Real-time fault early warning method and device based on environment monitoring, terminal and storage medium

Abstract

The invention discloses a real-time fault early warning method and device based on environment monitoring, a terminal and a storage medium, and belongs to the field of environment monitoring, and the method is applied to an environment monitoring network, and comprises the steps: obtaining the number information of a plurality of sensors, and grouping the plurality of sensors according to the number information and a preset grouping standard; environment parameter data collected by all sensors in each group are obtained, the working state of each group is monitored, and when an abnormal signal of any group is received, early warning response judgment is carried out according to the abnormal signal; when the number of the abnormal signals of the single group is not smaller than a preset signal threshold value, historical environment parameter data collected by each sensor in the corresponding group are obtained, and a dynamic baseline model is established according to the historical environment parameter data and the real-time environment parameter data; and according to the real-time environment parameter data and the dynamic baseline model, a fault analysis result is generated in combination with a preset parameter threshold. According to the method provided by the invention, a multi-level fault early warning response mechanism is introduced, accurate identification of different degrees of faults in the environment monitoring system can be realized, a targeted response strategy is adopted, unnecessary human resources are saved, and the processing efficiency of the monitored environment faults is improved.

Description

Technical Field

[0001] The present application relates to the field of environmental monitoring, and in particular to a real-time fault warning method, device, terminal and storage medium based on environmental monitoring. Background Art

[0002] In the field of environmental monitoring, with the continuous development of science and technology, environmental monitoring systems have achieved efficient and real-time detection of various environmental parameters (such as temperature, humidity, air quality, etc.). Such systems usually rely on advanced sensor networks, can accurately collect various environmental data, and provide strong support for environmental regulation and management through data processing and analysis technology.

[0003] Although current environmental monitoring systems have made significant progress in data collection and processing, they still face many challenges in fault warning. Traditional environmental monitoring systems often lack macroscopic control over the overall operating status of the system. As long as a sensor failure occurs, the same warning will be issued regardless of the fault condition. Under normal monitoring conditions, abnormalities may occur due to failures of individual sensors themselves or occasional instability of the environment. Such abnormalities are not enough to affect the overall monitoring. When the sensor detects it, the system will still issue an early warning, which may waste human resources to troubleshoot. In addition, when multi-sensor data anomalies or system-level failures occur, it is difficult to know the root cause of the problem in a timely and accurate manner, which in turn affects further troubleshooting and the continuity and accuracy of environmental monitoring.

[0004] In addition, with the continuous expansion of environmental monitoring application scenarios, such as constant temperature laboratories and precision equipment storage environments, the requirements for system stability and reliability are also increasing. Therefore, how to introduce a more accurate real-time fault warning mechanism based on the traditional environmental monitoring system has become an urgent problem to be solved in this field. Summary of the invention

[0005] In order to more accurately warn of possible faults in an environmental monitoring system, reduce unnecessary waste of resources, and ensure the accuracy and reliability of environmental parameters, the present invention proposes a real-time fault warning method, device, terminal and storage medium based on environmental monitoring.

[0006] In the first aspect, the present application provides a real-time fault warning method based on environmental monitoring, which adopts the following technical solution: A real-time fault warning method based on environmental monitoring, the method is applied to an environmental monitoring network, the environmental monitoring network includes a central processing unit and a plurality of sensors, the central processing unit is used to receive real-time environmental parameter data collected by the plurality of sensors, and comprises the following steps: Acquire serial number information of the plurality of sensors, and group the plurality of sensors according to the serial number information and a preset grouping standard; Acquire the environmental parameter data collected by all the sensors in each of the groups, monitor the working status of each of the groups, and when an abnormal signal of any of the groups is received, make an early warning response judgment according to the abnormal signal; When the number of abnormal signals in a single group is not less than a preset signal threshold, historical environmental parameter data collected by each sensor in the corresponding group is obtained, and a dynamic baseline model is established based on the historical environmental parameter data and the real-time environmental parameter data; A fault analysis result is generated based on the real-time environmental parameter data and the dynamic baseline model in combination with preset parameter thresholds.

[0007] By adopting the above technical solution, early warning response judgment is made for abnormal signals emitted by sensors, different early warning mechanisms are introduced, and multi-level early warning is made according to the judgment results. It is possible to accurately identify and respond to faults of different degrees in the environmental monitoring system, save resources, and improve the early warning speed. For relatively serious faults, a dynamic baseline model is established for the anomaly to conduct a more detailed fault analysis, which can accurately locate the root cause of the fault; and by grouping sensors, the approximate range of abnormal sensors is preliminarily determined. For some groups with low importance, it is unnecessary to waste too much resources for analysis, which can save unnecessary waste of resources.

[0008] Preferably, the step of acquiring the environmental parameter data collected by all the sensors and monitoring the working status of each of the groups specifically includes the following steps: Sending a status confirmation signal to all the sensors in each of the groups to monitor the working status of all the sensors; When the number of response signals to the status confirmation signal received in any of the groups is different from the number of sensors in the corresponding group, marking all the status confirmation signals that have not received a response and summarizing them as abnormal signals; When the number of response signals to the status confirmation signal in any of the groups is the same as the number of sensors in the corresponding group, the environmental parameter data of the sensors in the current group are obtained, and the data quality of the environmental parameter data is monitored. When the data quality is abnormal, all the environmental parameter data with abnormal data quality are summarized as the abnormal signal.

[0009] By adopting the above technical solution, the working status of each group is monitored by sending status confirmation signals and monitoring the data quality of environmental parameter data, so as to obtain the number of abnormal signals in each group, and the number of abnormal signals is convenient for subsequent judgment of the warning level.

[0010] Preferably, when an abnormal signal of any of the groups is received, performing an early warning response judgment according to the abnormal signal comprises the following steps: The results of the early warning response judgment include primary early warning response, intermediate early warning response and advanced early warning response, which are judged according to the number of abnormal signals; If the number of abnormal signals in the current group is 1, the primary warning response is performed on the sensors in the current group; If the number of abnormal signals in the current group is greater than 1 and less than a preset signal threshold, an intermediate warning response is performed on the sensors in the current group; If the number of the abnormal signals in the current group is not less than the signal threshold, an advanced warning response is performed on the sensors in the current group.

[0011] By adopting the above technical solution, the judgment rules for multi-level warnings are explained in detail, ensuring the rationality of multi-level warning responses.

[0012] Preferably, the method further comprises the following steps: When the primary warning response is performed, a primary warning signal is issued, wherein the primary warning signal includes information of the current group; When the intermediate warning response is performed, an intermediate warning signal is issued, and the intermediate warning signal includes the information of the current group and the fault details.

[0013] Preferably, the acquiring of the historical environmental parameter data collected by each sensor in the corresponding group and establishing a dynamic baseline model according to the historical environmental parameter data and the real-time environmental parameter data specifically includes the following steps: Determine the monitored environmental parameters, collect historical environmental parameter data within a preset time range, determine the baseline value of each environmental parameter, obtain the current timestamp, initialize the dynamic baseline model using sensor data within a preset number of frames closest to the current timestamp, and use the dynamic baseline model to monitor each environmental parameter in real time.

[0014] Preferably, the generating of the fault analysis result based on the real-time environmental parameter data and the dynamic baseline model in combination with a preset parameter threshold specifically includes the following steps: When performing the advanced warning response, the environmental parameter data collected by all the sensors in the corresponding group are compared with the baseline data of the dynamic baseline model, the deviation value between the environmental parameter data and the baseline data is calculated, a deviation time range is set, and all the environmental parameter data whose deviation values ​​exceed the preset parameter threshold within the deviation time range are obtained and marked as faulty environmental parameter data.

[0015] By adopting the above technical solution, a dynamic baseline model is established through historical environmental parameter data and the data recently collected by the sensor. By using this model, more accurate data monitoring of faulty sensors can be performed during advanced response warnings, thereby obtaining accurate fault environmental parameter data and ensuring the accuracy of the final results.

[0016] Preferably, the method further comprises the following steps: Analyze the fault environment parameter data to obtain the number information of the sensor corresponding to the fault environment parameter data, and generate and send an advanced warning signal according to the fault environment parameter data; A fault analysis result is generated based on the primary warning signal, the intermediate warning signal and the fault data advanced warning signal.

[0017] By adopting the above technical solution, the faulty sensor can be accurately known through advanced early warning response, and all early warning results are summarized to make the fault warning results more intuitive, making it easier for managers to take countermeasures.

[0018] In the second aspect, the present application provides a real-time fault warning device based on environmental monitoring, which adopts the following technical solution: A real-time fault warning device based on environmental monitoring includes the following modules: A sensor grouping module is configured to obtain number information of the plurality of sensors, and group the plurality of sensors according to the number information and a preset grouping standard; An early warning response judgment module is configured to obtain the environmental parameter data collected by all the sensors in each of the groups, monitor the working status of each of the groups, and when an abnormal signal of any of the groups is received, make an early warning response judgment according to the abnormal signal; A dynamic baseline model building module is configured to obtain historical environmental parameter data collected by each sensor in the corresponding group when the number of abnormal signals in a single group is not less than a preset signal threshold, and to build a dynamic baseline model based on the historical environmental parameter data and the real-time environmental parameter data; The fault analysis module is configured to generate a fault analysis result based on the real-time environmental parameter data and the dynamic baseline model in combination with a preset parameter threshold.

[0019] By adopting the above technical solutions, a real-time fault warning system has been built, which provides the necessary software technical support for the fault warning of environmental monitoring, realizes comprehensive, timely and accurate fault warning, and provides strong support for the stable operation of the environmental monitoring system.

[0020] In a third aspect, the present application provides a smart terminal, which adopts the following technical solution: An intelligent terminal includes a memory and a processor, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the real-time fault warning method based on environmental monitoring as described above.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, wherein at least one instruction, at least one program, code set or instruction set is stored in the readable storage medium, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the real-time fault warning method based on environmental monitoring as described above.

[0022] In summary, this application has at least the following beneficial effects: 1. This application introduces a multi-level fault warning response mechanism, which is divided according to the severity of the fault. It can accurately identify faults of different degrees in the environmental monitoring system and adopt targeted response strategies, saving unnecessary human resources and improving the efficiency of handling monitoring environment faults.

[0023] 2. This application can respond to faults in real time according to preset strategies and rules, and at the same time conduct effective analysis of the faults, effectively guide management personnel to troubleshoot and handle the faults, ensure the accuracy of the monitoring results, and ensure the stability and reliability of environmental monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of a real-time fault warning method based on environmental monitoring according to an embodiment of the present application; Figure 2 is a flow chart of step S2 of the real-time fault warning method based on environmental monitoring in an embodiment of the present application; Figure 3 It is a structural diagram of a real-time fault warning device based on environmental monitoring according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] The present application provides a real-time fault warning method, device, terminal and storage medium based on environmental monitoring. In order to make the purpose, technical solution and advantages of the present application clearer, the implementation method of the present application will be further described in detail below.

[0026] The following is a further detailed description of an embodiment of a real-time fault warning method based on environmental monitoring of the present application in conjunction with the drawings of the specification.

[0027] The present application discloses a real-time fault warning method based on environmental monitoring. The method is applied to an environmental monitoring network. The environmental monitoring network includes a central processing unit and multiple sensors. The central processing unit is used to receive real-time environmental parameter data collected by multiple sensors. The environmental parameters include temperature parameters, humidity parameters, light parameters, etc. The implementation subject of the method of this embodiment is the central processing unit. The method flow is as follows: Figure 1 As shown, the following steps are included: S1. Acquire serial number information of multiple sensors, and group the multiple sensors according to the serial number information and a preset grouping standard.

[0028] In a specific implementation method, each sensor in the environmental monitoring network is numbered, the central processing unit obtains the numbering information of each sensor, sets a grouping standard, and groups all the sensors. The grouping standard in this embodiment is to group every 20 sensors with adjacent numbers in order of numbering.

[0029] In other implementations, the grouping criteria may also be grouping by sensor type, such as grouping all temperature sensors into one group and grouping all humidity sensors into one group; the grouping criteria may also be grouping by region, such as grouping all sensors in a region into one group, etc.

[0030] S2. Obtain the environmental parameter data collected by all sensors in each group, monitor the working status of each group, and when an abnormal signal from any group is received, make an early warning response judgment based on the abnormal signal. The process is as follows: Figure 2 As shown, the specific steps include: S21. Send a status confirmation signal to all sensors in each group to monitor the working status of all sensors.

[0031] S22. When the number of response signals to the status confirmation signal received in any group is different from the number of sensors in the corresponding group, it is determined that the working status of the group is abnormal.

[0032] In this embodiment, if the connection between the central processing unit and the sensor is normal, then each sensor will send a response signal to the status confirmation signal. If the number of response signals received by the central processing unit from the sensors is different from the number of sensors, then it can be determined that the sensor that did not send a response signal did not receive the status confirmation signal, or the sensor sent a response signal but the central processing unit did not receive it. In both cases, the abnormality can be determined as an abnormal connection between the central processing unit and the sensor.

[0033] S23. Mark all the status confirmation signals that have not received a response and summarize them as abnormal signals, and count the number of abnormal signals.

[0034] In this implementation, the number of abnormal signals at this time represents the number of sensors with abnormal connections in the current group.

[0035] S24: When the number of response signals to the status confirmation signal in any group is the same as the number of sensors in the corresponding group, it means that all sensors are connected normally. At this time, the environmental parameter data of the sensors in the current group are obtained to monitor the data quality of the environmental parameter data.

[0036] The data quality of environmental parameter data includes data frequency and data stability. The method for monitoring data frequency is: preset a transmission frequency for the sensor, and when the transmission frequency is lower than the preset minimum frequency, it is determined that the data frequency is abnormal; for example, in this embodiment, the preset transmission frequency is 15 data points per minute, and when the actual transmission frequency is lower than 12 data points per minute, it is determined that the data frequency is abnormal.

[0037] The method for monitoring data stability is: when the environmental parameter data fluctuates beyond a preset range within a preset period of time, it is determined that the data stability is abnormal; for example, in the present embodiment, for the temperature parameter data monitored by the temperature sensor, if there is a fluctuation of more than ±5°C within 1 hour at any time point, it is determined that the stability of the temperature parameter data is abnormal. The remaining environmental parameter data are set according to demand and will not be repeated here.

[0038] In this embodiment, when any one of the data frequency and the data stability of a certain sensor is abnormal, it is determined that the data quality of the environmental parameter data of the sensor is abnormal.

[0039] S25. When data quality is abnormal, all environmental parameter data with abnormal data quality are summarized as abnormal signals, and the number of abnormal signals is counted.

[0040] It should be noted that the number of abnormal signals is based on the number of sensors. If one or more environmental parameter data of a sensor is abnormal, only one abnormal signal is counted.

[0041] S26. When an abnormal signal of any group is received, an early warning response judgment is made according to the number of abnormal signals. It should be noted that the central processing unit does not start abnormal response judgment as soon as an abnormal signal is received, but judges once per receiving cycle within a fixed receiving cycle. In this embodiment, the receiving cycle is 5 minutes, and the duration can be set according to actual conditions.

[0042] The results of the early warning response judgment include primary early warning response, intermediate early warning response and advanced early warning response.

[0043] If the number of abnormal signals in the current group is 1, a primary warning response is made to the sensors in the current group. Since the number of abnormal signals is 1, it is very likely that a sensor itself has an abnormality, which is not universal, and only one abnormality will not affect the overall environmental monitoring. Therefore, it is regarded as a primary fault and there is no need to pay too much attention to it and waste manpower. It only needs to indicate that there is an abnormality.

[0044] When a primary warning response is performed, a primary warning signal is issued, for example, in this embodiment, the management personnel is reminded by an interface prompt. The primary warning signal includes information about the current group, that is, the interface prompt includes which group the current sensor is in.

[0045] If the number of abnormal signals in the current group is greater than 1 and less than the preset signal threshold, an intermediate warning response is made to the sensors in the current group; the setting standard of this signal threshold should be less than the number of sensors that can cause significant impact. Therefore, in a specific implementable method, the signal threshold can be set according to the number of sensors. For example, if the number of sensors in a group is 20, then the signal threshold can be 5.

[0046] When an intermediate warning response is carried out, an intermediate warning signal is issued, and the central processing unit sends a warning signal that is more urgent than the primary warning signal, such as notifying the management personnel through text messages, emails, etc. At the same time, detailed information of the fault is recorded, such as the fault type is abnormal data frequency or abnormal data stability, etc., to facilitate subsequent analysis and processing.

[0047] If the number of abnormal signals in the current group is not less than the signal threshold, an advanced warning response is performed on the sensors in the current group.

[0048] At this time, if an advanced warning response is triggered, the central processing unit needs to carry out the corresponding response process.

[0049] S3. When the number of abnormal signals in a single group is not less than the preset signal threshold, an advanced warning response is triggered, and the historical environmental parameter data collected by each sensor in the corresponding group is obtained. A dynamic baseline model is established based on the historical environmental parameter data, which specifically includes the following steps: S31. Determine the monitored environmental parameters, collect historical environmental parameter data within a preset time range, screen and clean the historical environmental parameter data, and perform statistics to obtain statistical results. For example, in this embodiment, the method of calculating the average value can be used to perform statistics to obtain the average value of the historical environmental parameter data of all environmental parameters, and use it as the baseline value of each environmental parameter.

[0050] S32. Obtain sensor data within a preset number of frames of the current timestamp and the closest timestamp, for example, data of a few hours or days closest to the current timestamp. In this embodiment, sensor data collected within 3 hours of the current timestamp is selected, and the dynamic baseline model is established and initialized with this data.

[0051] S33. Use a dynamic baseline model to monitor various environmental parameters in real time.

[0052] It should be noted that the process of establishing a dynamic baseline model is a process of continuous iteration and optimization, which requires continuous collection and analysis of real-time environmental parameter data, and dynamic adjustment of baseline values ​​according to changes in environmental parameter data. Through this process, it can be ensured that the model can accurately reflect the normal range of environmental parameters, obtain environmental parameters where failures occur, and provide strong support for the stable operation of the environmental monitoring system. Since the process of establishing a dynamic baseline model is a public technology, it will not be repeated here.

[0053] S4. Generate fault analysis results based on real-time environmental parameter data and dynamic baseline models in combination with preset parameter thresholds, which specifically includes the following steps.

[0054] S41. When performing an advanced warning response, the environmental parameter data collected by all sensors in the corresponding group are compared with the baseline data of the dynamic baseline model, and the deviation value between the environmental parameter data and the baseline data is calculated. The deviation value can be measured by absolute value difference, percentage difference, standard deviation, etc. In this embodiment, the absolute value difference is used for calculation.

[0055] S42: Set a deviation time range, obtain all environmental parameter data whose deviation values ​​exceed a preset parameter threshold within the deviation time range, and mark them as faulty environmental parameter data.

[0056] The reason for setting the deviation time range is that a short deviation may be a temporary interference or a false alarm. In order to avoid the above situation, the deviation time range is set. As long as the deviation lasts for a period of time reaching this deviation time range and the deviation value exceeds the parameter threshold, the corresponding environmental parameter data is obtained and marked as faulty environmental parameter data.

[0057] S43, analyzing the fault environment parameter data, obtaining the number information of the sensor corresponding to the fault environment parameter data, and generating and sending an advanced warning signal according to the fault environment parameter data.

[0058] In the advanced warning response, since the fault level is the highest, it is not enough to just send a prompt signal as a high-level warning signal. In this embodiment, a dynamic baseline model is used to perform more detailed data monitoring, and further fault monitoring and analysis are performed to obtain more accurate and detailed fault information, including grouping information and fault type. Unlike the intermediate warning signal, the advanced warning signal also includes the sensor number information, which is convenient for management personnel to quickly locate the faulty sensor and implement measures to deal with the fault.

[0059] In other specific implementation methods, after the central processing unit sends a high-level warning signal, if the fault is not handled within a certain period of time, the faulty sensor will be automatically turned off and the backup sensor will be started to ensure the continuity of environmental monitoring. This time can be set according to actual needs.

[0060] S44. Generate a fault analysis result based on the primary warning signal, the intermediate warning signal and the fault data advanced warning signal.

[0061] The fault analysis result is to summarize the three levels of warning signals. The summary result can be a visual result such as a table. In this embodiment, it is a table, which records in detail the information of each group where the fault occurs, the warning response level of the group, and if it is an intermediate warning response, it will also include the fault type of the group. If it is a high-level warning level, in addition to the fault type, it will also include the number information of all faulty sensors in the group.

[0062] Based on the same inventive concept as above, the embodiment of the present application also discloses a real-time fault warning device based on environmental monitoring, the structure of which is as follows: Figure 3 As shown, the device includes the following modules: The sensor grouping module is configured to obtain serial number information of a plurality of sensors and group the plurality of sensors according to the serial number information and a preset grouping standard; The early warning response judgment module is configured to obtain the environmental parameter data collected by all sensors in each group, monitor the working status of each group, and when an abnormal signal of any group is received, make an early warning response judgment based on the abnormal signal; The dynamic baseline model building module is configured to obtain historical environmental parameter data collected by each sensor in the corresponding group when the number of abnormal signals in a single group is not less than a preset signal threshold, and to build a dynamic baseline model based on the historical environmental parameter data and the real-time environmental parameter data; The fault analysis module is configured to generate fault analysis results based on real-time environmental parameter data and a dynamic baseline model in combination with preset parameter thresholds.

[0063] In a specific feasible implementation scheme, the early warning response judgment module includes the following units: The first early warning response judgment unit is configured to send a status confirmation signal to all sensors in each group to monitor the working status of all sensors; when the number of response signals to the status confirmation signal received in any group is different from the number of sensors in the corresponding group, mark all the status confirmation signals that have not received a response and summarize them as abnormal signals; when the number of response signals to the status confirmation signal in any group is the same as the number of sensors in the corresponding group, obtain the environmental parameter data of the sensors in the current group, monitor the data quality of the environmental parameter data, and when the data quality is abnormal, summarize all the environmental parameter data with abnormal data quality as abnormal signals.

[0064] The second early warning response judgment unit is configured to make a judgment based on the number of abnormal signals; if the number of abnormal signals in the current group is 1, a primary early warning response is made to the sensors in the current group; if the number of abnormal signals in the current group is greater than 1 and less than a preset signal threshold, an intermediate early warning response is made to the sensors in the current group; if the number of abnormal signals in the current group is not less than the signal threshold, an advanced early warning response is made to the sensors in the current group.

[0065] The third early warning response judgment unit is configured to send out a primary early warning signal when performing a primary early warning response, and the primary early warning signal includes information of the current group; and to send out an intermediate early warning signal when performing an intermediate early warning response, and the intermediate early warning signal includes information of the current group and fault details.

[0066] In a specific implementation scheme, the dynamic baseline model establishment module includes the following units: The dynamic baseline model building unit is configured to determine the monitored environmental parameters, collect historical environmental parameter data within a preset time range, determine the baseline value of each environmental parameter, obtain the current timestamp, initialize the dynamic baseline model using the sensor data within a preset number of frames closest to the current timestamp, and use the dynamic baseline model to monitor each environmental parameter in real time.

[0067] In a specific implementation scheme, the fault analysis module includes the following units: The first fault analysis unit is configured to compare the environmental parameter data collected by all sensors in the corresponding group with the baseline data of the dynamic baseline model when performing an advanced warning response, calculate the deviation value between the environmental parameter data and the baseline data, set a deviation time range, obtain all environmental parameter data whose deviation values ​​exceed a preset parameter threshold within the deviation time range, and mark them as faulty environmental parameter data.

[0068] A second fault analysis unit is configured to analyze the fault environment parameter data, obtain the number information of the sensor corresponding to the fault environment parameter data, and generate and send an advanced warning signal according to the fault environment parameter data; The third fault analysis unit is configured to generate a fault analysis result according to the primary warning signal, the intermediate warning signal and the fault data advanced warning signal.

[0069] From the above function introduction, it can be seen that a real-time fault warning device based on environmental monitoring in this application has built a standard fault warning system. By introducing a multi-level warning mechanism, it ensures the pertinence and accuracy of the warning process, helps to achieve effective use of resources, and promotes the sustained, rapid and healthy development of the economy and society.

[0070] Based on the same inventive concept mentioned above, an embodiment of the present application also discloses a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set. The at least one instruction, at least one program, code set or instruction set can be loaded and executed by a processor to implement the real-time fault warning method based on environmental monitoring provided in the above method embodiment.

[0071] Also based on the same inventive concept mentioned above, an embodiment of the present application also discloses a computer-readable storage medium, in which at least one instruction, at least one program, code set or instruction set is stored. The at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the real-time fault warning method based on environmental monitoring as described above.

[0072] Those skilled in the art will appreciate that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in the computer-readable storage medium, which may include, for example, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0073] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A real-time fault warning method based on environmental monitoring, the method is applied to an environmental monitoring network, the environmental monitoring network includes a central processing unit and a plurality of sensors, the central processing unit is used to receive real-time environmental parameter data collected by the plurality of sensors, characterized in that: The steps include: Acquire serial number information of the plurality of sensors, and group the plurality of sensors according to the serial number information and a preset grouping standard; Acquire the environmental parameter data collected by all the sensors in each of the groups, monitor the working status of each of the groups, and when an abnormal signal of any of the groups is received, make an early warning response judgment according to the abnormal signal; When the number of abnormal signals in a single group is not less than a preset signal threshold, historical environmental parameter data collected by each sensor in the corresponding group is obtained, and a dynamic baseline model is established based on the historical environmental parameter data and the real-time environmental parameter data; A fault analysis result is generated based on the real-time environmental parameter data and the dynamic baseline model in combination with preset parameter thresholds.

2. The real-time fault warning method based on environmental monitoring according to claim 1 is characterized in that: The step of acquiring the environmental parameter data collected by all the sensors and monitoring the working status of each group specifically includes the following steps: Sending a status confirmation signal to all the sensors in each of the groups to monitor the working status of all the sensors; When the number of response signals to the status confirmation signal received in any of the groups is different from the number of sensors in the corresponding group, marking all the status confirmation signals that have not received a response and summarizing them as abnormal signals; When the number of response signals to the status confirmation signal in any of the groups is the same as the number of sensors in the corresponding group, the environmental parameter data of the sensors in the current group are obtained, and the data quality of the environmental parameter data is monitored. When the data quality is abnormal, all the environmental parameter data with abnormal data quality are summarized as the abnormal signal.

3. The real-time fault warning method based on environmental monitoring according to claim 1 is characterized in that: When receiving an abnormal signal of any of the groups, performing an early warning response judgment according to the abnormal signal specifically includes the following steps: The results of the early warning response judgment include primary early warning response, intermediate early warning response and advanced early warning response, which are judged according to the number of abnormal signals; If the number of abnormal signals in the current group is 1, the primary warning response is performed on the sensors in the current group; If the number of abnormal signals in the current group is greater than 1 and less than a preset signal threshold, an intermediate warning response is performed on the sensors in the current group; If the number of the abnormal signals in the current group is not less than the signal threshold, an advanced warning response is performed on the sensors in the current group.

4. The real-time fault warning method based on environmental monitoring according to claim 3 is characterized in that: The following steps are also included: When the primary warning response is performed, a primary warning signal is issued, wherein the primary warning signal includes information of the current group; When the intermediate warning response is performed, an intermediate warning signal is issued, and the intermediate warning signal includes the information of the current group and the fault details.

5. The real-time fault warning method based on environmental monitoring according to claim 1 is characterized in that: The acquiring of the historical environmental parameter data collected by each sensor in the corresponding group, and establishing a dynamic baseline model according to the historical environmental parameter data and the real-time environmental parameter data specifically includes the following steps: Determine the monitored environmental parameters, collect historical environmental parameter data within a preset time range, determine the baseline value of each environmental parameter, obtain the current timestamp, initialize the dynamic baseline model using sensor data within a preset number of frames closest to the current timestamp, and use the dynamic baseline model to monitor each environmental parameter in real time.

6. The real-time fault early warning method based on environmental monitoring according to claim 4 is characterized in that: The method of generating a fault analysis result based on the real-time environmental parameter data and the dynamic baseline model in combination with a preset parameter threshold specifically includes the following steps: When performing the advanced warning response, the environmental parameter data collected by all the sensors in the corresponding group are compared with the baseline data of the dynamic baseline model, the deviation value between the environmental parameter data and the baseline data is calculated, a deviation time range is set, and all the environmental parameter data whose deviation values ​​exceed the preset parameter threshold within the deviation time range are obtained and marked as faulty environmental parameter data.

7. The real-time fault warning method based on environmental monitoring according to claim 6 is characterized in that: The following steps are also included: Analyze the fault environment parameter data to obtain the number information of the sensor corresponding to the fault environment parameter data, and generate and send an advanced warning signal according to the fault environment parameter data; A fault analysis result is generated based on the primary warning signal, the intermediate warning signal and the fault data advanced warning signal.

8. A real-time fault warning device based on environmental monitoring, characterized in that: Includes the following modules: A sensor grouping module is configured to obtain number information of the plurality of sensors, and group the plurality of sensors according to the number information and a preset grouping standard; An early warning response judgment module is configured to obtain the environmental parameter data collected by all the sensors in each of the groups, monitor the working status of each of the groups, and when an abnormal signal of any of the groups is received, make an early warning response judgment according to the abnormal signal; A dynamic baseline model building module is configured to obtain historical environmental parameter data collected by each sensor in the corresponding group when the number of abnormal signals in a single group is not less than a preset signal threshold, and to build a dynamic baseline model based on the historical environmental parameter data and the real-time environmental parameter data; The fault analysis module is configured to generate a fault analysis result based on the real-time environmental parameter data and the dynamic baseline model in combination with a preset parameter threshold.

9. An intelligent terminal, characterized in that: It includes a memory and a processor, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the real-time fault warning method based on environmental monitoring as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The readable storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the real-time fault warning method based on environmental monitoring as described in any one of claims 1 to 7.

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