Abnormity detection method based on digital display pressure gauge, electronic equipment and related device

Abstract

The invention discloses an anomaly detection method based on a digital display pressure gauge, an electronic device and a related device, the anomaly detection method is applied to a relay controller in an Internet of Things system, the Internet of Things system further comprises n digital display pressure gauges and a cloud server, the method comprises the steps of obtaining pressure parameters of the n digital display pressure gauges in a preset time period, obtaining n pressure parameter sets, the n digital display pressure gauges are arranged at different positions in the target device; each digital display pressure gauge in the n digital display pressure gauges corresponds to one environment sensor; acquiring an environment data set in a preset time period through an environment sensor corresponding to each digital display pressure gauge in the n digital display pressure gauges to obtain n environment data sets; determining n pressure values according to the n pressure parameter sets and the n environment data sets; performing anomaly detection on the target device according to the n pressure values to obtain a target anomaly detection result; and synchronizing the target anomaly detection result to the cloud server. According to the invention, the accuracy of anomaly detection can be ensured.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things or flow and pressure sensing technology, and particularly relates to an abnormal detection method, an electronic device and a related device based on a digital display pressure gauge. Background Art

[0002] At present, unidirectional flow and pressure sensors are commonly used to test or confirm whether a test item has air leakage. For example, whether a device (such as a plastic bottle) has holes or air leakage. Currently, a digital display pressure gauge is used for abnormal detection, but the accuracy of abnormal detection is relatively low. Therefore, the problem of how to ensure the accuracy of abnormal detection needs to be solved urgently. Summary of the Invention

[0003] An embodiment of the present application provides an abnormal detection method, an electronic device and a related device based on a digital display pressure gauge, which can ensure the accuracy of abnormal detection.

[0004] In a first aspect, an embodiment of the present application provides an abnormal detection method, which is applied to a relay controller in an Internet of Things system. The Internet of Things system further includes n digital display pressure gauges and a cloud server, where n is an integer greater than 1. The method includes:

[0005] Obtain pressure parameters of the n digital display pressure gauges in a preset time period to obtain n pressure parameter sets. The n digital display pressure gauges are arranged at different positions inside the target device; each digital display pressure gauge in the n digital display pressure gauges corresponds to an environmental sensor;

[0006] Obtain an environmental data set of the preset time period through the environmental sensor corresponding to each digital display pressure gauge in the n digital display pressure gauges to obtain n environmental data sets;

[0007] Determine n pressure values according to the n pressure parameter sets and the n environmental data sets;

[0008] Perform abnormal detection on the target device according to the n pressure values to obtain a target abnormal detection result;

[0009] Synchronize the target abnormal detection result to the cloud server.

[0010] In a second aspect, an embodiment of the present application provides an abnormal detection device based on a digital display pressure gauge, which is applied to a relay controller in an Internet of Things system. The Internet of Things system further includes n digital display pressure gauges and a cloud server, where n is an integer greater than 1. The device includes: an acquisition unit, a determination unit, a detection unit and a synchronization unit, where,

[0011] The obtaining unit is configured to obtain the pressure parameters of the n digital pressure gauges in a preset time period, so as to obtain n pressure parameter sets, where the n digital pressure gauges are arranged at different positions inside the target device; each of the n digital pressure gauges corresponds to an environmental sensor; the environmental data sets in the preset time period are obtained through the environmental sensor corresponding to each of the n digital pressure gauges, so as to obtain n environmental data sets;

[0012] The determining unit is configured to determine n pressure values according to the n pressure parameter sets and the n environmental data sets;

[0013] The detecting unit is configured to perform anomaly detection on the target device according to the n pressure values, so as to obtain a target anomaly detection result;

[0014] The synchronizing unit is configured to synchronize the target anomaly detection result to the cloud server.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the processor, and the programs include instructions for executing the steps in the first aspect of the embodiment of the present application.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0018] Implementing the embodiments of the present application has the following beneficial effects:

[0019] It can be seen that the anomaly detection method, electronic device, and related device based on a digital display pressure gauge described in the embodiments of the present application are applied to a relay controller in an Internet of Things system. The Internet of Things system further includes n digital display pressure gauges and a cloud server, where n is an integer greater than 1. Pressure parameters of the n digital display pressure gauges in a preset time period are obtained to obtain n pressure parameter sets. The n digital display pressure gauges are arranged at different positions inside the target device; each of the n digital display pressure gauges corresponds to an environmental sensor. Environmental data sets in the preset time period are obtained through the environmental sensors corresponding to each of the n digital display pressure gauges to obtain n environmental data sets. n pressure values are determined based on the n pressure parameter sets and the n environmental data sets. Anomaly detection is performed on the target device according to the n pressure values to obtain a target anomaly detection result. The target anomaly detection result is synchronized to the cloud server. Since the environmental data set is used to calibrate the corresponding digital display pressure gauge, the detection accuracy of the digital display pressure gauge can be improved. Furthermore, the accuracy of anomaly detection can be ensured. In addition, synchronizing the target anomaly detection result to the cloud server can implement corresponding warning operations, thereby ensuring safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 is a flowchart of an anomaly detection method based on a digital display pressure gauge provided by an embodiment of the present application;

[0022] Figure 2 is a schematic structural diagram of an Internet of Things system for implementing an anomaly detection method based on a digital display pressure gauge provided by an embodiment of the present application;

[0023] Figure 3 is a schematic diagram of a scenario where a digital display pressure gauge is set on a target device provided by an embodiment of the present application;

[0024] Figure 4 is a schematic structural diagram of a digital display pressure gauge provided by an embodiment of the present application;

[0025] Figure 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0026] Figure 6 is a block diagram of the functional units of an anomaly detection device based on a digital display pressure gauge provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The terms "first", "second", etc. in the description, claims, and the above-mentioned drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may also include steps or units not listed in a possible example, or other steps or units inherent to these processes, methods, products, or devices in a possible example.

[0028] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appearing at various positions in the description does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0029] In order to enable those skilled in the art of this technology to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0030] The electronic devices involved in the embodiments of this application may include but are not limited to: relay controllers, edge servers, local controllers, local servers, etc., which are not limited herein. Of course, the electronic devices may also include at least one of the following: smart phones, tablet computers, intelligent robots, in-vehicle devices, wearable devices, computing devices, or other processing devices connected to a wireless modem, as well as various forms of user equipment (UE), mobile station (MS), terminal device, etc., which are not limited herein.

[0031] Please refer to Figure 1 , Figure 1 is a schematic flowchart of an abnormal detection method based on a digital display pressure gauge provided by an embodiment of this application, which is applied to a relay controller in an Internet of Things system. The Internet of Things system further includes n digital display pressure gauges and a cloud server, where n is an integer greater than 1. The abnormal detection method based on the digital display pressure gauge includes:

[0032] 101. Obtain the pressure parameters of the n digital pressure gauges within a preset time period to obtain n sets of pressure parameters. The n digital pressure gauges are arranged at different positions inside the target device; each digital pressure gauge in the n digital pressure gauges corresponds to an environmental sensor.

[0033] In specific implementation, as Figure 2 shown, the Internet of Things system includes a relay controller, n digital pressure gauges, and a cloud server, where n is an integer greater than 1. The relay controller is communicatively connected to the n digital pressure gauges. Specifically, the relay controller is communicatively connected to the n digital pressure gauges through an RS485 interface. The relay controller is communicatively connected to the cloud server. Among them, the transmission methods between the relay controller and the cloud server can include at least one of the following: network cable, 4G, wireless fidelity (Wi-Fi), etc., which are not limited here.

[0034] Among them, the digital pressure gauge can implement at least one of the following functions: reading the current pressure value, reading the status of the pressure gauge, etc., which are not limited here.

[0035] Among them, the cloud server can provide an API interface for output. Specifically, for example, the API interface can be a query interface. Another example is that the API interface can include a push interface, such as enabling the push interface, etc.

[0036] Among them, the relay controller realizes the parameter configuration of the digital pressure gauge. Specifically, the setting mode, switch state, output mode, etc. are not limited here. The housing of the relay controller includes a metal housing, and the metal housing can adopt an aluminum shell upper and lower structure.

[0037] Among them, the relay controller can implement at least one of the following functions: display function, menu function, etc., which are not limited here. The display function can include at least one of the following functions: time (allowing automatic time calibration, server time or international time), time zone definition, local IP, server address, relay device name, status display (number of online devices 12 / 32, number of table faults, number of records), etc., which are not limited here. The menu function can include at least one of the following: setting the local IP, server IP, testing the function of connecting to the server, querying records (fault records, set value alarm records, alarm records, network offline records, etc.), etc., which are not limited here.

[0038] Among them, the cloud server may include at least one of the following functions: basic setting function (device binding, setting common parameters of the relay controller, etc.), report function (querying uploaded records, reports of abnormal push, background query records (fault records, set value alarm records, alarm records, network offline records, etc.)), online viewing function (displaying the number of currently online devices, alarm numbers, and fault numbers on a large screen; monitoring multiple screens of the current table readings, with red alarms when exceeding the set value, etc.), push service (actively specifying to push to the PLC, mobile terminal push service (such as enterprise WeChat, WeChat, phone, SMS, etc.)), etc., which are not limited herein. The cloud server includes a metal shell, and the metal shell may adopt an upper and lower aluminum shell structure.

[0039] Among them, the target device may include at least one of the following: gas storage tank, conveying pipeline, liquefied gas tank, filter tank, plastic bottle, etc., which are not limited herein.

[0040] Among them, the preset time period can be set in advance or be the system default.

[0041] In specific implementation, as Figure 3 shown, n digital pressure gauges are arranged at different positions inside the target device, and the distances between adjacent digital pressure gauges may be equal or unequal.

[0042] In specific implementation, each of the n digital pressure gauges corresponds to an environmental sensor. For example, as Figure 4 shown, the digital pressure gauge may integrate the environmental sensor, or the environmental sensor may be arranged outside the digital pressure gauge. For example, the distance between the environmental sensor and the digital pressure gauge is within a preset range, and the preset range can be set in advance or be the system default.

[0043] Among them, each pressure parameter set in the n pressure parameter sets may include at least one pressure parameter, and the pressure parameter may include a pressure value.

[0044] 102. Obtain the environmental data sets for the preset time period through the environmental sensors corresponding to each of the n digital pressure gauges, and obtain n environmental data sets.

[0045] In specific implementation, the environmental sensors corresponding to each of the n digital pressure gauges may obtain the environmental data sets for the preset time period, and obtain n environmental data sets. Each environmental data set may include at least one environmental data, and the environmental data may include at least one of the following: environmental temperature, environmental humidity, magnetic field interference intensity, etc., which are not limited herein.

[0046] Among them, the environmental sensor can be used to capture the surrounding environment of the digital pressure gauge. To a certain extent, the surrounding environment will also affect the performance of the digital pressure gauge. The corresponding digital pressure gauge can be calibrated using the environmental data set, which can improve the detection accuracy of the digital pressure gauge.

[0047] 103. Determine n pressure values according to the n pressure parameter sets and the n environmental data sets.

[0048] In specific implementation, each digital pressure gauge can correspond to a pressure parameter set and an environmental data set. The pressure parameter set reflects the actual pressure situation, and the environmental data set reflects the environmental impact. Furthermore, the corresponding digital pressure gauge can be calibrated using the environmental data set, which can improve the detection accuracy of the digital pressure gauge.

[0049] Optionally, step 102 above, determining n pressure values according to the n pressure parameter sets and the n environmental data sets, can be implemented as follows:

[0050] Perform fitting according to the first pressure parameter set to obtain the first fitted straight line segment of the preset time period. The first pressure parameter set is any one of the n pressure parameter sets.

[0051] Determine the slope of the first fitted straight line segment to obtain the first slope.

[0052] Determine the mean value of the first fitted straight line segment to obtain the first mean value.

[0053] Determine the first standard deviation and the first average value of the environmental data set corresponding to the first pressure parameter set.

[0054] Determine the pressure value corresponding to the first pressure parameter set according to the first slope, the first mean value, the first standard deviation, and the first average value.

[0055] In specific implementation, since the digital pressure gauge collects pressure parameters at preset time intervals, each pressure parameter corresponds to a sampling moment, and the preset time interval can be set in advance or default by the system.

[0056] Furthermore, taking the first pressure parameter set as an example, where the first pressure parameter set is any one of the n pressure parameter sets, fitting can be performed according to each pressure parameter in the first pressure parameter set and the corresponding sampling time to obtain the first fitted straight line segment for a preset time period. That is, each pressure parameter in the first pressure parameter set and the corresponding sampling time can be regarded as a coordinate point, with the horizontal axis of this coordinate point being time and the vertical axis being the pressure parameter. Based on this method, multiple coordinate points can be obtained, and fitting is performed based on these multiple coordinate points to obtain the first fitted straight line segment. Then, the slope of the first fitted straight line segment is determined to obtain the first slope. The slope reflects the pressure change trend or the stability of the target device.

[0057] Of course, the mean of the first fitted straight line segment can also be determined to obtain the first mean, and the first mean reflects the pressure level inside the target device.

[0058] Next, the first standard deviation and the first average value of the environmental data set corresponding to the first pressure parameter set can also be determined. That is, standard deviation calculation can be performed on the environmental data set corresponding to the first pressure parameter set to obtain the first standard deviation, and mean calculation can be performed on the environmental data set corresponding to the first pressure parameter set to obtain the first average value. The first standard deviation reflects the stability of the environment, and the first average value reflects the environmental level.

[0059] Finally, the pressure value corresponding to the first pressure parameter set can be determined based on the first slope, the first mean, the first standard deviation, and the first average value. In this way, on the one hand, since the slope reflects the pressure change trend or the stability of the target device, and the first mean reflects the pressure level inside the target device, the corresponding pressure situation can be preliminarily determined. On the other hand, the first standard deviation reflects the stability of the environment, and the first average value reflects the environmental level, so the environmental data set can be used to calibrate the corresponding digital pressure gauge, which can improve the detection accuracy of the digital pressure gauge.

[0060] Optionally, for the above steps of determining the pressure value corresponding to the first pressure parameter set according to the first slope, the first mean, the first standard deviation, and the first average value, it can be implemented in the following manner:

[0061] Determine the first adjustment parameter corresponding to the first slope;

[0062] Determine the first reference pressure value according to the first adjustment parameter and the first mean;

[0063] Determine the first reference accuracy corresponding to the first average value;

[0064] Determine the first correction parameter corresponding to the first standard deviation;

[0065] Modify the first reference accuracy according to the first correction parameter to obtain the first accuracy;

[0066] Determine the pressure value corresponding to the first pressure parameter set according to the first accuracy and the first reference pressure value.

[0067] In specific implementation, the mapping relationship between the preset slope and the adjustment parameter can be stored in advance. Furthermore, the first adjustment parameter corresponding to the first slope can be determined based on this mapping relationship, and then the first reference pressure value can be determined according to the first adjustment parameter and the first mean value. Specifically, the first reference pressure value = (1 + the first adjustment parameter) * the first mean value. That is, since the slope reflects the pressure change trend or the stability of the target device, and the first mean value reflects the pressure level inside the target device, the corresponding pressure situation can be initially determined.

[0068] Among them, the value range of the adjustment parameter can be set in advance or defaulted by the system. For example, the value range of the adjustment parameter is -0.1 to 0.1.

[0069] Then, the mapping relationship between the preset mean value and the accuracy can also be stored in advance. Furthermore, the first reference accuracy corresponding to the first mean value can be determined based on this mapping relationship, and the mapping relationship between the preset standard deviation and the correction parameter can be stored in advance. Furthermore, the first correction parameter corresponding to the first standard deviation can be determined based on this mapping relationship, and then the first reference accuracy is corrected according to the first correction parameter to obtain the first accuracy. The first accuracy = (1 + the first correction parameter) * the first reference accuracy. Finally, the pressure value corresponding to the first pressure parameter set can be determined according to the first accuracy and the first reference pressure value, that is, the pressure value corresponding to the first pressure parameter set = (1 + the first accuracy) * the first reference pressure value. The first standard deviation reflects the stability of the environment, and the first mean value reflects the environmental level. Based on this environmental level, the corresponding accuracy is determined. Since the accuracy of the digital pressure gauge is different in different environments, that is, the accuracy suitable for the environment can be deeply adapted, the corresponding digital pressure gauge can be calibrated using the environmental data set, and the detection accuracy of the digital pressure gauge can be improved.

[0070] Among them, the value range of the correction parameter can be set in advance or defaulted by the system. For example, the value range of the correction parameter is -0.02 to 0.02.

[0071] 104. Perform anomaly detection on the target device according to the n pressure values to obtain a target anomaly detection result.

[0072] In the embodiments of the present application, the target anomaly detection result may include at least one of the following: whether there is an anomaly, the anomaly location, the anomaly degree, the anomaly type, etc., which are not limited herein.

[0073] Among them, the abnormal types may include at least one of the following: wear, air leakage, instability, hole, etc., which are not limited herein.

[0074] In a specific implementation, the target device can be detected for abnormalities according to n pressure values to obtain a target abnormality detection result. Since the corresponding digital pressure gauge is calibrated using the environmental data set, the detection accuracy of the digital pressure gauge can be improved, and further, the accuracy of the abnormality detection can be ensured.

[0075] Optionally, step 104 above, detecting the target device for abnormalities according to the n pressure values to obtain a target abnormality detection result, can be implemented as follows:

[0076] Determine the first environmental data mean value in the n environmental data sets;

[0077] Determine the reference pressure value corresponding to the first environmental data mean value;

[0078] Determine the absolute value of the difference between each of the n pressure values and the reference pressure value to obtain n absolute values;

[0079] Select the maximum value among the n absolute values. When the maximum value is greater than or equal to the preset absolute value, determine that the target device has an abnormality, obtain the absolute values among the n absolute values that are greater than the preset absolute value, and obtain the pressure values corresponding to these absolute values to obtain at least one pressure value, and determine the target abnormality detection result according to the at least one pressure value.

[0080] Among them, the preset absolute value can be set in advance or be the system default. The preset absolute value can be related to the environmental data, or it can also be related to the device attribute parameters of the target device. The device attribute parameters may include at least one of the following: device model, device material, device storage substance attribute, etc., which are not limited herein. The substance attribute may include at least one of the following: substance type, substance density, substance concentration, substance capacity, substance volume, etc., which are not limited herein.

[0081] In a specific implementation, the mean value of all environmental data in the n environmental data sets can be determined to obtain the first environmental data mean value. The mapping relationship between the preset environmental data mean value and the pressure value can also be stored in advance. Furthermore, the reference pressure value corresponding to the first environmental data mean value can be determined based on this mapping relationship, and then the absolute value of the difference between each of the n pressure values and the reference pressure value can be determined to obtain n absolute values. The maximum value among the n absolute values is selected. When the maximum value is greater than or equal to the preset absolute value, it is determined that the target device has an abnormality and the abnormality is significant.

[0082] Next, it is possible to obtain the absolute values among the n absolute values that are greater than the preset absolute value, and then obtain the pressure values corresponding to these absolute values to obtain at least one pressure value. Based on the at least one pressure value, the target anomaly detection result is determined. Since there are absolute values greater than the preset absolute value among the absolute values, it indicates that there is a pressure anomaly. For example, it is possible to determine the mean value of the at least one pressure value to obtain the target first mean value, determine the mean value of the other pressure values except the at least one pressure value to obtain the target second mean value, determine the absolute value of the difference between the target second mean value and the target first mean value to obtain the target absolute value, and according to the mapping relationship between the preset absolute value and the anomaly level, determine the target anomaly level corresponding to the target absolute value. Obtain the positions of the digital pressure gauges corresponding to the at least one pressure value to obtain at least one position, and based on the at least one position, determine the target anomaly position. According to the target anomaly position and the target anomaly level, determine the target anomaly detection result, that is, the target anomaly detection result includes the target anomaly position and the target anomaly level. In this way, the accuracy of anomaly detection can be ensured. It can not only determine the anomaly position and the degree of anomaly, but also provide corresponding warning operations.

[0083] Optionally, the following steps may further be included:

[0084] When the maximum value is less than the preset absolute value, group the n digital pressure gauges according to the n environmental data sets to obtain m groups, and each group includes at least two digital pressure gauges;

[0085] According to the n pressure values, obtain the pressure values corresponding to the m groups to obtain m groups of pressure values;

[0086] Determine the standard deviations corresponding to the m groups of pressure values to obtain m third standard deviations;

[0087] Determine the standard deviation of the n pressure values to obtain a fourth standard deviation;

[0088] Perform anomaly detection on the target device according to the fourth standard deviation and the m third standard deviations to obtain the target anomaly detection result.

[0089] In a specific implementation, when the maximum value is less than the preset absolute value, it indicates that the abnormality is general. Then, n digital pressure gauges can be grouped according to n environmental data sets to obtain m groups, with each group including at least two digital pressure gauges. That is, the digital pressure gauges with similar environments can be grouped together. Next, according to the n pressure values, the pressure values corresponding to the m groups are obtained, resulting in m groups of pressure values. Then, the standard deviation corresponding to each group of pressure values in the m groups of pressure values is determined to obtain m third standard deviations. Additionally, the standard deviation operation is performed on the n pressure values to obtain a fourth standard deviation. Finally, the target device can be subjected to anomaly detection based on the fourth standard deviation and the m third standard deviations to obtain a target anomaly detection result. That is, when the abnormality is general, the pressure parameters can be grouped based on the environmental conditions. Since the environmental differences are small, the environmental interference can be further reduced, ensuring the accuracy of the comparison between the pressure data within the group. In this way, the accuracy and sensitivity of the anomaly detection can be ensured.

[0090] Optionally, the above step of performing anomaly detection on the target device based on the fourth standard deviation and the m third standard deviations to obtain the target anomaly detection result can be implemented as follows:

[0091] When the fourth standard deviation is greater than the first preset threshold, it is determined that the target device has an abnormality;

[0092] Select the maximum value among the m third standard deviations to obtain a target third standard deviation;

[0093] Obtain the positions of the digital pressure gauges corresponding to the target third standard deviation to obtain multiple positions;

[0094] Determine the geometric center of the multiple positions;

[0095] Determine the target anomaly detection result based on the geometric center.

[0096] Among them, the first preset threshold can be set in advance or be the system default.

[0097] In a specific implementation, when the fourth standard deviation is greater than the first preset threshold, it is determined that the target device has an abnormality, and generally, specifically, the maximum value among the m third standard deviations can be selected to obtain the target third standard deviation. It is also possible to obtain the positions of the digital pressure gauges corresponding to the target third standard deviation to obtain multiple positions, and then determine the geometric center of the multiple positions. Based on the geometric center, the target abnormality detection result can be determined. The preset size range in the geometry can be determined as the target abnormal position. Then, the reference abnormality degree can be determined. Specifically, the first deviation degree between the preset absolute value and the maximum value can be determined. The first deviation degree = (preset absolute value - maximum value) / preset absolute value. That is, the mapping relationship between the preset deviation degree and the abnormality degree can be stored in advance. Furthermore, based on this mapping relationship, the reference abnormality degree corresponding to the first deviation degree can be determined.

[0098] Next, the mapping relationship between the preset standard deviation and the optimization parameter can be stored in advance. Furthermore, based on this mapping relationship, the first optimization parameter corresponding to the target third standard deviation can be determined. According to the first optimization parameter and the reference abnormality degree, the target abnormality degree can be determined. The target abnormality degree = (1 + first optimization parameter) * reference abnormality degree. According to the target abnormal position and the target abnormality degree, the target abnormality detection result can be determined. That is, the target abnormality detection result includes the target abnormal position and the target abnormality degree, ensuring the accuracy of the comparison between the pressure data within the group. In this way, the accuracy and sensitivity of the abnormality detection can be ensured.

[0099] Optionally, the following steps may also be included:

[0100] When the fourth standard deviation is less than or equal to the first preset threshold, determine the maximum value and the minimum value of the m third standard deviations;

[0101] Determine the difference between the maximum value and the minimum value to obtain the first difference;

[0102] When the first difference is greater than or equal to the preset difference, determine that the target device has an abnormality; execute the step of selecting the maximum value among the m third standard deviations to obtain the target third standard deviation;

[0103] When the first difference is less than the preset difference, determine that the target device is normal.

[0104] Among them, the preset difference can be set in advance or default by the system.

[0105] In a specific implementation, when the fourth standard deviation is less than or equal to the first preset threshold, the maximum and minimum values of the m third standard deviations are determined, the difference between the maximum value and the minimum value is determined to obtain a first difference, and when the first difference is greater than or equal to a preset difference, it is determined that the target device is abnormal. Correspondingly, the step of selecting the maximum value among the m third standard deviations to obtain the target third standard deviation can be executed. In this way, the accuracy and sensitivity of anomaly detection can be ensured.

[0106] Correspondingly, when the first difference is less than the preset difference, it is determined that the target device is normal. In this way, the accuracy and sensitivity of anomaly detection can be ensured.

[0107] Optionally, grouping the n digital pressure gauges according to the n environmental data sets to obtain m groups may include the following steps:

[0108] Determine the mean value of each environmental data set in the n environmental data sets to obtain n mean values;

[0109] Sort the n mean values from largest to smallest to obtain the sorted n mean values;

[0110] Group the sorted n mean values to obtain the m groups.

[0111] In a specific implementation, the mean value of each environmental data set in the n environmental data sets can be determined to obtain n mean values. Then, the n mean values are sorted from largest to smallest to obtain the sorted n mean values. Finally, the sorted n mean values can be grouped to obtain m groups. In this way, the pressure parameters can be grouped based on the environmental conditions, that is, if the environmental differences are small, the environmental interference can be further reduced, and the accuracy of the comparison between the pressure data within the group can be ensured. In this way, the accuracy and sensitivity of anomaly detection can be ensured.

[0112] 105. Synchronize the target anomaly detection result to the cloud server.

[0113] In a specific implementation, the target anomaly detection result can be synchronized to the cloud server. Since the environmental data sets are used to calibrate the corresponding digital pressure gauges, the detection accuracy of the digital pressure gauges can be improved, and further, the accuracy of anomaly detection can be ensured.

[0114] In a specific implementation, the mapping relationship between the preset anomaly detection results and the warning parameters can be stored in advance. Further, the target warning parameter corresponding to the target anomaly detection result can be determined based on this mapping relationship, and the target warning parameter can also be synchronized to the cloud server. The cloud server can specify the warning strategy corresponding to the target warning parameter and send it to the corresponding warning terminal.

[0115] Among them, the target warning parameter may include at least one of the following: warning method, warning level, warning object, etc., which are not limited here.

[0116] It can be seen that the abnormal detection method based on the digital display pressure gauge described in the embodiments of the present application is applied to the relay controller in the Internet of Things system. The Internet of Things system further includes n digital display pressure gauges and a cloud server, where n is an integer greater than 1. The pressure parameters of the n digital display pressure gauges in a preset time period are obtained to obtain n pressure parameter sets. The n digital display pressure gauges are arranged at different positions inside the target device; each of the n digital display pressure gauges corresponds to an environmental sensor. The environmental data sets in the preset time period are obtained through the environmental sensors corresponding to each of the n digital display pressure gauges to obtain n environmental data sets. n pressure values are determined according to the n pressure parameter sets and the n environmental data sets. The target device is subjected to abnormal detection according to the n pressure values to obtain the target abnormal detection result. The target abnormal detection result is synchronized to the cloud server. Since the environmental data set is used to calibrate the corresponding digital display pressure gauge, the detection accuracy of the digital display pressure gauge can be improved. Furthermore, the accuracy of abnormal detection can be ensured. In addition, synchronizing the target abnormal detection result to the cloud server can implement the corresponding warning operation, thereby ensuring safety.

[0117] Consistent with the above embodiments, please refer to Figure 5 , Figure 5 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device includes a processor, a memory, a communication interface, and one or more programs. Among them, the above one or more programs are stored in the above memory and are configured to be executed by the above processor. In the embodiments of the present application, the electronic device includes a relay controller applied to the Internet of Things system. The Internet of Things system further includes n digital display pressure gauges and a cloud server, where n is an integer greater than 1. The above programs include instructions for performing the following steps:

[0118] Obtain the pressure parameters of the n digital display pressure gauges in a preset time period to obtain n pressure parameter sets. The n digital display pressure gauges are arranged at different positions inside the target device; each of the n digital display pressure gauges corresponds to an environmental sensor;

[0119] Obtain the environmental data sets in the preset time period through the environmental sensors corresponding to each of the n digital display pressure gauges to obtain n environmental data sets;

[0120] Determine n pressure values according to the n pressure parameter sets and the n environmental data sets;

[0121] Perform abnormal detection on the target device according to the n pressure values to obtain the target abnormal detection result;

[0122] Synchronize the target anomaly detection result to the cloud server.

[0123] Optionally, in determining the n pressure values based on the n pressure parameter sets and the n environmental data sets, the above program includes instructions for performing the following steps:

[0124] Perform fitting based on the first pressure parameter set to obtain the first fitted straight line segment for the preset time period, where the first pressure parameter set is any one of the n pressure parameter sets;

[0125] Determine the slope of the first fitted straight line segment to obtain the first slope;

[0126] Determine the mean of the first fitted straight line segment to obtain the first mean;

[0127] Determine the first standard deviation and the first average value of the environmental data set corresponding to the first pressure parameter set;

[0128] Determine the pressure value corresponding to the first pressure parameter set based on the first slope, the first mean, the first standard deviation, and the first average value.

[0129] Optionally, in determining the pressure value corresponding to the first pressure parameter set based on the first slope, the first mean, the first standard deviation, and the first average value, the above program includes instructions for performing the following steps:

[0130] Determine the first adjustment parameter corresponding to the first slope;

[0131] Determine the first reference pressure value based on the first adjustment parameter and the first mean;

[0132] Determine the first reference accuracy corresponding to the first average value;

[0133] Determine the first correction parameter corresponding to the first standard deviation;

[0134] Correct the first reference accuracy according to the first correction parameter to obtain the first accuracy;

[0135] Determine the pressure value corresponding to the first pressure parameter set based on the first accuracy and the first reference pressure value.

[0136] Optionally, in performing anomaly detection on the target device based on the n pressure values to obtain the target anomaly detection result, the above program includes instructions for performing the following steps:

[0137] Determine the first environmental data mean in the n environmental data sets;

[0138] Determine the reference pressure value corresponding to the mean value of the first environmental data;

[0139] Determine the absolute value of the difference between each of the n pressure values and the reference pressure value to obtain n absolute values;

[0140] Select the maximum value among the n absolute values. When the maximum value is greater than or equal to the preset absolute value, determine that the target device is abnormal, obtain the absolute values among the n absolute values that are greater than the preset absolute value, and obtain the pressure values corresponding to these absolute values to obtain at least one pressure value, and determine the target abnormal detection result according to the at least one pressure value.

[0141] Optionally, the above program further includes instructions for performing the following steps:

[0142] When the maximum value is less than the preset absolute value, group the n digital pressure gauges according to the n environmental data sets to obtain m groups, and each group includes at least two digital pressure gauges;

[0143] According to the n pressure values, obtain the pressure values corresponding to the m groups to obtain m sets of pressure values;

[0144] Determine the standard deviation corresponding to the m sets of pressure values to obtain m third standard deviations;

[0145] Determine the standard deviation of the n pressure values to obtain a fourth standard deviation;

[0146] Perform abnormal detection on the target device according to the fourth standard deviation and the m third standard deviations to obtain the target abnormal detection result.

[0147] Optionally, in terms of performing abnormal detection on the target device according to the fourth standard deviation and the m third standard deviations to obtain the target abnormal detection result, the above program includes instructions for performing the following steps:

[0148] When the fourth standard deviation is greater than the first preset threshold, determine that the target device is abnormal;

[0149] Select the maximum value among the m third standard deviations to obtain a target third standard deviation;

[0150] Obtain the positions of the digital pressure gauges corresponding to the target third standard deviation to obtain multiple positions;

[0151] Determine the geometric center of the multiple positions;

[0152] Determine the target abnormal detection result according to the geometric center.

[0153] Optionally, the above program further includes instructions for performing the following steps:

[0154] When the fourth standard deviation is less than or equal to the first preset threshold, determine the maximum value and the minimum value of the m third standard deviations;

[0155] Determine the difference between the maximum value and the minimum value to obtain a first difference;

[0156] When the first difference is greater than or equal to a preset difference, determine that the target device is abnormal; perform the step of selecting the maximum value among the m third standard deviations to obtain a target third standard deviation;

[0157] When the first difference is less than the preset difference, determine that the target device is normal.

[0158] It can be seen that the relay controller described in the embodiments of the present application is applied to the middle Internet of Things system. The Internet of Things system further includes n digital pressure gauges and a cloud server, where n is an integer greater than 1. The pressure parameters of the n digital pressure gauges in a preset time period are obtained to obtain n pressure parameter sets. The n digital pressure gauges are arranged at different positions inside the target device; each of the n digital pressure gauges corresponds to an environmental sensor. The environmental data sets in the preset time period are obtained through the environmental sensors corresponding to each of the n digital pressure gauges to obtain n environmental data sets. n pressure values are determined according to the n pressure parameter sets and the n environmental data sets. The target device is subjected to abnormal detection according to the n pressure values to obtain a target abnormal detection result. The target abnormal detection result is synchronized to the cloud server. Since the environmental data set is used to calibrate the corresponding digital pressure gauge, the detection accuracy of the digital pressure gauge can be improved. Furthermore, the accuracy of abnormal detection can be ensured. In addition, synchronizing the target abnormal detection result to the cloud server can implement corresponding warning operations, thereby ensuring safety.

[0159] Figure 6 It is a functional unit composition block diagram of an abnormal detection device 600 based on a digital pressure gauge involved in the embodiments of the present application. The abnormal detection device 600 based on a digital pressure gauge is applied to a relay controller in an Internet of Things system. The Internet of Things system further includes n digital pressure gauges and a cloud server, where n is an integer greater than 1. The abnormal detection device 600 based on a digital pressure gauge includes: an acquisition unit 601, a determination unit 602, a detection unit 603, and a synchronization unit 604. Among them,

[0160] The obtaining unit 601 is configured to obtain the pressure parameters of the n digital pressure gauges in a preset time period, so as to obtain n pressure parameter sets. The n digital pressure gauges are arranged at different positions inside the target device; each of the n digital pressure gauges corresponds to an environmental sensor; the environmental data sets in the preset time period are obtained through the environmental sensors corresponding to each of the n digital pressure gauges, so as to obtain n environmental data sets;

[0161] The determining unit 602 is configured to determine n pressure values according to the n pressure parameter sets and the n environmental data sets;

[0162] The detecting unit 603 is configured to perform anomaly detection on the target device according to the n pressure values, so as to obtain a target anomaly detection result;

[0163] The synchronizing unit 604 is configured to synchronize the target anomaly detection result to the cloud server.

[0164] Optionally, in terms of determining the n pressure values according to the n pressure parameter sets and the n environmental data sets, the determining unit 602 is specifically configured to:

[0165] Perform fitting according to the first pressure parameter set to obtain the first fitting straight line segment in the preset time period, where the first pressure parameter set is any one of the n pressure parameter sets;

[0166] Determine the slope of the first fitting straight line segment to obtain a first slope;

[0167] Determine the mean value of the first fitting straight line segment to obtain a first mean value;

[0168] Determine the first standard deviation and the first average value of the environmental data set corresponding to the first pressure parameter set;

[0169] Determine the pressure value corresponding to the first pressure parameter set according to the first slope, the first mean value, the first standard deviation, and the first average value.

[0170] Optionally, in terms of determining the pressure value corresponding to the first pressure parameter set according to the first slope, the first mean value, the first standard deviation, and the first average value, the determining unit 602 is specifically configured to:

[0171] Determine a first adjustment parameter corresponding to the first slope;

[0172] Determine a first reference pressure value according to the first adjustment parameter and the first mean value;

[0173] Determine a first reference accuracy corresponding to the first average value;

[0174] Determine a first correction parameter corresponding to the first standard deviation;

[0175] Correct the first reference accuracy according to the first correction parameter to obtain a first accuracy;

[0176] Determine the pressure value corresponding to the first pressure parameter set according to the first accuracy and the first reference pressure value.

[0177] Optionally, in terms of performing anomaly detection on the target device according to the n pressure values to obtain a target anomaly detection result, the detection unit 603 is specifically configured to:

[0178] Determine a first environmental data mean value in the n environmental data sets;

[0179] Determine a reference pressure value corresponding to the first environmental data mean value;

[0180] Determine the absolute value of the difference between each pressure value in the n pressure values and the reference pressure value to obtain n absolute values;

[0181] Select the maximum value among the n absolute values. When the maximum value is greater than or equal to a preset absolute value, determine that the target device has an anomaly, obtain the absolute values among the n absolute values that are greater than the preset absolute value, and obtain the pressure value corresponding to the absolute value to obtain at least one pressure value, and determine the target anomaly detection result according to the at least one pressure value.

[0182] Optionally, the anomaly detection device 600 based on the digital pressure gauge is further specifically configured to:

[0183] When the maximum value is less than the preset absolute value, group the n digital pressure gauges according to the n environmental data sets to obtain m groups, and each group includes at least two digital pressure gauges;

[0184] According to the n pressure values, obtain the pressure values corresponding to the m groups to obtain m groups of pressure values;

[0185] Determine the standard deviations corresponding to the m groups of pressure values to obtain m third standard deviations;

[0186] Determine the standard deviation of the n pressure values to obtain a fourth standard deviation;

[0187] Perform anomaly detection on the target device according to the fourth standard deviation and the m third standard deviations to obtain the target anomaly detection result.

[0188] Optionally, in terms of performing anomaly detection on the target device based on the fourth standard deviation and the m third standard deviations to obtain the target anomaly detection result, the anomaly detection device 600 based on a digital pressure gauge is specifically configured to:

[0189] When the fourth standard deviation is greater than a first preset threshold, determine that the target device has an anomaly;

[0190] Select the maximum value among the m third standard deviations to obtain a target third standard deviation;

[0191] Obtain the positions of the digital pressure gauges corresponding to the target third standard deviation to obtain multiple positions;

[0192] Determine the geometric center of the multiple positions;

[0193] Determine the target anomaly detection result according to the geometric center.

[0194] Optionally, the anomaly detection device 600 based on a digital pressure gauge is further specifically configured to:

[0195] When the fourth standard deviation is less than or equal to the first preset threshold, determine the maximum value and the minimum value of the m third standard deviations;

[0196] Determine the difference between the maximum value and the minimum value to obtain a first difference;

[0197] When the first difference is greater than or equal to a preset difference, determine that the target device has an anomaly; perform the step of selecting the maximum value among the m third standard deviations to obtain a target third standard deviation;

[0198] When the first difference is less than the preset difference, determine that the target device is normal.

[0199] It can be seen that the anomaly detection device based on a digital pressure gauge described in the embodiments of the present application is applied to a relay controller in an Internet of Things system. The Internet of Things system further includes n digital pressure gauges and a cloud server, where n is an integer greater than 1. Pressure parameters of the n digital pressure gauges in a preset time period are obtained to obtain n pressure parameter sets. The n digital pressure gauges are arranged at different positions inside the target device; each of the n digital pressure gauges corresponds to an environmental sensor. Environmental data sets in the preset time period are obtained through the environmental sensors corresponding to each of the n digital pressure gauges to obtain n environmental data sets. n pressure values are determined based on the n pressure parameter sets and the n environmental data sets. Anomaly detection is performed on the target device based on the n pressure values to obtain a target anomaly detection result. The target anomaly detection result is synchronized to the cloud server. Since the environmental data set is used to calibrate the corresponding digital pressure gauge, the detection accuracy of the digital pressure gauge can be improved. Furthermore, the accuracy of anomaly detection can be ensured. In addition, synchronizing the target anomaly detection result to the cloud server can implement corresponding warning operations, thereby ensuring safety.

[0200] It can be understood that the functions of the respective program modules of the anomaly detection device based on a digital pressure gauge in this embodiment can be specifically implemented according to the methods in the above method embodiments. The specific implementation process can refer to the relevant descriptions in the above method embodiments and will not be elaborated here.

[0201] The embodiments of the present application further provide a computer storage medium. The computer storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to execute some or all of the steps of any of the methods described in the above method embodiments. The above computer includes an electronic device.

[0202] The embodiments of the present application further provide a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to cause a computer to execute some or all of the steps of any of the methods described in the above method embodiments. The computer program product can be a software installation package. The above computer includes an electronic device.

[0203] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0204] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0205] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in an electrical or other form.

[0206] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0207] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0208] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in each embodiment of the present application. And the aforementioned memory includes: various media such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0209] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and this program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (abbreviation: ROM), a random access memory (abbreviation: RAM), a magnetic disk, an optical disk, etc.

[0210] The above has introduced the embodiments of the present application in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation on the present application.

Claims

1. An abnormality detection method based on a digital pressure gauge, characterized in that: A relay controller applied to an Internet of Things system, wherein the Internet of Things system further includes n digital pressure gauges and a cloud server, where n is an integer greater than 1, and the method includes: Obtaining pressure parameters of the n digital pressure gauges in a preset time period to obtain n pressure parameter sets, wherein the n digital pressure gauges are arranged at different positions inside the target device; each of the n digital pressure gauges corresponds to an environmental sensor; Acquire the environmental data set of the preset time period through the environmental sensor corresponding to each of the n digital pressure gauges to obtain n environmental data sets; Determining n pressure values ​​according to the n pressure parameter sets and the n environmental data sets; Performing anomaly detection on the target device according to the n pressure values ​​to obtain a target anomaly detection result; The target anomaly detection result is synchronized to the cloud server.

2. The method according to claim 1, characterized in that The determining of n pressure values ​​according to the n pressure parameter sets and the n environmental data sets includes: Performing fitting according to the first pressure parameter set to obtain a first fitting straight line segment of the preset time period, wherein the first pressure parameter set is any one of the n pressure parameter sets; Determine the slope of the first fitting straight line segment to obtain a first slope; Determine the mean of the first fitting straight line segment to obtain a first mean; Determine a first standard deviation and a first mean value of an environmental data set corresponding to the first pressure parameter set; A pressure value corresponding to the first pressure parameter set is determined according to the first slope, the first mean, the first standard deviation and the first average value.

3. The method according to claim 2, characterized in that The determining the pressure value corresponding to the first pressure parameter set according to the first slope, the first mean, the first standard deviation and the first average value includes: determining a first adjustment parameter corresponding to the first slope; Determine a first reference pressure value according to the first adjustment parameter and the first average value; Determine a first reference accuracy corresponding to the first average value; determining a first correction parameter corresponding to the first standard deviation; Correcting the first reference accuracy according to the first correction parameter to obtain a first accuracy; A pressure value corresponding to the first pressure parameter set is determined according to the first accuracy and the first reference pressure value.

4. The method according to any one of claims 1 to 3, characterized in that: The performing abnormality detection on the target device according to the n pressure values ​​to obtain a target abnormality detection result includes: Determine a first environmental data mean value among the n environmental data sets; Determine a reference pressure value corresponding to the mean value of the first environmental data; Determine the absolute value of the difference between each of the n pressure values ​​and the reference pressure value to obtain n absolute values; A maximum value among the n absolute values ​​is selected, and when the maximum value is greater than or equal to a preset absolute value, it is determined that an abnormality exists in the target device, an absolute value corresponding to the preset absolute value among the n absolute values ​​is obtained, and a pressure value corresponding to the absolute value is obtained to obtain at least one pressure value, and the target abnormality detection result is determined according to the at least one pressure value.

5. The method according to claim 4, characterized in that The method further comprises: When the maximum value is less than the preset absolute value, grouping the n digital pressure gauges according to the n environmental data sets to obtain m groups, each group including at least two digital pressure gauges; According to the n pressure values, obtaining the pressure values ​​corresponding to the m groups to obtain m groups of pressure values; Determine the standard deviations corresponding to the m groups of pressure values ​​to obtain m third standard deviations; Determining a standard deviation of the n pressure values ​​to obtain a fourth standard deviation; Anomaly detection is performed on the target device according to the fourth standard deviation and the m third standard deviations to obtain the target anomaly detection result.

6. The method according to claim 5, characterized in that The performing abnormality detection on the target device according to the fourth standard deviation and the m third standard deviations to obtain the target abnormality detection result includes: When the fourth standard deviation is greater than a first preset threshold, determining that an abnormality exists in the target device; Selecting the maximum value among the m third standard deviations to obtain a target third standard deviation; Obtaining the position of the digital pressure gauge corresponding to the third standard deviation of the target, and obtaining multiple positions; determining the geometric centers of the plurality of locations; The target anomaly detection result is determined according to the geometric center.

7. The method according to claim 6, characterized in that The method further comprises: When the fourth standard deviation is less than or equal to the first preset threshold, determining the maximum value and the minimum value of the m third standard deviations; Determine a difference between the maximum value and the minimum value to obtain a first difference; When the first difference is greater than or equal to a preset difference, determining that the target device is abnormal; executing the step of selecting the maximum value of the m third standard deviations to obtain a target third standard deviation; When the first difference is smaller than the preset difference, it is determined that the target device is normal.

8. An abnormality detection device based on a digital pressure gauge, characterized in that: A relay controller used in an Internet of Things system, wherein the Internet of Things system further includes n digital pressure gauges and a cloud server, where n is an integer greater than 1, and the device includes: an acquisition unit, a determination unit, a detection unit, and a synchronization unit, wherein: The acquisition unit is used to acquire pressure parameters of the n digital pressure gauges in a preset time period to obtain n pressure parameter sets, wherein the n digital pressure gauges are arranged at different positions inside the target device; each of the n digital pressure gauges corresponds to an environmental sensor; and an environmental data set of the preset time period is acquired through the environmental sensor corresponding to each of the n digital pressure gauges to obtain n environmental data sets; The determining unit is used to determine n pressure values ​​according to the n pressure parameter sets and the n environmental data sets; The detection unit is used to perform abnormality detection on the target device according to the n pressure values ​​to obtain a target abnormality detection result; The synchronization unit is used to synchronize the target anomaly detection result to the cloud server.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store one or more programs and is configured to be executed by the processor, wherein the programs include instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 7.

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