A detection method, system and device for monitoring abnormal state of instrument box

Abstract

The present invention discloses a detection method, system and device for abnormal state of a monitoring instrument box, the method comprising: obtaining initial sensing data and initial positioning data of the monitoring instrument box by responding to a detection request sent by a user terminal, selecting corresponding sensing detection strategies and positioning detection strategies according to the initial sensing data and initial positioning data, polling the real-time sensing data and real-time positioning data of the monitoring instrument box, generating a first polling result and a second polling result, and using the first polling result and the second polling result to determine whether the monitoring instrument box is in an abnormal state. During the entire detection process, the terminal can detect the abnormal state of the monitoring instrument box online through the set sensing detection strategy and positioning detection strategy, thereby improving the detection accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment status detection, and in particular to a detection method, system and equipment for monitoring abnormal status of an instrument box. Background Art

[0002] As power grid construction continues to advance, the use of power monitoring instruments to monitor the status of power facilities has become an increasing focus of attention. Power monitoring instruments are used in conjunction with monitoring instrument boxes. In order to improve the operational reliability of power facilities, it is necessary to strengthen the protection of monitoring instrument boxes.

[0003] To strengthen the protection of monitoring instrument boxes, currently, electronic anti-theft devices such as infrared detection alarm and vibration alarm are mainly installed on monitoring instrument boxes. However, due to the complex distribution environment of monitoring instrument boxes, it is easy to trigger the electronic anti-theft devices to send out false positives when encountering severe weather such as strong winds and lightning, small rodents or large vehicles passing by, and maintenance staff need to go to the location of the monitoring instrument box to find out that a false positive has been sent. Therefore, the detection accuracy of abnormal status of monitoring instrument boxes needs to be further improved. Summary of the invention

[0004] The present invention provides a method, system and device for detecting abnormal state of a monitoring instrument box, which solves the technical problem that the detection accuracy of abnormal state of a monitoring instrument box is low mainly through electronic anti-theft devices in the prior art.

[0005] A first aspect of the present invention provides a method for detecting an abnormal state of a monitoring instrument box, comprising:

[0006] Respond to the detection request sent by the user end and obtain the initial sensing data and initial positioning data of the monitoring instrument box;

[0007] According to the initial sensor data, a sensor detection strategy is used to poll the real-time sensor data of the monitoring instrument box to generate a first polling result;

[0008] According to the initial positioning data, a positioning detection strategy is used to poll the real-time positioning data of the monitoring instrument box to generate a second polling result;

[0009] The first polling result and the second polling result are used to determine whether the monitoring instrument box is in an abnormal state.

[0010] Optionally, the step of polling the real-time sensor data of the monitoring instrument box using a sensor detection strategy according to the initial sensor data to generate a first polling result includes:

[0011] Collecting real-time sensor data of the monitoring instrument box according to a preset sensor sampling step;

[0012] Performing a difference operation on the real-time sensing data and the initial sensing data according to the sensing sampling step length to generate a sensing change value;

[0013] According to the comparison result between the sensor change value and a preset first threshold value, the real-time posture data of the monitoring instrument box is collected to generate a first polling result.

[0014] Optionally, the step of collecting the real-time posture data of the monitoring instrument box and generating a first polling result according to the comparison result between the sensor change value and a preset first threshold value includes:

[0015] Determining whether the sensor change value is greater than a preset first threshold;

[0016] If yes, real-time posture data of the monitoring instrument box is collected according to a preset sliding window step size to form a posture series;

[0017] Obtaining the predicted time corresponding to the sensor change value, and selecting the target real-time posture data corresponding to the predicted time from the posture series;

[0018] Generate predicted posture data and posture interval corresponding to the predicted moment according to the posture sequence through a preset sliding window and a preset posture prediction model;

[0019] Generate a first polling result according to the target real-time posture data, the predicted posture data and the posture interval, and jump to execute the step of performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate a sensor change value;

[0020] If not, the process jumps to the step of performing a difference operation on the real-time sensing data and the initial sensing data according to the sensing sampling step length to generate a sensing change value.

[0021] Optionally, the step of generating predicted posture data and posture interval corresponding to the predicted moment according to the posture series by using a preset sliding window and a preset posture prediction model comprises:

[0022] Adopting a preset sliding window to intercept the posture series, and obtaining a plurality of sliding window data corresponding to the prediction time;

[0023] Inputting all the sliding window data into a preset posture prediction model, and outputting corresponding predicted posture data;

[0024] Calculate the mean and variance of all the sliding window data;

[0025] Performing sum operation and difference operation on the mean value and the variance value respectively to generate corresponding sliding window sum value and sliding window difference value;

[0026] The sliding window difference and the sliding window sum are used to construct a posture interval.

[0027] Optionally, the step of generating a first polling result according to the target real-time posture data, the predicted posture data and the posture interval and jumping to execute the step of performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate a sensor change value includes:

[0028] Performing a difference operation between the predicted posture data and the target real-time posture data to generate a posture difference;

[0029] Generate a first polling result according to whether the posture difference is within the range of the posture interval;

[0030] Jump to execute the step of performing difference calculation on the real-time sensing data and the initial sensing data according to the sensing sampling step to generate a sensing change value.

[0031] Optionally, the step of polling the real-time positioning data of the monitoring instrument box using a positioning detection strategy according to the initial positioning data to generate a second polling result includes:

[0032] Collecting real-time positioning data of the monitoring instrument box according to a preset positioning sampling step;

[0033] According to the positioning sampling step, performing a difference operation on the real-time positioning data and the initial positioning data to construct a positioning change value;

[0034] The positioning change value is compared with a preset second threshold to generate a second polling result.

[0035] Optionally, the posture difference includes a plurality of sub-posture differences, and the posture interval includes a plurality of sub-posture intervals corresponding to each of the sub-posture differences; and the step of using the first polling result and the second polling result to determine whether the monitoring instrument box is in an abnormal state includes:

[0036] Analyze the first polling result, if any of the sub-attitude differences is not within the range of the corresponding sub-attitude interval, then the monitoring instrument box has an abnormal attitude value;

[0037] Counting the number of abnormal values ​​of the posture;

[0038] When the number is greater than a preset number threshold, it is determined that the monitoring instrument box is in an abnormal posture state;

[0039] The second polling result is analyzed, and if the positioning change value is greater than the second threshold, it is determined that the monitoring instrument box is in a positioning abnormal state.

[0040] Optionally, after the step of using the first polling result and the second polling result to determine whether the monitoring instrument box is in an abnormal state, the following steps are included:

[0041] If the monitoring instrument box is in an abnormal state, a corresponding abnormal signal is generated and sent to the user end.

[0042] A second aspect of the present invention provides a detection system for monitoring abnormal status of an instrument box, comprising:

[0043] The initial data acquisition module is used to respond to the detection request sent by the user end and obtain the initial sensing data and initial positioning data of the monitoring instrument box;

[0044] A first polling result generating module, configured to poll the real-time sensor data of the monitoring instrument box using a sensor detection strategy according to the initial sensor data to generate a first polling result;

[0045] A second polling result generating module, configured to poll the real-time positioning data of the monitoring instrument box using a positioning detection strategy according to the initial positioning data to generate a second polling result;

[0046] The abnormal state determination module is used to determine whether the monitoring instrument box is in an abnormal state by using the first polling result and the second polling result.

[0047] The third aspect of the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for detecting an abnormal state of a monitoring instrument box as described in any one of the first aspect of the present invention.

[0048] It can be seen from the above technical solutions that the present invention has the following advantages:

[0049] The present invention obtains the initial sensing data and initial positioning data of the monitoring instrument box by responding to the detection request sent by the user end, selects the corresponding sensing detection strategy and positioning detection strategy according to the initial sensing data and the initial positioning data, polls the real-time sensing data and real-time positioning data of the monitoring instrument box, generates the first polling result and the second polling result, and uses the first polling result and the second polling result to determine whether the monitoring instrument box is in an abnormal state. During the entire detection process, the terminal can detect the abnormal state of the monitoring instrument box online through the set sensing detection strategy and positioning detection strategy, thereby improving the detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 A flowchart of a method for detecting abnormal status of a monitoring instrument box provided in the first embodiment of the present invention;

[0052] Figure 2 A flowchart of a method for detecting abnormal status of a monitoring instrument box provided in Embodiment 2 of the present invention;

[0053] Figure 3 This is a flowchart of the steps of a detection system for monitoring abnormal status of an instrument box provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0054] The embodiments of the present invention provide a method, system and device for detecting abnormal status of a monitoring instrument box, which are used to solve the technical problem that the abnormal status of a monitoring instrument box is mainly detected by electronic anti-theft devices in the prior art, but the detection accuracy is low.

[0055] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 , Figure 1 A flowchart of the steps of a method for detecting an abnormal state of a monitoring instrument box provided in Embodiment 1 of the present invention.

[0057] The present invention provides a method for detecting an abnormal state of a monitoring instrument box, comprising:

[0058] Step 101: Respond to a detection request sent by a user terminal and obtain initial sensing data and initial positioning data of a monitoring instrument box.

[0059] A detection request refers to a request message sent by a user end of any detection application that supports monitoring abnormal status of an instrument box.

[0060] The initial sensing data refers to the infrared sensing data of the monitoring instrument box in the current normal working state. It can be understood that the infrared sensing data can map the infrared radiation energy of the environment where the monitoring instrument box is located.

[0061] The initial positioning data refers to the positioning coordinates of the monitoring instrument box when it is in the current normal working state.

[0062] It is understandable that a PIR infrared sensor and a GPS / Beidou positioning module are provided on the monitoring instrument box. The PIR infrared sensor can be used to detect infrared radiation from the environment. When a human body or an animal enters the sensing range of the PIR infrared sensor, the infrared radiation emitted by the human body or the animal can be detected. The GPS / Beidou positioning module can be used to collect the positioning coordinates of the monitoring instrument box in real time to determine the position change of the monitoring instrument box.

[0063] In an embodiment of the present invention, when a detection request is received from any user terminal that supports a detection application for monitoring abnormal status of a monitoring instrument box, the initial sensing data of the monitoring instrument box is collected through the PIR infrared sensor on the monitoring instrument box, and the initial positioning data of the monitoring instrument box is collected through the GPS / Beidou positioning module.

[0064] Step 102: According to the initial sensor data, a sensor detection strategy is adopted to poll the real-time sensor data of the monitoring instrument box to generate a first polling result.

[0065] The sensor detection strategy refers to the detection strategy that polls real-time sensor data.

[0066] Real-time sensor data refers to the infrared sensor data of the monitoring instrument box collected after the initial sensor data is determined.

[0067] The first polling result refers to the result generated by polling the real-time sensor data.

[0068] In the embodiment of the present invention, the initial sensor data is used as a benchmark, the real-time sensor data of the monitoring instrument box is collected by the PIR infrared sensor on the monitoring instrument box, and the real-time sensor data is polled using a sensor detection strategy to generate a corresponding first polling result.

[0069] Step 103: According to the initial positioning data, a positioning detection strategy is adopted to poll the real-time positioning data of the monitoring instrument box to generate a second polling result.

[0070] Positioning detection strategy refers to a detection strategy that polls real-time positioning data.

[0071] Real-time positioning data refers to the positioning coordinates of the monitoring instrument box collected after the initial positioning data is determined.

[0072] The second polling result refers to the result generated by polling the real-time positioning data.

[0073] In an embodiment of the present invention, the real-time positioning data of the monitoring instrument box is collected by the GPS / Beidou positioning module on the monitoring instrument box. Based on the initial positioning data, the real-time positioning data is polled using a positioning detection strategy to generate a corresponding second polling result.

[0074] Step 104: Use the first polling result and the second polling result to determine whether the monitoring instrument box is in an abnormal state.

[0075] The abnormal state refers to a state in which the monitoring instrument box is disturbed by external factors when no relevant adjustment instructions are received from the user end, causing the monitoring instrument box to deviate from the current normal working state.

[0076] In the embodiment of the present invention, by respectively analyzing the first polling result and the second polling result, it is determined whether the monitoring instrument box is in an abnormal state.

[0077] In the embodiment of the present invention, by responding to the detection request sent by the user terminal, the initial sensing data and initial positioning data of the monitoring instrument box are obtained, and the corresponding sensing detection strategy and positioning detection strategy are selected according to the initial sensing data and the initial positioning data, and the real-time sensing data and real-time positioning data of the monitoring instrument box are polled to generate the first polling result and the second polling result, and the first polling result and the second polling result are used to determine whether the monitoring instrument box is in an abnormal state. During the entire detection process, the terminal can detect the abnormal state of the monitoring instrument box online through the set sensing detection strategy and positioning detection strategy, thereby improving the detection accuracy.

[0078] See also Figure 2 , Figure 2 A flowchart of the steps of a method for detecting an abnormal state of a monitoring instrument box provided in Embodiment 2 of the present invention.

[0079] The present invention provides a method for detecting an abnormal state of a monitoring instrument box, comprising:

[0080] Step 201: Respond to a detection request sent by a user terminal and obtain initial sensing data and initial positioning data of a monitoring instrument box.

[0081] In the embodiment of the present invention, the specific implementation process of step 201 is similar to step 101 and will not be repeated here.

[0082] Step 202: Collect real-time sensor data of the monitoring instrument box according to a preset sensor sampling step.

[0083] The sensor sampling step refers to the unit time length of real-time sensor data sampling.

[0084] In an embodiment of the present invention, after determining the initial sensing data, according to the set sensing sampling step, during the operation of the monitoring instrument box, the real-time sensing data of the monitoring instrument box is collected in real time by the PIR infrared sensor on the monitoring instrument box according to the sensing sampling step.

[0085] Step 203: perform difference calculation on the real-time sensing data and the initial sensing data according to the sensing sampling step length to generate a sensing change value.

[0086] The sensing change value refers to the change in infrared radiation energy between the real-time sensing data and the initial sensing data.

[0087] In an embodiment of the present invention, starting from determining the initial sensor data, while the PIR infrared sensor collects real-time sensor data according to the sensor sampling step, a difference operation is performed on the real-time sensor data and the initial sensor data according to the time node corresponding to the sensor sampling step to generate a corresponding sensor change value.

[0088] Step 204: According to the comparison result between the sensor change value and the preset first threshold value, the real-time posture data of the monitoring instrument box is collected to generate a first polling result.

[0089] Optionally, step 204 includes the following sub-steps:

[0090] S1, determining whether the sensor change value is greater than a preset first threshold;

[0091] S2. If yes, collect the real-time posture data of the monitoring instrument box according to the preset sliding window step length to form a posture series;

[0092] S3, obtaining the predicted time corresponding to the sensor change value, and selecting the target real-time posture data corresponding to the predicted time from the posture series;

[0093] S4, generating predicted posture data and posture interval corresponding to the predicted time according to the posture sequence through a preset sliding window and a preset posture prediction model;

[0094] S5, generating a first polling result according to the target real-time posture data, the predicted posture data and the posture interval, and jumping to execute the step of performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate a sensor change value;

[0095] S6. If not, jump to the step of performing difference calculation on the real-time sensing data and the initial sensing data according to the sensing sampling step length to generate a sensing change value.

[0096] The first threshold refers to an energy threshold that allows the infrared radiation energy of the working environment to deviate from the infrared radiation energy corresponding to the initial sensing data when the monitoring instrument box is in the current normal working state.

[0097] Sliding window step size refers to the sliding step size of the sliding window. A sliding window refers to a window with a set window width and sliding step size, which can move in a certain direction.

[0098] Real-time attitude data refers to the attitude data of the monitoring instrument box collected in real time. It can be understood that the attitude data includes heading angle, pitch angle and roll angle, and the real-time attitude data includes real-time heading angle, real-time pitch angle and real-time roll angle.

[0099] The posture series refers to the series formed by arranging the real-time posture data in the order of collection time.

[0100] The prediction time refers to the time when posture prediction is performed.

[0101] The target real-time posture data refers to the real-time posture data corresponding to the prediction time.

[0102] The posture prediction model refers to a model that uses the real-time posture data of several moments before the prediction moment for self-regression to generate the predicted posture data corresponding to the prediction moment.

[0103] The predicted attitude data refers to the attitude data corresponding to the predicted time determined by the attitude prediction model, including the predicted heading angle, predicted pitch angle and predicted roll angle.

[0104] The posture interval refers to an interval used to determine whether the difference between the predicted posture data and the target real-time posture data is abnormal.

[0105] It can be understood that an inertial measurement module is provided on the monitoring instrument box, including a three-axis gyroscope and three-directional accelerometers, for measuring the posture data of the monitoring instrument box.

[0106] In an embodiment of the present invention, it is determined whether the sensor change value is greater than a preset first threshold value; if it is greater than, it means that the infrared radiation energy in the working environment of the monitoring instrument box changes abnormally, and it is necessary to collect the posture data of the monitoring instrument box for further judgment, then the inertial measurement module on the monitoring instrument box collects the real-time posture data of the monitoring instrument box according to the preset sliding window step, and the collected real-time posture data are composed of a posture series, and the predicted time corresponding to the sensor change value is obtained at the same time, based on the posture series, the predicted posture data and posture interval corresponding to the predicted time are generated through the preset sliding window and the preset posture prediction model, and the real-time posture data corresponding to the predicted time is selected from the posture series as the target real-time posture data, and after the first polling result is constructed using the target real-time posture data, the predicted posture data and the posture interval, the step of performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate the sensor change value continues to be jumped to execute; if it is not greater than, it means that the working environment of the monitoring instrument box is still in the current normal working state, then the step of performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate the sensor change value continues to be jumped to execute.

[0107] It can be understood that after generating the first polling result, in addition to using the first polling result to determine whether the monitoring instrument box is in an abnormal state, it is still necessary to continue to determine whether the change in infrared radiation energy in the working environment of the monitoring instrument box is still abnormal. If it is abnormal, continue with the posture prediction judgment at the next prediction moment.

[0108] Optionally, the posture prediction model is specifically:

[0109] x` t =a1x t-1 +…+a p x t-p +ε t ;

[0110] Among them, a1,…,a p is the model parameter, ε t The mean at time t is θ and the variance is δ 2 White noise, x` t is the predicted posture data at time t, x` t Include (α` t ,β` t ,γ` t ), α` t ,β` t ,γ` t are the predicted heading angle, predicted pitch angle and predicted roll angle at time t, respectively, t-p ,…,x t-1 They are the real-time posture data from time tp to time t-1 respectively.

[0111] It is understandable that ε t It can be set to follow a normal distribution with a mean of 0 and a variance of 0.01.

[0112] Furthermore, the model order p of the posture prediction model is calculated by the BIC criterion:

[0113]

[0114] p = argmin BIC(p);

[0115] Where N is the window width of the sliding window, is the p-order posture prediction model residual.

[0116] Furthermore, the least squares method is used to estimate the model parameters of the posture prediction model:

[0117] A=(X T X) -1 +X T Y;

[0118] A=[a1,…,a p ];

[0119] X=[x t-1 ,…,x t-p ];

[0120] Y=[x` t ];

[0121] Among them, A is the parameter matrix, X is the data matrix, and Y is the prediction matrix.

[0122] Furthermore, the posture prediction model after determining the model order and model parameters is used to predict the posture data x at time t+1 t+1 Make predictions:

[0123] x` t+1 =a1x t +…+a p x t+1-p +ε t+1 ;

[0124] Among them, ε t+1 The mean at time t+1 is θ and the variance is δ 2 White noise, x` t+1 Include (α` t+1 ,β` t+1 ,γ` t+1 ), α` t+1 ,β` t+1 ,γ` t+1are the predicted heading angle, predicted pitch angle and predicted roll angle at time t+1, respectively, t+1-p ,…,x t They are the real-time posture data from time t+1-p to time t.

[0125] Optionally, sub-step S4 includes:

[0126] A preset sliding window is used to intercept the posture series to obtain multiple sliding window data corresponding to the prediction time;

[0127] Input all sliding window data into the preset posture prediction model and output the corresponding predicted posture data;

[0128] Calculate the mean and variance of all sliding window data;

[0129] Perform sum and difference operations on the mean and variance values ​​respectively to generate corresponding sliding window sum and sliding window difference;

[0130] The attitude interval is constructed using sliding window difference and sliding window sum.

[0131] Sliding window data refers to the real-time posture data selected by the sliding window.

[0132] The sliding window sum value refers to the sum of the mean and variance values ​​of all sliding window data.

[0133] The sliding window difference refers to the difference between the mean and variance of all sliding window data.

[0134] In an embodiment of the present invention, after determining the prediction moment, a preset sliding window is used to extract corresponding multiple real-time posture data from the posture series as sliding window data according to the corresponding window width, and all the sliding window data are used to generate corresponding predicted posture data through a preset posture prediction model, and a mean operation is performed on all the sliding window data to determine the corresponding average value, and a variance operation is performed on all the sliding window data using the average value to form a corresponding variance value, and a sum operation is performed on the average value and the variance value to generate a corresponding sliding window sum value, and a difference operation is performed on the average value and the variance value to generate a corresponding sliding window difference value, and a posture interval is constructed with the sliding window difference value and the sliding window sum value as the interval endpoints respectively.

[0135] For example, if the window width of the sliding window is 4, to obtain the predicted posture data of t=6, the real-time posture data from t=2 to t=5 are selected from the posture series through the sliding window as the corresponding sliding window data.

[0136] Optionally, sub-step S5 includes:

[0137] The predicted posture data and the target real-time posture data are used to perform difference calculation to generate the posture difference;

[0138] Generate a first polling result according to whether the attitude difference is within the attitude interval;

[0139] Jump to the execution step of performing difference calculation on the real-time sensing data and the initial sensing data according to the sensing sampling step to generate a sensing change value.

[0140] The posture difference refers to the difference between the predicted posture data and the real-time posture data.

[0141] In an embodiment of the present invention, a difference operation is performed between the predicted posture data and the target real-time posture data to generate a corresponding posture difference. Based on whether the posture difference is within the range of the posture interval, that is, whether the posture difference is greater than the sliding window sum value or less than the sliding window difference value, a corresponding first polling result is generated, and the process jumps to execute a step of performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate a sensor change value.

[0142] Step 205: collect real-time positioning data of the monitoring instrument box according to the preset positioning sampling step.

[0143] Positioning sampling step refers to the unit time length of real-time positioning data sampling.

[0144] In an embodiment of the present invention, after determining the initial positioning data, the positioning sampling step is set. During the operation of the monitoring instrument box, the real-time positioning data of the monitoring instrument box is collected in real time through the GPS / Beidou positioning module on the monitoring instrument box according to the positioning sampling step.

[0145] Step 206: perform difference calculation on the real-time positioning data and the initial positioning data according to the positioning sampling step length to construct a positioning change value.

[0146] The positioning change value refers to the change in infrared radiation energy between the real-time sensing data and the initial sensing data.

[0147] In an embodiment of the present invention, starting from determining the initial positioning data, the real-time positioning data is collected according to the positioning sampling step by the GPS / Beidou positioning module, and the difference operation is performed on the real-time positioning data and the initial positioning data according to the time node corresponding to the positioning sampling step to construct the corresponding positioning change value.

[0148] Step 207: Compare the positioning change value with a preset second threshold value to generate a second polling result.

[0149] The second threshold refers to the distance threshold of the positioning coordinates corresponding to the initial positioning data that the monitoring instrument box is allowed to deviate from when in the current normal working state.

[0150] Optionally, the second threshold may be set within a circular range with a radius of 12 m and a positioning coordinate corresponding to the initial positioning data as the center of the circle.

[0151] In the embodiment of the present invention, the positioning change value is compared with a preset first threshold value according to the positioning sampling step, and the generated comparison result is used as the corresponding second polling result.

[0152] Step 208: Use the first polling result and the second polling result to determine whether the monitoring instrument box is in an abnormal state.

[0153] Optionally, the posture difference includes a plurality of sub-posture differences, and the posture interval includes a plurality of sub-posture intervals corresponding to each sub-posture difference; step 208 includes the following sub-steps:

[0154] Analyze the first polling result. If any sub-attitude difference is not within the range of the corresponding sub-attitude interval, the monitoring instrument box has an abnormal attitude value.

[0155] Count the number of posture outliers;

[0156] When the number is greater than a preset number threshold, it is determined that the monitoring instrument box is in an abnormal posture state;

[0157] The second polling result is analyzed, and if the positioning change value is greater than the second threshold, it is determined that the monitoring instrument box is in a positioning abnormal state.

[0158] It can be understood that the multiple sub-attitude differences and the corresponding sub-attitude intervals correspond to the heading angle, pitch angle and roll angle of the attitude data, respectively.

[0159] An abnormal attitude value refers to abnormal target real-time attitude data, that is, an abnormal target real-time heading angle, target real-time pitch angle, or target real-time roll angle.

[0160] The quantity threshold refers to the number of abnormal posture values ​​for determining that the monitoring instrument box has an abnormal posture state. Optionally, the quantity threshold is set to two.

[0161] The abnormal state includes the abnormal posture state and the abnormal positioning state. The abnormal posture state refers to the state where the posture of the monitoring instrument box is abnormal. The abnormal positioning state refers to the state where the positioning coordinates of the monitoring instrument box are abnormal.

[0162] In an embodiment of the present invention, the first polling result is analyzed. If any sub-attitude difference is not within the range of the corresponding sub-attitude interval, that is, the sub-attitude difference corresponding to the target real-time heading angle, the target real-time pitch angle or the target real-time roll angle is not within the range of the corresponding sub-attitude interval, it is determined that the monitoring instrument box has an abnormal attitude value, and the number of abnormal attitude values ​​is counted to determine whether the number is greater than the preset number threshold. If it is greater, it is determined that the monitoring instrument box has an abnormal attitude state; otherwise, if any sub-attitude difference is within the range of the corresponding sub-attitude interval, that is, there is no abnormal attitude value, then it is determined that the monitoring instrument box does not have an abnormal attitude state. At the same time, the second polling result is analyzed. If the positioning change value is greater than the second threshold, it is determined that the monitoring instrument box has an abnormal positioning state; otherwise, if the positioning change value is less than or greater than the second threshold, it is determined that the monitoring instrument box does not have an abnormal positioning state.

[0163] Optionally, after step 208, the method further includes:

[0164] If the monitoring instrument box is in an abnormal state, a corresponding abnormal signal is generated and sent to the user end.

[0165] The abnormal signal refers to the signal corresponding to converting the abnormal state from the information state to the signal state.

[0166] In an embodiment of the present invention, if the monitoring instrument box is in an abnormal state, that is, there is an abnormal posture state or an abnormal positioning state, or both an abnormal posture state and an abnormal positioning state, a corresponding abnormal signal is generated according to the corresponding abnormal state and sent to the user end.

[0167] Optionally, a GSM-SMS module may be provided on the monitoring instrument box to send abnormal signals to the user end.

[0168] In an embodiment of the present invention, by responding to a detection request sent by a user terminal, the initial sensing data and initial positioning data of the monitoring instrument box are obtained, and the real-time sensing data and real-time positioning data of the monitoring instrument box are respectively collected according to a preset sensing sampling step and a preset positioning sampling step, and a difference operation is performed between the real-time sensing data and the initial sensing data to generate a sensing change value, and real-time posture data is collected according to a comparison result between the sensing change value and a preset first threshold to generate a first polling result, and a difference operation is performed between the real-time positioning data and the initial positioning data to construct a positioning change value, and the positioning change value is compared with a preset second threshold to generate a second polling result, and the first polling result and the second polling result are analyzed to determine whether the monitoring instrument box is in an abnormal state. In the entire detection process, the terminal can detect the abnormal state of the monitoring instrument box online through the set sensing detection strategy and positioning detection strategy, thereby improving the detection accuracy.

[0169] See also Figure 3 , Figure 3 This is a flowchart of the steps of a detection system for monitoring abnormal status of an instrument box provided in Embodiment 3 of the present invention.

[0170] The present invention provides a detection system for monitoring abnormal status of an instrument box, comprising:

[0171] The initial data acquisition module 301 is used to respond to the detection request sent by the user terminal and acquire the initial sensing data and initial positioning data of the monitoring instrument box;

[0172] A first polling result generating module 302 is used to poll the real-time sensor data of the monitoring instrument box using a sensor detection strategy according to the initial sensor data to generate a first polling result;

[0173] The second polling result generating module 303 is used to poll the real-time positioning data of the monitoring instrument box using the positioning detection strategy according to the initial positioning data to generate a second polling result;

[0174] The abnormal state determination module 304 is used to determine whether the monitoring instrument box is in an abnormal state by using the first polling result and the second polling result.

[0175] Optionally, the first polling result generating module 302 includes:

[0176] The real-time sensor data acquisition submodule is used to collect the real-time sensor data of the monitoring instrument box according to the preset sensor sampling step;

[0177] The sensor change value generation submodule is used to perform difference calculation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate the sensor change value;

[0178] The first polling result generating submodule is used to collect the real-time posture data of the monitoring instrument box to generate the first polling result according to the comparison result between the sensor change value and the preset first threshold value.

[0179] Optionally, the first polling result generating submodule includes:

[0180] A first threshold judgment unit, used to judge whether the sensing change value is greater than a preset first threshold;

[0181] The posture sequence forming unit is used to collect the real-time posture data of the monitoring instrument box according to the preset sliding window step length to form a posture sequence;

[0182] A prediction time processing unit is used to obtain the prediction time corresponding to the sensor change value, and select the target real-time posture data corresponding to the prediction time from the posture series;

[0183] A posture prediction unit, used to generate predicted posture data and posture interval corresponding to the prediction time according to the posture series through a preset sliding window and a preset posture prediction model;

[0184] A first polling result generating unit is used to generate a first polling result according to the target real-time posture data, the predicted posture data and the posture interval, and jump to execute the step of performing a difference operation between the real-time sensor data and the initial sensor data according to the sensor sampling step length to generate a sensor change value;

[0185] The jump execution unit is used for, if not, jumping to the execution step of performing difference calculation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate the sensor change value.

[0186] Optionally, the posture prediction unit is specifically used for:

[0187] A preset sliding window is used to intercept the posture series to obtain multiple sliding window data corresponding to the prediction time;

[0188] Input all sliding window data into the preset posture prediction model and output the corresponding predicted posture data;

[0189] Calculate the mean and variance of all sliding window data;

[0190] Perform sum and difference operations on the mean and variance values ​​respectively to generate corresponding sliding window sum and sliding window difference;

[0191] The attitude interval is constructed using sliding window difference and sliding window sum.

[0192] Optionally, the first polling result generating unit is specifically used for:

[0193] The predicted posture data and the target real-time posture data are used to perform difference calculation to generate the posture difference;

[0194] Generate a first polling result according to whether the attitude difference is within the attitude interval;

[0195] Jump to the execution step of performing difference calculation on the real-time sensing data and the initial sensing data according to the sensing sampling step to generate a sensing change value.

[0196] Optionally, the second polling result generating module 303 is specifically used for:

[0197] Collect real-time positioning data of the monitoring instrument box according to the preset positioning sampling step;

[0198] According to the positioning sampling step, the real-time positioning data and the initial positioning data are differenced to construct the positioning change value;

[0199] The positioning change value is compared with a preset second threshold value to generate a second polling result.

[0200] Optionally, the posture difference includes a plurality of sub-posture differences, and the posture interval includes a plurality of sub-posture intervals corresponding to each sub-posture difference; the abnormal state determination module 304 is specifically used for:

[0201] Analyze the first polling result. If any sub-attitude difference is not within the range of the corresponding sub-attitude interval, the monitoring instrument box has an abnormal attitude value.

[0202] Count the number of posture outliers;

[0203] When the number is greater than a preset number threshold, it is determined that the monitoring instrument box is in an abnormal posture state;

[0204] The second polling result is analyzed, and if the positioning change value is greater than the second threshold, it is determined that the monitoring instrument box is in a positioning abnormal state.

[0205] Optionally, the system further includes an abnormality reminder module, which is used to:

[0206] If the monitoring instrument box is in an abnormal state, a corresponding abnormal signal is generated and sent to the user end.

[0207] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for detecting an abnormal state of a monitoring instrument box as in any embodiment of the present invention.

[0208] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0209] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0210] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0211] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0212] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting abnormal state of a monitoring instrument box, characterized in that: include: Respond to the detection request sent by the user end and obtain the initial sensing data and initial positioning data of the monitoring instrument box; According to the initial sensor data, a sensor detection strategy is adopted to poll the real-time sensor data of the monitoring instrument box to generate a first polling result, including: collecting the real-time sensor data of the monitoring instrument box according to a preset sensor sampling step; performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate a sensor change value; judging whether the sensor change value is greater than a preset first threshold; if so, collecting the real-time posture data of the monitoring instrument box according to a preset sliding window step to form a posture series; obtaining the predicted moment corresponding to the sensor change value, and selecting the target real-time posture data corresponding to the predicted moment from the posture series; intercepting the posture series using a preset sliding window to obtain multiple sliding window data corresponding to the predicted moment; The sliding window data is input into a preset posture prediction model to output corresponding predicted posture data; the average value and variance value of all the sliding window data are calculated; the sum operation and difference operation are performed on the average value and the variance value respectively to generate corresponding sliding window sum value and sliding window difference value; the sliding window difference value and the sliding window sum value are used as interval endpoints to construct a posture interval; according to the target real-time posture data, the predicted posture data and the posture interval, a first polling result is generated and a step of performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate a sensor change value is jumped to execute; if not, the step of performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate a sensor change value is jumped to execute; According to the initial positioning data, the real-time positioning data of the monitoring instrument box is polled by using a positioning detection strategy to generate a second polling result, including: collecting the real-time positioning data of the monitoring instrument box according to a preset positioning sampling step; performing a difference operation on the real-time positioning data and the initial positioning data according to the positioning sampling step to construct a positioning change value; comparing the positioning change value with a preset second threshold value to generate a second polling result; The first polling result and the second polling result are used to determine whether the monitoring instrument box is in an abnormal state.

2. The method for detecting abnormal state of a monitoring instrument box according to claim 1, characterized in that: The step of generating a first polling result according to the target real-time posture data, the predicted posture data and the posture interval and jumping to the step of performing a difference operation on the real-time sensor data and the initial sensor data according to the sensor sampling step to generate a sensor change value comprises: Performing a difference operation between the predicted posture data and the target real-time posture data to generate a posture difference; Generate a first polling result according to whether the posture difference is within the range of the posture interval; Jump to execute the step of performing difference calculation on the real-time sensing data and the initial sensing data according to the sensing sampling step to generate a sensing change value.

3. The method for detecting abnormal state of a monitoring instrument box according to claim 2, characterized in that: The posture difference value includes a plurality of sub-posture differences, and the posture interval includes a plurality of sub-posture intervals corresponding to each of the sub-posture differences; The step of using the first polling result and the second polling result to determine whether the monitoring instrument box is in an abnormal state includes: Analyze the first polling result, if any of the sub-attitude differences is not within the range of the corresponding sub-attitude interval, then the monitoring instrument box has an abnormal attitude value; Counting the number of abnormal values ​​of the posture; When the number is greater than a preset number threshold, it is determined that the monitoring instrument box is in an abnormal posture state; The second polling result is analyzed, and if the positioning change value is greater than the second threshold, it is determined that the monitoring instrument box is in a positioning abnormal state.

4. The method for detecting abnormal state of a monitoring instrument box according to claim 1, characterized in that: After the step of using the first polling result and the second polling result to determine whether the monitoring instrument box is in an abnormal state, the method further includes: If the monitoring instrument box is in an abnormal state, a corresponding abnormal signal is generated and sent to the user end.

5. A detection system for monitoring abnormal status of an instrument box, characterized in that: The method for detecting an abnormal state of a monitoring instrument box as claimed in claim 1 comprises: The initial data acquisition module is used to respond to the detection request sent by the user end and obtain the initial sensing data and initial positioning data of the monitoring instrument box; A first polling result generating module, configured to poll the real-time sensor data of the monitoring instrument box using a sensor detection strategy according to the initial sensor data to generate a first polling result; A second polling result generating module, configured to poll the real-time positioning data of the monitoring instrument box using a positioning detection strategy according to the initial positioning data to generate a second polling result; The abnormal state determination module is used to determine whether the monitoring instrument box is in an abnormal state by using the first polling result and the second polling result.

6. An electronic device, characterized in that: It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for detecting an abnormal state of a monitoring instrument box as described in any one of claims 1 to 4.

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