Split type intelligent monitoring device operation state method based on connecting cable

Abstract

The invention discloses a split type intelligent monitoring device operation state method based on a connection cable, and relates to the technical field of intelligent monitoring, and the method comprises the steps: monitoring a power supply state and a data transmission state of a sensing device and a communication control device in real time, and generating a real-time state data stream; analyzing the real-time state data flow by using a data processing algorithm, and judging whether the connection state of the sensing device and the communication control device is abnormal or not; and automatically switching to a standby sensing device or a communication control device, and reconfiguring a power supply and data transmission path to recover the normal operation of the equipment. According to the split type intelligent monitoring device operation state method based on the connecting cable, stable power supply can be provided through the connecting cable, it is ensured that the device continuously operates at a high position or in a complex environment, efficient data transmission capacity is achieved, real-time transmission of high-resolution videos and images is supported, and the system is suitable for popularization and application. The data transmission delay is reduced, and the system response speed is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an operating status method of a split-type intelligent monitoring device based on a connecting cable. Background Art

[0002] With the rapid development of Internet of Things technology, modern industry, energy management, smart buildings and other fields are increasingly dependent on monitoring systems. Traditional monitoring devices are mostly centralized, with complex structures, difficulty in fault location and poor flexibility. Split-type monitoring devices have gradually become mainstream due to their detachability, flexibility and easy maintenance. However, in practical applications, such split-type monitoring devices face some challenges, especially in the process of power supply and data transmission, where faults are often encountered.

[0003] At present, most existing technologies focus on fault detection, but lack a comprehensive, real-time monitoring mechanism, especially for the status monitoring of power supply and data transmission. As a result, it is difficult to detect and quickly switch to backup equipment in time when a fault occurs, which can easily cause the equipment to shut down for a long time or work instability. Moreover, in an environment where multiple devices work together, how to reasonably schedule power and data transmission paths to avoid a larger system crash caused by a single point of failure is one of the technical challenges. Most existing fault recovery methods rely on manual intervention, with a low degree of automation, and cannot achieve real-time, intelligent fault switching and recovery. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing split-type intelligent monitoring devices have poor stability in power supply and data transmission, especially in complex environments, which are prone to equipment failure and poor data transmission, resulting in reduced efficiency and reliability of the monitoring system; in addition, the existing devices lack flexibility in the installation, disassembly and maintenance of the equipment, making it difficult to meet the needs of daily maintenance and upgrades. How to improve the compatibility, reliability and operation and maintenance efficiency of the equipment while ensuring stable power supply and efficient data transmission is an optimization problem to be solved by the present invention.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable, comprising:

[0007] Deploy a monitoring module on the connecting cable to monitor the power supply status and data transmission status of the sensor device and the communication control device in real time, and generate a real-time status data stream;

[0008] Analyze the real-time status data stream using data processing algorithms to determine whether the connection status between the sensor device and the communication control device is abnormal, and identify the fault type based on the connection status determination result;

[0009] Based on the identified fault type, it automatically switches to the backup sensing device or communication control device, and reconfigures the power supply and data transmission path to restore normal operation of the equipment.

[0010] As a preferred solution of the method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable according to the present invention, wherein: the generating of the real-time status data stream includes monitoring the power supply status of the sensor device, collecting and recording the voltage, current and power, and forming a power supply data stream;

[0011] Monitor the data transmission status of the communication control device, collect and record the signal strength, data packet loss rate and transmission delay, and form a data transmission data stream.

[0012] As a preferred solution of the method for measuring the operating status of a split-type intelligent monitoring device based on a connecting cable described in the present invention, wherein: the analysis of the real-time status data stream includes performing a time series analysis on the power supply data stream and the data transmission data stream, calculating the power fluctuation and the change of the data transmission status in each time period; comparing the calculation result with the preset normal operating threshold, and determining that the data in a certain time period exceeds the normal range as an abnormal state;

[0013] The abnormal status includes power failure, data transmission failure and connection failure.

[0014] As a preferred solution of the method for detecting the operating status of a split-type intelligent monitoring device based on a connection cable according to the present invention, wherein: the identification of the fault type includes identifying a power fault if an abnormal value of current, voltage or power is detected and exceeds a preset normal range, and determining whether it is necessary to switch the power supply path;

[0015] If data packet loss, signal strength reduction, or transmission delay exceeding a preset tolerance value is detected, it is identified as a data transmission failure and a decision is made as to whether the data transmission path needs to be switched;

[0016] If a power failure and a data transmission failure are detected at the same time, it is identified as a connection failure, and it is determined whether it is necessary to switch to a backup sensing device or a backup communication control device.

[0017] As a preferred solution of the method for detecting the operating status of a split-type intelligent monitoring device based on a connecting cable according to the present invention, the automatic switching to the backup sensing device or the communication control device includes calculating the power demand of the backup sensing device when a power failure is identified, selecting the backup sensing device according to the current power supply status, and switching the power supply path to the backup sensing device;

[0018] When a data transmission failure is identified, the data bandwidth requirement of the backup communication control device is calculated, and the most suitable backup communication control device is selected according to the current transmission capacity, and the data transmission path is switched to the backup communication control device;

[0019] During the switching process, the power demand and data bandwidth demand of the backup device are calculated in real time, and a backup path is selected;

[0020] During the switching process, the power demand and data bandwidth of the backup device are calculated in real time, and the backup path is selected to ensure the normal operation of the device.

[0021] As a preferred solution of the method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable according to the present invention, wherein: the automatic switching to the backup sensing device or the communication control device further includes monitoring the power fluctuation of the backup sensing device or the signal strength and data transmission delay of the backup communication control device;

[0022] Adjust the working status of the backup equipment based on the detection results, and adjust the load of the backup equipment according to the real-time monitoring data.

[0023] As a preferred solution of the method for operating status of a split-type intelligent monitoring device based on a connecting cable described in the present invention, wherein: the normal operation of the device is restored including starting a self-check process after the switching is completed to check the connection status and working status of all devices;

[0024] If an abnormality is found during the self-check process, an alarm will be issued immediately and the rollback operation will be initiated to restore to the normal state before the switch.

[0025] Another object of the present invention is to provide a split-type intelligent monitoring device, which can solve the problems of unstable power supply, high data transmission delay and frequent equipment failures in existing monitoring devices by constructing a split-type intelligent monitoring device based on connecting cables.

[0026] To solve the above technical problems, the present invention provides the following technical solutions: a split-type intelligent monitoring device, comprising: a data monitoring module, used to monitor the power supply status and data transmission status of the sensor device and the communication control device in real time, and generate a real-time status data stream; a fault analysis module, used to analyze the real-time status data stream using a data processing algorithm, determine whether the connection status between the sensor device and the communication control device is abnormal, and identify the fault type based on the connection status judgment result; an equipment switching module, used to automatically switch to a backup sensor device or communication control device based on the identified fault type, and reconfigure the power supply and data transmission path to restore the normal operation of the equipment.

[0027] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable are implemented.

[0028] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable as described above.

[0029] Beneficial effects of the present invention: The method for the operation status of a split-type intelligent monitoring device based on connecting cables provided by the present invention can not only provide a stable power supply through the connecting cables to ensure the continuous operation of the equipment in high places or complex environments, but also has efficient data transmission capabilities, supports real-time transmission of high-resolution videos and images, reduces data transmission delays, and improves system response speed. At the same time, the high reliability of the connecting cables enables the equipment to work stably in harsh environments such as high temperature, low temperature, and humidity, reduces the failure rate, and extends the service life of the equipment. In addition, the present invention also provides a flexible customized design, which can adjust the equipment configuration according to the requirements of different scenarios, and improves the maintainability and extensibility of the equipment. Overall, the present invention greatly improves the stability, reliability and adaptability of the intelligent monitoring device, and reduces the operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0031] Figure 1 An overall flow chart of a method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable provided by an embodiment of the present invention.

[0032] Figure 2 An overall structural diagram of a method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0035] Example 1

[0036] Reference Figure 1 , is an embodiment of the present invention, and provides a method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable, comprising:

[0037] Deploy a monitoring module on the connecting cable to monitor the power supply status and data transmission status of the sensor device and the communication control device in real time, and generate a real-time status data stream;

[0038] Analyze the real-time status data stream using data processing algorithms to determine whether the connection status between the sensor device and the communication control device is abnormal, and identify the fault type based on the connection status determination result;

[0039] Based on the identified fault type, it automatically switches to the backup sensing device or communication control device, and reconfigures the power supply and data transmission path to restore normal operation of the equipment.

[0040] Generating a real-time status data stream includes monitoring the power supply status of the sensor device, collecting and recording voltage, current and power, and forming a power supply data stream;

[0041] Monitor the data transmission status of the communication control device, collect and record the signal strength, data packet loss rate and transmission delay, and form a data transmission data stream.

[0042] Specifically, in this embodiment, the connection cable is connected to the sensor device and the communication control device through the monitoring module, and the monitoring module collects the power data (voltage, current, power) from the sensor device and the data transmission status (signal strength, packet loss rate, transmission delay) of the communication control device in real time. The power data is converted into a digital signal through an analog-to-digital converter (ADC) and a timestamp is added to generate a power supply data stream and a data transmission data stream.

[0043] Power data collection is performed once per second. During each sampling, the voltage and current are measured and recorded respectively, and the power is obtained by calculating the product of the current and voltage. If the collected power parameters (such as voltage, current or power) exceed the preset threshold within a certain period, the data is automatically marked as abnormal, triggering the subsequent analysis process. Data transmission status collection The data transmission status is recorded by the monitoring module regularly to record the signal strength, packet loss rate and transmission delay. The signal strength is calculated by the wireless signal strength indicator (RSSI); the packet loss rate is calculated by comparing the number of packets transmitted and received in each data period; the transmission delay is obtained by measuring the round-trip time (RTT) of the packet.

[0044] Furthermore, in order to improve the accuracy of data collection, this embodiment processes the original collected data through a data preprocessing algorithm to reduce the impact of noise on the results. The power data is smoothed through a weighted moving average filtering algorithm to remove the impact of periodic fluctuations and random noise, ensuring sensitivity to abnormal fluctuations.

[0045] When there are large fluctuations in current and voltage, the power data is compared with historical data to determine whether it is a long-term load fluctuation of the power equipment to ensure that there will be no false alarms due to instantaneous data fluctuations. At this time, the abnormal data is marked and the data is filtered and smoothed. Analysis of data transmission status For the delay and packet loss rate of the data transmission status, the Kalman filter algorithm is used to process the collected signals to reduce the impact of instantaneous interference, so that the stability of data transmission can be analyzed more accurately. When the packet loss rate or delay exceeds the preset standard, the state is marked as abnormal for subsequent fault diagnosis.

[0046] Furthermore, this embodiment also integrates an adaptive adjustment mechanism based on big data analysis, which uploads the real-time collected power data and data transmission status to a remote server for real-time analysis. The server automatically adjusts the collection cycle and preset thresholds according to the operating status of the equipment in different environments to adapt to data changes in complex environments. For example, in an environment with large power fluctuations, the data collection frequency can be automatically increased to improve data accuracy and reduce errors.

[0047] The cloud data processing server will analyze the collected power data based on cloud computing technology, learn the performance of the equipment under different operating conditions, deduce the law of equipment failure through historical data, provide early warning and optimization solutions, and ensure that the equipment can operate normally in different working environments. Adaptive data stream generation combines cloud computing and local data collection, and can dynamically adjust the priority and frequency of data collection according to the real-time operation of the equipment, ensuring that the entire monitoring network works efficiently and with low latency, avoiding differences in monitoring effects caused by too frequent or untimely collection.

[0048] Analyzing the real-time status data stream includes performing time series analysis on the power supply data stream and the data transmission data stream, calculating the power fluctuation and the change of the data transmission status in each time period; comparing the calculation results with the preset normal operation threshold, and determining that the data in a certain time period exceeds the normal range as an abnormal state;

[0049] Abnormal conditions include power failure, data transmission failure, and connection failure.

[0050] Specifically, in this embodiment, the data analysis module performs a timing analysis on the generated power supply data stream and data transmission data stream. First, the fluctuation range of the power data in each time period is calculated, and the calculation result is compared with the preset normal operation threshold. When the power fluctuation exceeds the preset threshold, it is considered that a power failure has occurred; at the same time, the delay and packet loss rate of the data transmission status are also analyzed. If it is found that the data in a certain period exceeds the preset standard, it is determined to be a data transmission failure.

[0051] Power fluctuation analysis uses Fourier transform to convert power data from the time domain to the frequency domain, identifying periodic and sudden fluctuations in power supply to ensure early detection of potential power problems. If periodic fluctuations are detected that exceed the preset threshold range, it is considered a potential fault and an early warning is issued. Data transmission status analysis uses a sliding window algorithm to gradually analyze data such as signal strength, delay, and packet loss rate in each time period, and monitor the stability of the data link in real time. If the delay exceeds the preset tolerance range or the packet loss rate is too high, it will be immediately judged as a data transmission failure and corresponding fault diagnosis will be performed.

[0052] Furthermore, in order to improve the accuracy of the analysis, this embodiment introduces a machine learning algorithm to classify and predict power fluctuations and data transmission anomalies in combination with historical data. Through algorithms such as decision trees or random forests, more complex abnormal patterns can be identified in real-time data, improving the accuracy of fault prediction and identification.

[0053] Historical data analysis: Based on the accumulated historical data model training analysis algorithm, the abnormal identification strategy is gradually optimized. When there is a large difference between the new real-time data and the historical data, the model will be used to determine whether it is a new type of fault to avoid misjudgment. Abnormal pattern optimization: Through multi-dimensional data analysis, complex fault modes can be identified, especially the compound fault mode of power failure and data transmission failure. Machine learning algorithms are used to optimize fault identification capabilities to ensure timely and accurate identification when new fault modes occur.

[0054] Furthermore, this embodiment further introduces a deep learning (DNN) model to improve the accuracy and efficiency of fault identification by extracting multi-level features of power data streams and data transmission status data streams. The deep learning model can automatically learn the complex laws of power fluctuations and data transmission anomalies, and combine multi-dimensional data for fusion analysis, thereby achieving early warning of faults.

[0055] The application of deep learning models, through the training of historical data by deep neural networks, can accurately identify the complex patterns of various power fluctuations and data transmission states. Especially in the face of sudden failures, the deep learning model can determine whether there is an abnormality in real time through feature extraction, further improving the intelligent level of fault diagnosis. Fault prediction and optimization, deep learning models can learn rules from nonlinear and time-series related data. With the assistance of multiple data sources, they can predict the occurrence of potential faults and optimize subsequent work processes based on the prediction results to avoid affecting the normal operation of equipment due to the occurrence of faults.

[0056] Identifying the fault type includes identifying a power fault if an abnormal value of current, voltage or power is detected and exceeds a preset normal range, and determining whether it is necessary to switch the power supply path;

[0057] If data packet loss, signal strength reduction, or transmission delay exceeding a preset tolerance value is detected, it is identified as a data transmission failure and a decision is made as to whether the data transmission path needs to be switched;

[0058] If a power failure and a data transmission failure are detected at the same time, it is identified as a connection failure, and it is determined whether it is necessary to switch to a backup sensing device or a backup communication control device.

[0059] Specifically, when an abnormal power state is detected, the current, voltage, and power abnormal value detection modules are used to determine whether the power parameters exceed the preset normal range. If exceeded, it is marked as a power failure and subsequent steps are executed. For abnormal data transmission status, parameters such as packet loss rate, signal strength, and delay are calculated to determine whether a data transmission failure has occurred. If both a power failure and a data transmission failure are detected at the same time, it is determined to be a connection failure.

[0060] Power fault identification: When any of the current, voltage and power exceeds the preset normal range, the power fault mark will be triggered immediately. These data will be recorded, and the fault will be identified and diagnosed. Data transmission fault identification: packet loss rate, signal strength and delay are important indicators for judging data transmission failure. If the packet loss rate exceeds the set threshold or the delay is too long within a period of time, it will be marked as a data transmission failure.

[0061] Furthermore, in order to improve the accuracy of fault identification, multi-channel data fusion technology is introduced to conduct a comprehensive analysis of the fault type through multiple data sources such as power, signal strength, and delay. The combined mode of power failure and data transmission failure can effectively avoid misjudgment of a single data source. Multi-dimensional data fusion, when judging the type of fault, can effectively reduce misjudgment caused by a single data source and improve the accuracy of fault identification by fusing and analyzing data from multiple dimensions such as power and data transmission status. Joint fault pattern recognition, when a power failure and a data transmission failure occur at the same time, can accurately determine the cause of the failure through joint analysis of multiple data sources, avoiding wrong decisions caused by single data source analysis.

[0062] Furthermore, with the support of deep learning models, the fault type can be optimized through rule knowledge base and expert experience. Combining deep learning models with traditional rule bases, comprehensive judgments can be made based on historical fault records and real-time monitoring data to ensure the accuracy and reliability of fault type identification.

[0063] Combined with expert rules, artificial intelligence and rule knowledge base can enhance the flexibility and accuracy of fault identification based on the judgment of deep learning models. Especially when facing unknown types of faults, the rule base can provide additional information support to help determine the type of fault. The fault library and self-learning mechanism will continuously learn new fault modes and update the fault library as the equipment operation data accumulates. When a new fault type is detected, the fault library can update the rules and retrain the model to improve the predictability of future faults.

[0064] Automatically switching to a backup sensing device or a communication control device includes, when a power failure is identified, calculating the power demand of the backup sensing device, selecting the backup sensing device according to the current power supply status, and switching the power supply path to the backup sensing device;

[0065] When a data transmission failure is identified, the data bandwidth requirement of the backup communication control device is calculated, and the most suitable backup communication control device is selected according to the current transmission capacity, and the data transmission path is switched to the backup communication control device;

[0066] During the switching process, the power demand and data bandwidth demand of the backup equipment are calculated in real time, and the backup path is selected;

[0067] During the switching process, the power demand and data bandwidth of the backup device are calculated in real time, and the backup path is selected to ensure the normal operation of the device.

[0068] Specifically, in this embodiment, after a power failure is identified, the power demand of the backup sensor device is first calculated to determine whether the backup device can operate normally under the current power supply state. If the power demand of the backup sensor device is less than or equal to the current power supply capacity, the backup sensor device will be automatically selected and the power supply path will be switched to the backup device. If the power demand of the backup device exceeds the current supply capacity, the power path will be optimized and additional power supply sources will be added to ensure that the backup sensor device can operate normally.

[0069] When a power failure is detected, the existing power supply capacity will be analyzed first and compared with the power demand of the backup sensing device. If the current power is sufficient, the power path will be automatically switched; if the power supply is insufficient, power dispatch will be carried out to allocate more power resources to ensure that the backup equipment can operate. Similarly, when data transmission fails, the bandwidth requirements of the backup communication control device will be evaluated and matched according to the current transmission bandwidth. If the bandwidth requirements of the backup communication control device are within the current transmission capacity, it will automatically switch to the backup communication control device.

[0070] Furthermore, in this embodiment, in order to improve the smoothness of the switching process, a function of real-time calculation of the power demand and data bandwidth demand of the backup device is added. By real-time monitoring of the backup device, it can be ensured that during the switching process, the switching of the power supply and data transmission path will not cause a short shutdown of the device or data loss. Specifically, the load of the backup device will be predicted before the switching, and the timing and parameters of the switching operation will be dynamically adjusted according to the current device load and status.

[0071] When it is necessary to switch the power supply, it will first evaluate the power consumption of the backup device and the current distribution of power resources. If the power consumption of the backup device is high, it will ensure the normal operation of the backup device by reducing the power demand of other devices to avoid failures caused by improper power distribution. When switching to the backup communication control device, it will adjust the priority of data transmission according to the current network load to ensure the priority transmission of important data and avoid data loss or delay during the switching process.

[0072] Furthermore, this embodiment introduces an adaptive algorithm, which combines the changes in real-time power demand and data bandwidth demand, and can intelligently adjust the switching strategy according to environmental changes. For example, when the device is running under high load conditions, the selection of backup equipment can be automatically optimized, and the switching preparation of power and data paths can be made in advance to ensure the continuous and stable operation of the equipment. Through intelligent prediction and adjustment, it is possible to predict and prepare for switching in advance before a fault occurs to avoid operation interruption. By continuously collecting operating data and using historical data for learning, the scheduling strategy of power and data transmission paths will be optimized in real time. Especially when the equipment load fluctuates greatly, the working status of the backup equipment will be automatically adjusted in advance to ensure that there will be no power or data transmission bottlenecks. When switching paths, the switching timing will be dynamically determined based on the status of the current device to avoid switching operations when the load is high, thereby reducing the impact of the switching process on the operation of the device.

[0073] Automatically switching to the backup sensing device or communication control device also includes monitoring power fluctuations of the backup sensing device or signal strength and data transmission delay of the backup communication control device;

[0074] Adjust the working status of the backup equipment based on the detection results, and adjust the load of the backup equipment according to the real-time monitoring data.

[0075] Specifically, in this embodiment, in addition to monitoring the power and data transmission status of the main device, the monitoring module also needs to monitor the power fluctuation of the backup sensor device and the signal strength and data transmission delay of the backup communication control device. After the backup device is connected to the network, the power fluctuation data of the backup device will be collected in real time, and the working state of the backup device will be dynamically adjusted according to the collected signal strength and data transmission delay. In particular, when the load of the backup device changes greatly, the working mode of the device will be adjusted in time according to the load situation to ensure that the backup device can operate normally and stably during emergency switching. After the backup device is connected, the power consumption of the backup device will be continuously monitored, the amplitude of the power fluctuation will be calculated, and the situation of excessive fluctuation will be marked. When the power fluctuation amplitude of the backup device is large, the power path will be automatically optimized to ensure the stability of the power supply. The data transmission status of the backup device will be monitored in real time to ensure that the transmission bandwidth and signal strength of the backup device after access are within the normal range. If the signal strength and transmission delay of the backup device are abnormal, the working mode of the backup device will be automatically adjusted to optimize the data transmission path.

[0076] Furthermore, in the monitoring process of the standby device, this embodiment introduces a rule-based intelligent adjustment mechanism. When the power demand or data transmission capacity of the standby device changes, the working state of the standby device will be automatically adjusted according to the set rules. Specifically, when the power demand of the standby device increases, the load of other devices will be automatically reduced to ensure that the standby device can continue to operate; when the signal strength of the standby device decreases, the data transmission priority of the device will be automatically increased to ensure the stability of the standby device. The power and data transmission status of the standby device will be collected in real time, combined with environmental data for analysis, and the operation strategy of the standby device will be automatically adjusted. For example, when the standby device is short of power, its working mode can be adjusted to a low power mode to reduce power consumption; when the transmission delay of the standby device is too long, other devices will be scheduled first to ensure that the standby device can smoothly transition to the working state. According to the real-time status of the standby device, the load of the standby device can be intelligently adjusted, and dynamic load balancing can be performed when the device load is too high to avoid the standby device from being affected by overload and affecting normal operation.

[0077] Furthermore, this embodiment further introduces a self-learning capability based on big data, which can automatically identify the optimal working state of the backup equipment under different operating environments by analyzing long-term operating data. Through machine learning algorithms, the working strategy of the backup equipment can be gradually optimized, and the adaptability of the backup equipment under extreme conditions can be improved to ensure that the equipment can be connected and stably operated in the optimal working state when switching. By analyzing a large amount of historical data, the operating characteristics and failure modes of the backup equipment in different environments are learned. Based on the self-learning algorithm, the working state adjustment strategy of the backup equipment can be continuously optimized, and it can quickly adapt and make reasonable adjustments in the new environment. When multiple backup devices work together, the collaborative relationship between the backup devices can be automatically adjusted according to the working state of the equipment to ensure that the resources between the devices are reasonably allocated, so as to improve the stability and efficiency of the entire monitoring network.

[0078] Restoring normal operation of the equipment includes starting the self-test process after the switch is completed to check the connection status and working status of all devices;

[0079] If an abnormality is found during the self-check process, an alarm will be issued immediately and the rollback operation will be initiated to restore to the normal state before the switch.

[0080] Specifically, in the present embodiment, the recovery process of the device first starts the self-test function to perform a comprehensive test on the power and data transmission paths that have been switched to the backup device. The self-test process includes detecting whether the power supply path is stable, whether the data transmission path is normal, and whether the working state of the backup device meets the predetermined operating standards. If any abnormality is found during the self-test process, an alarm will be immediately issued, and the rollback operation will be initiated to restore to the normal state before the switch. The self-test process will first detect whether the power path is stable to ensure that the backup device can obtain sufficient power support. Next, the stability of the data transmission path will be detected to ensure the normal transmission of data. Finally, the status of the backup device will be checked to ensure that it is working properly. When an abnormality is found during the self-test process, an alarm will be immediately issued to remind the operator to check. The alarm mechanism is not limited to sound and light alarms, but also includes sending alarm signals to the outside through a communication module.

[0081] Furthermore, in order to improve the efficiency of the recovery operation, the present embodiment introduces a real-time monitoring mechanism. During the recovery process, the recovery of the power and data transmission paths will be tracked in real time, and the recovery strategy will be dynamically adjusted according to the progress of the recovery. If any new problems are encountered during the recovery process, the recovery strategy will be adjusted immediately to ensure rapid recovery to a normal state. During the recovery operation, the execution order of the recovery steps will be automatically optimized based on the real-time feedback of the recovery progress. If it is found that the recovery of a certain path is unstable, the path will be given priority to ensure that the recovery progress is not delayed. After restoring to the backup device and completing the self-test, an integrity check will be performed to ensure that all connections and device functions are restored to normal. If it is found during the inspection that a certain part cannot be restored, the fallback operation will be started again to restore the device to the most stable state.

[0082] Example 2

[0083] Reference Figure 2 , is an embodiment of the present invention, providing a split-type intelligent monitoring device operation status system based on a connecting cable, comprising:

[0084] The data monitoring module 100 is used to monitor the power supply status and data transmission status of the sensor device and the communication control device in real time and generate a real-time status data stream;

[0085] The fault analysis module 200 is used to analyze the real-time status data stream using a data processing algorithm to determine whether the connection status between the sensor device and the communication control device is abnormal, and identify the fault type based on the connection status determination result;

[0086] The device switching module 300 is used to automatically switch to a backup sensing device or a communication control device based on the identified fault type, and reconfigure the power supply and data transmission path to restore the normal operation of the device.

[0087] Example 3

[0088] An embodiment of the present invention is different from the first two embodiments in that:

[0089] 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 invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0090] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0091] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0092] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable, characterized in that: include: Deploy a monitoring module on the connecting cable to monitor the power supply status and data transmission status of the sensor device and the communication control device in real time, and generate a real-time status data stream; Analyze the real-time status data stream using data processing algorithms to determine whether the connection status between the sensor device and the communication control device is abnormal, and identify the fault type based on the connection status determination result; Based on the identified fault type, it automatically switches to the backup sensing device or communication control device, and reconfigures the power supply and data transmission path to restore normal operation of the equipment.

2. The method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable according to claim 1, characterized in that: Generating the real-time status data stream includes monitoring the power supply status of the sensor device, collecting and recording the voltage, current and power, and forming the power supply data stream; Monitor the data transmission status of the communication control device, collect and record the signal strength, data packet loss rate and transmission delay, and form a data transmission data stream.

3. The method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable according to claim 2, characterized in that: The analysis of the real-time status data stream includes performing time series analysis on the power supply data stream and the data transmission data stream, calculating the power fluctuation and the change of the data transmission status in each time period; comparing the calculation result with the preset normal operation threshold value, and determining that the data in a certain time period exceeds the normal range as an abnormal state; The abnormal status includes power failure, data transmission failure and connection failure.

4. The method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable according to claim 3, characterized in that: The identification of the fault type includes identifying a power fault if an abnormal value of current, voltage or power is detected and exceeds a preset normal range, and determining whether it is necessary to switch the power supply path; If data packet loss, signal strength reduction, or transmission delay exceeding a preset tolerance value is detected, it is identified as a data transmission failure and a decision is made as to whether the data transmission path needs to be switched; If a power failure and a data transmission failure are detected at the same time, it is identified as a connection failure, and it is determined whether it is necessary to switch to a backup sensing device or a backup communication control device.

5. The method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable according to claim 4, characterized in that: The automatic switching to the backup sensing device or the communication control device includes, when a power failure is identified, calculating the power demand of the backup sensing device, selecting the backup sensing device according to the current power supply status, and switching the power supply path to the backup sensing device; When a data transmission failure is identified, the data bandwidth requirement of the backup communication control device is calculated, and the most suitable backup communication control device is selected according to the current transmission capacity, and the data transmission path is switched to the backup communication control device; During the switching process, the power demand and data bandwidth demand of the backup device are calculated in real time, and a backup path is selected; During the switching process, the power demand and data bandwidth of the backup device are calculated in real time, and the backup path is selected to ensure the normal operation of the device.

6. The method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable according to claim 5, characterized in that: The automatic switching to the backup sensing device or the communication control device further includes monitoring power fluctuations of the backup sensing device or signal strength and data transmission delay of the backup communication control device; Adjust the working status of the backup equipment based on the detection results, and adjust the load of the backup equipment according to the real-time monitoring data.

7. The method for monitoring the operating status of a split-type intelligent monitoring device based on a connecting cable according to claim 6, characterized in that: The restoring the normal operation of the device includes starting a self-check process after the switching is completed to check the connection status and working status of all devices; If an abnormality is found during the self-check process, an alarm will be issued immediately and the rollback operation will be initiated to restore to the normal state before the switch.

8. A split-type intelligent monitoring device using the split-type intelligent monitoring device operation status method based on connecting cables as described in any one of claims 1 to 7, characterized in that: include: A data monitoring module (100) is used to monitor the power supply status and data transmission status of the sensor device and the communication control device in real time, and generate a real-time status data stream; A fault analysis module (200) is used to analyze the real-time status data stream using a data processing algorithm, determine whether the connection status between the sensor device and the communication control device is abnormal, and identify the fault type based on the connection status determination result; The device switching module (300) is used to automatically switch to a backup sensing device or a communication control device based on the identified fault type, and reconfigure the power supply and data transmission path to restore the normal operation of the device.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for operating status of a split-type intelligent monitoring device based on a connecting cable as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for operating status of a split-type intelligent monitoring device based on a connecting cable as described in any one of claims 1 to 7 are implemented.

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