A remote monitoring test control management method, system and storage medium

Abstract

The application relates to a remote monitoring test control management method and system and a storage medium, and belongs to the field of data transmission.The method comprises the following steps: acquiring device data transmitted by all connection points based on a remote terminal and storing the device data of each connection point in a corresponding information database; constructing a single attribute data structure for the device data transmitted by each connection point within a preset test time period; determining the distribution type of the device data based on the single attribute data structure; constructing a probability distribution model of the device data based on the distribution type; screening out abnormal data in the device data based on the probability distribution model; storing the abnormal data in a preset abnormal database, and transmitting the device data without the abnormal data to an upper computer. The application has the effect that the upper computer can accurately and effectively perform remote monitoring on devices or sensors.

Description

Technical Field

[0001] This application relates to the field of data transmission, and in particular to a remote monitoring, testing, control and management method, system and storage medium. Background Technology

[0002] RTU stands for Remote Terminal Control System, used for monitoring and controlling signals and industrial equipment at remote work sites. As a core device in an enterprise's integrated automation system, the RTU mainly includes signal input modules, signal output modules, microprocessors, wireless communication equipment, and power supplies. RTUs are currently widely used in industries such as meteorology, hydrology, and geology.

[0003] In existing technologies, RTUs connect to devices or sensors through multiple interfaces to acquire data generated by the devices or sensors, such as voltage data and gravity data, and transmit the data to a host computer. This allows the host computer to perform statistical analysis on the data, thereby enabling remote monitoring of the devices or sensors.

[0004] Regarding the aforementioned prior art, the applicant believes that when the RTU receives data generated by the device or sensor through multiple interfaces and transmits the data to the host computer, the device or sensor may generate abnormal data due to instability of the device itself, external environment or human factors. If the abnormal data is transmitted to the host computer for statistical analysis through the RTU, it will cause the statistical analysis results of the host computer to be inaccurate, thereby causing the host computer to be unable to perform accurate and effective remote monitoring of the device or sensor. Summary of the Invention

[0005] To enable a host computer to perform accurate and effective remote monitoring of equipment or sensors, this application provides a remote monitoring, testing, control, and management method, system, and storage medium.

[0006] Firstly, the remote monitoring, testing, control, and management method provided in this application adopts the following technical solution:

[0007] A remote monitoring, testing, control, and management method includes:

[0008] The device data transmitted from all connection points is obtained through a remote terminal, and the device data of each connection point is stored in the corresponding information database.

[0009] A single-attribute data structure is constructed for the device data transmitted at each connection point within a preset test time period;

[0010] Based on the single-attribute data structure, determine the distribution type of the device data;

[0011] Construct a probability distribution model for the device data based on the distribution type;

[0012] Abnormal data in the device data is filtered out based on the probability distribution model;

[0013] The abnormal data is stored in a preset abnormal database, and the device data with the abnormal data removed is transmitted to the host computer.

[0014] By adopting the above technical solution, abnormal data is filtered out from the device data obtained based on the remote terminal through a probability distribution model, and the device data with abnormal data removed is transmitted to the host computer, thereby facilitating the host computer to perform accurate and effective remote monitoring of the devices or sensors corresponding to the connection points.

[0015] Optionally, determining the distribution type of the device data based on the single-attribute data structure includes:

[0016] Sample data for a single attribute is obtained based on the single attribute data structure.

[0017] The frequency distribution of the sample data is statistically analyzed, and the probability density function of the sample data is obtained.

[0018] Based on the preset probability distribution structure and the probability density function, the distribution type of the device data is determined.

[0019] By adopting the above technical solution, the distribution type of equipment data is determined by the probability density function of a single attribute and the preset probability distribution structure. After determining the distribution type of equipment data, the probability distribution model of equipment data can be constructed, which facilitates the screening of abnormal data.

[0020] Optionally, the step of filtering out abnormal data from the device data based on the probability distribution model includes:

[0021] Based on the probability distribution model, the mean data is obtained;

[0022] In the sample data, the data object that differs the most from the mean data is obtained, and the maximum and minimum extreme values ​​are obtained based on the probability distribution model;

[0023] Determine whether the data object is greater than the mean data;

[0024] If it is greater than the maximum value, the data object is substituted into the preset first formula based on the maximum extreme value to obtain the first frequency value;

[0025] Determine whether the first frequency value is less than or equal to a preset standard frequency value;

[0026] If the value is less than or equal to the value, the data object is determined to be abnormal data.

[0027] By adopting the above technical solution, the mean data is first obtained based on the constructed probability distribution model. After obtaining the data object, the first formula is used to determine whether the data object corresponding to the abnormal attribute is abnormal data, so as to facilitate the screening of abnormal data and then the accurate data is transmitted to the host computer, which facilitates the host computer to perform accurate and effective remote monitoring of the device or sensor corresponding to the connection point.

[0028] Optionally, the method further includes:

[0029] If the data object is not greater than the mean data, based on the minimum extreme value, the data object is substituted into the preset second formula to obtain the second frequency value;

[0030] Determine whether the second frequency value is less than or equal to the standard frequency value;

[0031] If the value is less than or equal to the value, the data object is determined to be abnormal data.

[0032] By adopting the above technical solution, when the data object is no larger than the mean data, the second frequency value obtained by the second formula is compared with the standard frequency value, and the data object corresponding to the abnormal attribute can be filtered as abnormal data, so that the accurate data is transmitted to the host computer, which facilitates the host computer to perform accurate and effective remote monitoring of the device or sensor corresponding to the connection point.

[0033] Optionally, after storing the abnormal data in a preset abnormal database and transmitting the device data (with the abnormal data removed) to the host computer, the process includes:

[0034] The frequency of abnormalities in the device data of each connection point during the test period is calculated based on the abnormal data.

[0035] Connection points with abnormal frequencies exceeding a preset frequency threshold are identified as abnormal connection points; these abnormal connection points include visible connection points and closed connection points.

[0036] If the abnormal connection point is the visualization connection point, a visualization chart is generated from the device data of the abnormal connection point, and an alarm message is generated based on the time point corresponding to the abnormal data.

[0037] By adopting the above technical solution, if the frequency of abnormal data during the test period is greater than the frequency threshold, it indicates that the equipment or sensor may be malfunctioning. At this time, when the abnormal connection point is a visual connection point, a visual chart of the abnormal connection point is generated, and an alarm message is generated to facilitate timely handling by the back-end management personnel.

[0038] Optionally, after determining the connection points corresponding to abnormal frequencies greater than a preset frequency threshold as abnormal connection points, the method further includes:

[0039] If the abnormal connection point is the closed connection point, obtain the information database corresponding to the closed connection point, and determine the information database as a closed database;

[0040] Obtain the responsible terminal identifier from the closed database;

[0041] Set control permissions for the closed device corresponding to the closed connection point for the responsible terminal corresponding to the responsible terminal identifier.

[0042] By adopting the above technical solution, when the abnormal connection point is a closed connection point, the control authority of the responsible terminal over the closed connection point is set, which facilitates the responsible terminal to handle abnormal connection points that may occur in a timely manner.

[0043] Optionally, the responsible terminal includes a primary responsible terminal and a secondary responsible terminal; the control permissions include primary control permissions and viewing permissions; the responsible terminal identifier includes a primary responsible terminal identifier and a secondary responsible terminal identifier; the primary responsible terminal identifier corresponds to the primary responsible terminal, and the secondary responsible terminal identifier corresponds to the secondary responsible terminal; there is one primary responsible terminal and multiple secondary responsible terminals;

[0044] The step of setting control permissions for the closed device corresponding to the closed connection point for the responsible terminal corresponding to the responsible terminal identifier includes:

[0045] The main responsible terminal is given master control permissions for the closed device, enabling the main responsible terminal to control the closed device;

[0046] The secondary responsible terminal is granted viewing permissions for the closed device, enabling it to view the control flow of the primary responsible terminal over the closed device.

[0047] By adopting the above technical solution, different control permissions are set for the primary and secondary responsible terminals to facilitate effective management of equipment or sensors.

[0048] Optionally, after setting control permissions for the closed device corresponding to the closed connection point on the responsible terminal corresponding to the responsible terminal identifier, the following steps are included:

[0049] Determine whether a control command sent by the main responsible terminal has been received;

[0050] If received, the control command is sent to the enclosed device via the remote terminal, causing the enclosed device to execute the control command.

[0051] By adopting the above technical solution, upon receiving the control command sent by the responsible terminal, the closed device can execute the control command, which facilitates the responsible terminal to remotely control and manage the closed device.

[0052] Secondly, the remote monitoring, testing, control, and management system provided in this application adopts the following technical solution:

[0053] A remote monitoring, testing, control and management system includes a memory, a processor and a computer program stored in the memory and capable of running on the processor. When the processor loads and executes the computer program, the aforementioned remote monitoring, testing, control and management method is adopted.

[0054] By adopting the above technical solution, a computer program is generated from the above remote monitoring, testing, control and management method and stored in the memory so that it can be loaded and executed by the processor. Thus, a smart terminal is made based on the memory and the processor, which is convenient to use.

[0055] Thirdly, the computer-readable storage medium provided in this application adopts the following technical solution:

[0056] A computer-readable storage medium storing a computer program, wherein the computer program, when loaded and executed by a processor, employs the aforementioned remote monitoring, testing, control, and management method.

[0057] By adopting the above technical solution, the above remote monitoring, testing, control and management method is used to generate a computer program and store it in a computer-readable storage medium so that it can be loaded and executed by the processor. The computer-readable storage medium facilitates the reading and storage of the computer program.

[0058] In summary, this application has at least one of the following beneficial technical effects:

[0059] 1. Abnormal data is filtered out from device data acquired through remote terminals using a probability distribution model, and the device data with abnormal data removed is transmitted to the host computer, thereby facilitating accurate and effective remote monitoring of the devices or sensors corresponding to the connection points by the host computer.

[0060] 2. First, the mean data is obtained based on the constructed probability distribution model. After obtaining the data objects, the first formula is used to determine whether the data objects corresponding to the abnormal attributes are abnormal data, so as to facilitate the filtering of abnormal data and the transmission of accurate data to the host computer, which can facilitate the host computer to perform accurate and effective remote monitoring of the devices or sensors corresponding to the connection points.

[0061] 3. Set different control permissions for the primary and secondary responsible terminals to facilitate effective management of equipment or sensors. Attached Figure Description

[0062] Figure 1 This is a flowchart illustrating one implementation of a remote monitoring, testing, control, and management method according to an embodiment of this application.

[0063] Figure 2 This is a flowchart illustrating one implementation of a remote monitoring, testing, control, and management method according to an embodiment of this application.

[0064] Figure 3 This is a flowchart illustrating one implementation of a remote monitoring, testing, control, and management method according to an embodiment of this application.

[0065] Figure 4 This is a flowchart illustrating one implementation of a remote monitoring, testing, control, and management method according to an embodiment of this application.

[0066] Figure 5 This is a flowchart illustrating one implementation of a remote monitoring, testing, control, and management method according to an embodiment of this application.

[0067] Figure 6 This is a flowchart illustrating one implementation of a remote monitoring, testing, control, and management method according to an embodiment of this application.

[0068] Figure 7 This is a flowchart illustrating one implementation of a remote monitoring, testing, control, and management method according to an embodiment of this application.

[0069] Figure 8 This is a flowchart illustrating one implementation of a remote monitoring, testing, control, and management method according to an embodiment of this application. Detailed Implementation

[0070] The following is in conjunction with the appendix Figures 1 to 8 This application will be described in further detail.

[0071] This application discloses a remote monitoring, testing, control, and management method.

[0072] Reference Figure 1 A remote monitoring, testing, control, and management method includes the following steps:

[0073] S101. Obtain device data transmitted from all connection points based on the remote terminal and store the device data of each connection point in the corresponding information database.

[0074] A remote terminal unit (RTU) is a remote terminal control system, also known as a telemetry terminal unit. It is used to monitor and control signals and industrial equipment at remote work sites. Since a remote terminal connects to several devices or sensors, the interface between the remote terminal and each device or sensor is called a connection point. Device data refers to the operating data generated by the device or sensor, such as voltage data, current data, device operating time, and device temperature.

[0075] The information database is used to store device data. Specifically, each remote terminal connection point corresponds to one information database, meaning that each information database stores only the data transmitted from that single connection point of the remote terminal.

[0076] In this embodiment, the remote terminal is connected to the device or sensor via an RS485 serial port. The current executing entity refers to the industrial internet platform. The device data acquired by the remote terminal through the device or sensor can be uploaded to the industrial internet platform via a wired or wireless network. Specifically, the wireless network can be a 4G network, a 5G network, etc.

[0077] S102. Construct a single-attribute data structure for the device data transmitted by each connection point within a preset test time period.

[0078] The test period is manually set, for example, from 8:00 AM to 12:00 PM. The attribute data structure describes the set of attribute data for each target object, for example, matrix A = [a...]. 11 ,a 21 ,…,a m1 A single-attribute data structure refers to an m×1 matrix. For example, if the device data transmitted by connection point A during the test period is 20, 50, 30, 40, then the single-attribute data structure built based on the device data is A = [20, 50, 30, 40].

[0079] S103. Based on the single-attribute data structure, determine the distribution type of device data.

[0080] Distribution types include discrete and continuous distributions. Discrete distributions mainly include binomial distribution, Poisson distribution, discrete uniform distribution, geometric distribution, and hypergeometric distribution. In the first embodiment, the distribution type is determined based on a histogram. A histogram is a commonly used graph to display the distribution of quantitative data. Through a histogram, the shape, center position, and dispersion of the data distribution can be visually observed, thereby determining the distribution type of the device data packets. In the second embodiment, the probability density function of a single attribute is first statistically obtained, and then the distribution type of the device data is determined by the probability density function and a preset probability distribution structure.

[0081] Continuous distributions mainly include normal distribution, t-distribution, F-distribution, chi-square distribution, exponential distribution, Gamma-distribution, and Beta-distribution. In the first embodiment, the distribution type is determined based on the histogram; in the second embodiment, the probability density function of a single attribute is first obtained statistically, and then the distribution type of the device data is determined by the probability density function and a preset probability distribution structure.

[0082] S104. Construct a probability distribution model for device data based on distribution type.

[0083] In this embodiment, after the distribution type of the device data is known, the device data is input into a preset probability model based on machine learning, and a probability distribution model that conforms to the distribution type is output. Specifically, the probability model is learned based on an existing dataset. The distribution type of the existing dataset is consistent with the distribution type of the device data. Therefore, after inputting the device data based on machine learning, the distribution type that conforms to the existing dataset can be output. At this time, the output distribution type is the distribution type of the device data.

[0084] S105. Filter out abnormal data in the device data based on the probability distribution model.

[0085] After constructing the probability distribution model, the probability that the device data conforms to the probability distribution model can be calculated, and device data that is lower than the preset probability threshold can be identified as abnormal data.

[0086] S106. Store the abnormal data in a preset abnormal database and transfer the device data with the abnormal data removed to the host computer.

[0087] The anomaly database is used to store abnormal data transmitted from all connection points. Device data with abnormal data removed is transmitted to the host computer so that the host computer can perform accurate and effective remote monitoring of the devices or sensors corresponding to the connection points.

[0088] The implementation principle of this embodiment is as follows: abnormal data is filtered out from the device data obtained based on the remote terminal through a probability distribution model, and the device data with abnormal data removed is transmitted to the host computer, thereby facilitating the host computer to perform accurate and effective remote monitoring of the device or sensor corresponding to the connection point.

[0089] exist Figure 1 In step S103 of the illustrated embodiment, the distribution type of the device data can be determined using a probability density function. Specifically, through... Figure 2 The embodiments shown will be described in detail.

[0090] Reference Figure 2 Based on a single-attribute data structure, the distribution type of device data is determined, including the following steps:

[0091] S201. Obtain sample data for a single attribute based on a single attribute data structure.

[0092] As shown in step S102, a single-attribute data structure refers to an m×1 matrix. For example, if the device data transmitted by connection point A during the test period is 20, 50, 30, 40, then the single-attribute data structure constructed based on the device data is A = [20, 50, 30, 40], and the sample data refers to 20, 50, 30, 40.

[0093] S202. Analyze the frequency distribution of the sample data and obtain the probability density function of the sample data.

[0094] In this embodiment, after obtaining the sample data, i.e. based on the goodness of fit x 2 The test can be used to test whether the population of sample data follows a certain known distribution or a certain type of distribution. In addition, the population of sample data can be tested based on the Kolmogorov test or the Smirnov test, which can yield the probability density function of the sample data.

[0095] S203. Based on the preset probability distribution structure and probability density function, determine the distribution type of the device data.

[0096] The probability distribution structure is preset. After obtaining the probability density function of the sample data, the similarity between the probability distribution structure and the probability density function is measured based on the Hellinger distance. If the similarity is greater than a preset similarity threshold, the distribution type of the device data can be determined. In probability theory and statistics, the Hellinger distance is used to measure the similarity between two probability distributions.

[0097] The remote monitoring, testing, control and management method provided in this embodiment determines the distribution type of equipment data through a single-attribute probability density function and a preset probability distribution structure. After determining the distribution type of equipment data, a probability distribution model of equipment data can be constructed, which facilitates the screening of abnormal data.

[0098] exist Figure 1 In step S105 of the illustrated embodiment, after constructing the probability distribution model, the mean data can be obtained. At this point, it can be determined whether the device data is abnormal data using a preset formula and the mean data. Specifically, through... Figure 3 The embodiments shown will be described in detail.

[0099] Reference Figure 3 The process of filtering out abnormal data from device data based on a probability distribution model includes the following steps:

[0100] S301. Obtain mean data based on the probability distribution model.

[0101] The mean value refers to the expected value of a probability distribution model, used to reflect the central tendency of the data. If the probability distribution model is a normal distribution, the formula is as follows:

[0102]

[0103] Where μ represents the mean and σ represents the variance, the mean data μ can be obtained at this point.

[0104] S302. Obtain the data object that differs the most from the mean data in the sample data, and obtain the maximum and minimum extreme values ​​based on the probability distribution model.

[0105] The largest difference from the mean data indicates the largest absolute value of the difference. Taking step S201 as an example, if the sample data are 20, 50, 30, 40, and the mean is calculated to be 25, then the data object with the largest difference from the mean data is 50.

[0106] It should be noted that after obtaining sample data 50, there is no sample data 50 in the sample data at this time. That is, the data object with the largest difference from the mean data is obtained again for the remaining sample data. If there are still sample data 20, 30, 40, the data object with the largest difference from the mean data is 40, and so on.

[0107] The probability distribution function is obtained based on the probability distribution model. The minimum value of the probability distribution function is the minimum extremum, and the maximum value of the probability distribution function is the maximum extremum.

[0108] S303. Determine whether the data object is greater than the mean data.

[0109] Since the largest difference from the mean data indicates the largest absolute value of the difference from the mean data, when the mean data is 20, the differences between 0 and 40 and the mean data are both the largest, but 40 is greater than the mean data and 0 is less than the mean data.

[0110] S304. If it is greater than the maximum value, substitute the data object into the preset first formula based on the maximum extreme value, and obtain the first frequency value.

[0111] The first formula is the anomaly detection formula for maximum extrema, and the first formula is as follows:

[0112]

[0113] Where y is the first frequency value, m is the attribute of the sample data, for example, in the single-attribute data structure A = [20, 50, 30, 40], the attribute of the sample data 50 is 2; a max This is the maximum extreme value.

[0114] When the data object is greater than the mean data, the data object is substituted into the first formula to obtain the first frequency value.

[0115] S305. Determine whether the first frequency value is less than or equal to the preset standard frequency value.

[0116] S306. If it is less than or equal to, the data object is determined to be abnormal data.

[0117] In this embodiment, the standard frequency value is 0.15. If the first frequency value is less than or equal to the standard frequency value, it indicates that the data object is abnormal data. If the first frequency value is greater than the standard frequency value, it indicates that the data object is normal data.

[0118] The remote monitoring, testing, control and management method provided in this embodiment first obtains mean data based on the constructed probability distribution model. After obtaining the data object, it uses a first formula to determine whether the data object corresponding to the abnormal attribute is abnormal data, thereby facilitating the screening of abnormal data and enabling accurate data transmission to the host computer. This allows the host computer to perform accurate and effective remote monitoring of the device or sensor corresponding to the connection point.

[0119] exist Figure 3 In the illustrated implementation, when the data object is not greater than the average data, it can be determined whether the device data is abnormal based on a preset second formula and the average data. Specifically, through... Figure 4 The embodiments shown will be described in detail.

[0120] Reference Figure 4 The remote monitoring, testing, control, and management method also includes the following steps:

[0121] S401. If the data object is not greater than the mean data, based on the minimum extreme value, substitute the data object into the preset second formula and obtain the second frequency value.

[0122] The second formula is the anomaly detection formula for minimum extrema, and the second formula is as follows:

[0123]

[0124] Where y' is the first frequency value, m is the attribute of the sample data, for example, in the single-attribute data structure A=[20,50,30,40], the attribute of the sample data 50 is 2; a min It is the minimum extreme value.

[0125] When the data object is not greater than, i.e. less than or equal to, the mean data, the data object is substituted into the second formula to obtain the second frequency value.

[0126] S402. Determine whether the second frequency value is less than or equal to the standard frequency value.

[0127] S403. If it is less than or equal to, the data object is determined to be abnormal data.

[0128] In this embodiment, the standard frequency value is 0.15. If the second frequency value is less than or equal to the standard frequency value, it indicates that the data object is abnormal data. If the second frequency value is greater than the standard frequency value, it indicates that the data object is normal data.

[0129] The remote monitoring, testing, control, and management method provided in this embodiment compares the second frequency value obtained by the second formula with the standard frequency value when the data object is not greater than the average data. This allows the data object corresponding to the abnormal attribute to be filtered as abnormal data, thereby enabling accurate data transmission to the host computer. This facilitates accurate and effective remote monitoring of the device or sensor corresponding to the connection point by the host computer.

[0130] exist Figure 1 After step S106 in the illustrated embodiment, abnormal connection points can be divided into visible connection points and closed connection points to facilitate separate management of abnormal connection points through these two methods. Specifically, through... Figure 5 The embodiments shown will be described in detail.

[0131] Reference Figure 5 After storing the abnormal data in a preset abnormal database and transmitting the device data with the abnormal data removed to the host computer, the process includes the following steps:

[0132] S501. Calculate the frequency of abnormal device data at each connection point during the test period based on abnormal data.

[0133] In this embodiment, the total number of device data during the test period is first obtained, and the number of abnormal data is obtained. The abnormal frequency is then calculated by dividing the number of abnormal data by the total number of data.

[0134] S502. The connection points corresponding to abnormal frequencies that are greater than the preset frequency threshold are identified as abnormal connection points; abnormal connection points include visible connection points and closed connection points.

[0135] If the abnormal frequency is greater than the frequency threshold, the connection point where the abnormal frequency occurs is determined to be an abnormal connection point, indicating that the device or sensor connected to the abnormal connection point may be malfunctioning.

[0136] If the device data transmitted at connection point a needs to be publicly displayed on the current executing entity, then connection point a is called a visible connection point; if the device data transmitted at connection point b needs to be transmitted covertly, that is, cannot be publicly displayed on the current executing entity, then connection point b is called a closed connection point.

[0137] S503. If the abnormal connection point is a visual connection point, generate a visual chart of the device data of the abnormal connection point, and generate alarm information based on the time point corresponding to the abnormal data.

[0138] When the abnormal connection point is a visual connection point, a line chart of the device data is generated. Specifically, the x-axis of the line chart represents the test period, and the y-axis represents the device data corresponding to each time point. After generating the line chart, the time points corresponding to the abnormal data are obtained. At this point, alarm information is generated based on the time points, which helps back-end staff to promptly determine the cause of the abnormal data.

[0139] The remote monitoring test control management method provided in this embodiment indicates that if the frequency of abnormal data occurring during the test period is greater than the frequency threshold, it means that the equipment or sensor may be malfunctioning. At this time, when the abnormal connection point is a visual connection point, a visual chart of the abnormal connection point is generated, and an alarm message is generated to facilitate timely handling by the back-end management personnel.

[0140] exist Figure 5 After step S502 of the illustrated implementation, when the abnormal connection point is a closed connection point, control permissions can be set for the closed connection point to facilitate its management. Specifically, through... Figure 6 The embodiments shown will be described in detail.

[0141] Reference Figure 6 After identifying the connection points corresponding to abnormal frequencies exceeding a preset frequency threshold as abnormal connection points, the process includes the following steps:

[0142] S601. If the abnormal connection point is a closed connection point, obtain the information database corresponding to the closed connection point and determine the information database as a closed database.

[0143] If the abnormal connection point is a closed connection point, it indicates that the device data transmitted through the closed connection point is being transmitted covertly. In this case, the information database corresponding to the closed connection point is obtained, and the information database is determined to be a closed database.

[0144] S602. Obtain the responsible terminal identifier from the closed database.

[0145] In this embodiment, each closed database stores several responsible terminal identifiers. The responsible terminal identifier is a unique code for the responsible terminal. Specifically, each responsible terminal identifier is preset by humans, and the responsible terminal can be a mobile phone, tablet, or computer, etc.

[0146] S603. Set control permissions for the closed devices corresponding to the closed connection points for the responsible terminal corresponding to the responsible terminal identifier.

[0147] A closed device refers to the device corresponding to a closed connection point. If the closed device corresponding to the closed connection point may malfunction, the responsible terminal is set to control the closed device to effectively prevent data leakage of the closed device.

[0148] The remote monitoring, testing, control, and management method provided in this embodiment sets control permissions for the responsible terminal on closed connection points when the abnormal connection point is a closed connection point, so that the responsible terminal can handle abnormal connection points that may occur in a timely manner.

[0149] exist Figure 6 In step S603 of the illustrated implementation, the responsible terminal can be divided into a primary responsible terminal and a secondary responsible terminal to facilitate the determination of the control permissions of the primary and secondary responsible terminals, thereby facilitating the management of the closed connection point. Specifically, through... Figure 7 The embodiments shown will be described in detail.

[0150] Reference Figure 7 The responsible terminals include a primary responsible terminal and a secondary responsible terminal; control permissions include primary control permissions and viewing permissions; the responsible terminal identifiers include the primary responsible terminal identifier and the secondary responsible terminal identifier; the primary responsible terminal identifier corresponds to the primary responsible terminal, and the secondary responsible terminal identifier corresponds to the secondary responsible terminal; there is one primary responsible terminal and multiple secondary responsible terminals;

[0151] To set control permissions for the closed devices corresponding to the closed connection points for the responsible terminal corresponding to the responsible terminal identifier, the following steps are included:

[0152] S701. Set the master control permissions for the closed device on the main responsible terminal, so that the main responsible terminal can control the closed device.

[0153] Master control authority refers to the primary responsible terminal's ability to control the enclosed device. In this embodiment, there is only one primary responsible terminal, meaning only one primary responsible terminal can control the enclosed device.

[0154] S702. Set viewing permissions for the secondary responsible terminal to view the closed device, so that the secondary responsible terminal can view the control process of the primary responsible terminal on the closed device.

[0155] Viewing permissions mean that the secondary responsible terminal can view the control flow of the primary responsible terminal to the closed device. That is, every time the primary responsible terminal controls the closed device, an operation record is generated. All operation records of the primary responsible terminal to the closed device constitute the control flow.

[0156] The remote monitoring, testing, control, and management method provided in this embodiment sets different control permissions for the primary and secondary responsible terminals in order to effectively manage the equipment or sensors.

[0157] exist Figure 6 After step S603 of the illustrated implementation, the main responsible terminal can remotely control the closed device corresponding to the closed connection point by sending control commands. Specifically, through... Figure 8 The embodiments shown will be described in detail.

[0158] Reference Figure 8 After setting control permissions for the closed devices corresponding to the closed connection points on the responsible terminal corresponding to the responsible terminal identifier, the following steps are included:

[0159] S801. Determine whether a control command has been received from the main responsible terminal.

[0160] Control commands refer to instructions issued by the main responsible terminal to control the closed equipment to perform corresponding operations, such as turning off the power or restarting.

[0161] S802. If received, the control command will be sent to the closed device via the remote terminal, so that the closed device will execute the control command.

[0162] If a control command is received, the current executing entity will immediately transmit the control command to the closed device via a remote terminal. Upon receiving the control command, the closed device will execute it. If the current executing entity does not receive a control command, no action will be taken.

[0163] The remote monitoring, testing, control, and management method provided in this embodiment enables the closed device to execute control commands upon receiving control commands from the responsible terminal, facilitating remote control and management of the closed device by the responsible terminal.

[0164] This application also discloses a remote monitoring, testing, control and management system, including a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the remote monitoring, testing, control and management method described in the above embodiments is used when the processor executes the computer program.

[0165] This application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it employs the remote monitoring, testing, control, and management method described in the above embodiments.

[0166] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0167] The remote monitoring, testing, control and management method described in the above embodiments is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the above method.

[0168] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A remote monitoring, testing, control, and management method, characterized in that, include: The device data transmitted from all connection points is obtained through a remote terminal, and the device data of each connection point is stored in the corresponding information database. A single-attribute data structure is constructed for the device data transmitted at each connection point within a preset test time period; Based on the single-attribute data structure, determine the distribution type of the device data; Construct a probability distribution model for the device data based on the distribution type; Abnormal data in the device data is filtered out based on the probability distribution model; The abnormal data is stored in a preset abnormal database, and the device data with the abnormal data removed is transmitted to the host computer. The frequency of abnormalities in the device data of each connection point during the test period is calculated based on the abnormal data. Connection points with abnormal frequencies exceeding a preset frequency threshold are identified as abnormal connection points; these abnormal connection points include visible connection points and closed connection points. If the abnormal connection point is the visualization connection point, generate a visualization chart from the device data of the abnormal connection point, and generate alarm information based on the time point corresponding to the abnormal data; If the abnormal connection point is the closed connection point, obtain the information database corresponding to the closed connection point, and determine the information database as a closed database; Obtain the responsible terminal identifier from the closed database; Set control permissions for the closed device corresponding to the closed connection point for the responsible terminal corresponding to the responsible terminal identifier.

2. The remote monitoring, testing, control, and management method according to claim 1, characterized in that, The step of determining the distribution type of the device data based on the single-attribute data structure includes: Sample data for a single attribute is obtained based on the single attribute data structure. The frequency distribution of the sample data is statistically analyzed, and the probability density function of the sample data is obtained. Based on the preset probability distribution structure and the probability density function, the distribution type of the device data is determined.

3. The remote monitoring, testing, control, and management method according to claim 2, characterized in that, The step of filtering out abnormal data from the device data based on the probability distribution model includes: Based on the probability distribution model, the mean data is obtained; In the sample data, the data object that differs the most from the mean data is obtained, and the maximum and minimum extreme values ​​are obtained based on the probability distribution model; Determine whether the data object is greater than the mean data; If it is greater than the maximum value, the data object is substituted into the preset first formula based on the maximum extreme value to obtain the first frequency value; Determine whether the first frequency value is less than or equal to a preset standard frequency value; If the value is less than or equal to the value, the data object is determined to be abnormal data.

4. The remote monitoring, testing, control, and management method according to claim 3, characterized in that, The method further includes: If the data object is not greater than the mean data, based on the minimum extreme value, the data object is substituted into the preset second formula to obtain the second frequency value; Determine whether the second frequency value is less than or equal to the standard frequency value; If the value is less than or equal to the value, the data object is determined to be abnormal data.

5. The remote monitoring, testing, control, and management method according to claim 1, characterized in that, The responsible terminal includes a primary responsible terminal and a secondary responsible terminal; the control permissions include primary control permissions and viewing permissions. The responsible terminal identifier includes the primary responsible terminal identifier and the secondary responsible terminal identifier; The primary responsible terminal identifier corresponds to the primary responsible terminal, and the secondary responsible terminal identifier corresponds to the secondary responsible terminal; there is one primary responsible terminal and multiple secondary responsible terminals; The step of setting control permissions for the closed device corresponding to the closed connection point for the responsible terminal corresponding to the responsible terminal identifier includes: The main responsible terminal is given master control permissions for the closed device, enabling the main responsible terminal to control the closed device; The secondary responsible terminal is granted viewing permissions for the closed device, enabling it to view the control flow of the primary responsible terminal over the closed device.

6. The remote monitoring, testing, control, and management method according to claim 5, characterized in that, After setting control permissions for the closed device corresponding to the closed connection point on the responsible terminal corresponding to the responsible terminal identifier, the following steps are included: Determine whether a control command sent by the main responsible terminal has been received; If received, the control command is sent to the enclosed device via the remote terminal, causing the enclosed device to execute the control command.

7. A remote monitoring, testing, control, and management system, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it employs the method described in any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it employs the method described in any one of claims 1 to 6.

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