormal or occurs frequently, it may cause equipment failure, performance degradation, and even endanger the stability and safety of the equipment. By monitoring and analyzing the vibration situation, potential problems can be discovered in a timely manner, and measures can be taken to repair or improve them, thereby reducing the size of the potential risks and ensuring the reliability and safety of vehicle-mounted monitoring equipment.

[0058] Therefore, understanding the vibration conditions of the installation environment of vehicle-mounted monitoring equipment can not only help improve the performance and stability of the equipment, but also effectively analyze and evaluate potential hidden dangers, providing strong support for the reliable operation of the equipment.

[0059] The vibration data of the vehicle monitoring device is collected by using an acceleration sensor at a rated time interval, and each collection forms the vibration data of a time point. The vibration peak value is determined by detecting the maximum value in the window using the sliding window technology; the amplitude of the vibration data in each window is calculated, for example, it can be obtained by the following formula: Formula, where x, y and z represent the acceleration data on the three axes respectively, and the maximum vibration amplitude in each window is obtained.

[0060] Collect all historical maximum vibration amplitude update data, including timestamps, arrange the timestamps in chronological order to form a time series, calculate the time interval for every two adjacent timestamps, and obtain a time series of time intervals; calculate the average of all time intervals to obtain the historical average update time interval, set a sliding window with a window size equal to the rated number of historical records, slide the window on the time series, calculate the average time interval in each window, and for each sliding window, use the average time interval of the most recent record to calculate the difference with the average time interval of the previous record, and then divide it by the difference in the average time interval of the previous record to obtain the update change index.

[0061] The finer change trend reflects the dynamic changes in the frequency of updating the maximum vibration amplitude of the installation environment of the vehicle-mounted monitoring equipment. Specifically, a larger update change index indicates that the frequency of updating the maximum vibration amplitude has a significant increasing trend in the newer time period compared to the previous time period, reflecting the relative frequency of vibration events in the environment in which the vehicle-mounted monitoring equipment is located. On the contrary, a smaller update change index indicates that the frequency of updating the maximum vibration amplitude is relatively stable or decreasing, indicating that the vibration events in the environment in which the vehicle-mounted monitoring equipment is located are relatively few or tend to be stable. Therefore, the degree of change in the finer change trend can be used to identify the dynamic changes in the vibration conditions in the environment in which the vehicle-mounted monitoring equipment is located, providing valuable information for real-time monitoring.

[0062] The operation process of the software feature extraction module includes the following:

[0063] Analyzing the correlation between fault events and other events plays an important role in analyzing and evaluating the size of hidden dangers in vehicle-mounted monitoring equipment. By gaining a deep understanding of the relationship between fault events and other events, potential problem patterns and abnormal behaviors can be revealed. This helps identify the root causes that may lead to equipment hidden dangers and provides a more comprehensive perspective, making it possible to conduct a detailed assessment of equipment hidden dangers. Specifically, correlation analysis can help determine whether the fault event is related to specific operations, environmental conditions, or other events, thereby identifying possible sources of hidden dangers. This in-depth correlation analysis helps to detect potential problems early and provide guidance for taking effective preventive maintenance and repair measures, ultimately improving the reliability and safety of vehicle-mounted monitoring equipment.

[0064] Retrieve the operation log of the vehicle monitoring equipment, including records of fault events and other related events;

[0065] Classify the logs, identify and extract fault events and other events respectively;

[0066] Using the Spearman association rule analysis method, we construct frequent item sets, which are combinations of events that often occur together. For example, we can find out whether a failure event is often accompanied by other specific events.

[0067] Calculate the support of frequent itemsets and the confidence of association rules.

[0068] Support indicates how often a combination of events occurs in the entire log, while confidence indicates the probability of another event occurring if one event occurs.

[0069] The lift is obtained by dividing the support of the fault event and other events occurring simultaneously by the product of the support of the fault event occurring alone and the support of other events occurring alone.

[0070] The fault impact trend index is obtained by taking the sum of the lifts of all association rules and dividing it by the number of association rules.

[0071] The fault impact trend index is used to comprehensively reflect the impact trend of faults on other events in the association rules between different fault types and other events in the operation log of the vehicle monitoring equipment. The change of this index reflects the degree of correlation between the fault event and other events, as well as the intensity of the impact of the fault on other events. When the fault impact trend index is larger, it means that the correlation between the fault event and other events is stronger, and the impact trend of the fault on other events is more significant. Conversely, when the index is smaller, it means that the impact trend of the fault event on other events is relatively weak. Therefore, the size of the fault impact trend index directly reflects the correlation intensity and impact degree between the fault event and other events in the operation status of the vehicle monitoring equipment.

[0072] The operation process of the comprehensive assessment module includes the following:

[0073] The update change index and the fault impact trend index are normalized and mapped to a unified interval. Normalization can eliminate the dimension effect and make different indicators comparable. Secondly, machine learning technology is used to assign weight coefficients to the two indexes to reflect their importance in the comprehensive evaluation. The normalized update change index and fault impact trend index are weighted and summed according to the weight coefficients to obtain the pre-evaluation coefficient. For example, it can be obtained by the following formula: PCI = w1·uci+w2·fiti;

[0074] Where PCI represents the pre-assessment coefficient, uci and fiti are the update change index and fault impact trend index, respectively, w1 and w2 are the weight coefficients of the update change index and fault impact trend index, respectively, and both are greater than 0.

[0075] The pre-assessment coefficient is used to comprehensively reflect the usage status and potential hidden dangers of the vehicle-mounted monitoring equipment hardware and software. When the pre-assessment coefficient is larger, it means that the update change and failure impact trend of the vehicle-mounted monitoring equipment is relatively high, and there are potential problems or hidden dangers that are more serious. Conversely, when the coefficient is smaller, it means that the update change and failure impact trend of the vehicle-mounted monitoring equipment is relatively low, indicating that the equipment condition is relatively stable or the problem is relatively minor. Therefore, the size of the pre-assessment coefficient directly reflects the relative degree of the comprehensive condition of the vehicle-mounted monitoring equipment and provides an estimate of the overall hidden danger situation of the equipment.

[0076] The operation process of the warning signal presentation module includes the following:

[0077] The pre-assessment coefficient is compared with the warning analysis threshold to determine whether the overall status of the vehicle monitoring equipment exceeds the set warning standard;

[0078] If the pre-assessment coefficient is greater than or equal to the early warning analysis threshold, it means that the overall status or hidden danger of the vehicle-mounted monitoring equipment has reached or exceeded the set warning level, which means that the vehicle-mounted monitoring equipment has undergone major update changes and failure impacts, which requires attention and further monitoring, maintenance or repair measures to generate a warning signal.

[0079] On the contrary, if the pre-evaluation coefficient is less than the early warning analysis threshold, it means that the overall status of the vehicle monitoring equipment is relatively normal and does not meet the early warning standard. It means that the trend of the equipment in terms of updates and failures is relatively stable, and there are no obvious problems or hidden dangers at present, but continuous monitoring is still required to ensure the reliability of the equipment and not generate signals.

[0080] The present invention normalizes the update change index and the fault impact trend index, and uses the weight coefficient to perform weighted summation on the two indexes, so as to flexibly adjust the importance of different indexes in the comprehensive evaluation. Such weight distribution can be customized according to actual needs and business priorities, so that the pre-evaluation coefficient is closer to the actual focus. Comparison with the early warning analysis threshold provides an intuitive judgment mechanism. When the pre-evaluation coefficient exceeds the early warning analysis threshold, it indicates that there may be potential problems or hidden dangers in the overall condition of the vehicle-mounted monitoring equipment. The generation of a warning signal helps to attract attention in time and take corresponding countermeasures, thereby reducing the risk of equipment failure and improving the reliability and safety of the equipment.

[0081] The operation process of the dynamic adjustment module includes the following:

[0082] Use the GPS module in the vehicle monitoring device to regularly obtain the real-time location data of the vehicle;

[0083] Calculate the distance between the vehicle's current location and the preset maintenance point using GPS coordinate data. This can be done by calculating the geographic distance between two points, using longitude and latitude information to calculate the shortest distance between two points on a sphere;

[0084] When the actual distance of the vehicle is greater than or equal to the distance warning threshold, the warning mechanism is triggered;

[0085] Based on the distance that triggers the warning, the warning analysis threshold is dynamically adjusted. For example, it can be calculated by the following formula:

[0086] Where DTY represents the dynamically adjusted warning analysis threshold, JTY represents the initial warning analysis threshold, KM and JS represent the distance and distance warning threshold, respectively, and ε is the correction coefficient of the distance warning threshold to the distance ratio, which is greater than 0.

[0087] The dynamically adjusted early warning analysis threshold is used to replace the initial early warning analysis threshold.

[0088] The present invention can realize accurate monitoring of the vehicle's motion state by calculating the distance between the vehicle's current position and a preset maintenance point, set a distance warning threshold and trigger a corresponding warning mechanism, and dynamically adjust the warning analysis threshold. The system can flexibly adjust the sensitivity of the warning according to actual conditions, and conveniently lower the signal generation threshold of the pre-evaluation threshold according to the distance between the vehicle and the maintenance point, which helps to achieve early detection of potential problems and can dynamically adjust the warning strategy according to the vehicle's operating conditions, thereby improving the safety, efficiency and controllability of vehicle operations and better, more efficiently and more intelligently supervising corporate assets.

[0089] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0090] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0091] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0092] In the several embodiments provided in the present application, it should be understood that the disclosed systems and devices can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0095] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0096] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0097] Finally: 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, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An enterprise operation supervision system for enterprise management, characterized in that: include: Data statistics module, cloud platform, environmental feature extraction module, software feature extraction module, comprehensive evaluation module, warning signal presentation module, dynamic adjustment module; The data statistics module obtains the communication quality level of the vehicle-mounted monitoring equipment, and chooses to store the operation data locally or send the operation data to the cloud platform according to the quality level; The cloud platform is used to receive the operating data sent by the data statistics module and to build an operating environment for the environmental feature extraction module, software feature extraction module, comprehensive evaluation module, warning signal presentation module and dynamic adjustment module; The environmental feature extraction module identifies the vibration cycle and extracts the timestamp corresponding to the maximum vibration amplitude, analyzes the update time change trend to obtain the update change index, and sends the update change index to the comprehensive evaluation module; The software feature extraction module calculates the degree of improvement between different fault types and other events in the operation log of the vehicle monitoring equipment through log mining and association rule analysis, obtains the fault impact trend index, and sends the fault impact trend index to the comprehensive evaluation module; The comprehensive evaluation module uses the update change index and the fault impact trend index to perform comprehensive processing to obtain a pre-evaluation coefficient, and sends the pre-evaluation coefficient to the early warning signal presentation module; The warning signal presentation module compares the pre-assessment coefficient with the warning analysis threshold, and presents warning signals of different degrees according to the comparison results; The dynamic adjustment module uses GPS to calculate the distance between the maintenance point and the vehicle to determine whether to dynamically adjust the warning analysis threshold; The operation process of the environmental feature extraction module includes the following: The vibration data of the vehicle monitoring device is collected at a rated time interval using an acceleration sensor. Each collection forms vibration data of a time point. The vibration peak value is determined by detecting the maximum value in the window using the sliding window technology. The amplitude of the vibration data in each window is calculated. Collect all historical maximum vibration amplitude update data, including timestamps, arrange the timestamps in chronological order to form a time series, and calculate the time interval for every two adjacent timestamps to obtain a time series of time intervals; Calculate the average of all time intervals to get the historical average update time interval, set a sliding window with a window size equal to the rated number of historical records, slide the window on the time series, calculate the average time interval in each window, and for each sliding window, use the average time interval of the most recent record to calculate the difference with the average time interval of the previous record, and then divide it by the difference in the average time interval of the previous record to get the update change index; The operation process of the software feature extraction module includes the following: Retrieve the operation log of the vehicle monitoring equipment, including the records of fault events and other related events; classify the log, identify and extract the fault events and other events respectively; use the Spearman association rule analysis method to construct frequent item sets, that is, combinations of events that often appear together; Calculate the support of frequent itemsets and the confidence of association rules; The lift is obtained by dividing the support of the fault event and other events occurring simultaneously by the product of the support of the fault event occurring alone and the support of other events occurring alone. The fault impact trend index is obtained by taking the sum of the lifts of all association rules and dividing it by the number of association rules.

2. The enterprise operation supervision system for enterprise management according to claim 1, characterized in that: The operation process of the data statistics module includes the following: Collect multiple signal strength data of the vehicle-mounted monitoring equipment in the current unit time, calculate the signal average value and signal standard deviation value, compare the signal average value with the signal strength threshold, and compare the signal standard deviation value with the discrete threshold. If the signal average value is not greater than the signal strength threshold, or the signal standard deviation value is not less than the discrete threshold, the operation data is first stored in the local storage container. Once the signal average value is greater than or equal to the signal strength threshold, and the signal standard deviation value is less than the discrete threshold, the operation data of the vehicle-mounted monitoring equipment is sent to the cloud platform.

3. The enterprise operation supervision system for enterprise management according to claim 2 is characterized by: The operation process of the comprehensive assessment module includes the following: The update change index and the fault impact trend index are normalized and mapped to a unified interval, and the normalized update change index and the fault impact trend index are weighted and summed according to the weight coefficient to obtain the pre-evaluation coefficient.

4. The enterprise operation supervision system for enterprise management according to claim 3 is characterized by: The operation process of the warning signal presentation module includes the following: The pre-assessment coefficient is compared with the early warning analysis threshold, and if the pre-assessment coefficient is greater than or equal to the early warning analysis threshold, a warning signal is generated; Otherwise, no signal is generated.

5. The enterprise operation supervision system for enterprise management according to claim 4 is characterized by: The operation process of the dynamic adjustment module includes the following: Use the GPS module in the vehicle monitoring device to regularly obtain the real-time location data of the vehicle; Calculate the distance between the vehicle's current location and the preset maintenance point using GPS coordinate data; When the actual distance of the vehicle is greater than or equal to the distance warning threshold, the warning mechanism is triggered; Dynamically adjust the warning analysis threshold based on the distance that triggers the warning; The dynamically adjusted early warning analysis threshold is used to replace the initial early warning analysis threshold.

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