Remote operation and maintenance processing method and device based on big data analysis

Abstract

The invention discloses a remote operation and maintenance processing method and device based on big data analysis, and relates to the technical field of operation and maintenance. The method comprises the following steps: receiving first detection data which is sent by target edge computing equipment and aims at a first enterprise; determining first enterprise data according to the first enterprise; determining a second enterprise corresponding to the first enterprise data, judging the first detection data, and determining that the first detection data is in a fault state; calling a target processing measure matched with the fault state from the second enterprise; determining to-be-maintained equipment according to the target processing measure, and receiving a target video sent by the target AR equipment; processing the target video based on the target processing measure to obtain a maintenance distribution map; and sending the maintenance distribution map to the target edge computing device so as to display the maintenance distribution map on the target edge computing device. By implementing the technical scheme provided by the invention, the problems that an enterprise depends on field operation of field personnel when executing maintenance and repair tasks, and the maintenance efficiency is relatively low can be effectively solved.

Description

Technical Field

[0001] This application relates to the field of operation and maintenance technologies, and particularly to a remote operation and maintenance processing method and device based on big data analysis. Background Art

[0002] With the continuous expansion of the business scope of enterprises, the amount of enterprise data is growing rapidly at an alarming rate. In order to efficiently manage this data and fully tap its potential value, enterprises urgently need to build a set of efficient and comprehensive operation and maintenance management systems to ensure the integrity, accuracy, and security of the data.

[0003] This operation and maintenance management system not only covers the regular inspections, maintenance, and necessary upgrades of enterprise hardware facilities (such as key physical infrastructures like production equipment and office equipment) and software systems (such as core business systems like enterprise resource planning), but also aims to significantly reduce the risk of business interruption caused by equipment failures or system outages through preventive measures. To ensure the continuous and stable operation of enterprises, it is particularly important to regularly maintain key data types within the enterprise. These key data types widely cover multiple aspects such as transaction data, master data, metadata, sensitive data, and log data. Since these data types play a crucial role in enterprise operations, their accuracy and security are directly related to enterprise business decisions and operational efficiency. However, currently, most enterprises still rely on on-site personnel for on-site operations when performing maintenance and repair tasks, that is, operation and maintenance personnel need to go to each enterprise location for on-site operations, but the maintenance efficiency of on-site operations is relatively low.

[0004] Therefore, there is an urgent need for a remote operation and maintenance processing method and device based on big data analysis that can solve the above technical problems. Summary of the Invention

[0005] This application provides a remote operation and maintenance processing method and device based on big data analysis, which can effectively solve the problem that enterprises rely on on-site personnel for on-site operations when performing maintenance and repair tasks, and the maintenance efficiency is relatively low.

[0006] In a first aspect, the present application provides a remote operation and maintenance processing method based on big data analysis. The method includes: receiving first detection data sent by a target edge computing device for a first enterprise, where the first detection data includes performance data, sensitive data, user behavior data, and log data; determining first enterprise data corresponding to the first enterprise, where the first enterprise data includes enterprise domain data, enterprise scale data, enterprise operation and maintenance data, and enterprise business data; determining a second enterprise based on the first enterprise data, judging the first detection data, and determining that the first detection data is in a fault state; retrieving a target processing measure matching the fault state from the second enterprise; determining a device to be maintained based on the target processing measure, and receiving a target video sent by a target AR device, where the target video is a video of a user using the target AR device to scan the device to be maintained in the first enterprise; processing the target video based on the target processing measure to obtain a maintenance distribution map; and sending the maintenance distribution map to the target edge computing device for displaying the maintenance distribution map on the target edge computing device.

[0007] By adopting the above technical solution, receiving the first detection data from the target edge computing device, which covers multiple aspects such as performance, sensitivity, user behavior, and logs, provides a comprehensive information basis for subsequent fault judgment and processing. Integrating the first enterprise data corresponding to the first enterprise can more accurately understand the enterprise's operation status and potential needs. Based on the first detection data, the fault state can be intelligently judged, which reduces the need for manual judgment and improves the accuracy and speed of judgment. Determining the second enterprise from the first enterprise data and then retrieving the target processing measure matching the fault state from the second enterprise realizes the rapid positioning of the fault processing scheme. Then, processing the target video based on the target processing measure to generate a maintenance distribution map and directly sending the maintenance distribution map to the target edge computing device enables it to be displayed on the target edge computing device, realizing the remote operation of maintenance and repair tasks, thus effectively solving the problem that current enterprises rely on on-site personnel for on-site operations during maintenance and repair tasks and the maintenance efficiency is relatively low.

[0008] Optionally, determining the second enterprise based on the first enterprise data specifically includes: searching for a third enterprise with the same enterprise domain data from multiple enterprises; obtaining second enterprise data corresponding to the third enterprise; calculating the similarity between the first enterprise data and the second enterprise data to obtain a target similarity value; judging whether the target similarity value is greater than a preset similarity threshold; when the target similarity value is greater than the preset similarity threshold, confirming that the third enterprise and the first enterprise are similar enterprises, and outputting the third enterprise as the second enterprise.

[0009] By adopting the above technical solution, obtaining the second enterprise data corresponding to the third enterprise with the same domain data as that of the first enterprise and calculating the similarity with the first enterprise data can accurately identify the enterprises similar to the first enterprise in multiple dimensions. When the target similarity value exceeds the preset similarity threshold, it can automatically confirm that the third enterprise and the first enterprise are similar enterprises and output it as the second enterprise. This step provides an opportunity for the enterprise to learn from the best practices of similar enterprises. Especially when facing a failure state, it can quickly find a solution from the handling measures of similar enterprises, improving the maintenance efficiency and reducing the operation and maintenance costs.

[0010] Optionally, judging the first detection data to obtain a failure state specifically includes: obtaining the system configuration information corresponding to the first enterprise; determining the second detection data according to the system configuration information; judging whether the first detection data is consistent with the second detection data; when the first detection data is inconsistent with the second detection data, confirming that the first detection data is in a failure state.

[0011] By adopting the above technical solution, obtaining the system configuration information corresponding to the first enterprise and determining the second detection data according to this information can quickly compare the first detection data (i.e., the actually collected data) with the second detection data (i.e., the data expected to be obtained according to the system configuration information), so as to timely discover the inconsistency between the two. When the two are inconsistent, it often indicates an abnormal state of the system or device, that is, a failure state, which can significantly improve the accuracy of failure identification.

[0012] Optionally, retrieving the target handling measure matching the failure state from the second enterprise specifically includes: after determining that the first enterprise is in a failure state, retrieving the preset database corresponding to the second enterprise; inputting the failure state into the preset database for query to obtain multiple handling measures; obtaining the multiple usage times corresponding to the multiple handling measures, one handling measure corresponding to one usage time, and the usage time being the total usage times corresponding to each handling measure; sorting the multiple usage times from largest to smallest to obtain the target sorting result; obtaining the target usage time, where the target usage time is the usage time corresponding to the one ranked first in the target sorting result; and outputting the handling measure corresponding to the target usage time as the target handling measure.

[0013] By adopting the above technical solution, after determining that the first enterprise is in a failure state, it can quickly retrieve multiple handling measures from the preset database corresponding to the second enterprise (the enterprise similar to the first enterprise), and then by counting the usage times of each handling measure and sorting them from largest to smallest, it can efficiently screen out the most effective handling measure, that is, the target handling measure. This screening method avoids the cumbersome process of manually trying different handling measures one by one and improves the handling efficiency.

[0014] Optionally, after determining the device to be maintained according to the target processing measure, the method further includes: generating a prompt message according to the device to be maintained, and sending the prompt message to the target edge computing device, so that the target edge computing device determines a collection instruction according to the prompt message, and sends the collection instruction to the user side. The collection instruction is used to prompt the user to use the target AR device to perform video collection on the device to be maintained.

[0015] By adopting the above technical solution, the prompt message generated by the device to be maintained is sent to the target edge computing device, which can quickly generate a targeted collection instruction and immediately send it to the user side. The user uses the target AR device to perform video collection on the device to be maintained according to the collection instruction. This process not only simplifies the on-site operation, but also improves the accuracy and integrity of the collection.

[0016] Optionally, processing the target video based on the target processing measure to obtain a maintenance distribution map, specifically including: identifying the target video to obtain the target model corresponding to the device to be maintained; adjusting the target processing measure according to the target model to obtain multiple maintenance steps; extracting multiple parts to be maintained from the multiple maintenance steps, and numbering the multiple parts to be maintained, with one part to be maintained corresponding to one number; after marking the multiple parts to be maintained, splicing the marked images to obtain a maintenance distribution map, and the marked image is an image of the part to be maintained after marking.

[0017] By adopting the above technical solution, identifying the target video can accurately determine the target model of the device to be maintained. Since different models of devices may require different processing measures and maintenance steps, through model matching, it can be ensured that the subsequent provided maintenance steps are specific to the device, thereby improving the accuracy and effectiveness of maintenance. Adjusting the target processing measure according to the target model to generate multiple maintenance steps for the device of this model, extracting the parts to be maintained from the maintenance steps, and numbering them. This numbering method helps maintenance personnel quickly identify and locate the parts that need to be maintained, reducing confusion and errors during the maintenance process. After confirming that all parts to be maintained have been marked, a maintenance distribution map is generated. The maintenance distribution map intuitively shows the maintenance requirements of the device and the distribution of the parts to be maintained, which helps maintenance personnel better understand the maintenance tasks, formulate reasonable maintenance plans, and improve work efficiency.

[0018] Optionally, after sending the maintenance distribution map to the target edge computing device for display on the target edge computing device, the method further includes: obtaining a first location corresponding to the device to be maintained; calculating a target distance between the first location and a second location, where the second location is the location of the target edge computing device in the first enterprise; if the target distance is greater than a preset distance, determining to send the maintenance distribution map to the target AR device so that the user can view the maintenance distribution map according to the target AR device.

[0019] By adopting the above technical solution, obtaining the first location of the device to be maintained and the second location of the target edge computing device enables the calculation of the target distance between the two. This ability of location perception and distance calculation provides key information about the device location and the maintainer's location for the enterprise; when the target distance is greater than the preset distance, sending the maintenance distribution map to the target AR device allows the user to remotely view the maintenance distribution map through the AR device, and the user can arrange their actions more flexibly, improving the maintenance efficiency.

[0020] In a second aspect of the present application, a remote operation and maintenance processing device based on big data analysis is provided. The device includes a receiving unit, a processing unit, and a sending unit; the receiving unit receives first detection data sent by the target edge computing device for the first enterprise, and the first detection data includes performance data, sensitive data, user behavior data, and log data; the processing unit determines first enterprise data corresponding to the first enterprise, and the first enterprise data includes enterprise domain data, enterprise scale data, enterprise operation and maintenance data, and enterprise business data; determines a second enterprise according to the first enterprise data, judges the first detection data, and determines that the first detection data is in a fault state; retrieves a target processing measure matching the fault state from the second enterprise; determines the device to be maintained according to the target processing measure, and receives a target video sent by the target AR device, where the target video is a video of the user using the target AR device to scan the device to be maintained in the first enterprise; processes the target video based on the target processing measure to obtain a maintenance distribution map; the sending unit sends the maintenance distribution map to the target edge computing device for display on the target edge computing device.

[0021] Optionally, the receiving unit is used to find a third enterprise with the same enterprise domain data from multiple enterprises; obtain second enterprise data corresponding to the third enterprise; the processing unit is used to calculate the similarity between the first enterprise data and the second enterprise data to obtain a target similarity value; judge whether the target similarity value is greater than a preset similarity threshold; when the target similarity value is greater than the preset similarity threshold, confirm that the third enterprise and the first enterprise are similar enterprises and output the third enterprise as the second enterprise.

[0022] Optionally, the receiving unit is configured to obtain the system configuration information corresponding to the first enterprise; the processing unit is configured to determine the second detection data according to the system configuration information; determine whether the first detection data is consistent with the second detection data; when the first detection data is inconsistent with the second detection data, confirm that the first detection data is in a fault state.

[0023] Optionally, after determining that the first enterprise is in a fault state, the receiving unit is configured to retrieve the preset database corresponding to the second enterprise; the processing unit is configured to input the fault state into the preset database for query to obtain multiple processing measures; the receiving unit is configured to obtain the multiple usage times corresponding to the multiple processing measures, one processing measure corresponding to one usage time, and the usage time being the total usage times corresponding to each processing measure; the processing unit is configured to sort the multiple usage times from largest to smallest to obtain a target sorting result; the receiving unit is configured to obtain the target usage time, and the target usage time is the usage time corresponding to the one ranked first in the target sorting result; the processing unit is configured to output the processing measure corresponding to the target usage time as the target processing measure.

[0024] Optionally, the sending unit is configured to generate a prompt message according to the device to be maintained, and send the prompt message to the target edge computing device, so that the target edge computing device determines a collection instruction according to the prompt message and sends the collection instruction to the user side, and the collection instruction is used to prompt the user to use the target AR device to perform video collection on the device to be maintained.

[0025] Optionally, the processing unit is configured to identify the target video to obtain the target model corresponding to the device to be maintained; adjust the target processing measure according to the target model to obtain multiple maintenance steps; extract multiple parts to be maintained from the multiple maintenance steps, and mark serial numbers for the multiple parts to be maintained, one part to be maintained corresponding to one serial number; after marking the multiple parts to be maintained, splice the marked images to obtain a maintenance distribution map, and the marked image is an image of the parts to be maintained after being marked.

[0026] Optionally, the receiving unit is configured to obtain the first position corresponding to the device to be maintained; the processing unit is configured to calculate the target distance between the first position and the second position, and the second position is the position of the target edge computing device in the first enterprise; the sending unit is configured to determine to send the maintenance distribution map to the target AR device if the target distance is greater than the preset distance, so that the user can view the maintenance distribution map according to the target AR device.

[0027] In a third aspect of the present application, an electronic device is provided, and the electronic device includes a processor, a memory, a user interface and a network interface. The memory is used for storing instructions, the user interface and the network interface are used for communicating with other devices, and the processor is used for executing the instructions stored in the memory, so that an electronic device executes the method according to any one of the above in the present application.

[0028] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method of any one of the above in the present application is executed.

[0029] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Receive the first detection data from the target edge computing device. These data cover multiple aspects such as performance, sensitivity, user behavior, and logs, providing a comprehensive information basis for subsequent fault judgment and handling. Integrate the first enterprise data corresponding to the first enterprise to more accurately understand the operation status and potential needs of the enterprise. According to the first detection data, the fault status can be intelligently judged. This process reduces the need for manual judgment, improves the accuracy and speed of judgment. Determine the second enterprise from the first enterprise data, and then retrieve the target processing measures matching the fault status from the second enterprise, achieving a quick positioning of the fault handling solution. Then, based on the target processing measures, process the target video to generate a maintenance distribution map, and directly send the maintenance distribution map to the target edge computing device for display on the target edge computing device, realizing the remote operation of maintenance and repair tasks, thus effectively solving the problem that current enterprises rely on on-site personnel for on-site operation during maintenance and repair tasks and the relatively low maintenance efficiency.

[0030] 2. Identify the target model of the device to be maintained for the target video. Since different models of devices may require different processing measures and maintenance steps, through model matching, it can be ensured that the subsequent provided maintenance steps are specific to the device, thereby improving the accuracy and effectiveness of maintenance. Adjust the target processing measures according to the target model to generate multiple maintenance steps for this model of device. Extract the components to be maintained from the maintenance steps and mark them with serial numbers. This marking method helps maintenance personnel quickly identify and locate the components to be maintained, reducing confusion and errors during the maintenance process. After confirming that all the components to be maintained have been marked, generate a maintenance distribution map. The maintenance distribution map intuitively shows the maintenance requirements of the device and the distribution of the components to be maintained, helping maintenance personnel better understand the maintenance tasks, formulate reasonable maintenance plans, and improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a schematic flowchart of a remote operation and maintenance processing method based on big data analysis provided by an embodiment of the present application; Figure 2 is a schematic structural diagram of a remote operation and maintenance processing device based on big data analysis provided by an embodiment of the present application; Figure 3It is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0032] Explanation of reference numerals: 201, receiving unit; 202, processing unit; 203, transmitting unit; 300, electronic device; 301, processor; 302, memory; 303, user interface; 304, network interface; 305, communication bus. Detailed implementation manners

[0033] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0034] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for instance" aims to present relevant concepts in a specific manner.

[0035] In the description of the embodiments of the present application, the meaning of the term "plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0036] With the continuous expansion of the enterprise business scope, the enterprise data volume is growing rapidly at an astonishing speed. In order to efficiently manage this data and fully explore its potential value, the enterprise urgently needs to build a set of efficient and comprehensive operation and maintenance management systems to ensure the integrity, accuracy and security of the data.

[0037] This operation and maintenance management system not only covers regular inspections, maintenance, and necessary upgrades of an enterprise's hardware facilities (such as key physical infrastructures like production equipment and office equipment) and software systems (such as core business systems like enterprise resource planning), but also aims to significantly reduce the risk of business interruption caused by equipment failures or system outages through preventive measures. To ensure the continuous and stable operation of the enterprise, it is particularly important to perform regular maintenance on key data types within the enterprise. These key data types widely cover various aspects such as transaction data, master data, metadata, sensitive data, and log data. Since these data types play a crucial role in the enterprise's operation, their accuracy and security are directly related to the enterprise's business decisions and operational efficiency. However, currently, most enterprises still rely on on-site personnel for on-site operations when performing maintenance and repair tasks, but the maintenance efficiency of manual personnel going to the site for on-site operations is relatively low.

[0038] Therefore, how to solve the problem that enterprises rely on on-site manual operations when performing maintenance and repair tasks, with relatively low maintenance efficiency. A remote operation and maintenance processing method based on big data analysis provided by an embodiment of this application is applied to a server. The server in this application refers to a platform that provides remote operation and maintenance services for enterprises. Figure 1 It is a schematic flowchart of a remote operation and maintenance processing method based on big data analysis provided by an embodiment of this application. Refer to Figure 1 This method includes the following steps S101 - step S107.

[0039] S101: Receive the first detection data sent by the target edge computing device for the first enterprise.

[0040] In the above S101, in the production or operation environment of the first enterprise, deploy the target edge computing device. The first enterprise is any enterprise to be maintained. Ensure that the target edge computing device is correctly connected to data collection devices such as sensors and high-definition cameras, and can collect performance data, sensitive data, user behavior data, and log data in real time. After the target edge computing device collects data in real time, it sends the data to the receiving end of the data processing center or server through a local area network or direct connection. After the server receives the first detection data, it performs preliminary processing and parsing on the first detection data to ensure the accuracy and integrity of the data.

[0041] S102: Determine the first enterprise data corresponding to the first enterprise.

[0042] In the above S102, after determining the first enterprise, the basic information of the first enterprise, including enterprise field data, enterprise scale data, etc., can be queried through the address or company name corresponding to the first enterprise via a public query website (such as the National Enterprise Credit Information Publicity System, Credit China, etc.) or a commercial query platform. Then, operation and maintenance data and business data are obtained from within the enterprise, which are usually stored in the enterprise's internal database or management system. The queried enterprise information is integrated with the internal data to form complete first enterprise data, and at this time, the first enterprise data includes enterprise field data, enterprise scale data, enterprise operation and maintenance data, and enterprise business data.

[0043] S103: Determine the second enterprise based on the first enterprise data, and judge the first detection data to determine that the first detection data is in a fault state.

[0044] In the above S103, after determining the first enterprise data corresponding to the first enterprise, determining the second enterprise according to the first enterprise data specifically includes: searching for a third enterprise with the same enterprise field data from multiple enterprises; obtaining the second enterprise data corresponding to the third enterprise; calculating the similarity between the first enterprise data and the second enterprise data to obtain a target similarity value; determining whether the target similarity value is greater than a preset similarity threshold; when the target similarity value is greater than the preset similarity threshold, confirming that the third enterprise and the first enterprise are similar enterprises, and outputting the third enterprise as the second enterprise. Specifically, the enterprise field data of the first enterprise can be clarified first, which usually includes key information such as the industry to which the enterprise belongs, the main business, the type of products or services, etc. Establish a database containing the enterprise field data of multiple enterprises. These enterprises may come from different industries or fields, but the field data of each enterprise has been clearly recorded. Using database query technology, search for a third enterprise with the same or highly similar enterprise field data as the first enterprise in the constructed enterprise field database. This can be achieved through methods such as keyword matching and semantic analysis. Screen out the third enterprise that is closest to the enterprise field data of the first enterprise from the matching results. At this time, the third enterprise can be one or multiple. The specific number of the third enterprise can be determined according to the number of enterprises with the same enterprise field data as the first enterprise. The third enterprise can be understood as a set of other enterprises with the same enterprise field as the first enterprise. Then obtain the second enterprise data corresponding to any one enterprise from the third enterprises. At this time, the second enterprise data includes the basic information, business data, financial data, technical data, etc. of the enterprise. Clean and organize the collected second enterprise data, remove redundant information, and ensure the accuracy and consistency of the data. Then calculate the similarity between the first enterprise data and the second enterprise data in turn. Common similarity calculation methods include Euclidean distance, cosine similarity, Pearson correlation coefficient, etc. Using the selected similarity calculation method, calculate the first enterprise data and the second enterprise data to obtain a target similarity value. Then, according to the actual needs and business scenarios, set a reasonable preset similarity threshold. This threshold is used to determine whether the similarity between the first enterprise and the third enterprise is high enough to confirm that they are similar enterprises. Compare the calculated target similarity value with the preset similarity threshold. If the target similarity value is greater than the preset similarity threshold, it is considered that the similarity between the first enterprise and the third enterprise is relatively high. When the target similarity value is greater than the preset similarity threshold, officially confirm that the third enterprise and the first enterprise are similar enterprises. This means that the third enterprise has a high similarity with the first enterprise in terms of field data, business data, etc. Output the confirmed third enterprise as the second enterprise.When identifying similar enterprises, if the third enterprise contains multiple enterprises, the enterprise data corresponding to each enterprise is successively calculated for similarity with the first enterprise data according to the above calculation method to obtain multiple similarity values. Then, the maximum similarity value is selected from the multiple similarity values, and this similarity value is compared with a preset similarity threshold. Based on the comparison result, it is determined whether the first enterprise and the third enterprise are similar enterprises. For example, the preset similarity threshold can be set to 0.9. The first enterprise is A. The third enterprise B is determined according to the enterprise field data of the first enterprise A. Then, the second enterprise data corresponding to the third enterprise B is obtained, and the similarity value between the first enterprise data and the second enterprise data is calculated. If the similarity value is 0.95, at this time, the similarity value is greater than the preset similarity threshold, and it can be defaulted that the first enterprise A and the third enterprise B are similar enterprises. The third enterprise B can be output as the second enterprise. At this time, the second enterprise refers to the enterprise similar to the first enterprise.

[0045] In addition, when the target similarity value is less than or equal to the preset similarity threshold, it is confirmed that the first enterprise and the third enterprise are not similar enterprises. It is necessary to obtain other enterprises with the same enterprise field data as the first enterprise from multiple enterprises, calculate the similarity between the other enterprises and the first enterprise, and find the second enterprise most similar to the first enterprise according to the similarity.

[0046] Further, after determining the second enterprise from the first enterprise data, the first detection data is judged to determine that the first detection data is in a fault state, which specifically includes: obtaining the system configuration information corresponding to the first enterprise; determining the second detection data according to the system configuration information; judging whether the first detection data is consistent with the second detection data; when the first detection data is inconsistent with the second detection data, confirming that the first detection data is in a fault state. Specifically, it is necessary to clarify the specific scope of the system configuration information of the first enterprise system, which may include hardware configuration (such as server model, CPU, memory, hard disk, etc.), software configuration (such as operating system version, application program version, etc.), network configuration (such as IP address, subnet mask, gateway, etc.) and security configuration, etc. According to the type of system configuration information, select an appropriate acquisition method. For example, for hardware configuration information, it can be obtained by physically checking the server or using commands provided by the operating system (such as systeminfo for Windows and lshw for Linux); for software configuration information, the installation directory, version file of the operating system or application program can be viewed or corresponding query commands can be used; for network configuration information, network configuration commands (such as ipconfig for Windows and ifconfig for Linux) can be used to obtain it. Perform corresponding operations according to the selected acquisition method to obtain the system configuration information of the first enterprise. This may include steps such as logging in to the server, executing commands, and viewing files. Record and organize the obtained system configuration information to form a clear and accurate system configuration information report. Analyze the obtained system configuration information to understand the configuration of the system in terms of hardware, software, network, and security, etc. Determine the indicators to be detected according to the analysis results. These indicators may include CPU usage rate, memory occupancy rate, remaining hard disk space, network bandwidth usage rate, security setting status, etc. Use corresponding detection tools or commands to detect the determined detection indicators to obtain the second detection data. For example, the task manager or performance monitor can be used to detect the usage of CPU and memory, and the network monitor can be used to detect the usage of network bandwidth, etc. Then compare the first detection data (which may be previously obtained or preset reference data) with the second detection data. This may require aligning and comparing the two sets of data according to the same detection indicators. Analyze the comparison results to find the differences between the two sets of data. These differences may include numerical differences, status differences, or configuration differences, etc. According to the analysis results, judge whether the first detection data is consistent with the second detection data. If the two sets of data do not differ much in key detection indicators and meet the preset consistency standard, they can be considered consistent; otherwise, they are considered inconsistent. When the first detection data is inconsistent with the second detection data, it is necessary to confirm whether the first detection data is in a fault state. This may require judging in combination with the actual situation and running state of the system. Analyze the reasons for the inconsistency.Possible causes include changes in system configuration (such as hardware upgrades, software updates, etc.), system being attacked or infected by viruses, internal system failures, etc.

[0047] Furthermore, when the first detection data is consistent with the second detection data, it is confirmed that the first detection data is in a normal state.

[0048] S104: Retrieve the target handling measures that match the fault state from the second enterprise.

[0049] In the above S104, after detecting that the first detection data is in a fault state, it is necessary to query from the second enterprise the handling measures that match the fault state of the first enterprise. Retrieving the target handling measures that match the fault state from the second enterprise specifically includes: after determining that the first enterprise is in a fault state, retrieving the corresponding preset database of the second enterprise; inputting the fault state into the preset database for query to obtain multiple handling measures; obtaining the multiple usage times corresponding to the multiple handling measures, where one handling measure corresponds to one usage time, and the usage time is the total usage times corresponding to each handling measure; sorting the multiple usage times from largest to smallest to obtain the target sorting result; obtaining the target usage time, where the target usage time is the usage time corresponding to the one ranked first in the target sorting result; and outputting the handling measure corresponding to the target usage time as the target handling measure.

[0050] Specifically, after determining that the first enterprise is in a fault state, since the first enterprise and the second enterprise are similar enterprises, it is possible to query in the second enterprise what measures will be taken when an enterprise is in a fault state. First, obtain the preset database corresponding to the second enterprise. The preset database stores the processing measures that need to be taken when a fault occurs during the maintenance process. The preset database can be on a specific server within the second enterprise. When accessing the preset database corresponding to the second enterprise, the corresponding access permission needs to be obtained. Then, verify the identity of the accessing user. If the verification is correct, the query for the processing measures corresponding to the fault state can begin in the preset database. According to the specific description and characteristics of the fault state, construct the corresponding query statement. This may require using SQL language or other database query languages. The preset database will return the records that match the query conditions. Extract the processing measures related to the fault state from the returned records. Analyze the processing measure records extracted from the preset database to obtain the total usage times corresponding to each processing measure. The total usage times refer to the total usage times of the processing measure from its existence to the current time. During the analysis process, establish a mapping relationship between the processing measure and the usage times. To obtain accurate usage times, each time the corresponding target processing measure is output, 1 is added to the usage times of this processing measure. This helps to sort and select the usage times subsequently. Use the selected sorting algorithm to sort the usage times corresponding to each processing measure. The sorting result will be a list arranged in descending order of usage times. According to the processing measure information in the first record, obtain the target processing measure. This will be the measure used to solve the fault state subsequently. The effective processing and solution of the fault state are realized. S105: Determine the device to be maintained according to the target processing measure, and receive the target video sent by the target AR device.

[0051] In the above S105, after determining the target processing measure, according to the fault diagnosis method and maintenance steps mentioned in the target processing measure, determine the type and location of the device that needs to be maintained. These devices may be key devices on the production line or auxiliary devices that support the operation of the enterprise.

[0052] In addition, after determining the device to be maintained corresponding to the target processing measure, a prompt message is generated according to the device to be maintained, and the prompt message is sent to the target edge computing device, so that the target edge computing device determines a collection instruction according to the prompt message and sends the collection instruction to the user side. The collection instruction is used to prompt the user to use the target AR device to perform video collection on the device to be maintained. Specifically, after determining the device to be maintained, the target edge computing device corresponding to the first enterprise is obtained, and the information related to the device to be maintained is generated into a prompt message and sent to the target edge computing device. Network communication technologies (such as Wi-Fi, Bluetooth, cellular network, etc.) can be used to send the constructed device information packet to the target edge computing device. After receiving the device information packet, the target edge computing device parses it to obtain the detailed information of the device to be maintained. According to the detailed information of the device to be maintained, the target edge computing device generates a collection instruction. This instruction will guide the user on how to use the target AR device to perform video collection on the device to be maintained. The collection instruction is converted into a format suitable for the user to receive, such as a text message, a QR code, a notification within an application, etc. Through the selected communication channel, the collection instruction is sent to the user. This may be a text message containing detailed steps, or a QR code that guides the user to open the AR device and scan it. The user receives the collection instruction. The user prepares the target AR device according to the prompt of the collection instruction. This may be a dedicated AR glasses, AR helmet or other AR device. The user uses the AR device to perform video collection on the device to be maintained according to the instructions of the collection instruction. This may involve adjusting parameters such as the device position and focal length to ensure that the quality of the collected video meets the maintenance requirements. After the user completes the video collection, the video data is uploaded to a specified server. This is usually achieved through network communication technologies. Therefore, the target video sent by the target AR device is received, and the target video is the video of the user using the target AR device to scan the device to be maintained in the first enterprise. The target AR device can capture the images and videos of the device in real time and send them to the server. The server receives the target video sent by the target AR device and performs preliminary processing and parsing.

[0053] S106: Process the target video based on the target processing measure to obtain a maintenance distribution map.

[0054] In the above S106, after obtaining the target video corresponding to the first enterprise, the target video is processed based on the target processing measures to obtain a maintenance distribution map, which specifically includes: identifying the target video to obtain the target model corresponding to the equipment to be maintained; adjusting the target processing measures according to the target model to obtain multiple maintenance steps; extracting multiple parts to be overhauled from the multiple maintenance steps, and numbering the multiple parts to be overhauled, with one part to be overhauled corresponding to one number; after marking the multiple parts to be overhauled, splicing the marked images to obtain a maintenance distribution map, and the marked image is an image of the parts to be overhauled marked.

[0055] Specifically, the target video can be preprocessed first, including denoising, enhancing contrast, stabilizing the picture, etc., to improve the accuracy of subsequent recognition. Using computer vision technology, information such as the appearance features and operating status of the equipment is extracted from the video. This may involve algorithms such as image recognition and object detection. The extracted features are matched with a preset equipment model database. The database contains information such as the models, appearance features, and key components of various equipment. By comparing the similarity or using specific recognition algorithms, the equipment model in the target video is determined. The identified equipment model is verified to ensure that it is consistent with the equipment in the video. This may require manual confirmation or the use of more advanced recognition techniques. According to the equipment model, the target processing measures related to this model are obtained from the maintenance database. These measures may include preventive maintenance, fault repair, etc. According to the specific situation of the equipment (such as the usage environment, operating time, historical fault records, etc.), the target processing measures are adjusted. This may require combining professional knowledge and experience. The adjusted processing measures are refined into specific maintenance steps. Each step should clearly describe the operations to be performed, the tools or equipment required, and the possible risks and preventive measures. Then, each maintenance step is reviewed to identify the parts that need to be overhauled or replaced. At this time, the parts to be overhauled refer to a certain icon or application in the equipment. The extracted parts to be overhauled are classified and sorted for subsequent number marking and maintenance. Each part to be overhauled is numbered. The numbers should be unique and easy to identify for tracking and management during the maintenance process. After each part to be overhauled is numbered, a marked map can be obtained. Then, carefully check whether each part to be overhauled has been correctly marked and ensure that there is no omission. According to the marking results of the parts to be overhauled, multiple marked maps are obtained, and then the multiple marked maps are sorted and integrated in ascending order of the numbers to obtain a maintenance distribution map. The map should include the location, number of the parts to start maintenance, and the location and picture of the parts to end maintenance, etc.

[0056] For example, the maintenance steps are determined according to the model of the device to be repaired. If the maintenance steps include 5 steps, the icons to be clicked in the 5 steps are extracted in sequence to obtain the parts to be repaired. Different numerical serial numbers are assigned to each part to be repaired according to the maintenance order, and the earlier the maintenance order, the earlier the numerical serial number. After each numerical serial number is marked, a marked diagram can be obtained. The number of marked diagrams is the same as the number corresponding to the numerical serial number. Then, multiple marked diagrams are sorted according to the numerical serial numbers to obtain multiple frames of maintenance images, and the multiple frames of maintenance images are integrated. Integration can be understood as integrating them onto one picture or becoming multiple frames of images to obtain a maintenance distribution diagram.

[0057] S107: Send the maintenance distribution diagram to the target edge computing device so that the maintenance distribution diagram can be displayed on the target edge computing device.

[0058] In the above S107, the generated maintenance distribution diagram is sent to the target edge computing device. Ensure the transmission speed and accuracy of the data so that the target edge computing device can display the maintenance distribution diagram in real time. After receiving the maintenance distribution diagram, the target edge computing device displays it to the operation and maintenance personnel through a display screen or other output devices. According to the information in the maintenance distribution diagram, the operation and maintenance personnel formulate a detailed maintenance plan and arrange maintenance personnel to carry out maintenance work. It can effectively utilize edge computing and AR technology to achieve rapid diagnosis and repair of the fault status in the first enterprise. This can not only improve the maintenance efficiency and quality, but also reduce the operation and maintenance costs and downtime, creating greater value for the enterprise.

[0059] In addition, after sending the maintenance distribution map to the target edge computing device, the maintenance personnel can choose whether to use an AR device to repair the fault state based on the actual maintenance requirements, which specifically includes: obtaining the first position corresponding to the device to be maintained; calculating the target distance between the first position and the second position, where the second position is the position of the target edge computing device in the first enterprise; if the target distance is greater than the preset distance, it is determined to send the maintenance distribution map to the target AR device so that the user can view the maintenance distribution map according to the target AR device. Specifically, the first position of the device to be maintained can be obtained first. This can be achieved based on the built-in GPS module of the device, network positioning (such as Wi-Fi positioning, Bluetooth positioning, etc.), or through a positioning system deployed within the enterprise (such as RFID, UWB, etc.). Using the selected positioning technology, collect the real-time position information of the device to be maintained. This usually involves the device sending a position request to the positioning system and then receiving the position data returned by the system. Verify and correct the collected position data to ensure its accuracy and reliability. This may involve steps such as comparing with other known position information and using algorithms for error correction. Then, clarify the specific position of the target edge computing device in the enterprise. This can usually be achieved by referring to the device layout diagram within the enterprise, using positioning technology to obtain the device position, etc. According to the coordinate information of the first position and the second position, select a suitable distance calculation method. Common methods include the Euclidean distance formula (applicable to points in a plane or space), Manhattan distance (applicable to grid-like layouts), etc. Use the selected distance calculation method to calculate the target distance between the first position and the second position. This usually involves steps such as mathematical operations and data processing. Verify the calculation result to ensure its accuracy and reasonableness. Then, store the target distance in the database for subsequent query and use. Then, according to the actual situation and requirements of the enterprise, set a suitable preset distance. This distance should be able to reflect the necessity of using an AR device for remote maintenance guidance when the distance between the device to be maintained and the target edge computing device is too far. Compare the calculated target distance with the preset distance. If the target distance is greater than the preset distance, it indicates that the device to be maintained is far from the target edge computing device, and it is appropriate to use an AR device for remote maintenance guidance. If the sending condition is met (i.e., the target distance is greater than the preset distance), then decide to send the maintenance distribution map to the target AR device. This usually involves steps such as packing the maintenance distribution map data, selecting a suitable communication protocol and channel for transmission, etc. After the user receives the maintenance distribution map through the target AR device, they can use the functions of the AR device (such as augmented reality display, interactive operations, etc.) to view and understand the content of the maintenance distribution map. This helps the user better understand the condition of the device to be repaired, the repair steps, and the required tools, etc.

[0060] The embodiment of the present application also provides a remote operation and maintenance processing device based on big data analysis.Figure 2 It is a schematic structural diagram of a remote operation and maintenance processing device based on big data analysis provided by an embodiment of the present application. Refer to Figure 2 , the device includes a receiving unit 201, a processing unit 202, and a sending unit 203.

[0061] The receiving unit 201 receives the first detection data for the first enterprise sent by the target edge computing device. The first detection data includes performance data, sensitive data, user behavior data, and log data.

[0062] The processing unit 202 determines the first enterprise data corresponding to the first enterprise. The first enterprise data includes enterprise domain data, enterprise scale data, enterprise operation and maintenance data, and enterprise business data; determines the second enterprise according to the first enterprise data, judges the first detection data, and determines that the first detection data is in a fault state; retrieves the target processing measure matching the fault state from the second enterprise; determines the device to be maintained according to the target processing measure, and receives the target video sent by the target AR device. The target video is a video of the user using the target AR device to scan the device to be maintained in the first enterprise; processes the target video based on the target processing measure to obtain a maintenance distribution map.

[0063] The sending unit 203 sends the maintenance distribution map to the target edge computing device for displaying the maintenance distribution map on the target edge computing device.

[0064] In a possible implementation manner, the receiving unit 201 is used to find the third enterprise with the same enterprise domain data from multiple enterprises; obtain the second enterprise data corresponding to the third enterprise; the processing unit 202 is used to calculate the similarity between the first enterprise data and the second enterprise data to obtain the target similarity value; judge whether the target similarity value is greater than the preset similarity threshold; when the target similarity value is greater than the preset similarity threshold, confirm that the third enterprise and the first enterprise are similar enterprises, and output the third enterprise as the second enterprise.

[0065] In a possible implementation manner, the receiving unit 201 is used to obtain the system configuration information corresponding to the first enterprise; the processing unit 202 is used to determine the second detection data according to the system configuration information; judge whether the first detection data is consistent with the second detection data; when the first detection data is inconsistent with the second detection data, confirm that the first detection data is in a fault state.

[0066] In a possible implementation, the receiving unit 201 is configured to retrieve a preset database corresponding to a second enterprise after determining that the first enterprise is in a fault state; the processing unit 202 is configured to input the fault state into the preset database for query to obtain a plurality of processing measures; the receiving unit 201 is configured to obtain a plurality of usage times corresponding to the plurality of processing measures, where one processing measure corresponds to one usage time, and the usage time is the total usage time corresponding to each processing measure; the processing unit 202 is configured to sort the plurality of usage times from largest to smallest to obtain a target sorting result; the receiving unit 201 is configured to obtain a target usage time, where the target usage time is the usage time corresponding to the first place in the target sorting result; the processing unit 202 is configured to output the processing measure corresponding to the target usage time as a target processing measure.

[0067] In a possible implementation, the sending unit 203 is configured to generate a prompt message according to the device to be maintained, and send the prompt message to a target edge computing device, so that the target edge computing device determines a collection instruction according to the prompt message and sends the collection instruction to the user side. The collection instruction is used to prompt the user to use the target AR device to perform video collection on the device to be maintained.

[0068] In a possible implementation, the processing unit 202 is configured to identify the target video to obtain a target model corresponding to the device to be maintained; adjust the target processing measure according to the target model to obtain a plurality of maintenance steps; extract a plurality of parts to be maintained from the plurality of maintenance steps, and mark serial numbers for the plurality of parts to be maintained, where one part to be maintained corresponds to one serial number; after marking the plurality of parts to be maintained, splice the marked images to obtain a maintenance distribution map, and the marked image is an image obtained by marking the parts to be maintained.

[0069] In a possible implementation, the receiving unit 201 is configured to obtain a first position corresponding to the device to be maintained; the processing unit 202 is configured to calculate a target distance between the first position and a second position, where the second position is the position of the target edge computing device in the first enterprise; the sending unit 203 is configured to determine to send the maintenance distribution map to the target AR device if the target distance is greater than a preset distance, so that the user can view the maintenance distribution map according to the target AR device.

[0070] It should be noted that when the device provided in the above embodiments implements its functions, only the above-mentioned division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be elaborated here.

[0071] This application also discloses an electronic device. Referring to Figure 3 , Figure 3 FIG. Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 302, and at least one communication bus 305.

[0072] Among them, the communication bus 305 is used to implement connection communication between these components.

[0073] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0074] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0075] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines, and executes various functions of the server and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 302, and by calling data stored in the memory 302. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate one or a combination of several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes operating systems, user interfaces, and application requests, etc.; the GPU is responsible for rendering and drawing the content required to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may also not be integrated into the processor 301 and may be implemented separately by a single chip.

[0076] Among them, the memory 302 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 302 includes a non-transitory computer-readable storage medium. The memory 302 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 302 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 302 may also be at least one storage device located far from the aforementioned processor 301.

[0077] As Figure 3 shown, the memory 302, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for remote operation and maintenance processing based on big data analysis.

[0078] In Figure 3 the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user to obtain the data input by the user; and the processor 301 can be used to call the application program for remote operation and maintenance processing stored in the memory 302, and when executed by one or more processors, enable the electronic device to execute the methods described in one or more of the above embodiments.

[0079] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0080] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0082] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, in each embodiment of the present application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0084] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. And the aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks or optical discs that can store program codes.

[0085] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the practice of the present disclosure, those skilled in the art will easily think of other implementation schemes of the present disclosure. The present application aims to cover any variations, uses or adaptive changes of the present disclosure, and these variations, uses or adaptive changes follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not recorded in the present disclosure.

Claims

1. A remote operation and maintenance processing method based on big data analysis, characterized in that: The method comprises: Receiving first detection data for a first enterprise sent by a target edge computing device, where the first detection data includes performance data, sensitive data, user behavior data, and log data; Determine first enterprise data corresponding to the first enterprise, the first enterprise data including enterprise domain data, enterprise scale data, enterprise operation and maintenance data, and enterprise business data; Determine a second enterprise according to the first enterprise data, judge the first detection data, and determine that the first detection data is in a fault state; Retrieving a target processing measure matching the fault state from the second enterprise; Determine a device to be maintained according to the target processing measure, and receive a target video sent by a target AR device, where the target video is a video of a user using the target AR device to scan the device to be maintained in the first enterprise; Processing the target video based on the target processing measure to obtain a maintenance distribution map; The maintenance distribution map is sent to the target edge computing device so as to display the maintenance distribution map on the target edge computing device.

2. The method according to claim 1, characterized in that The determining the second enterprise according to the first enterprise data specifically includes: Find a third enterprise having the same domain data as the enterprise from among multiple enterprises; Acquire the second enterprise data corresponding to the third enterprise; Calculating similarity between the first enterprise data and the second enterprise data to obtain a target similarity value; Determine whether the target similarity value is greater than a preset similarity threshold; When the target similarity value is greater than the preset similarity threshold, the third enterprise is confirmed to be similar to the first enterprise, and the third enterprise is output as the second enterprise.

3. The method according to claim 1, characterized in that The judging the first detection data to determine that the first detection data is in a fault state specifically includes: Obtaining system configuration information corresponding to the first enterprise; Determine second detection data according to the system configuration information; Determining whether the first detection data is consistent with the second detection data; When the first detection data is inconsistent with the second detection data, it is confirmed that the first detection data is in the fault state.

4. The method according to claim 3, characterized in that The retrieving a target processing measure matching the fault state from the second enterprise specifically includes: After determining that the first enterprise is in the fault state, retrieving a preset database corresponding to the second enterprise; Inputting the fault status into the preset database for query to obtain multiple processing measures; Acquire multiple usage times corresponding to multiple processing measures, where one processing measure corresponds to one usage time, and the usage time is the total usage times corresponding to each processing measure; Sort the plurality of usage times from largest to smallest to obtain a target sorting result; Obtaining the target usage count, where the target usage count is the usage count corresponding to the first target in the target sorting result; The processing measure corresponding to the target usage count is output as the target processing measure.

5. The method according to claim 1, characterized in that After determining the equipment to be maintained according to the target processing measures, the method further includes: Generate prompt information based on the device to be maintained, and send the prompt information to the target edge computing device, so that the target edge computing device determines a collection instruction based on the prompt information, and sends the collection instruction to the user end, wherein the collection instruction is used to prompt the user to use the target AR device to perform video collection on the device to be maintained.

6. The method according to claim 5, characterized in that The processing of the target video based on the target processing measure to obtain a maintenance distribution map specifically includes: Identify the target video to obtain the target model corresponding to the device to be maintained; Adjusting the target treatment measures according to the target model to obtain multiple maintenance steps; Extracting a plurality of parts to be repaired from the plurality of repair steps, and marking the plurality of parts to be repaired with serial numbers, wherein one part to be repaired corresponds to one serial number; After marking a plurality of the components to be repaired, the marked images are spliced ​​to obtain the maintenance distribution map, wherein the marked images are images of the components to be repaired that are marked.

7. The method according to claim 1, characterized in that After sending the maintenance distribution map to the target edge computing device so as to display the maintenance distribution map on the target edge computing device, the method further includes: Acquire a first position corresponding to the equipment to be maintained; Calculate a target distance between the first location and a second location, where the second location is a location of the target edge computing device in the first enterprise; If the target distance is greater than the preset distance, it is determined to send the maintenance distribution map to the target AR device so that the user can view the maintenance distribution map according to the target AR device.

8. A remote operation and maintenance processing device based on big data analysis, characterized in that: The device comprises a receiving unit (201), a processing unit (202) and a sending unit (203). The receiving unit (201) receives first detection data for a first enterprise sent by a target edge computing device, wherein the first detection data includes performance data, sensitive data, user behavior data, and log data; The processing unit (202) determines first enterprise data corresponding to the first enterprise, wherein the first enterprise data includes enterprise domain data, enterprise scale data, enterprise operation and maintenance data, and enterprise business data; Determine the second enterprise according to the first enterprise data, judge the first detection data, and obtain the fault status; Retrieving a target processing measure matching the fault state from the second enterprise; Determine a device to be maintained according to the target processing measure, and receive a target video sent by a target AR device, where the target video is a video of a user using the target AR device to scan the device to be maintained in the first enterprise; Processing the target video based on the target processing measure to obtain a maintenance distribution map; The sending unit (203) sends the maintenance distribution map to the target edge computing device, so that the maintenance distribution map is displayed on the target edge computing device.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (302), a user interface (303) and a network interface (304), wherein the memory (302) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (302) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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