An intelligent monitoring method, device, electronic device and storage medium for equipment failures

Abstract

The present application provides a method, device, electronic device and storage medium for intelligent monitoring of equipment failures. The method includes: obtaining monitoring items of a target device; obtaining the topological relationship between devices connected to an operation and maintenance platform, and obtaining monitoring data of the target device based on the monitoring items; determining whether the target monitoring item is abnormal data; if it is determined to be abnormal data, adding the abnormal data to a corresponding abnormal data array, and determining the abnormal median; determining the warning range of the abnormal median of the monitoring item parameters, and after the monitoring item parameters of the target device fall within the corresponding warning range of the abnormal median, warning that a failure has occurred in the target monitoring item of the target device. In the present application, by using a two-step method to perform abnormal monitoring and automatic warning on the monitoring item data of the target device, the abnormal monitoring granularity of the device can be refined to the monitoring item level, improving the fine granularity and accuracy of equipment failure monitoring.

Description

Technical Field

[0001] The present application relates to the technical field of equipment monitoring. Specifically, it relates to an intelligent equipment fault monitoring method, device, electronic equipment and storage medium. Background Art

[0002] For traffic facilities, taking highways as an example, there are various electromechanical facilities installed on them. Decentralized management and monitoring will consume a huge amount of manpower and material resources, and the response is slow. It is impossible to detect the faults and problems of electromechanical facilities in time, resulting in adverse effects on normal operation after the faults of electromechanical facilities.

[0003] For example, the electromechanical facilities of highways have the characteristics of diversity, complexity and geographical distribution; this makes it difficult to conduct unified equipment monitoring among the electromechanical facilities of highways. Summary of the Invention

[0004] The problem solved by the present application is that it is difficult to conduct efficient and precise monitoring of traffic equipment faults.

[0005] To solve the above problems, the first aspect of the present application provides an intelligent equipment fault monitoring method, including:

[0006] Obtain the monitoring items of the target equipment configured in advance, where the monitoring items include monitoring item parameters and parameter ranges; obtain the topological relationship between the equipment connected to the operation and maintenance platform constructed in advance, and obtain the monitoring data of the target equipment based on the monitoring items;

[0007] Judge whether the target monitoring item is abnormal data according to the fault analysis algorithm corresponding to the target monitoring item loaded in advance;

[0008] If it is determined that the target monitoring item is abnormal data, add the abnormal data to the abnormal data array corresponding to the target monitoring item, and determine the abnormal median of the monitoring item parameters;

[0009] Determine the warning range of the abnormal median of the monitoring item parameters according to the abnormal data. After the monitoring item parameters of the target equipment fall into the corresponding warning range of the abnormal median, warn that the target monitoring item of the target equipment has a fault.

[0010] The second aspect of the present application provides an intelligent equipment fault monitoring device, which includes:

[0011] A monitoring configuration module, configured to obtain the monitoring items of the target equipment configured in advance, where the monitoring items include monitoring item parameters and parameter ranges;

[0012] An equipment topology module, configured to obtain the topological relationship between the equipment connected to the operation and maintenance platform constructed in advance, and obtain the monitoring data of the target equipment based on the monitoring items;

[0013] Anomaly determination module, configured to determine whether the target monitoring item is abnormal data according to a pre-loaded fault analysis algorithm corresponding to the target monitoring item; if it is determined that the target monitoring item is abnormal data, add the abnormal data to the abnormal data array corresponding to the target monitoring item, and determine the abnormal median of the monitoring item parameters;

[0014] Device warning module, configured to determine the warning range of the abnormal median of the monitoring item parameters according to the abnormal data, and warn that the target monitoring item of the target device fails after the monitoring item parameters of the target device fall into the corresponding warning range of the abnormal median.

[0015] A third aspect of the present application provides an electronic device, which includes: a memory and a processor;

[0016] The memory is used to store programs;

[0017] The processor is coupled to the memory and is configured to execute the program to:

[0018] Obtain the monitoring items of the target device configured in advance, where the monitoring items include monitoring item parameters and parameter ranges; obtain the topological relationship between devices accessing the operation and maintenance platform constructed in advance, and obtain the monitoring data of the target device based on the monitoring items;

[0019] Determine whether the target monitoring item is abnormal data according to a pre-loaded fault analysis algorithm corresponding to the target monitoring item;

[0020] If it is determined that the target monitoring item is abnormal data, add the abnormal data to the abnormal data array corresponding to the target monitoring item, and determine the abnormal median of the monitoring item parameters;

[0021] Determine the warning range of the abnormal median of the monitoring item parameters according to the abnormal data, and warn that the target monitoring item of the target device fails after the monitoring item parameters of the target device fall into the corresponding warning range of the abnormal median.

[0022] A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to implement the above-mentioned device fault intelligent monitoring method.

[0023] An intelligent monitoring method for equipment faults disclosed in this application pre-configures the monitoring items of the equipment and pre-constructs the topological relationship between the equipment connected to the access platform, obtains the monitoring item data, and determines whether the target monitoring item data is abnormal data according to the pre-loaded fault analysis algorithm corresponding to the target monitoring item. If it is abnormal data, the abnormal data is added to the abnormal data group corresponding to the target monitoring item, and the abnormal median of the residual data of the monitoring item is determined; then, the warning range of the abnormal median of the monitoring item parameters is determined according to the abnormal data, and further, whether to warn that the target monitoring item of the target equipment fails is determined according to whether the monitoring item data falls within the warning range. By adopting this solution, the target monitoring item data of the target equipment can be remotely and real-time monitored. First, the fault analysis algorithm corresponding to the target item is used to determine whether the target monitoring item is abnormal data; if it is abnormal data, it is added to the abnormal data array to quickly obtain the abnormal median, and then the warning range of the abnormal median is determined. After the monitoring item parameters of the target equipment fall within the warning range of the corresponding abnormal median, it is warned that the target monitoring item of the target equipment fails. By adopting this method, the abnormal judgment of the monitoring data is divided into two steps, and the judgment benchmark (abnormal median) of the abnormal data can be updated in real time, so as to improve the pertinence and efficiency of the abnormal data judgment, reduce frequent warnings, and further, the automatic monitoring and warning of data abnormalities in the dimension of the target monitoring item of the target equipment can be realized, and the efficiency and accuracy of abnormal monitoring are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flowchart of an intelligent monitoring method for equipment faults according to an embodiment of the present application;

[0025] Figure 2 is a flowchart of step S100 of an intelligent monitoring method for equipment faults according to an embodiment of the present application;

[0026] Figure 3 is a flowchart of step S100 of an intelligent monitoring method for equipment faults according to another embodiment of the present application;

[0027] Figure 4 is a flowchart of step S200 of an intelligent monitoring method for equipment faults according to an embodiment of the present application;

[0028] Figure 5 is a flowchart of step S205 of an intelligent monitoring method for equipment faults according to an embodiment of the present application;

[0029] Figure 6 is a flowchart of step S400 of an intelligent monitoring method for equipment faults according to an embodiment of the present application;

[0030] Figure 7 is a structural block diagram of an intelligent monitoring device for equipment faults according to an embodiment of the present application;

[0031] Figure 8 It is a structural block diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0032] To make the above objects, features, and advantages of the present application more obvious and understandable, the following will describe in detail the specific embodiments of the present application with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0033] It should be noted that unless otherwise specified, the technical terms or scientific terms used in the present application should have the ordinary meanings understood by those skilled in the art to which the present application belongs.

[0034] Taking the highway as an example, there are various electromechanical facilities installed on it. Decentralized management and monitoring will consume a huge amount of manpower and material resources, and the response is relatively slow. It is impossible to detect problems with electromechanical facilities in a timely manner, resulting in adverse effects on normal operation after the electromechanical facilities fail.

[0035] The electromechanical facilities on the highway have the characteristics of diversity, complexity, and geographical distribution; this makes there many different characteristics among the electromechanical facilities on the highway, and it is very difficult to conduct unified intelligent monitoring of equipment failures.

[0036] To solve the above problems, the present application provides a new intelligent monitoring solution for equipment failures, which can solve the problem that different electromechanical facilities cannot be monitored uniformly by pre-configuring and constructing a topological relationship.

[0037] An embodiment of the present application provides an intelligent monitoring method for equipment failures. This method can be executed by an intelligent monitoring device for equipment failures, and this intelligent monitoring device for equipment failures can be integrated in electronic devices such as pads, computers, servers, computers, server clusters, data centers, etc. As Figure 1 , it is a flowchart of an intelligent monitoring method for equipment failures according to an embodiment of the present application; wherein, the intelligent monitoring method for equipment failures includes:

[0038] S100, obtaining the monitoring items of the target device pre-configured, where the monitoring items include monitoring item parameters and parameter ranges;

[0039] Among them, the target device / equipment can be the electromechanical facilities on the highway, and specifically can be an exit mixed lane traffic light, a telephone switch, a tunnel camera, a toll station network switch, an MTC dedicated lane fog lamp, etc.

[0040] Among them, the monitored items of the device are the parameters of the device and the value ranges of the parameters; the monitored items of devices of the same type are the same. Taking a tunnel camera as an example: the parameters of this device include operating voltage, operating grounding resistance, service life, and the status of the indicator lights on the camera, and each parameter has a corresponding value range.

[0041] Among them, the specific values of the monitored items of the device, that is, the specific values of the parameters, can be transmitted by the manufacturer of the device, or obtained through manual detection, or directly obtained through a preset method, or automatically reported by the device.

[0042] S200, obtain the topological relationship between devices in the pre-constructed access operation and maintenance platform, and obtain the monitoring data of the target device based on the monitored items;

[0043] There is a topological relationship between devices. For example, a switch connects and controls multiple servers. Therefore, the switch can be regarded as the parent device, and the multiple servers it connects and controls are the child devices; the server controls multiple fog lights, then the server and the fog lights it controls can be regarded as parent and child devices.

[0044] Through the topological relationship, the correlation between devices can be mined, so as to increase the accuracy of the subsequent execution results through this correlation.

[0045] S300, determine whether the target monitored item is abnormal data according to the pre-loaded fault analysis algorithm corresponding to the target monitored item;

[0046] Obtain the parameters and their parameter values of each item of the monitoring data of each index, judge the monitoring data index items of each dimension of each device obtained, and determine the abnormal data among them.

[0047] Among them, the monitoring data index items of each dimension of each device obtained can be input into a preset algorithm. For the convenience of distinction, each piece of data information corresponding to each dimension of each device has a unique number.

[0048] S400, if it is determined that the target monitored item is abnormal data, add the abnormal data to the abnormal data array corresponding to the target monitored item, and determine the abnormal median of the parameters of the monitored item;

[0049] S500, determine the warning range of the abnormal median of the parameters of the monitored item according to the abnormal data, and after the monitored item parameters of the target device fall into the corresponding warning range of the abnormal median, warn that the target monitored item of the target device has a fault.

[0050] In this application, early warnings are given to the monitoring parameters of the device based on the abnormal monitoring data of the device, so as to systematically monitor by utilizing the high consistency between the historical data and the current data of the device, avoiding the problems brought by the different characteristics of the devices.

[0051] In addition, by obtaining the pre-configured monitoring items and the pre-constructed topological relationships, the relevance between devices is mined, and the relevance between devices is introduced into the judgment of abnormal data, increasing the accuracy of the judgment.

[0052] In one implementation, as Figure 2 shown, in S100, obtain the monitoring items of the pre-configured target device, and the monitoring items include monitoring item parameters and parameter ranges, including:

[0053] S101, through the interactive input method, input the monitoring items of the pre-configured target device;

[0054] Among them, the interactive input method is to obtain input information by interacting with the input person. For example, an input interface is displayed on a computer, and the input person inputs information in the input interface through a mouse or keyboard.

[0055] Among them, the monitoring items of the pre-configured device are pre-configured according to the type of the device, and each type of device only needs to be configured once.

[0056] Among them, the data type of the monitoring item parameters can be 7 basic data types such as Int32, float, double, enum, bool, text, date, as well as the struct structure type and the array array type or a combination thereof. The specific combination method is not limited here.

[0057] Among them, the combination rule of the data types is that the struct structure type can contain any number of basic data types, and the array array type can contain any number of basic data types and struct structure types. That is to say, the data in the struct structure type can contain multiple basic data types; the data in the array array type can contain multiple basic data types and multiple struct structure types, and the included struct structure types can further contain multiple basic data types.

[0058] S102, convert the configured monitoring items into JSON format for storage.

[0059] In one implementation, steps S101 - S102 are implemented at the front - end of the computer. For example, after the input personnel enter the monitored items of the device configuration on the computer interface, the computer directly converts the monitored items of the device (type) into JSON format for storage.

[0060] Among them, JSON (JavaScript Object Notation) is a lightweight data interchange format. It is based on a subset of ECMAScript (the JavaScript specification formulated by the European Computer Manufacturers Association) and uses a text format completely independent of programming languages to store and represent data.

[0061] In this step, storing the data in JSON format can be that the front - end directly sends the JSON - formatted data to the back - end.

[0062] In one implementation, in step S102, obtaining and parsing the stored monitored items are implemented at the back - end of the computer.

[0063] Among them, obtaining the monitored items can be that the front - end directly sends the JSON - formatted data to the back - end so that the back - end can obtain it.

[0064] In one implementation, as Figure 3 shown, obtaining the monitored items of the pre - configured target device includes:

[0065] S1031, read the monitored items and determine the data type of the monitored items;

[0066] Based on the combination rules of the data types described above, the basic data type, struct structure type, and array array type have their distinct characteristics, and the data type of the monitored items can be determined according to these characteristics. The specific determination method will not be elaborated here.

[0067] S1032, when the monitored item is of the basic data type, parse the monitored item to determine the monitored item parameters and parameter ranges of the device;

[0068] Based on the combination rules of the data types described above, the basic data type does not contain other data types inside, so the basic data type can be directly parsed. Through parsing, the monitored items in JSON format can be parsed out to determine the monitored item parameters and parameter ranges in the monitored items.

[0069] S1033, when the monitored item is of the structure type, loop - parse the basic data type data in the structure type to determine the monitored item parameters and parameter ranges of the device;

[0070] Based on the combination rules of the data types described above, the struct structure type can contain any number of basic data types. That is to say, the data of the struct structure type can contain multiple basic data types.

[0071] When parsing the monitoring items of the structure type, parsing the data of the struct structure type can obtain the data of the basic data types contained in the data. Therefore, it is necessary to perform cyclic parsing to parse out the monitoring items in JSON format and determine the monitoring item parameters and parameter ranges in the monitoring items.

[0072] S1034, when the monitoring item is of array type, cyclically parse the basic data type data and structure type data in the array type to determine the monitoring item parameters and parameter ranges of the device.

[0073] Based on the combination rules of the data types described above, the struct structure type can contain any number of basic data types, and the array array type can contain any number of basic data types and struct structure types. That is to say, the data of the struct structure type can contain multiple basic data types; the data of the array array type can contain multiple basic data types and multiple struct structure types, and the contained struct structure types can further contain multiple basic data types.

[0074] When parsing the monitoring items of the array type, parsing the data of the array array type can obtain the data of the basic data types or struct structure types contained in the data. Therefore, it is necessary to perform cyclic parsing to parse out the monitoring items in JSON format and determine the monitoring item parameters and parameter ranges in the monitoring items.

[0075] In one implementation, when cyclically parsing the basic data type data and structure type data in the array type, when parsing the structure type data, cyclically parse the basic data type data in the structure type.

[0076] It should be noted that by cyclically parsing the data of the array array type, the data of the basic data types (the corresponding data can be directly parsed) or struct structure types contained in the data can be obtained. However, this method of large loop cannot parse out the specific data within the struct structure type. Therefore, it is also necessary to perform a small loop on the data of the struct structure type until the data of the struct structure type is completely parsed, and then the small loop ends, and then the next large loop is performed.

[0077] In one embodiment, steps 101-102 and steps S1031-S1034 can be two independent solutions, and the monitoring items of the target device are configured first in different ways.

[0078] In one embodiment, when obtaining other JSON-format data of the device, the JSON-format data can also be parsed according to steps S1031-S1034.

[0079] In one embodiment, as Figure 4 shown, S200, obtaining the topological relationship between devices of the pre-built access operation and maintenance platform, and obtaining the monitoring data of the target device based on the monitoring items, includes:

[0080] S201, obtaining the topological relationship between devices of the pre-configured access operation and maintenance platform;

[0081] Among them, the topological relationship between devices is determined according to the actual situation. Specifically, the input personnel can configure the topological relationship between the devices of the system; or the system directly configures the topological relationship of the electromechanical facilities involved in materials such as the communication architecture; or the system can first configure the topological relationship and then display it for manual correction.

[0082] It should be noted that in the topological relationship, the parent device and the child device can be of the same type or different types. Multiple child devices of the same parent device can be of the same type or different types.

[0083] S202, obtaining the cascading index grouping and sub-index weights of each type of parent-child device with a topological relationship that are pre-set;

[0084] Among them, the types of the parent-child device, the type of the parent device, and the type of the child device are independent concepts; there is no special naming rule for the types of the parent-child device, and it can be uniquely determined directly by serial numbers or identifiers, etc.

[0085] Among them, in the types of the parent-child device, if there are two parent-child devices, and their parent device types are the same but their child device types are different, then the types of these two parent-child devices are different; if their parent device types are different but their child device types are the same, then the types of these two parent-child devices are different; if their parent device types are different and their child device types are different, then the types of these two parent-child devices are different. Only when their parent device types are the same and their child device types are the same, then the types of these two parent-child devices are the same.

[0086] Among them, for the parent and child devices with a topological relationship, if the types of the parent device and the child device are the same, they have the same parameter items and parameter value ranges, and the parameter items and value ranges can be the parameter items and value ranges of the parent and child devices of this type.

[0087] Among them, for the parent and child devices with a topological relationship, if the types of the parent device and the child device are different, it means that there are the same parts and unique parts in the parameter items of the parent device and the child device; that is to say, it includes the monitoring item parameters that the parent device has and the corresponding child device does not have (parent-only), the monitoring item parameters that the parent device has and the corresponding child device also has (common), and the monitoring item parameters that the child device has and the corresponding parent device does not have (child-only). For the parent and child devices, the parent-only monitoring item parameters generally have practical significance and can be not considered in this application.

[0088] Among them, the cascaded index grouping and sub-index weights of the parent and child devices are to select all or part of the common and child-only monitoring item parameters and set the weights occupied by each child device under the child-only monitoring item parameters; through the weights occupied by the child devices and the values of the monitoring item parameters of the child devices, the total value can be calculated, and this total value is the value of the monitoring item parameter added to the parent device.

[0089] That is to say, select a part from the child-only monitoring item parameters and add these monitoring items as additional attributes to the attributes of the parent device, and the specific parameter values corresponding to the attributes are calculated from the weights and parameter values of the child devices.

[0090] Among them, each type of parent and child devices has a set of index groupings, and the cascaded index groupings of the parent and child devices of the same type are the same.

[0091] Among them, the setting of the cascaded index grouping and sub-index weights of the parent and child devices can be determined according to the actual situation. The specific setting can be set by the input personnel through interactive input, or can be set by the system reading and manual correction, etc.

[0092] For example, in the parent and child devices of server - fog lamp, the monitoring item parameters of the server include network latency (common), and the monitoring item parameters of the fog lamp include network latency (common), working voltage (child-only), and service life (child-only); then the working voltage and network latency can be used as the cascaded index grouping of the parent and child devices, and the weights of 5 fog lamps are all set to 0.2; then the parent device additionally has the monitoring item parameter of working voltage, and the parameter value of this parameter is the average value of the working voltages of 5 fog lamps.

[0093] S203, when the target device is a child device among the parent and child devices with a topological relationship, collect the real-time monitoring data of the target device;

[0094] Among them, in this step, the real-time monitoring data of the device collected can be in JSON format; the parsing of this data can be carried out through steps S1031 - S1034.

[0095] Among them, the real-time monitoring data of the device can be directly reported and obtained by the device, or obtained by other means.

[0096] Among them, the reporting interval of the real-time data can be 1s, so that the real-time data can be obtained in a timely manner.

[0097] Among them, for devices that can directly obtain monitoring data, the real-time monitoring data of the device can be directly collected, for example, the child device in the parent-child device.

[0098] Here, it should be noted that in this step, it is default that all devices are parent-child devices with a topological relationship. On this basis, the child device is considered as a device that can directly obtain monitoring data. If the devices connected to the operation and maintenance platform also include other device types, such as devices that do not have a topological relationship and are independent systems, then this device can also be regarded as a device that can directly obtain monitoring data. At this time, when the target device is a child device in the parent-child device with a topological relationship or an independent system device, the real-time monitoring data of the target device is collected.

[0099] S204, when the target device is the parent device in the parent-child device with a topological relationship, collect the real-time monitoring data of the parent-child device;

[0100] For the child device in the parent-child device, its monitoring data can be directly obtained; but for the parent device in the parent-child device, it is not certain that all the monitoring data can be directly obtained.

[0101] S205, based on the cascaded index grouping and the sub-index weights, determine the monitoring data of the parent device.

[0102] According to the cascaded index grouping and sub-index weight settings of the parent-child device, determine the monitoring parameter items and corresponding parameter values of the parent device.

[0103] In one implementation, as Figure 5 shown, the S205, based on the cascaded index grouping and the sub-index weights, determine the monitoring data of the parent device in the parent-child device with a topological relationship, including:

[0104] S2051, when the parent device and the child device in the parent-child device are of the same type, retain the real-time monitoring data of the collected parent device;

[0105] S2052, when the types of the parent device and the child device in the parent-child device are different, retain the monitoring data of the monitoring item parameters that the parent device has and the corresponding child device does not have, and calculate the monitoring data of this monitoring item parameter of the parent device according to the monitoring data of the monitoring item parameters that the child device has and the parent device does not have.

[0106] In one implementation, the S205 to determine the monitoring data of the parent device in the parent-child devices with a topological relationship based on the cascaded index grouping and the sub-index weights includes: determining whether the current device has a superior device, and if so, obtaining the statuses of the indicators for calculating the status of the parent device participated by all items of the peer devices; counting the statuses of the indicators of the associated parent devices of all peer devices; and calculating the status of the parent device indicators according to the cascaded index grouping and based on the index weights. When the parent device and the child device are of the same type, the status calculation of a certain dimension of the parent device is made by comparing the monitored data with a preset threshold; when the parent device and the child device are not of the same type, the method for calculating a certain dimension of the parent device is to count all sub-dimensions, then count the statuses of each sub-dimension of all child devices, and calculate according to the formula 1 / number of child devices * (each sub-dimension * status * weight) based on the preset weights of each sub-dimension; determining the statuses of the associated indicators of the parent device, and if there is an abnormality, repeating the above steps.

[0107] In one implementation, the determination of the abnormal data in the monitoring data of the device includes:

[0108] When the monitoring data of the device exceeds the parameter range of the corresponding monitoring item parameter, determine this monitoring data as abnormal data;

[0109] For example, the parameter range of the working voltage of the fog lamp is 210V - 221V; if the working voltage reported by the fog lamp is 209V, then this data is abnormal data.

[0110] When the device is associated with a preset algorithm, process the monitoring data according to the preset algorithm to determine the abnormal data in the monitoring data.

[0111] Among them, the monitoring data index items of each dimension of each device obtained can be input into the preset algorithm to directly output the abnormal data.

[0112] Among them, the preset algorithm can be a pre-loaded analysis algorithm, and its loading process is as follows: define the fault analysis algorithm interface; provide the default fault analysis algorithm implementation; use the SPI extensible mechanism of java to configure the interface and implementation in the directory of the specified path; use the class loader to load the custom fault algorithm analysis implementation; if not, use the default implementation.

[0113] Among them, the pre-loaded analysis algorithm can be provided by the manufacturer, the customer, or the input personnel.

[0114] Among them, the preset algorithm can also be: loop through the intelligent monitoring index items of device failures to compare with real-time monitoring data; judge whether its value is within the normal value range (provided by the customer or the manufacturer), and make judgments on the status of each index of the device; when the monitored value is not within the preset threshold (provided by the customer or the manufacturer), it is determined that the data of the device in this dimension is abnormal data / fault data, and the abnormal data / fault data information is added to the fault data group corresponding to the data identifier.

[0115] Among them, the storage or transmission of the abnormal data can be direct storage or transmission, or it can be grouped and stored or transmitted based on the parameter of each monitoring item of the device type. The abnormal data of the same monitoring item parameter of the same device type is in one group. For example, all abnormal data of the working voltage of fog lights are stored in the group of the working voltage parameter of the fog light type.

[0116] In one implementation, as Figure 6 shown, the same target monitoring item of different devices corresponds to the same abnormal data array; in S400, adding the abnormal data to the abnormal data array corresponding to the target monitoring item and determining the abnormal median of the monitoring item parameter includes:

[0117] S401, adding the abnormal data to the abnormal data array corresponding to the target monitoring item;

[0118] S402, allocating the abnormal data in the abnormal data array into large top heap elements and small top heap elements. Among them, the maximum value of the large top heap elements is less than the minimum value of the small top heap elements, and when the abnormal data is even, the number of large top heap elements is the same as the number of small top heap elements; when the abnormal data is odd, the number of large top heap elements differs from the number of small top heap elements by one;

[0119] S403, determining the abnormal median of the abnormal data array according to the average value of the maximum value of the large top heap elements and the minimum value of the small top heap elements.

[0120] In this way, every time an abnormal data is added, the abnormal median in the current abnormal data array after adding the abnormal data can be obtained, so as to perform subsequent early warnings based on the abnormal median.

[0121] In one implementation, S500, determining the early warning range of the abnormal median of the monitoring item parameter according to the abnormal data. After the monitoring item parameter of the target device falls into the corresponding early warning range of the abnormal median, it is warned that the target monitoring item of the target device has a failure. Specifically:

[0122] The abnormal median of the monitored item parameter is the median of all abnormal data of the monitored item parameter; among them, the abnormal data of the same monitored item parameter of the same device type are grouped together, and the abnormal median of the monitored item parameter is the median of the abnormal data within the corresponding group.

[0123] Among them, the abnormal data within the group are sorted, and the abnormal data at the middle position is the abnormal median, or the average value of the two abnormal data at the middle position is the abnormal median.

[0124] Among them, to determine whether the value of the detected item parameter is close to the abnormal median, a proximity threshold can be set. If the absolute value of the difference between the monitored item parameter value and the abnormal median is less than the proximity threshold, it is considered to be close to the abnormal median and a warning is issued.

[0125] Among them, to determine whether the value of the detected item parameter is close to the abnormal median, a proximity threshold can be set. The warning range of the abnormal median is the abnormal median ± proximity threshold. If the monitored item parameter value falls within the warning range of the abnormal median, it is considered to be close to the abnormal median and a warning is issued.

[0126] In one implementation, in S500, according to the abnormal data, the warning range of the abnormal median of the monitored item parameter is determined. After the monitored item parameter of the target device falls within the corresponding warning range of the abnormal median, it is warned that the target monitored item of the target device has a fault. Specifically:

[0127] Traverse the monitoring item parameters configured in the system, and regard one monitoring item as a logical group; each group stores two abnormal medians, one is the abnormal median of data greater than the threshold range, and the other is the abnormal median data less than the threshold range; for the same monitoring item parameter, the threshold ranges of the parent and child devices are the same, and the abnormal medians are also the same; two data structures, a max heap and a min heap, are defined in each group; the data volumes stored in the max heap and the min heap are kept consistent; the max heap is used to store fault data less than the abnormal median, and the min heap is used to store fault data greater than the abnormal median; judge whether the max heap is empty, if it is empty, put the fault data into the max heap; if the max heap is not empty, judge whether the fault data is less than the maximum value of the max heap, if it is less, then add it to the max heap; if the fault data is greater than the maximum value of the max heap, then judge whether the min heap is empty, if it is empty, then add it to the min heap; if the min heap is not empty, judge whether it is less than the minimum value of the min heap, if it is less, then add it to the max heap; if the fault element is greater than the minimum value of the min heap, then add it to the min heap; adjust the elements of the max heap and the min heap to make the quantities on both sides consistent in the case of even numbers and differ by 1 in the case of odd numbers; regularly traverse all normal devices in a loop, obtain the abnormal median according to the dimension - take the average of the maximum value of the max heap and the minimum value of the min heap, and compare whether the monitoring item data is close to the abnormal median, if it is close, then give an early warning.

[0128] For example: The abnormal data of the working voltage of the fog lamp are A1, A2, A3, A4, A5, and the five abnormal data increase in sequence; the order of obtaining the abnormal data is A4, A2, A5, A3, A1 respectively; first obtain A4, the max heap is empty, so A4 is the max heap, and there is no change in the subsequent loop; then obtain A2, which is less than A4, and it is classified into the max heap. Then judge that the quantities on both sides differ by 2, and adjust the max heap so that the max heap is A2 and the min heap is A4, and there is no change in the subsequent loop. At this time, the abnormal median is the average value of A2 and A4; then obtain A5, which is greater than A2 and greater than A4, and add it to the min heap, and there is no change in the subsequent loop. At this time, the abnormal median is the average value of A2 and A4; then obtain A3, which is greater than A2 and less than A4, and add it to the max heap, and there is no change in the subsequent loop. At this time, the abnormal median is the average value of A3 and A4; finally obtain A1, add it to the max heap, and there is no change in the subsequent loop. At this time, the abnormal median is the average value of A3 and A4.

[0129] Regularly loop to obtain the working voltage of all normal fog lamps. If the working voltage is close to the abnormal median, then give an early warning.

[0130] Among them, to judge whether the detected item parameter value is close to the abnormal median, a proximity threshold can be set. If the absolute value of the difference between the monitoring item parameter value and the abnormal median is less than the proximity threshold, it is considered to be close to the abnormal median and an early warning is given.

[0131] Among them, to determine whether the parameter value of the detection item is close to the abnormal median, a proximity threshold can be set. The warning range of the abnormal median is the abnormal median ± the proximity threshold. If the parameter value of the monitoring item falls within the warning range of the abnormal median, it is considered to be close to the abnormal median and a warning is issued.

[0132] In this application, when the monitoring and analysis algorithm is loaded, an extensible mechanism is used to provide an extension point, enabling the loading of multiple monitoring and analysis algorithms.

[0133] In this application, the configuration and parsing of the monitoring items achieve the goal of dynamic configuration and parsing.

[0134] In this application, the fault analysis calculation algorithm (with a preset default calculation algorithm) is dynamically obtained, and based on the monitoring parameter configuration, the fault status of the monitoring item is analyzed in real time; in this application, the cascade relationship and weight between the monitoring items are established, the topological relationship of the device is established, and based on the association relationship, the status of the monitoring item of the parent device is calculated; in this application, the fault data is obtained in real time, the fault median of each monitoring item is generated, and the fault of the monitoring item is analyzed in multiple dimensions.

[0135] The solution of this application can cover the monitoring of various devices, with a finer granularity and more accurate accuracy for the monitoring of a single device itself; at the same time, it can also monitor network topology devices and analyze the status of associated devices; through the accumulation and calculation of historical data, early warnings can be made for different types of devices.

[0136] The embodiment of this application provides a device fault intelligent monitoring device for implementing the device fault intelligent monitoring method described above in this application. The following provides a detailed description of the device fault intelligent monitoring device.

[0137] As Figure 7 shown, the device fault intelligent monitoring device includes:

[0138] A monitoring configuration module 101, configured to obtain the monitoring items of the target device configured in advance, where the monitoring items include monitoring item parameters and parameter ranges;

[0139] A device topology module 102, configured to obtain the topological relationship between devices accessing the operation and maintenance platform pre-constructed, and obtain the monitoring data of the target device based on the monitoring items;

[0140] An abnormality determination module 103, configured to determine whether the target monitoring item is abnormal data according to the fault analysis algorithm corresponding to the target monitoring item pre-loaded; if it is determined that the target monitoring item is abnormal data, add the abnormal data to the abnormal data array corresponding to the target monitoring item, and determine the abnormal median of the monitoring item parameters;

[0141] The device warning module 104 is used to determine the warning range of the abnormal median of the monitoring item parameters according to the abnormal data, and warn that the target monitoring item of the target device fails after the monitoring item parameters of the target device fall into the corresponding warning range of the abnormal median.

[0142] In one implementation, the monitoring configuration module 101 is further used for:

[0143] Input the monitoring items of the pre-configured target device in an interactive input manner; convert the configured monitoring items into JSON format for storage.

[0144] In one implementation, the monitoring configuration module 101 is further used for:

[0145] Read the monitoring items, and judge the data types of the monitoring items; in the case where the monitoring item is a basic data type, parse the monitoring item to determine the monitoring item parameters and parameter ranges of the device; in the case where the monitoring item is a structure type, loop to parse the basic data type data in the structure type to determine the monitoring item parameters and parameter ranges of the device; in the case where the monitoring item is an array type, loop to parse the basic data type data and structure type data in the array type to determine the monitoring item parameters and parameter ranges of the device.

[0146] In one implementation, the monitoring configuration module 101 is further used for:

[0147] When parsing the structure type data, loop to parse the basic data type data in the structure type.

[0148] In one implementation, the device topology module 102 is further used for:

[0149] Obtain the topology relationship between the devices accessing the operation and maintenance platform configured in advance; obtain the cascading index grouping and sub-index weights of each type of parent-child devices with topology relationship; when the target device is a child device among the parent-child devices with topology relationship, collect the real-time monitoring data of the target device.

[0150] In one implementation, the device topology module 102 is further used for:

[0151] When the target device is a parent device among the parent-child devices with topology relationship, collect the real-time monitoring data of the parent-child devices; based on the cascading index grouping and the sub-index weights, determine the monitoring data of the parent device.

[0152] In one implementation, the device topology module 102 is further used for:

[0153] When the types of the parent device and the child device in the parent-child device are the same, the real-time monitoring data of the collected parent device is retained; when the types of the parent device and the child device in the parent-child device are different, the monitoring data of the monitoring item parameters that the parent device has and the corresponding child device does not have is retained, and the monitoring data of the monitoring item parameters of the parent device is calculated according to the monitoring data of the monitoring item parameters that the child device has and the parent device does not have.

[0154] In one implementation, the anomaly determination module 103 is further configured to:

[0155] Add the anomaly data to the anomaly data array corresponding to the target monitoring item; allocate the anomaly data in the anomaly data array as large top heap elements and small top heap elements, where the maximum value of the large top heap elements is less than the minimum value of the small top heap elements, and when the number of the anomaly data is even, the number of the large top heap elements is the same as the number of the small top heap elements, and when the number of the anomaly data is odd, the number of the large top heap elements differs from the number of the small top heap elements by one; determine the anomaly median of the anomaly data array according to the average value of the maximum value of the large top heap elements and the minimum value of the small top heap elements.

[0156] The device fault intelligent monitoring device provided by the above embodiments of the present application and the device fault intelligent monitoring method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0157] The internal functions and structures of the device fault intelligent monitoring device are described above. As Figure 8 shown, in practice, the device fault intelligent monitoring device can be implemented as an electronic device, including: a memory 301 and a processor 303.

[0158] The memory 301 can be configured to store programs.

[0159] In addition, the memory 301 can also be configured to store various other data to support operations on the electronic device. Examples of these data include instructions for any application program or method for operating on the electronic device, contact data, phone book data, messages, pictures, videos, etc.

[0160] The memory 301 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disk.

[0161] A processor 303, coupled to a memory 301, for executing a program in the memory 301 for:

[0162] Obtaining monitoring items of a pre-configured target device, where the monitoring items include monitoring item parameters and parameter ranges; obtaining the topological relationship between devices of a pre-constructed access operation and maintenance platform, and obtaining monitoring data of the target device based on the monitoring items;

[0163] Judging whether the target monitoring item is abnormal data according to a pre-loaded fault analysis algorithm corresponding to the target monitoring item;

[0164] If it is determined that the target monitoring item is abnormal data, adding the abnormal data to an abnormal data array corresponding to the target monitoring item, and determining the abnormal median of the monitoring item parameters;

[0165] Determining a warning range of the abnormal median of the monitoring item parameters according to the abnormal data, and after the monitoring item parameters of the target device fall into the corresponding warning range of the abnormal median, warning that the target monitoring item of the target device has a fault.

[0166] In one implementation, the processor 303 is specifically configured to:

[0167] Inputting the monitoring items of the pre-configured target device in an interactive input manner; converting the configured monitoring items into JSON format for storage.

[0168] In one implementation, the processor 303 is specifically configured to:

[0169] Reading the monitoring items, and judging the data type of the monitoring items; in the case where the monitoring item is a basic data type, parsing the monitoring item to determine the monitoring item parameters and parameter ranges of the device; in the case where the monitoring item is a structure type, circularly parsing the basic data type data in the structure type to determine the monitoring item parameters and parameter ranges of the device; in the case where the monitoring item is an array type, circularly parsing the basic data type data and structure type data in the array type to determine the monitoring item parameters and parameter ranges of the device.

[0170] In one implementation, the processor 303 is specifically configured to:

[0171] When parsing the structure type data, circularly parsing the basic data type data in the structure type.

[0172] In one implementation, the processor 303 is specifically configured to:

[0173] Obtain the topological relationship between devices of a pre-configured access operation and maintenance platform; obtain the cascading index grouping and sub-index weights of each type of parent-child devices with topological relationships that are pre-set; when the target device is a child device among parent-child devices with topological relationships, collect the real-time monitoring data of the target device.

[0174] In one implementation, the processor 303 is specifically configured to:

[0175] When the target device is a parent device among parent-child devices with topological relationships, collect the real-time monitoring data of the parent-child devices; based on the cascading index grouping and the sub-index weights, determine the monitoring data of the parent device.

[0176] In one implementation, the processor 303 is specifically configured to:

[0177] When the parent device and the child device among the parent-child devices have the same type, retain the real-time monitoring data of the collected parent device; when the parent device and the child device among the parent-child devices have different types, retain the monitoring data of the monitoring item parameters that the parent device has and the corresponding child device does not have, and calculate the monitoring data of the monitoring item parameters of the parent device according to the monitoring data of the monitoring item parameters that the child device has and the parent device does not have.

[0178] In one implementation, the processor 303 is specifically configured to:

[0179] When the monitoring data of the device exceeds the parameter range of the corresponding monitoring item parameter, determine the monitoring data as abnormal data; when the device is associated with a preset algorithm, process the monitoring data according to the preset algorithm to determine the abnormal data in the monitoring data.

[0180] In one implementation, the processor 303 is specifically configured to:

[0181] Add the abnormal data to the abnormal data array corresponding to the target monitoring item; allocate the abnormal data in the abnormal data array as large top heap elements and small top heap elements, where the maximum value of the large top heap elements is less than the minimum value of the small top heap elements, and when the abnormal data is an even number, the number of large top heap elements is the same as the number of small top heap elements, and when the abnormal data is an odd number, the number of large top heap elements differs from the number of small top heap elements by one; determine the abnormal median of the abnormal data array according to the average value of the maximum value of the large top heap elements and the minimum value of the small top heap elements.

[0182] In this application, Figure 8 only some components are schematically shown, which does not mean that the electronic device only includes Figure 8The components shown.

[0183] The electronic device provided in this embodiment is based on the same inventive concept as the device fault intelligent monitoring method provided in the embodiments of the present application, and has the same beneficial effects as the methods adopted, run, or implemented by the application programs stored therein.

[0184] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0185] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0186] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0187] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0188] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0189] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0190] This application also provides a computer-readable storage medium corresponding to the device fault intelligent monitoring method provided by the foregoing embodiment. A computer program (i.e., a program product) is stored thereon. When the computer program is run by a processor, it will execute the device fault intelligent monitoring method provided by any of the foregoing embodiments.

[0191] Computer-readable media includes permanent and non-permanent, removable and non-removable media and can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0192] The computer-readable storage medium provided by the above embodiments of this application and the device fault intelligent monitoring method provided by the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored thereon.

[0193] It should be noted that a large number of specific details are set forth in the specification provided herein. However, it can be understood that the embodiments of the present application may be practiced without these specific details. In some instances, well-known structures and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0194] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, commodity or device comprising said element.

[0195] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An intelligent monitoring method for equipment failures, characterized in that, Including: Obtain the monitoring items of the target device pre-configured, where the monitoring items include monitoring item parameters and parameter ranges; Obtain the topological relationship between devices in the access operation and maintenance platform pre-constructed, and obtain the monitoring data of the target device based on the monitoring items; including: obtaining the topological relationship between devices in the access operation and maintenance platform pre-configured; obtaining the cascaded index groups and sub-index weights of each type of parent and child devices with topological relationships pre-set; when the target device is the parent device among the parent and child devices with topological relationships, collect the real-time monitoring data of the parent and child devices; based on the cascaded index groups and the sub-index weights, determine the monitoring data of the parent device; judge whether the target monitoring item is abnormal data according to the pre-loaded fault analysis algorithm corresponding to the target monitoring item; If it is determined that the target monitoring item is abnormal data, add the abnormal data to the abnormal data array corresponding to the target monitoring item, and determine the abnormal median of the monitoring item parameters; According to the abnormal data, determine the warning range of the abnormal median of the monitoring item parameters. After the monitoring item parameters of the target device fall into the corresponding warning range of the abnormal median, warn that the target monitoring item of the target device fails; Wherein, the same target monitoring item of different devices corresponds to the same abnormal data array.

2. The method according to claim 1, wherein The pre-configuring the monitoring items of the target device includes: Input the monitoring items of the target device pre-configured in an interactive input manner; Convert the configured monitoring items into JSON format for storage.

3. The method according to claim 1, wherein The obtaining the monitoring items of the target device pre-configured includes: Read the monitoring items and judge the data type of the monitoring items; When the monitoring item is of a basic data type, parse the monitoring item to determine the monitoring item parameters and parameter ranges of the device; When the monitoring item is of a structure type, loop to parse the basic data type data in the structure type to determine the monitoring item parameters and parameter ranges of the device; When the monitoring item is of an array type, loop to parse the basic data type data and structure type data in the array type to determine the monitoring item parameters and parameter ranges of the device.

4. The method according to any one of claims 1-3, characterized in that When the target device is a child device among the parent and child devices with topological relationships, collect the real-time monitoring data of the target device.

5. The method according to claim 1, wherein The determining the monitoring data of the parent device based on the cascaded index groups and the sub-index weights includes: When the parent device and the child device in the parent and child devices are of the same type, retain the real-time monitoring data of the collected parent device; When the parent device and the child device in the parent and child devices are of different types, retain the monitoring data of the monitoring item parameters that the parent device has and the corresponding child device does not have, and calculate the monitoring data of the monitoring item parameters of the parent device according to the monitoring data of the monitoring item parameters that the child device has and the parent device does not have.

6. The method according to claim 1, wherein The adding the abnormal data to the abnormal data array corresponding to the target monitoring item and determining the abnormal median of the monitoring item parameters includes: Add the abnormal data to the abnormal data array corresponding to the target monitoring item; Allocate the abnormal data in the abnormal data array as large top heap elements and small top heap elements, where the maximum value of the large top heap elements is less than the minimum value of the small top heap elements, and when the number of the abnormal data is even, the number of the large top heap elements is the same as the number of the small top heap elements, and when the number of the abnormal data is odd, the number of the large top heap elements differs from the number of the small top heap elements by one; Determine the abnormal median of the abnormal data array according to the average value of the maximum value of the large top heap elements and the minimum value of the small top heap elements.

7. An intelligent device fault monitoring device, characterized in that, Comprising: A monitoring configuration module, configured to obtain the monitoring items of the target device pre-configured, where the monitoring items include monitoring item parameters and parameter ranges; A device topology module, configured to obtain the topological relationship between devices in the access operation and maintenance platform pre-constructed, and obtain the monitoring data of the target device based on the monitoring items; including: obtaining the topological relationship between devices in the access operation and maintenance platform pre-configured; obtaining the cascaded index grouping and sub-index weights of each type of parent and child devices with topological relationships pre-set; when the target device is the parent device among the parent and child devices with topological relationships, collecting the real-time monitoring data of the parent and child devices; determining the monitoring data of the parent device based on the cascaded index grouping and the sub-index weights; An abnormality determination module, configured to determine whether the target monitoring item is abnormal data according to the fault analysis algorithm corresponding to the target monitoring item pre-loaded; if it is determined that the target monitoring item is abnormal data, add the abnormal data to the abnormal data array corresponding to the target monitoring item, and determine the abnormal median of the monitoring item parameters; A device warning module, configured to determine the warning range of the abnormal median of the monitoring item parameters according to the abnormal data, and warn that the target monitoring item of the target device fails after the monitoring item parameters of the target device fall into the corresponding warning range of the abnormal median; Wherein, the same target monitoring item of different devices corresponds to the same abnormal data array.

8. An electronic device, characterized in that, Comprising: A memory and a processor; The memory is used to store programs; ​ ​ ​ If it is determined that the target monitoring item is abnormal data, add the abnormal data to the abnormal data array corresponding to the target monitoring item, and determine the abnormal median of the monitoring item parameters; Determine the warning range of the abnormal median of the monitoring item parameters according to the abnormal data. After the monitoring item parameters of the target device fall into the corresponding warning range of the abnormal median, warn that the target monitoring item of the target device has a fault; Among them, the same target monitoring item of different devices corresponds to the same abnormal data array.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by a processor to implement the method according to any one of claims 1-6.

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