CN116186632A - Remote monitoring test control management method, system and storage medium - Google Patents
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Abstract
The application relates to a remote monitoring test control management method, a system and a storage medium, which belong to the field of data transmission, and the method comprises the following steps: acquiring equipment data transmitted by all connection points based on a remote terminal and storing the equipment data of each connection point in a corresponding information database; constructing a single attribute data structure for the equipment data transmitted by each connection point in a preset test time period; determining a 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 equipment data based on the probability distribution model; and storing the abnormal data in a preset abnormal database, and transmitting the equipment data from which the abnormal data are removed to an upper computer. The remote monitoring system has the effect of enabling the upper computer to accurately and effectively monitor equipment or a sensor.
Description
Technical Field
The present disclosure relates to the field of data transmission, and in particular, to a method and system for remote monitoring, testing, controlling and managing, and a storage medium.
Background
RTU refers to a remote terminal control system for monitoring and controlling signals at a remote job site and industrial equipment. The RTU is used as a core device for forming an enterprise comprehensive automation system and mainly comprises a signal input module, a signal output module, a microprocessor, wireless communication equipment, a power supply and the like. RTU is widely used in industries such as weather, hydrology, water conservancy and geology at present.
In the prior art, the RTU is connected with the device or the sensor through a plurality of interfaces, and is used for acquiring data generated by the device or the sensor, such as voltage data, gravity data, and the like, and transmitting the data to the upper computer, so that the upper computer can perform statistical analysis on the data, and further the upper computer can perform remote monitoring on the device or the sensor.
For the prior art, the applicant believes that when the RTU receives data generated by the device or the sensor through a plurality of interfaces and transmits the data to the upper computer, abnormal data may be generated by the device or the sensor due to instability of the device itself, external environment or artificial reasons, if the abnormal data is transmitted to the upper computer through the RTU for statistical analysis, the statistical analysis result of the upper computer is inaccurate, and the upper computer cannot accurately and effectively monitor the device or the sensor remotely.
Disclosure of Invention
In order to enable an upper computer to accurately and effectively remotely monitor equipment or a sensor, the application provides a remote monitoring test control management method, a remote monitoring test control management system and a storage medium.
In a first aspect, the present application provides a remote monitoring test control management method, which adopts the following technical scheme:
a remote monitoring test control management method comprises the following steps:
acquiring equipment data transmitted by all connection points based on a remote terminal and storing the equipment data of each connection point in a corresponding information database;
constructing a single attribute data structure for the equipment data transmitted by each connection point in a preset test time period;
determining a 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 equipment data based on the probability distribution model;
and storing the abnormal data in a preset abnormal database, and transmitting the equipment data from which the abnormal data are removed to an upper computer.
By adopting the technical scheme, the abnormal data is screened out from the equipment data acquired based on the remote terminal through the probability distribution model, and the equipment data with the abnormal data removed is transmitted to the upper computer, so that the upper computer can accurately and effectively monitor the equipment or the sensor corresponding to the connection point.
Optionally, the determining, based on the single attribute data structure, a distribution type of the device data includes:
acquiring sample data of a single attribute based on the single attribute data structure;
counting the frequency distribution of the sample data and obtaining a probability density function of the sample data;
and determining the distribution type of the equipment data based on a preset probability distribution structure and the probability density function.
By adopting the technical scheme, the distribution type of the equipment data is determined through the probability density function of the single attribute and the preset probability distribution structure, and the probability distribution model of the equipment data can be constructed after the distribution type of the equipment data is determined, so that the abnormal data can be conveniently screened.
Optionally, the screening out abnormal data in the device data based on the probability distribution model includes:
acquiring mean value data based on the probability distribution model;
acquiring a data object with the largest phase difference with the mean value data from the sample data, and acquiring a maximum extremum and a minimum extremum based on the probability distribution model;
judging whether the data object is larger than the mean value data or not;
if the data object is larger than the maximum extremum, substituting the data object into a preset first formula, and obtaining a first frequency value;
judging whether the first frequency value is smaller than or equal to a preset standard frequency value;
and if the data object is smaller than or equal to the abnormal data, judging the data object as abnormal data.
By adopting the technical scheme, the mean value data is firstly obtained based on the constructed probability distribution model, and after the data object is obtained, whether the data object corresponding to the abnormal attribute is abnormal data or not is judged through the first formula, so that the abnormal data can be conveniently screened out, the accurate data can be further transmitted to the upper computer, and the upper computer can conveniently and accurately and effectively remotely monitor the equipment or the sensor corresponding to the connection point.
Optionally, the method further comprises:
if the data object is not larger than the mean value data, substituting the data object into a preset second formula based on the minimum extremum value, and obtaining a second frequency value;
judging whether the second frequency value is smaller than or equal to the standard frequency value;
and if the data object is smaller than or equal to the abnormal data, judging the data object as abnormal data.
By adopting the technical scheme, when the data object is not greater than the mean value data, the second frequency value obtained through the second formula is compared with the standard frequency value, so that the data object corresponding to the abnormal attribute can be used as abnormal data for screening, and further accurate data is transmitted to the upper computer, and the upper computer can conveniently and accurately and effectively remotely monitor equipment or a sensor corresponding to the connection point.
Optionally, after the storing the abnormal data in a preset abnormal database and transmitting the device data from which the abnormal data is removed to an upper computer, the method includes:
calculating an abnormal frequency of the device data of each connection point in the test period based on the abnormal data;
judging a connection point corresponding to an abnormal frequency greater than a preset frequency threshold as an abnormal connection point; the abnormal connection points comprise visual connection points and closed connection points;
if the abnormal connection point is the visual connection point, generating a visual chart of the equipment data of the abnormal connection point, and generating alarm information based on a time point corresponding to the abnormal data.
By adopting the technical scheme, if the abnormal frequency of the abnormal data in the test time period is larger than the frequency threshold value, the equipment or the sensor is likely to be in fault, a visual chart of the abnormal connection point is generated when the abnormal connection point is a visual connection point, alarm information is generated, and the background manager can process the abnormal data in time conveniently.
Optionally, after the connection point corresponding to the abnormal frequency greater than the preset frequency threshold is determined as the abnormal connection point, the method further includes:
if the abnormal connection point is the closed connection point, acquiring the information database corresponding to the closed connection point, and judging the information database as a closed database;
acquiring a responsible terminal identifier from the closed database;
and setting control authority of the closed equipment corresponding to the closed connection point for the responsible terminal corresponding to the responsible terminal identifier.
By adopting the technical scheme, when the abnormal connection point is a closed connection point, the control authority of the responsible terminal to the closed connection point is set, so that the responsible terminal can timely process the abnormal connection point which is possibly abnormal.
Optionally, the responsible terminals include a primary responsible terminal and a secondary responsible terminal; the control authority comprises a master control authority and a viewing authority; the responsible terminal identification comprises a main responsible terminal identification and a secondary responsible terminal identification; the main responsible terminal identifier corresponds to the main responsible terminal, and the auxiliary responsible terminal identifier corresponds to the auxiliary responsible terminal; the number of the main responsible terminals is one, and the number of the auxiliary responsible terminals is a plurality;
the responsible terminal corresponding to the responsible terminal identifier sets a control authority for the closed equipment corresponding to the closed connection point, and the method comprises the following steps:
setting the master control authority of the closed equipment for the master responsible terminal to enable the master responsible terminal to control the closed equipment;
and setting the viewing authority of the closing equipment for the secondary responsible terminal, so that the secondary responsible terminal views the control flow of the primary responsible terminal on the closing equipment.
By adopting the technical scheme, different control authorities are set for the main responsible terminal and the auxiliary responsible terminal so as to effectively manage equipment or sensors.
Optionally, after the responsible terminal corresponding to the responsible terminal identifier sets the control authority for the closed device corresponding to the closed connection point, the method includes:
judging whether a control instruction sent by the main responsible terminal is received or not;
and if the control instruction is received, transmitting the control instruction to the closed equipment based on the remote terminal, and enabling the closed equipment to execute the control instruction.
By adopting the technical scheme, after receiving the control instruction sent by the responsible terminal, the responsible terminal can conveniently control and manage the closed equipment remotely even if the closed equipment executes the control instruction.
In a second aspect, the present application provides a remote monitoring test control management system, which adopts the following technical scheme:
the remote monitoring test control management system comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the remote monitoring test control management method is adopted when the processor loads and executes the computer program.
By adopting the technical scheme, the remote monitoring test control management method generates the computer program, stores the computer program in the memory and loads and executes the computer program by the processor, so that the intelligent terminal is manufactured according to the memory and the processor, and is convenient to use.
In a third aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium having a computer program stored therein, the computer program when loaded and executed by a processor employing the remote monitoring test control management method described above.
By adopting the technical scheme, the remote monitoring test control management method generates a computer program, stores the computer program in a computer readable storage medium to be loaded and executed by a processor, and facilitates the reading and storage of the computer program through the computer readable storage medium.
In summary, the present application has at least one of the following beneficial technical effects:
1. and screening out abnormal data from the equipment data acquired based on the remote terminal through a probability distribution model, and transmitting the equipment data from which the abnormal data is removed to the upper computer, so that the upper computer can accurately and effectively remotely monitor the equipment or the sensor corresponding to the connection point.
2. Firstly, average data are acquired based on a constructed probability distribution model, and after the data objects are acquired, whether the data objects corresponding to the abnormal attributes are abnormal data or not is judged through a first formula, so that the abnormal data can be conveniently screened out, accurate data are further transmitted to an upper computer, and the upper computer can conveniently and accurately and effectively remotely monitor equipment or sensors corresponding to the connection points.
3. Different control authorities are set for the main responsible terminal and the auxiliary responsible terminal so as to effectively manage the equipment or the sensor.
Drawings
Fig. 1 is a schematic flow chart of one implementation of a remote monitoring test control management method according to an embodiment of the present application.
Fig. 2 is a flow chart of one of the remote monitoring test control management methods according to the embodiment of the present application.
Fig. 3 is a flow chart of one implementation of a remote monitoring test control management method according to an embodiment of the present application.
Fig. 4 is a flow chart of one of the remote monitoring test control management methods according to the embodiment of the present application.
Fig. 5 is a flow chart of one of the remote monitoring test control management methods according to the embodiment of the present application.
Fig. 6 is a flow chart of one of the remote monitoring test control management methods according to the embodiment of the present application.
Fig. 7 is a flow chart of one of the remote monitoring test control management methods according to the embodiment of the present application.
Fig. 8 is a flow chart of one of the remote monitoring test control management methods according to the embodiment of the present application.
Detailed Description
The present application is described in further detail below in conjunction with figures 1 to 8.
The embodiment of the application discloses a remote monitoring test control management method.
Referring to fig. 1, a remote monitoring test control management method includes the steps of:
s101, 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.
Remote terminals refer to RTUs, which are collectively referred to as remote terminal control systems, also known as telemetry terminals, for monitoring and controlling signals at a remote job site and industrial equipment. Because the remote terminal is connected with a plurality of devices or sensors, the interface of the remote terminal connected with each device or sensor is a connection point. Device data refers to operational data generated by a device or sensor, such as voltage data, current data, device run time, device temperature, etc.
The information database is used for storing device data. Specifically, each connection point of the remote terminal corresponds to an information database, that is, only data transmitted by one connection point of the remote terminal is stored in one information database.
In this embodiment, the remote terminal is connected to the device or the sensor through an RS485 serial port, the current execution subject refers to an industrial internet platform, and device data acquired by the remote terminal through the device or the sensor can be uploaded to the industrial internet platform through a wired network or a wireless network. Specifically, the wireless network may be a 4G network, a 5G network, or the like.
S102, constructing a single attribute data structure for the device data transmitted by each connection point in a preset test time period.
The test period is set manually, for example 8:00 am to 12:00 am. The attribute data structure is used to describe the attribute data set of each target object, e.g., matrix a= [ a ] 11 ,a 21 ,…,a m1 ]I.e. the single attribute data structure refers to a matrix of m x 1. For example, if the device data transmitted by the a connection point is 20,50,30,40 in the test period, the single attribute data structure constructed based on the device data is a= [20,50,30,40]。
S103, determining the distribution type of the device data based on the single attribute data structure.
The distribution type includes a discrete type distribution and a continuous type distribution, and the discrete type distribution mainly includes a binomial distribution, a poisson distribution, a discrete uniform distribution, a geometric distribution, a super geometric distribution, and the like. In the first embodiment, the distribution type is judged from the histogram. A histogram is a common pattern used to show the distribution of quantitative data. The shape, the center position and the discrete degree of the data distribution can be visually seen through the histogram, and then the distribution type of the data packet of the equipment is judged; in the second embodiment, firstly, a probability density function of a single attribute is obtained through statistics, and secondly, the distribution type of the equipment data is determined through the probability density function and a preset sum probability distribution structure.
The continuous distribution mainly comprises normal distribution, t-distribution, F-distribution, chi-square distribution, exponential distribution, gamma-distribution, beta-distribution and the like. In the first embodiment, the distribution type is judged from the histogram; in the second embodiment, firstly, a probability density function of a single attribute is obtained through statistics, and secondly, the distribution type of the equipment data is determined through the probability density function and a preset sum probability distribution structure.
S104, constructing a probability distribution model of the equipment data based on the distribution type.
In this embodiment, after the distribution type of the device data is known, the device data is input to a preset probability model based on machine learning, and a probability distribution model conforming to the distribution type is output. Specifically, the probability model is obtained by learning based on the existing data set, and the distribution type of the existing data set is consistent with the distribution type of the equipment data, so that after the equipment data is input based on machine learning, the distribution type conforming to the existing data set can be output, and the output distribution type is the distribution type of the equipment data.
S105, screening out abnormal data in the equipment data based on the probability distribution model.
After the probability distribution model is built, the probability that the equipment data accords with the probability distribution model can be calculated, and the equipment data lower than a preset probability threshold value is judged to be abnormal data.
S106, storing the abnormal data in a preset abnormal database, and transmitting the equipment data with the abnormal data removed to the upper computer.
The abnormal database is used for storing the abnormal data transmitted by all the connection points, and transmitting the equipment data from which the abnormal data is removed to the upper computer, so that the upper computer can accurately and effectively monitor the equipment or the sensor corresponding to the connection points.
The implementation principle of the embodiment is as follows: and screening out abnormal data from the equipment data acquired based on the remote terminal through a probability distribution model, and transmitting the equipment data from which the abnormal data is removed to the upper computer, so that the upper computer can accurately and effectively remotely monitor the equipment or the sensor corresponding to the connection point.
In step S103 of the embodiment shown in fig. 1, the distribution type of the device data may be determined by a probability density function. The embodiment shown in fig. 2 is specifically described in detail.
Referring to fig. 2, determining a distribution type of device data based on a single attribute data structure includes the steps of:
s201, acquiring sample data of a single attribute based on the single attribute data structure.
As can be seen from step S102, the single attribute data structure refers to an mx 1 matrix. For example, if the device data transmitted by the a connection point is 20,50,30,40 in the test period, 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.
S202, counting the frequency distribution of the sample data, and obtaining a probability density function of the sample data.
In this embodiment, after obtaining the sample data, the fitting goodness x is based on 2 Whether the sample data is subjected to a certain known distribution or a certain class of distribution is checked, and besides, the sample data can be checked based on a Kerr Mo Geluo or a Schmidt test method, so that a probability density function of the sample data can be obtained.
S203, determining the distribution type of the equipment data based on a preset probability distribution structure and a probability density function.
The probability distribution structure is preset, after a probability density function of the sample data is obtained, namely the similarity between the probability distribution structure and the probability density function is measured based on the Hellinger distance, and if the similarity is larger than a preset similarity threshold value, the distribution type of the equipment data can be determined. In probability theory and statistical theory, the Hellinger distance is used to measure the similarity of two probability distributions.
According to the remote monitoring test control management method provided by the embodiment, the distribution type of the equipment data is determined through the probability density function of the single attribute and the preset probability distribution structure, and after the distribution type of the equipment data is determined, the probability distribution model of the equipment data can be built, so that the abnormal data can be conveniently screened.
In step S105 of the embodiment shown in fig. 1, after the probability distribution model is constructed, the mean value data may be obtained, and at this time, whether the device data is abnormal data may be determined according to a preset formula and the mean value data. The embodiment shown in fig. 3 is specifically described in detail.
Referring to fig. 3, the method for screening abnormal data in the device data based on the probability distribution model includes the following steps:
s301, acquiring mean value data based on a probability distribution model.
The mean value data refers to expected values of the probability distribution model and is used for reflecting the concentrated trend of the data. If the probability distribution model is normal distribution, the formula is as follows:
wherein μ represents the mean, σ represents the variance, and the mean data μ is obtained at this time.
S302, acquiring a data object with the largest phase difference with the mean value data in the sample data, and acquiring the maximum extremum and the minimum extremum based on the probability distribution model.
The maximum difference from the mean data represents the maximum absolute value of the difference from the mean data. Taking step S201 as an example, if the sample data is 20,50,30,40 and the average value is 25, the data object with the largest difference from the average value is 50.
It should be noted that after the sample data 50 is obtained, no sample data 50 in the sample data is obtained, that is, the data object with the largest difference from the mean value data is obtained again for the rest of the sample data, if the sample data still remains 20,30,40, the data object with the largest difference from the mean value data is 40, and so on.
And obtaining a probability distribution function based on the probability distribution model, wherein 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.
S303, judging whether the data object is larger than the mean value data.
Since the difference from the mean data is the largest, the absolute value of the difference from the mean data is the largest, so when the mean data is 20, the differences between 0 and 40 and the mean data are the largest, but 40 is larger than the mean data, and 0 is smaller than the mean data.
And S304, if the data object is larger than the maximum value, substituting the data object into a preset first formula, and obtaining a first frequency value.
The first formula is an abnormality discrimination formula of the maximum extremum, and the first formula is as follows:
wherein y is a first frequencyThe value, m, is the attribute of the sample data, e.g., in the single attribute data structure a= [20,50,30,40]]The attribute of the sample data 50 is 2; a, a max Is the maximum extremum.
When the data object is larger than the mean value data, substituting the data object into a first formula to obtain a first frequency value.
S305, judging whether the first frequency value is smaller than or equal to a preset standard frequency value.
S306, if the data object is smaller than or equal to the abnormal data, judging the data object to be abnormal data.
In this embodiment, the standard frequency value is 0.15, and if the first frequency value is less than or equal to the standard frequency value, it indicates that the data object is abnormal data. And if the first frequency value is larger than the standard frequency value, indicating that the data object is normal data.
According to the remote monitoring test control management method provided by the embodiment, firstly, mean value data is obtained based on the established probability distribution model, and after the data object is obtained, whether the data object corresponding to the abnormal attribute is abnormal data or not is judged through the first formula, so that the abnormal data can be conveniently screened out, accurate data can be further transmitted to the upper computer, and the upper computer can conveniently and accurately and effectively remotely monitor equipment or sensors corresponding to the connection points.
In the embodiment shown in fig. 3, when the data object is not larger than the mean value data, it may be determined whether the device data is abnormal data based on a preset second formula and the mean value data. The embodiment shown in fig. 4 is specifically described in detail.
Referring to fig. 4, the remote monitoring test control management method further includes the steps of:
s401, if the data object is not larger than the mean value data, substituting the data object into a preset second formula based on the minimum extremum, and obtaining a second frequency value.
The second formula is an abnormality discrimination formula of the minimum extremum, and the second formula is as follows:
wherein the method comprises the steps ofY' is the first frequency value, m is the attribute of the sample data, e.g. in the single attribute data structure a= [20,50,30,40]]The attribute of the sample data 50 is 2; a, a min Is the minimum extremum.
And substituting the data object into a second formula to obtain a second frequency value when the data object is not more than, i.e. less than or equal to, the mean value data.
S402, judging whether the second frequency value is smaller than or equal to the standard frequency value.
S403, if the data object is smaller than or equal to the abnormal data, judging the data object to be abnormal data.
In this embodiment, the standard frequency value is 0.15, and if the second frequency value is less than or equal to the standard frequency value, it indicates that the data object is abnormal data. And if the second frequency value is larger than the standard frequency value, indicating that the data object is normal data.
According to the remote monitoring test control management method provided by the embodiment, when the data object is not larger than the mean value data, the second frequency value obtained through the second formula is compared with the standard frequency value, so that the data object corresponding to the abnormal attribute can be used as abnormal data for screening, accurate data are further transmitted to the upper computer, and the upper computer can conveniently and accurately and effectively remotely monitor equipment or a sensor corresponding to the connection point.
After step S106 of the embodiment shown in fig. 1, the abnormal connection points may be divided into the visualized connection points and the closed connection points, so as to facilitate the management of the abnormal connection points through the visualized connection points and the closed connection points, respectively. The embodiment shown in fig. 5 is specifically described in detail.
Referring to fig. 5, after storing the abnormal data in a preset abnormal database and transmitting the device data from which the abnormal data is removed to an upper computer, the method comprises the steps of:
s501, calculating the abnormal frequency of the device data of each connection point in the test time period based on the abnormal data.
In this embodiment, the total data amount of the device data in the test period is first obtained, and the abnormal data amount of the abnormal data is obtained, and then the abnormal frequency is the abnormal data amount divided by the total data amount.
S502, judging a connection point corresponding to an abnormal frequency larger than a preset frequency threshold as an abnormal connection point; the abnormal connection points include a visualized connection point and a closed connection point.
If the abnormal frequency is greater than the frequency threshold, the connection point with the abnormal frequency is judged to be the abnormal connection point, and the fact that the equipment or the sensor connected with the abnormal connection point possibly fails is indicated.
If the equipment data transmitted at the point a is required to be displayed on the current execution main body in a public way, the point a is called as a visual connection point; if the device data transmitted at the b connection point needs to be subjected to secret transmission, that is, the device data cannot be subjected to public display on the current execution main body, the b connection point is called as a closed connection point.
S503, if the abnormal connection point is a visual connection point, generating a visual chart of equipment data of the abnormal connection point, and generating alarm information based on a time point corresponding to the abnormal data.
When the abnormal connection point is a visual connection point, generating a line graph of the equipment data, namely a visual chart. Specifically, the x-axis of the line graph is the time of the test period, and the y-axis of the line graph is the device data corresponding to each time. After the line graph is generated, a time point corresponding to the abnormal data can be obtained, and at the moment, alarm information is generated based on the time point, so that background staff can conveniently judge the generation reason of the abnormal data in time.
According to the remote monitoring test control management method provided by the embodiment, if the abnormal frequency of the abnormal data in the test time period is larger than the frequency threshold value, the equipment or the sensor is likely to be in fault, at the moment, a visual chart of the abnormal connection point is generated when the abnormal connection point is a visual connection point, alarm information is generated, and the background manager can process the abnormal data in time.
After step S502 in the embodiment shown in fig. 5, when the abnormal connection point is a closed connection point, the control authority may be set to the closed connection point to facilitate management of the closed connection point. The embodiment shown in fig. 6 is specifically described in detail.
Referring to fig. 6, after determining a connection point corresponding to an abnormal frequency greater than a preset frequency threshold as an abnormal connection point, the method includes the steps of:
s601, if the abnormal connection point is a closed connection point, acquiring an information database corresponding to the closed connection point, and judging the information database as a closed database.
If the abnormal connection point is a closed connection point, indicating that the equipment data transmitted by the closed connection point is in secret transmission, acquiring an information database corresponding to the closed connection point at the moment, and judging the information database as a closed database.
S602, acquiring a responsible terminal identifier from a closed database.
In this embodiment, each closed database stores a unique code corresponding to a plurality of responsible terminal identifiers, and specifically, each responsible terminal identifier is preset by a person, and the responsible terminal may be a mobile phone, a tablet or a computer.
S603, setting control authority of the closed equipment corresponding to the closed connection point for the responsible terminal corresponding to the responsible terminal identifier.
The closed equipment refers to equipment corresponding to the closed connection point, and if the closed equipment corresponding to the closed connection point possibly fails, the control authority of the terminal to the closed equipment is set, so that the data disclosure of the closed equipment is effectively prevented.
According to the remote monitoring test control management method provided by the embodiment, when the abnormal connection point is a closed connection point, the control authority of the responsible terminal to the closed connection point is set, so that the responsible terminal can timely process the abnormal connection point which is possibly abnormal.
In step S603 of the embodiment shown in fig. 6, the responsible terminals may be divided into a primary responsible terminal and a secondary responsible terminal, so as to determine the control rights of the primary responsible terminal and the secondary responsible terminal, so as to facilitate the management of the closed connection point. The embodiment shown in fig. 7 is specifically described.
Referring to fig. 7, the responsible terminals include a primary responsible terminal and a secondary responsible terminal; the control authority comprises a master control authority and a viewing authority; the responsible terminal identification comprises a main responsible terminal identification and a secondary responsible terminal identification; the main responsible terminal identification corresponds to the main responsible terminal, and the auxiliary responsible terminal identification corresponds to the auxiliary responsible terminal; one main responsible terminal and a plurality of auxiliary responsible terminals;
setting control authority of the closed equipment corresponding to the closed connection point for the responsible terminal corresponding to the responsible terminal identifier, comprising the following steps:
s701, setting a master control authority for the closing device for the master responsible terminal, so that the master responsible terminal controls the closing device.
The master control authority refers to that the master responsible terminal can control the closed equipment. In this embodiment, there is only one master responsible terminal, i.e. only one master responsible terminal may control the closed device.
S702, setting the viewing authority of the secondary responsible terminal on the closed equipment, so that the secondary responsible terminal views the control flow of the primary responsible terminal on the closed equipment.
The checking authority refers to that the secondary responsible terminal can check the control flow of the primary responsible terminal on the closed equipment, namely, the primary responsible terminal generates an operation record once controlling the closed equipment, and all the operation records of the primary responsible terminal on the closed equipment are the control flow.
According to the remote monitoring test control management method provided by the embodiment, different control authorities are set for the main responsible terminal and the auxiliary responsible terminal so as to effectively manage equipment or sensors.
After step S603 in the embodiment shown in fig. 6, the master responsible terminal may remotely control the enclosure device corresponding to the enclosure connection point by sending a control command. The embodiment shown in fig. 8 is specifically described.
Referring to fig. 8, after setting a control authority for a closed device corresponding to a closed connection point to a responsible terminal corresponding to a responsible terminal identification, the method includes the steps of:
s801, judging whether a control instruction sent by a main responsible terminal is received.
The control instruction refers to an instruction sent by the main responsible terminal for controlling the closed device to execute corresponding operation, such as power off, restarting, etc.
S802, if the control command is received, the control command is sent to the sealing equipment based on the remote terminal, and the sealing equipment executes the control command.
If the control instruction is received, the current execution main body issues the control instruction to the sealing equipment through the remote terminal, and the sealing equipment executes the control instruction after receiving the control instruction. If the current execution main body does not receive the control instruction, no action is performed.
According to the remote monitoring test control management method provided by the embodiment, when the control instruction sent by the responsible terminal is received, even if the closed equipment executes the control instruction, the responsible terminal can conveniently carry out remote control and management on the closed equipment.
The embodiment of the application also discloses a remote monitoring test control management system, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the remote monitoring test control management method in the embodiment is adopted when the processor executes the computer program.
The embodiment of the application also discloses a computer readable storage medium, and the computer readable storage medium stores a computer program, wherein the remote monitoring test control management method in the embodiment is adopted when the computer program is executed by a processor.
The computer program may be stored in a computer readable medium, where the computer program includes computer program code, where the computer program code may be in a source code form, an object code form, an executable file form, or some middleware form, etc., and the computer readable medium includes any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, etc., where the computer readable medium includes, but is not limited to, the above components.
The remote monitoring test control management method in the embodiment is stored in the computer readable storage medium through the computer readable storage medium, and is loaded and executed on a processor, so that the storage and application of the method are convenient.
The foregoing are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in any way, therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.
Claims (10)
1. The remote monitoring test control management method is characterized by comprising the following steps of:
acquiring equipment data transmitted by all connection points based on a remote terminal and storing the equipment data of each connection point in a corresponding information database;
constructing a single attribute data structure for the equipment data transmitted by each connection point in a preset test time period;
determining a 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 equipment data based on the probability distribution model;
and storing the abnormal data in a preset abnormal database, and transmitting the equipment data from which the abnormal data are removed to an upper computer.
2. The remote monitoring test control management method according to claim 1, wherein the determining the distribution type of the device data based on the single attribute data structure includes:
acquiring sample data of a single attribute based on the single attribute data structure;
counting the frequency distribution of the sample data and obtaining a probability density function of the sample data;
and determining the distribution type of the equipment data based on a preset probability distribution structure and the probability density function.
3. The method for remote monitoring test control management according to claim 2, wherein the screening out abnormal data in the device data based on the probability distribution model comprises:
acquiring mean value data based on the probability distribution model;
acquiring a data object with the largest phase difference with the mean value data from the sample data, and acquiring a maximum extremum and a minimum extremum based on the probability distribution model;
judging whether the data object is larger than the mean value data or not;
if the data object is larger than the maximum extremum, substituting the data object into a preset first formula, and obtaining a first frequency value;
judging whether the first frequency value is smaller than or equal to a preset standard frequency value;
and if the data object is smaller than or equal to the abnormal data, judging the data object as abnormal data.
4. A remote monitoring test control management method according to claim 3, wherein the method further comprises:
if the data object is not larger than the mean value data, substituting the data object into a preset second formula based on the minimum extremum value, and obtaining a second frequency value;
judging whether the second frequency value is smaller than or equal to the standard frequency value;
and if the data object is smaller than or equal to the abnormal data, judging the data object as abnormal data.
5. The remote monitoring test control management method according to claim 1, wherein after the storing the abnormal data in a preset abnormal database and transmitting the device data from which the abnormal data is removed to an upper computer, comprising:
calculating an abnormal frequency of the device data of each connection point in the test period based on the abnormal data;
judging a connection point corresponding to an abnormal frequency greater than a preset frequency threshold as an abnormal connection point; the abnormal connection points comprise visual connection points and closed connection points;
if the abnormal connection point is the visual connection point, generating a visual chart of the equipment data of the abnormal connection point, and generating alarm information based on a time point corresponding to the abnormal data.
6. The remote monitoring test control management method according to claim 5, further comprising, after the connection point corresponding to the abnormal frequency greater than the preset frequency threshold is determined as the abnormal connection point:
if the abnormal connection point is the closed connection point, acquiring the information database corresponding to the closed connection point, and judging the information database as a closed database;
acquiring a responsible terminal identifier from the closed database;
and setting control authority of the closed equipment corresponding to the closed connection point for the responsible terminal corresponding to the responsible terminal identifier.
7. The remote monitoring test control management method according to claim 6, wherein the responsible terminals include a primary responsible terminal and a secondary responsible terminal; the control authority comprises a master control authority and a viewing authority; the responsible terminal identification comprises a main responsible terminal identification and a secondary responsible terminal identification; the main responsible terminal identifier corresponds to the main responsible terminal, and the auxiliary responsible terminal identifier corresponds to the auxiliary responsible terminal; the number of the main responsible terminals is one, and the number of the auxiliary responsible terminals is a plurality;
the responsible terminal corresponding to the responsible terminal identifier sets a control authority for the closed equipment corresponding to the closed connection point, and the method comprises the following steps:
setting the master control authority of the closed equipment for the master responsible terminal to enable the master responsible terminal to control the closed equipment;
and setting the viewing authority of the closing equipment for the secondary responsible terminal, so that the secondary responsible terminal views the control flow of the primary responsible terminal on the closing equipment.
8. The remote monitoring test control management method according to claim 6, wherein after the responsible terminal corresponding to the responsible terminal identifier sets a control authority for the closed device corresponding to the closed connection point, the method comprises:
judging whether a control instruction sent by the main responsible terminal is received or not;
and if the control instruction is received, transmitting the control instruction to the closed equipment based on the remote terminal, and enabling the closed equipment to execute the control instruction.
9. A remote monitoring test control management system comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program is loaded and executed by the processor by the method of any one of claims 1 to 8.
10. A computer readable storage medium having a computer program stored therein, characterized in that the method according to any of claims 1 to 8 is employed when the computer program is loaded and executed by a processor.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116483290A (en) * | 2023-06-26 | 2023-07-25 | 深圳市亲邻科技有限公司 | Remote monitoring system and method for data storage device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116483290A (en) * | 2023-06-26 | 2023-07-25 | 深圳市亲邻科技有限公司 | Remote monitoring system and method for data storage device |
| CN116483290B (en) * | 2023-06-26 | 2024-02-09 | 深圳市亲邻科技有限公司 | Remote monitoring system and method for data storage device |
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