CN117456707B - Intelligent bus duct temperature and humidity abnormality early warning method and device - Google Patents
Intelligent bus duct temperature and humidity abnormality early warning method and device Download PDFInfo
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Abstract
The invention provides a method and a device for early warning of abnormal temperature and humidity of an intelligent bus duct, and relates to the technical field of intelligent early warning, wherein the method comprises the following steps: acquiring temperature and humidity data from a sensor node; comparing the temperature and humidity data with dynamic factors of the current environment state to obtain a comparison result; performing anomaly detection according to the comparison result to determine an anomaly node; the abnormal node is sent to a main control node, so that the main control node performs path planning according to the received abnormal node information to determine a final communication path; and sending a control instruction to the abnormal node according to the final communication path, so that the early warning device sends out an audible and visual alarm according to the control instruction, and the abnormal early warning of the temperature and the humidity of the bus duct is realized. The method and the device can adapt to the dynamic change of the environment, improve the accuracy of early warning, realize the accurate positioning of the abnormal nodes and improve the timeliness of early warning.
Description
Technical Field
The invention relates to the technical field of intelligent early warning, in particular to an intelligent bus duct temperature and humidity abnormality early warning method and device.
Background
With the continued advancement of technology, intelligent busway systems are becoming increasingly popular in many industrial and commercial applications. The bus duct is taken as an important component of the power system, and whether the running state is stable or not is directly related to the safety and the efficiency of the whole power system. However, in the operation process of the bus duct, due to the influence of various factors such as environmental factors, load changes, equipment aging and the like, the condition of abnormal temperature and humidity easily occurs, so that the normal operation of the bus duct can be influenced, and serious safety accidents can be caused.
At present, for the early warning of the abnormal temperature and humidity of the bus duct, a traditional threshold comparison method is generally adopted. The temperature and humidity data of the bus duct are collected through the sensor nodes, then the temperature and humidity data are compared with a preset threshold value, if the temperature and humidity data exceed the threshold value range, the bus duct is judged to be abnormal, and an alarm is triggered. However, this approach has significant limitations. Firstly, a fixed threshold cannot adapt to the dynamic change of the environment, and false alarm or missing alarm is easy to occur. Secondly, the traditional threshold comparison method cannot comprehensively analyze data of a plurality of sensor nodes, and cannot accurately position abnormal nodes.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the intelligent bus duct temperature and humidity abnormality early warning method and device, which not only can adapt to the dynamic change of the environment and improve the early warning accuracy, but also can realize the accurate positioning of abnormal nodes and improve the early warning timeliness.
In order to solve the technical problems, the technical scheme of the invention is as follows:
in a first aspect, a method for early warning of abnormal temperature and humidity of an intelligent bus duct, the method includes:
acquiring temperature and humidity data from a sensor node;
comparing the temperature and humidity data with dynamic factors of the current environment state to obtain a comparison result;
performing anomaly detection according to the comparison result to determine an anomaly node;
the abnormal node is sent to a main control node, so that the main control node performs path planning according to the received abnormal node information to determine a final communication path;
and sending a control instruction to the abnormal node according to the final communication path, so that the early warning device sends out an audible and visual alarm according to the control instruction, and the abnormal early warning of the temperature and the humidity of the bus duct is realized.
Further, acquiring temperature and humidity data from the sensor node includes:
Sending control signals to each sensor node to activate the built-in temperature and humidity sensor of the sensor node;
according to the control signal, the sensor acquires temperature and humidity data according to the set sampling frequency and resolution;
passing the temperature and humidity dataProcessing to obtain processed temperature and humidity data +.>Wherein->The temperature and humidity data input by the sensor are shown,Nrepresenting the window size of the moving average filter,wthe fusion weights are represented as such,kthe window size of the median filter is indicated,nindicating the current moment +.>Is shown at the momentnPrevious firstkThe time is the same;
the processed temperature and humidity dataAnd packaging the data packets into data packets so that the data packets are sent to the master control node through the wireless communication module.
Further, the data packet includes an identifier of the sensor, a time stamp, a temperature value and a humidity value.
Further, comparing the temperature and humidity data with the dynamic factor of the current environmental state to obtain a comparison result, including:
acquiring current environmental state information;
calculating a dynamic factor through a prediction model according to the current environment state information;
acquiring current temperature and humidity data from a sensor node;
time alignment is carried out on the current temperature and humidity data and the dynamic factors so as to obtain alignment data of the same time point corresponding to the current temperature and humidity data and the dynamic factors;
Carrying out standardization processing on the alignment data to obtain standardized data;
and comparing the standardized data with the dynamic factors to obtain a comparison result.
Further, performing anomaly detection according to the comparison result to determine an anomaly node, including:
setting an abnormality detection standard and a threshold according to actual requirements and application scenes;
obtaining a comparison result, comparing the comparison result with an abnormality detection standard according to the set abnormality detection standard, and calculating an abnormality score for each node;
comparing the abnormal score of each node with a set threshold value, and if the score of one node is greater than the threshold value, the corresponding node is a potential abnormal node;
analyzing the potential abnormal nodes to obtain an analysis result;
and determining abnormal nodes according to the analysis result.
Further, the abnormal node is sent to a master node, so that the master node performs path planning according to the received abnormal node information to determine a final communication path, and the method comprises the following steps:
establishing a communication link between the abnormal node and the main control node;
according to the actual application scene and the network environment, the abnormal node sends related information to the main control node through the established communication link, so that the main control node analyzes and processes the information to extract the abnormal node;
Constructing a search space between the path and the main control node and between the path and the abnormal node;
calculating path cost from the main control node to the abnormal node according to the search space;
traversing adjacent nodes in the search space from the main control node, and determining a final path reaching the abnormal node according to the calculated path cost;
and determining a final communication path according to the final path.
Further, calculating a dynamic factor according to the current environmental state information through a prediction model, including:
acquiring temperature and humidity real-time data of the current environment;
processing the temperature and humidity real-time data of the current environment to obtain a processing result;
constructing a prediction model according to the historical data and the prediction demandWherein->Is a dynamic factor predictor; />Is an intercept term of the predictive model; />，/>，…，/>Regression coefficients corresponding to the environmental state information; />，/>，…，/>Is the state information value of the current environment, +.>Representing the amount of environmental status information；
Training the prediction model to obtain a final prediction model;
and calculating a dynamic factor predicted value according to the final prediction model and the processing result.
In a second aspect, an abnormal temperature and humidity early warning device for an intelligent bus duct includes:
The acquisition module is used for acquiring temperature and humidity data from the sensor nodes; comparing the temperature and humidity data with dynamic factors of the current environment state to obtain a comparison result; performing anomaly detection according to the comparison result to determine an anomaly node;
the processing module is used for sending the abnormal node to the main control node so that the main control node performs path planning according to the received abnormal node information to determine a final communication path; and sending a control instruction to the abnormal node according to the final communication path, so that the early warning device sends out an audible and visual alarm according to the control instruction, and the abnormal early warning of the temperature and the humidity of the bus duct is realized.
In a third aspect, a computing device includes:
one or more processors;
and a storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method.
In a fourth aspect, a computer readable storage medium has a program stored therein, which when executed by a processor, implements the method.
The scheme of the invention at least comprises the following beneficial effects:
By comparing the temperature and humidity data with the dynamic factors of the current environment state, the method can adapt to the dynamic change of the environment, so that the temperature and humidity abnormality of the bus duct can be detected more accurately, the situation of false alarm and missing alarm can be reduced, and the reliability of early warning can be improved. According to the invention, the abnormality detection can be carried out according to the comparison result to determine the specific abnormal node, and the accurate positioning function enables maintenance personnel to quickly find the problem and take effective measures to repair, so that the fault recovery time is shortened, and the operation efficiency of the power system is improved.
By sending the abnormal node information to the main control node, the main control node can carry out path planning according to the received information to determine a final communication path, so that the abnormal information can be timely transmitted to related personnel, and the response can be carried out at the first time. In addition, a control instruction is sent to the abnormal node according to the final communication path, and the early warning device can send out an audible and visual alarm according to the instruction, so that abnormal early warning of the temperature and the humidity of the bus duct is realized. The timely early warning function is helpful for preventing potential safety accidents and guaranteeing the safety of personnel and equipment. The invention performs abnormality detection and path planning, improves the intelligent degree of the whole bus duct system, not only can reduce the frequency and the workload of manual inspection, but also can realize remote monitoring and management of the bus duct system and improve the automation level of the power system.
By timely early warning and accurately positioning the abnormal nodes, the method is beneficial to reducing unnecessary maintenance and replacement cost. Meanwhile, the early warning accuracy and timeliness are improved, and the safety accident risk caused by abnormal temperature and humidity can be reduced, so that the related loss is reduced.
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Fig. 1 is a schematic flow chart of an intelligent bus duct temperature and humidity abnormality early warning method according to an embodiment of the invention.
Fig. 2 is a schematic diagram of an intelligent bus duct temperature and humidity abnormality early warning device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention provides an intelligent bus duct temperature and humidity abnormality early warning method, which includes the following steps:
step 11, acquiring temperature and humidity data from a sensor node;
Step 12, comparing the temperature and humidity data with dynamic factors of the current environment state to obtain a comparison result;
step 13, performing anomaly detection according to the comparison result to determine an anomaly node;
step 14, the abnormal node is sent to a main control node, so that the main control node performs path planning according to the received abnormal node information to determine a final communication path;
and step 15, sending a control instruction to the abnormal node according to the final communication path, so that the early warning device sends out an audible and visual alarm according to the control instruction, and realizing abnormal early warning of the temperature and the humidity of the bus duct.
In the embodiment of the invention, the temperature and humidity data are compared with the dynamic factors of the current environment state, so that the method can adapt to the dynamic change of the environment, and the temperature and humidity abnormality of the bus duct can be detected more accurately, thereby being beneficial to reducing the situations of false alarm and missing alarm and improving the reliability of early warning. According to the invention, the abnormality detection can be carried out according to the comparison result to determine the specific abnormal node, and the accurate positioning function enables maintenance personnel to quickly find the problem and take effective measures to repair, so that the fault recovery time is shortened, and the operation efficiency of the power system is improved. By sending the abnormal node information to the main control node, the main control node can carry out path planning according to the received information to determine a final communication path, so that the abnormal information can be timely transmitted to related personnel, and the response can be carried out at the first time. In addition, a control instruction is sent to the abnormal node according to the final communication path, and the early warning device can send out an audible and visual alarm according to the instruction, so that abnormal early warning of the temperature and the humidity of the bus duct is realized. The timely early warning function is helpful for preventing potential safety accidents and guaranteeing the safety of personnel and equipment. The invention performs abnormality detection and path planning, improves the intelligent degree of the whole bus duct system, not only can reduce the frequency and the workload of manual inspection, but also can realize remote monitoring and management of the bus duct system and improve the automation level of the power system. By timely early warning and accurately positioning the abnormal nodes, the method is beneficial to reducing unnecessary maintenance and replacement cost. Meanwhile, the early warning accuracy and timeliness are improved, and the safety accident risk caused by abnormal temperature and humidity can be reduced, so that the related loss is reduced.
In a preferred embodiment of the present invention, the step 11 may include:
step 111, sending control signals to each sensor node to activate the temperature and humidity sensor built in the sensor node;
step 112, according to the control signal, the sensor collects temperature and humidity data according to the set sampling frequency and resolution;
step 113, passing the temperature and humidity dataProcessing to obtain processed temperature and humidity data +.>Wherein->The temperature and humidity data input by the sensor are shown,Nrepresenting the window size of the moving average filter,wthe fusion weights are represented as such,kthe window size of the median filter is indicated,nindicating the current moment +.>Is shown at the momentnPrevious firstkThe time is the same;
step 114, the processed temperature and humidity data is processedAnd packaging the data packet into a data packet, so that the data packet is sent to a main control node through a wireless communication module, wherein the data packet comprises the identification of the sensor, a time stamp, a temperature value and a humidity value.
In the embodiment of the invention, the built-in temperature and humidity sensor is activated by sending the control signal to each sensor node, so that the sensor can be ensured to work accurately when needed. The specific sampling frequency and the specific resolution are set to collect the data, so that the acquired temperature and humidity data can be ensured to be detailed and accurate, and the requirements of practical application are met. Noise and abnormal values in the original data can be effectively filtered through processing the temperature and humidity data, so that smoother and reliable processed temperature and humidity data are obtained, the accuracy of the data can be improved, and the robustness of the system can be enhanced. And the processed temperature and humidity data are packaged into a data packet, and the identification, the time stamp, the temperature value and the humidity value of the sensor are added, so that the integrity and the traceability of the data can be ensured. This allows the master node or other recipient to know clearly from which sensor the data came, when it was collected, and the specific temperature and humidity values. The wireless communication module is used for transmitting the data packet to the main control node, so that the real-time transmission and centralized management of data can be realized, the data transmission efficiency is improved, the wiring complexity is reduced, and the whole system is more flexible and extensible. Errors and losses in data transmission can be reduced by optimizing the data acquisition and processing process, so that the maintenance difficulty and the operation cost of the system are reduced.
In a preferred embodiment of the present invention, the step 114 may include:
step 1141, determining the length and format of each field according to a predefined packet structure. For example, the sensor identifier is a fixed-length character string or number, the timestamp is a value representing a specific date and time, and the temperature and humidity data is a floating point number or fixed point number;
in step 1142, a packet header is created according to the packet structure, and the packet starts with the header, which includes some key information, such as a packet length, a version number, a verification manner, etc., to help the receiving end (i.e. the master node) to correctly parse the packet. The identification of the insertion sensor, which in the corresponding field of the data packet, may be a serial number, MAC address or other form of unique identifier. And adding a time stamp for recording the data acquisition time into the data packet, wherein the time stamp adopts a standard date and time format, such as a UNIX time stamp, so that a receiving end can accurately know when the data is acquired. The processed temperature and humidity data is inserted into the corresponding position of the data packet, and the processed temperature and humidity data is filtered and smoothed to eliminate noise and abnormal values, so as to provide more accurate environmental state information, and the data can be floating point numbers, fixed point numbers or other suitable representation forms according to a defined format.
In step 1143, a check code is added at the end or head of the data packet in order to improve the reliability of the data. The check code is used for detecting whether the data packet is in error in the transmission process at the receiving end. When all the information is added into the data packet, the data packet is packaged, and the data packet is ready to be sent to the main control node through the wireless communication module. Through the steps, the processed temperature and humidity data and other metadata are effectively packaged in one structured data packet, so that the integrity, accuracy and consistency of the data are ensured, and the master node can reliably analyze the data.
In a preferred embodiment of the present invention, the step 12 may include:
step 121, obtaining current environmental state information;
step 122, calculating a dynamic factor through a prediction model according to the current environment state information;
step 123, acquiring current temperature and humidity data from the sensor node;
step 124, time alignment is performed on the current temperature and humidity data and the dynamic factor to obtain alignment data of the same time point corresponding to the current temperature and humidity data and the dynamic factor;
step 125, performing normalization processing on the alignment data to obtain normalized data;
Step 126, the normalized data is compared with the dynamic factor to obtain a comparison result.
In the embodiment of the invention, the method can adapt to the real-time change of the environment by acquiring the current environment state information and calculating the dynamic factor by using the prediction model, and the dynamic adaptability enables the processing of temperature and humidity data to be more accurate and can reflect the actual situation. The current temperature and humidity data and the dynamic factors are aligned in time, so that the same corresponding time points are ensured, errors caused by time offset are eliminated, and the comparison result is more accurate and reliable through time alignment processing. By normalizing the data, possible deviations between different sensors can be eliminated, making the data more comparable. The standardized data can reflect the temperature and humidity state of the environment more truly. The standardized data is compared with the dynamic factors, so that the abnormal conditions of the temperature and the humidity can be detected more accurately, the accurate comparison is helpful for finding potential problems in time, and corresponding measures are taken for repairing, so that the stable operation of the bus duct system is ensured. Due to the adoption of methods such as dynamic factors, time alignment and standardized processing, the method can reduce the possibility of false alarm and missing alarm and improve the accuracy of abnormality detection. This helps to reduce unnecessary maintenance costs and improves the reliability of the overall system. By combining the environmental state information, the prediction model and the intelligent algorithm, the method improves the intelligent degree of the whole early warning system. The system can be more autonomously adapted to environmental changes, the load of manual monitoring is reduced, and more efficient anomaly detection and early warning are realized.
In another preferred embodiment of the present invention, the step 124 may include:
step 1241, byCalculating the value of the temperature data at the corresponding time point of the dynamic factor; by passing throughCalculating the value of the humidity data at the time point corresponding to the dynamic factor, wherein +.>And->Respectively expressed at the time pointsTemperature data and humidity data after the alignment; />And->Indicated at the time point +.>Raw temperature data and humidity data; />And->Indicated at the time point +.>Raw temperature data and humidity data; /> =/> -/>Representing a time interval; />，/>And->Is a weight coefficient.
In another preferred embodiment of the present invention, the step 125 may include:
step 1251, byCarrying out standardization treatment on the aligned temperature data; by passing throughPerforming normalization treatment on the aligned humidity data, wherein +.>And->Respectively representing normalized temperature data and humidity data; />And->Respectively representing temperature data and humidity data after time alignment; />And->The median of the temperature data and the humidity data are respectively represented; />Andthe median absolute deviation of the temperature data and the humidity data are respectively represented; />Is a scaling factor; />Andthe quartile range of the temperature data and the humidity data are respectively represented; / >Is an adjustment parameter.
In another preferred embodiment of the present invention, the step 126 may include:
step 1261, byCalculating Euclidean distance between the temperature data and the dynamic factor; by passing throughCalculating Euclidean distance between the humidity data and the dynamic factor; wherein (1)>And->Respectively representing cosine similarity between the normalized temperature data and humidity data and the dynamic factor,/I>，And->Respectively represents the normalized temperature and humidity data in the first placeiValues at the respective time points; />And->Respectively represent the dynamic factors in the firstiThe values of the temperature and humidity data at the respective time points,grepresenting the time series length of the data.
Step 1262, determining the similarity or the difference between the standardized temperature and humidity data and the dynamic factor according to the Euclidean distance between the temperature data and the dynamic factor and the Euclidean distance between the humidity data and the dynamic factor, wherein the smaller the Euclidean distance is, the more similar the standardized data and the dynamic factor are; the larger the Euclidean distance is, the larger the difference between the Euclidean distances is, so that the consistency or the difference degree of the normalized temperature and humidity data and the dynamic factor can be evaluated by comparing the two Euclidean distances.
In a preferred embodiment of the present invention, the step 13 may include:
Step 131, setting an abnormality detection standard and a threshold according to the actual requirements and the application scene;
step 132, obtaining a comparison result, comparing the comparison result with an anomaly detection standard according to the set anomaly detection standard, and calculating an anomaly score for each node;
step 133, comparing the abnormal score of each node with a set threshold, if the score of one node is greater than the threshold, the corresponding node is a potential abnormal node;
step 134, analyzing the potential abnormal nodes to obtain an analysis result;
and step 135, determining an abnormal node according to the analysis result.
In the embodiment of the present invention, by setting the anomaly detection standard and the threshold according to the actual requirements and the application scenario in step 131, the anomaly detection is more flexible, and can adapt to different environments and requirements, and different applications may need to pay attention to different anomaly modes, so that the customization capability is very important. Step 132 calculates an anomaly score for each node that provides a quantitative way to evaluate the anomalies of the data, which can give more information to help the decision maker understand the data state more carefully. Step 134 further analyzes the potentially anomalous nodes to further identify or exclude potentially anomalous nodes and possibly discover the root cause of the anomaly. Step 135 determines the true outlier node based on the analysis result. Through the screening and analysis of the preceding steps, this step can provide a more accurate, more confident determination of abnormal nodes. By identifying abnormal nodes, corresponding measures can be taken to prevent or intervene. For example, in a sensor network, an abnormal node may indicate that the sensor is malfunctioning or being disturbed, and timely discovering and handling this problem may ensure the stability and accuracy of the overall network.
In another preferred embodiment of the present invention, the step 134 may include:
step 1341, byAnalyzing the potential abnormal nodes to obtain a more accurate analysis result, wherein +_>Represent the firstiAnomaly scores for the potential anomaly nodes; />，/>，/>，…，/>Respectively represent the firstiStatistical features of the potential anomaly nodes; />，/>，/>，…，/>Is the weight of the corresponding statistical feature; />Representing the number of statistical features +.>Represent the firstiEach potential abnormal node and the latestThe distance of the clustering center; />Is the weight of the cluster distance. Thus, by calculating the anomaly score +.>The true abnormal node can be identified more accurately, so that the accuracy and reliability of abnormality detection are improved.
In a preferred embodiment of the present invention, the step 14 may include:
step 141, establishing a communication link between the abnormal node and the main control node;
step 142, according to the actual application scene and the network environment, the abnormal node sends the related information to the main control node through the established communication link, so that the main control node analyzes and processes the information to extract the abnormal node;
step 143, constructing a search space between the path and the main control node and between the path and the abnormal node;
Step 144, calculating the path cost from the main control node to the abnormal node according to the search space;
step 145, starting from the main control node, traversing adjacent nodes in the search space, and determining a final path reaching the abnormal node according to the calculated path cost;
and step 146, determining a final communication path according to the final path.
In the embodiment of the invention, the communication link is established between the abnormal node and the main control node through the step 141, so that when the abnormality occurs, the related information can be rapidly transmitted to the main control node, thereby realizing rapid response and processing. Step 142 allows the abnormal node to send the relevant information to the master node for analysis and processing, ensuring that the master node can obtain complete and accurate abnormal information. Steps 143 to 145 construct and search paths from the master node to the abnormal node, ensure that an optimal one of a plurality of possible paths is selected, and help to save resources and improve processing efficiency. Through analysis and processing of the abnormal information, the main control node can more accurately judge which abnormalities are real and need to be processed, so that the false alarm rate is reduced, and unnecessary interference to normal nodes is reduced. Determining the final communication path (step 146) means that when an anomaly is handled, the communication resources can be efficiently utilized, reducing unnecessary communication overhead, and improving the operating efficiency of the overall network. The abnormal information is processed and transmitted according to the actual application scene and the network environment (step 142), so that different application requirements and network conditions can be adapted, and good flexibility and adaptability are shown.
In another preferred embodiment of the present invention, the step 144 may include:
step 1441 ofCalculate path cost->Wherein->、/>、/>And->Is a weight coefficient; the hop count represents the number of intermediate nodes which need to pass from the main control node to the abnormal node, and the complexity of the path and possible fault points are increased when one intermediate node is added, so that the hop count is positively related to the path cost; the transmission delay is the time required for transmitting the data packet from the main control node to the abnormal node, the time comprises processing delay, propagation delay and the like, and the longer the transmission delay is, the worse the instantaneity is, thus being positively related to the path cost; bandwidth occupancy refers to the network bandwidth occupied when using the path for data transmission, and high bandwidth occupancy may cause network congestion and reduce the performance of other traffic, thus interfacing with the pathDirect correlation of diameter cost; the security risk is an assessment of the security threat that may be faced with using the path, e.g., some paths may be more vulnerable to attack or eavesdropping, the higher the security risk, the higher the cost of the path should be.
In a preferred embodiment of the present invention, the step 122 may include:
step 1221, acquiring temperature and humidity real-time data of the current environment;
Step 1222, processing the temperature and humidity real-time data of the current environment to obtain a processing result;
step 1223, constructing a prediction model according to the historical data and the predicted demandWherein->Is a dynamic factor predictor; />Is an intercept term of the predictive model; />，/>，…，/>Regression coefficients corresponding to the environmental state information; />，/>，…，/>Is the state information value of the current environment, +.>Representing environmental conditionsThe amount of information;
step 1224, training the prediction model to obtain a final prediction model;
step 1225, calculating a dynamic factor predicted value according to the final prediction model and the processing result.
In the embodiment of the invention, the future environmental state can be predicted more accurately by acquiring the temperature and humidity real-time data of the current environment and combining the historical data, and the change trend of the environment can be captured by combining the real-time and historical data, so that the prediction precision is improved. The noise and the abnormal value in the data can be removed by processing the temperature and humidity real-time data, so that the quality and the accuracy of the data are ensured; by constructing a prediction model and introducing a dynamic factor predicted value, the process can adapt to the changes of different environments and conditions, and the dynamic factor can be adjusted according to the actual conditions of the environments, so that the prediction is more in line with the actual conditions. By using regression coefficients corresponding to the environmental state information, the process can comprehensively consider the influence of various environmental states on the prediction result; the final prediction model is obtained by training the prediction model, so that the model can have better performance in practical application, and the training process can optimize the parameters and the structure of the model to enable the model to reflect the change of the environmental state more accurately. And calculating a dynamic factor predicted value according to the final predicted model and the processing result, and providing real-time environment state predicted information for a decision maker.
In another preferred embodiment of the present invention, the step 1222 may include:
step 12221, byPreliminary processing is performed on the temperature and humidity data of the current environment to obtain an average value +.>Wherein->Is a weight of->In order to size the sliding window it is possible,tfor time (I)>For temperature or humidity data, ">Is an index value;
step 12222, pass-throughCalculating absolute deviation of average value->For detecting possible outliers;
step 12223, for a certain timeIf it deviates from the average by more thanvAbsolute deviation of times(whereinvA constant) then the data is considered an outlier and either culling or replacement is required.
As shown in fig. 2, the embodiment of the present invention further provides an intelligent bus duct temperature and humidity abnormality pre-warning device 20, including:
an acquisition module 21, configured to acquire temperature and humidity data from the sensor node; comparing the temperature and humidity data with dynamic factors of the current environment state to obtain a comparison result; performing anomaly detection according to the comparison result to determine an anomaly node;
the processing module 22 is configured to send the abnormal node to a master node, so that the master node performs path planning according to the received abnormal node information to determine a final communication path; and sending a control instruction to the abnormal node according to the final communication path, so that the early warning device sends out an audible and visual alarm according to the control instruction, and the abnormal early warning of the temperature and the humidity of the bus duct is realized.
Optionally, acquiring temperature and humidity data from the sensor node includes:
sending control signals to each sensor node to activate the built-in temperature and humidity sensor of the sensor node;
according to the control signal, the sensor acquires temperature and humidity data according to the set sampling frequency and resolution;
passing the temperature and humidity dataProcessing to obtain processed temperature and humidity data +.>Wherein->The temperature and humidity data input by the sensor are shown,Nrepresenting the window size of the moving average filter,wthe fusion weights are represented as such,kthe window size of the median filter is indicated,nindicating the current time of day and,is shown at the momentnPrevious firstkThe time is the same;
the processed temperature and humidity dataAnd packaging the data packets into data packets so that the data packets are sent to the master control node through the wireless communication module.
Optionally, the data packet includes an identifier of the sensor, a timestamp, a temperature value, and a humidity value.
Optionally, comparing the temperature and humidity data with a dynamic factor of the current environmental state to obtain a comparison result, including:
acquiring current environmental state information;
calculating a dynamic factor through a prediction model according to the current environment state information;
acquiring current temperature and humidity data from a sensor node;
Time alignment is carried out on the current temperature and humidity data and the dynamic factors so as to obtain alignment data of the same time point corresponding to the current temperature and humidity data and the dynamic factors;
carrying out standardization processing on the alignment data to obtain standardized data;
and comparing the standardized data with the dynamic factors to obtain a comparison result.
Optionally, performing anomaly detection according to the comparison result to determine an anomaly node, including:
setting an abnormality detection standard and a threshold according to actual requirements and application scenes;
obtaining a comparison result, comparing the comparison result with an abnormality detection standard according to the set abnormality detection standard, and calculating an abnormality score for each node;
comparing the abnormal score of each node with a set threshold value, and if the score of one node is greater than the threshold value, the corresponding node is a potential abnormal node;
analyzing the potential abnormal nodes to obtain an analysis result;
and determining abnormal nodes according to the analysis result.
Optionally, the abnormal node is sent to a master node, so that the master node performs path planning according to the received abnormal node information to determine a final communication path, and the method includes:
Establishing a communication link between the abnormal node and the main control node;
according to the actual application scene and the network environment, the abnormal node sends related information to the main control node through the established communication link, so that the main control node analyzes and processes the information to extract the abnormal node;
constructing a search space between the path and the main control node and between the path and the abnormal node;
calculating path cost from the main control node to the abnormal node according to the search space;
traversing adjacent nodes in the search space from the main control node, and determining a final path reaching the abnormal node according to the calculated path cost;
and determining a final communication path according to the final path.
Optionally, calculating the dynamic factor according to the current environmental state information through a prediction model includes:
acquiring temperature and humidity real-time data of the current environment;
processing the temperature and humidity real-time data of the current environment to obtain a processing result;
constructing a prediction model according to the historical data and the prediction demandWherein->Is a dynamic factor predictor; />Is an intercept term of the predictive model; />，/>，…，/>Regression coefficients corresponding to the environmental state information; />，/>，…，/>Is the state information value of the current environment, +. >Representing the amount of environmental status information;
training the prediction model to obtain a final prediction model;
and calculating a dynamic factor predicted value according to the final prediction model and the processing result.
It should be noted that the apparatus is an apparatus corresponding to the above method, and all implementation manners in the above method embodiment are applicable to this embodiment, so that the same technical effects can be achieved.
Embodiments of the present invention also provide a computing device comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
Furthermore, it should be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. Also, the steps of performing the series of processes described above may naturally be performed in chronological order in the order of description, but are not necessarily performed in chronological order, and some steps may be performed in parallel or independently of each other. It will be appreciated by those of ordinary skill in the art that all or any of the steps or components of the methods and apparatus of the present invention may be implemented in hardware, firmware, software, or any combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, as would be apparent to one of ordinary skill in the art upon reading the present specification.
The object of the invention can thus also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general purpose device. The object of the invention can thus also be achieved by merely providing a program product containing program code for implementing said method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. It is apparent that the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The steps of executing the series of processes may naturally be executed in chronological order in the order described, but are not necessarily executed in chronological order. Some steps may be performed in parallel or independently of each other.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (7)
1. An intelligent bus duct temperature and humidity abnormality early warning method is characterized by comprising the following steps:
acquiring temperature and humidity data from a sensor node, comprising: sending control signals to each sensor node to activate the built-in temperature and humidity sensor of the sensor node; according to the control signal, the sensor acquires temperature and humidity data according to the set sampling frequency and resolution; passing the temperature and humidity dataProcessing to obtain processed temperature and humidity data +.>Wherein->The temperature and humidity data input by the sensor are shown,Nrepresenting the window size of the moving average filter,wrepresenting fusion weights, ++>The window size of the median filter is indicated,nindicating the current moment +.>Indicated at the time +.>First->Temperature and humidity data of each moment->Representing the median function>Representing iteration variables in the moving average calculation; the processed temperature and humidity data are->Packaging the data packet into a data packet, so that the data packet is sent to a main control node through a wireless communication module, wherein the data packet comprises a sensor identifier, a time stamp, a temperature value and a humidity value;
comparing the temperature and humidity data with the dynamic factor of the current environment state to obtain a comparison result, wherein the comparison result comprises the following steps: acquiring current environmental state information; calculating a dynamic factor through a prediction model according to the current environment state information; acquiring current temperature and humidity data from a sensor node; time alignment is carried out on the current temperature and humidity data and the dynamic factors so as to obtain alignment data of the same time point corresponding to the current temperature and humidity data and the dynamic factors; carrying out standardization processing on the alignment data to obtain standardized data; comparing the standardized data with the dynamic factors to obtain a comparison result;
Performing anomaly detection according to the comparison result to determine an anomaly node;
the abnormal node is sent to a main control node, so that the main control node performs path planning according to the received abnormal node information to determine a final communication path;
and sending a control instruction to the abnormal node according to the final communication path, so that the early warning device sends out an audible and visual alarm according to the control instruction, and the abnormal early warning of the temperature and the humidity of the bus duct is realized.
2. The method for warning of temperature and humidity anomalies in an intelligent bus duct according to claim 1, wherein anomaly detection is performed according to the comparison result to determine anomaly nodes, comprising:
setting an abnormality detection standard and a threshold according to actual requirements and application scenes;
obtaining a comparison result, comparing the comparison result with an abnormality detection standard according to the set abnormality detection standard, and calculating an abnormality score for each node;
comparing the abnormal score of each node with a set threshold value, and if the score of one node is greater than the threshold value, the corresponding node is a potential abnormal node;
analyzing the potential abnormal nodes to obtain an analysis result;
and determining abnormal nodes according to the analysis result.
3. The method for warning of abnormal temperature and humidity of an intelligent bus duct according to claim 2, wherein the abnormal node is sent to a master control node, so that the master control node performs path planning according to the received abnormal node information to determine a final communication path, and the method comprises the following steps:
establishing a communication link between the abnormal node and the main control node;
according to the actual application scene and the network environment, the abnormal node sends related information to the main control node through the established communication link, so that the main control node analyzes and processes the information to extract the abnormal node;
constructing a search space between the path and the main control node and between the path and the abnormal node;
calculating path cost from the main control node to the abnormal node according to the search space;
traversing adjacent nodes in the search space from the main control node, and determining a final path reaching the abnormal node according to the calculated path cost;
and determining a final communication path according to the final path.
4. The intelligent bus duct temperature and humidity abnormality pre-warning method according to claim 3, characterized in that calculating a dynamic factor through a prediction model according to the current environmental state information comprises:
Acquiring temperature and humidity real-time data of the current environment;
processing the temperature and humidity real-time data of the current environment to obtain a processing result;
constructing a prediction model according to the historical data and the prediction demandWherein, wherein->Is a dynamic factor predictor; />Is an intercept term of the predictive model; />，/>，…，/>Regression coefficients corresponding to the environmental state information; />，/>，…，/>Is the state information value of the current environment, +.>Representing the amount of environmental status information;
training the prediction model to obtain a final prediction model;
and calculating a dynamic factor predicted value according to the final prediction model and the processing result.
5. An abnormal early warning device of intelligent bus duct humiture, its characterized in that includes:
the acquisition module is used for acquiring temperature and humidity data from the sensor node, and comprises the following components: sending control signals to each sensor node to activate the built-in temperature and humidity sensor of the sensor node; according to the control signal, the sensor is driven to acquire according to the set sampling frequency and resolutionCollecting temperature and humidity data; passing the temperature and humidity dataProcessing to obtain processed temperature and humidity data +.>Wherein->The temperature and humidity data input by the sensor are shown, NRepresenting the window size of the moving average filter,wrepresenting fusion weights, ++>The window size of the median filter is indicated,nindicating the current time of day and,indicated at the time +.>First->Temperature and humidity data of each moment->Representing the median function>Representing iteration variables in the moving average calculation; the processed temperature and humidity data are->Packaging the data packet into a data packet, so that the data packet is sent to a main control node through a wireless communication module, wherein the data packet comprises a sensor identifier, a time stamp, a temperature value and a humidity value; comparing the temperature and humidity data with the dynamic factor of the current environment state to obtain a comparison resultComprising: acquiring current environmental state information; calculating a dynamic factor through a prediction model according to the current environment state information; acquiring current temperature and humidity data from a sensor node; time alignment is carried out on the current temperature and humidity data and the dynamic factors so as to obtain alignment data of the same time point corresponding to the current temperature and humidity data and the dynamic factors; carrying out standardization processing on the alignment data to obtain standardized data; comparing the standardized data with the dynamic factors to obtain a comparison result; performing anomaly detection according to the comparison result to determine an anomaly node;
The processing module is used for sending the abnormal node to the main control node so that the main control node performs path planning according to the received abnormal node information to determine a final communication path; and sending a control instruction to the abnormal node according to the final communication path, so that the early warning device sends out an audible and visual alarm according to the control instruction, and the abnormal early warning of the temperature and the humidity of the bus duct is realized.
6. A computing device, comprising:
one or more processors;
storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1 to 4.
7. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program which, when executed by a processor, implements the method according to any of claims 1 to 4.
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