Data processing method and device for industrial monitoring

Abstract

The invention discloses a data processing method and device for industrial monitoring. The method comprises the steps of obtaining monitoring demand data; according to the monitoring demand data, routing inspection robot path planning processing is carried out to obtain routing inspection path data, and the routing inspection robot carries out routing inspection according to the routing inspection path data so as to monitor the industrial equipment; inspection data is obtained, and the inspection data is used for representing data obtained by inspection of the inspection robot; and performing industrial monitoring processing based on equipment feature extraction on the inspection data to obtain monitoring result data. The robot inspection route is planned according to the industrial equipment monitoring requirement, the robot conducts inspection according to the inspection route, operation chart data of the industrial equipment is collected and recognized, and the recognition result is output, so that the problem that in the prior art, industrial equipment monitoring is low in accuracy is solved; the technical effect of improving the monitoring accuracy and efficiency of the industrial equipment is achieved.

Description

Technical Field

[0001] The present application relates to the field of inspection, and in particular, to a data processing method and device for industrial monitoring. Background Art

[0002] In industrial production, safe operation of the plant is the basis for ensuring production efficiency and employee safety. As a key management activity, plant inspections are carried out in accordance with established safety standards and operating procedures through regular and detailed inspections of equipment, facilities and the environment.

[0003] With the continuous development of modern technology, industrial production has gradually become information-based and intelligent. Manual inspections are more dependent on inspectors, and have low efficiency and accuracy. For some industrial production scenarios that are difficult to inspect manually, such as industrial production scenarios that produce toxic gases, there is an urgent need to introduce intelligent inspection robots to inspect industrial plants to monitor whether industrial equipment is operating normally.

[0004] Therefore, this application is proposed to address the problem of low industrial monitoring efficiency in the prior art. Summary of the invention

[0005] The main purpose of this application is to provide a data processing method and device for industrial monitoring, so as to solve the technical problem of low efficiency of industrial monitoring in the prior art and achieve the technical effect of improving the accuracy and efficiency of industrial equipment monitoring.

[0006] In order to achieve the above-mentioned purpose, the first aspect of the present application proposes a data processing method for industrial monitoring, characterized in that it is applied to a patrol robot to enable the patrol robot to monitor industrial equipment, and the method comprises:

[0007] Acquiring monitoring demand data, wherein the monitoring demand data is relevant data used to indicate demand for industrial monitoring;

[0008] Performing inspection robot path planning processing according to the monitoring demand data to obtain inspection path data, and the inspection robot performs inspection according to the inspection path data to realize monitoring of industrial equipment;

[0009] Acquiring inspection data, wherein the inspection data is used to represent data obtained by the inspection robot;

[0010] The inspection data is subjected to industrial monitoring processing based on equipment feature extraction to obtain monitoring result data.

[0011] In some optional embodiments of the present application, the inspection data is subjected to industrial monitoring processing based on equipment feature extraction, and the monitoring result data obtained includes:

[0012] Performing recognition processing on the inspection data to obtain inspection image data, wherein the inspection image data is image data used to represent the inspection of industrial equipment;

[0013] Performing preprocessing based on image cropping on the inspection image data to obtain process inspection image data;

[0014] Performing deep learning-based chart recognition processing on the process inspection image data to obtain process monitoring result data, wherein the process monitoring result data is chart data for representing industrial equipment;

[0015] Performing multi-sample evaluation processing on the process monitoring result data to obtain the monitoring result data.

[0016] In some optional embodiments of the present application, the process inspection image data is subjected to chart recognition processing based on deep learning to obtain process monitoring result data including:

[0017] Performing a recognition process based on chart features on the process inspection image to obtain first chart feature data and second chart feature data, wherein the first chart feature data is feature data for indicating a chart type in the process inspection image data, and the second chart feature data is feature data for indicating a chart data indication in the process inspection image data;

[0018] Matching a template chart corresponding to the first chart feature data to obtain template chart data, wherein the template chart data is data for representing a template chart corresponding to a chart type in the process inspection data image data;

[0019] The second chart feature data and the template chart data are calculated and processed to obtain the process monitoring result data.

[0020] In some optional embodiments of the present application, the process inspection image data is subjected to chart recognition processing based on deep learning to obtain process monitoring result data including:

[0021] Performing position feature-based recognition processing on the process inspection image data to obtain position feature data, wherein the position feature data is used to represent the position feature data of the industrial equipment corresponding to the process inspection image;

[0022] Matching the industrial equipment corresponding to the position feature data in a preset industrial equipment model to obtain the industrial equipment data to be tested, wherein the industrial equipment data to be tested includes equipment chart template data;

[0023] The process inspection image data and the equipment chart template data are subjected to chart recognition processing based on a preset algorithm to obtain the process monitoring result data.

[0024] In some optional embodiments of the present application, performing multi-sample evaluation processing on the process monitoring result data to obtain the monitoring result data includes:

[0025] Performing evaluation processing on the process monitoring result data based on a preset number of samples to obtain multi-sample evaluation data;

[0026] The multiple sample evaluation data are judged to determine whether the multiple sample evaluation data meet the preset evaluation rules,

[0027] If the multi-sample evaluation data meets the preset evaluation rules, the monitoring result data is obtained;

[0028] If the multi-sample evaluation data does not satisfy the preset evaluation rules, the monitoring abnormality prompt data is output.

[0029] In some optional embodiments of the present application, the inspection robot path planning process is performed according to the monitoring demand data to obtain the inspection path data including:

[0030] Extracting characteristic points from the monitoring demand data to obtain characteristic point data, wherein the characteristic point data is data used to represent the location of the industrial equipment to be monitored;

[0031] Acquiring robot operation data, wherein the robot operation data includes operation position data of the inspection robot;

[0032] Path planning processing is performed on the operating position data and the characteristic point data to obtain the inspection path data.

[0033] According to a second aspect of the present application, a data processing device for industrial monitoring is proposed, which is applied to a patrol robot to enable the patrol robot to monitor industrial equipment, including:

[0034] A demand acquisition module, used to acquire monitoring demand data, wherein the monitoring demand data is relevant data used to represent demand industrial monitoring;

[0035] A path planning module is used to perform path planning processing of the inspection robot according to the monitoring demand data to obtain inspection path data, and the inspection robot performs inspection according to the inspection path data to realize monitoring of industrial equipment;

[0036] An inspection data acquisition module, used to acquire inspection data, wherein the inspection data is used to represent data obtained by the inspection robot;

[0037] The monitoring module is used to perform industrial monitoring processing on the inspection data based on equipment feature extraction to obtain monitoring result data.

[0038] In some optional embodiments of the present application, the data processing device according to claim 7 is characterized in that the monitoring module comprises:

[0039] An image recognition module, used to perform recognition processing on the inspection data to obtain inspection image data, wherein the inspection image data is image data used to represent the inspection of industrial equipment;

[0040] A preprocessing module, used for performing preprocessing on the inspection image data based on image cropping to obtain process inspection image data;

[0041] A chart recognition module, used for performing chart recognition processing based on deep learning on the process inspection image data to obtain process monitoring result data, wherein the process monitoring result data is chart data for representing industrial equipment;

[0042] The multi-sample evaluation module is used to perform multi-sample evaluation processing on the process monitoring result data to obtain the monitoring result data.

[0043] According to a third aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned data processing method for industrial monitoring.

[0044] According to the fourth aspect of the present application, an electronic device is proposed, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the above-mentioned data processing method for industrial monitoring.

[0045] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0046] In the present application, monitoring demand data is obtained, wherein the monitoring demand data is relevant data for representing the demand for industrial monitoring; patrol robot path planning is performed according to the monitoring demand data to obtain patrol path data, and the patrol robot performs patrol according to the patrol path data to realize monitoring of industrial equipment; patrol data is obtained, wherein the patrol data is data for representing the patrol robot patrol; the patrol data is processed for industrial monitoring based on equipment feature extraction to obtain monitoring result data. By planning the robot patrol route according to the industrial equipment monitoring demand, the robot patrols according to the patrol route, collects and identifies the operation chart data of the industrial equipment, and outputs the identification result, the problem of low accuracy in industrial equipment monitoring in the prior art is solved, and the technical effect of improving the accuracy and efficiency of industrial equipment monitoring is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0048] Figure 1 A flowchart of a data processing method for industrial monitoring provided in this application;

[0049] Figure 2 A flowchart of a data processing method for industrial monitoring provided in this application;

[0050] Figure 3 A flowchart of a data processing method for industrial monitoring provided in this application;

[0051] Figure 4 A flow chart of a data processing method for industrial monitoring provided in this application. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0053] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0054] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0055] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0056] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "socketed" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0057] In some optional embodiments of the present application, a data processing method for industrial monitoring is proposed, which is applied to a patrol robot to enable the patrol robot to monitor industrial equipment. Figure 1 A flowchart of a data processing method for industrial monitoring provided in this application, such as Figure 1 As shown, the method comprises the following steps:

[0058] S101: Acquire monitoring demand data;

[0059] The monitoring demand data is relevant data used to represent the demand for industrial monitoring, including relevant data of the industrial equipment to be monitored, such as the operating data of the industrial equipment to be monitored, the location data of the industrial equipment to be monitored, etc. The industrial equipment to be monitored is equipment in a preset industrial area, including multiple industrial equipment to be monitored. The multiple industrial equipment to be monitored can be industrial equipment of the same type, or multiple industrial equipment to be monitored can be industrial equipment of different types.

[0060] S102: performing path planning processing for the inspection robot according to the monitoring demand data to obtain inspection path data, and the inspection robot performs inspection according to the inspection path data to realize monitoring of the industrial equipment;

[0061] In the embodiment of the present application, the inspection path of the inspection robot in the preset industrial area is planned based on the operating status data and the location data of the industrial equipment. The inspection robot collects data of the running industrial equipment during the inspection process, and the industrial equipment is monitored by processing the collected data of the industrial equipment. The robot automatically plans the inspection path to monitor the industrial equipment, thereby improving the efficiency of industrial equipment monitoring.

[0062] In some optional embodiments of the present application, a data processing method for industrial monitoring is provided to implement path planning processing for an inspection robot according to monitoring demand data, the method comprising:

[0063] Extract and process the feature points of the monitoring demand data to obtain feature point data, wherein the feature point data is data used to represent the position of the industrial equipment to be monitored; obtain robot operation data, which includes the operation position data of the inspection robot; perform path planning on the operation position data and the feature point data to obtain inspection path data.

[0064] In an optional embodiment of the present application, feature point extraction and processing are performed on the monitoring demand data to obtain feature point data, and the method also includes: extracting operation characteristics of the detection demand data to obtain operation characteristic data, wherein the operation characteristic data is characteristic data used to represent the operation of industrial equipment, and the operation characteristic data includes operation frequency characteristic data and operation time characteristic data; determining the feature point data to be monitored based on the above-mentioned operation frequency characteristic data and operation time characteristic data, wherein the feature point data to be monitored is position data used to represent the feature point to be monitored, and the feature point to be monitored is the position data of the industrial equipment that currently needs to be inspected and monitored.

[0065] S103: Obtain inspection data;

[0066] Inspection data is used to represent data obtained by the inspection robot during inspection;

[0067] S104: Perform industrial monitoring processing on the inspection data based on equipment feature extraction to obtain monitoring result data.

[0068] In some optional embodiments of the present application, a data processing method for industrial monitoring is proposed. Figure 2 A flowchart of a data processing method for industrial monitoring provided in this application, such as Figure 2 As shown, the method comprises the following steps:

[0069] S201: Identify and process the inspection data to obtain inspection image data;

[0070] The inspection image data is image data used to represent the inspection of industrial equipment. The inspection image data is video stream data of industrial equipment collected during the robot inspection process. Industrial monitoring processing is performed based on the video frame images in the above video stream data to identify the operating status of the industrial equipment.

[0071] S202: performing preprocessing on the inspection image data based on image cropping to obtain process inspection image data;

[0072] The inspection image data is screened based on chart features to obtain video frame images with industrial equipment charts, and the inspection image data to be processed is obtained. The inspection image data to be processed is subjected to feature annotation processing. According to the annotated features, the inspection image to be processed is cropped according to preset cropping rules. The preset cropping rules can be that the proportion of the annotated features in the cropped image is higher than a preset value.

[0073] S203: performing deep learning-based chart recognition processing on the process inspection image data to obtain process monitoring result data;

[0074] The process monitoring result data is a chart data used to represent industrial equipment;

[0075] In some optional embodiments of the present application, a data processing method for industrial monitoring is proposed, comprising:

[0076] The process inspection image is subjected to recognition processing based on chart features to obtain first chart feature data and second chart feature data, wherein the first chart feature data is feature data for indicating the chart type in the process inspection image data, and the second chart feature data is feature data for indicating the chart data indication in the process inspection image data; a template chart corresponding to the first chart feature data is matched to obtain template chart data, wherein the template chart data is data of a template chart corresponding to the chart type in the process inspection image data; the second chart feature data and the template chart data are calculated and processed to obtain process monitoring result data.

[0077] In an optional embodiment of the present application, by identifying the chart type in the process inspection image data, matching the standard chart template corresponding to the chart type, such as if the chart type is a pointer-type chart, matching the standard chart template corresponding to the pointer-type chart, the standard chart template includes the display value range of the pointer-type table data; by identifying the data indication feature in the process inspection image data, such as if the data indication feature of the pointer-type chart is a pointer, identifying the pointer, calculating the chart data of the industrial equipment to be monitored in the process inspection image according to the pointer position data and the display value range in the above-mentioned standard chart template, and obtaining the process monitoring result data.

[0078] In some optional embodiments of the present application, a data processing method for industrial monitoring is proposed, comprising:

[0079] Perform position feature-based recognition processing on the process inspection image data to obtain position feature data, which is used to represent the position feature data of the industrial equipment corresponding to the process inspection image; match the industrial equipment corresponding to the position feature data in the preset industrial equipment model to obtain the industrial equipment data to be tested, which includes equipment chart template data; perform chart recognition processing based on a preset algorithm on the process inspection image data and the equipment chart template data to obtain process monitoring result data.

[0080] In an optional embodiment of the present application, by identifying the position in the process inspection image data where the robot took the inspection image, the position of the industrial equipment corresponding to the inspection image is determined to obtain inspection position feature data, and the corresponding industrial equipment is matched in a pre-built industrial equipment model based on the inspection position feature data. According to the equipment chart template of the industrial equipment, the process inspection image data is subjected to chart recognition processing based on a deep learning algorithm to obtain monitoring result data of the industrial equipment.

[0081] S204: Perform multi-sample evaluation processing on the process monitoring result data to obtain monitoring result data.

[0082] In some optional embodiments of the present application, a data processing method for industrial monitoring is proposed for multi-sample evaluation processing, the method comprising:

[0083] Performing multi-sample evaluation processing on process monitoring result data to obtain monitoring result data includes: performing evaluation processing on the process monitoring result data based on a preset number of samples to obtain multi-sample evaluation data; judging the multi-sample evaluation data to determine whether the multi-sample evaluation data satisfies preset evaluation rules, and if the multi-sample evaluation data satisfies the preset evaluation rules, obtaining monitoring result data; if the multi-sample evaluation data does not satisfy the preset evaluation rules, outputting monitoring abnormality prompt data.

[0084] The inspection robot collects video stream data of the equipment to be monitored, performs image frame extraction processing on the video stream data, and obtains multiple image frame data to be monitored; performs the above-mentioned image cropping preprocessing and deep learning-based chart recognition on the multiple image frame data to be detected, and obtains multiple process monitoring result data; compares the above-mentioned multiple process monitoring result data with the industrial equipment monitoring threshold, judges whether the multiple process monitoring result data meet the industrial equipment monitoring threshold rule, and obtains multi-sample evaluation data, which is data used to represent the ratio of process monitoring result data that meets the industrial equipment monitoring threshold rule to all samples; if the ratio of the process monitoring result data that meets the industrial equipment monitoring threshold rule to all samples is greater than or equal to the preset evaluation threshold, a monitoring result is obtained, and the monitoring result data includes the process monitoring result data that meets the industrial equipment monitoring threshold rule; if the ratio of the process monitoring result data that meets the industrial equipment monitoring threshold rule to all samples is less than the preset evaluation threshold, monitoring abnormality prompt data is output.

[0085] In the embodiment of the present application, by performing multi-sample evaluation processing on the process monitoring result data, the process monitoring result data corresponding to the multiple samples collected are evaluated and processed, thereby improving the accuracy of industrial equipment monitoring.

[0086] In some optional embodiments of the present application, a data processing device for industrial monitoring is proposed, which is applied to a patrol robot to enable the patrol robot to monitor industrial equipment. Figure 3 A schematic diagram of a data processing device for industrial monitoring provided in this application, such as Figure 3 As shown, the device comprises:

[0087] The demand acquisition module 31 is used to acquire monitoring demand data, wherein the monitoring demand data is relevant data used to represent the demand for industrial monitoring;

[0088] A path planning module 32 is used to perform path planning processing for the inspection robot according to the monitoring demand data to obtain inspection path data, and the inspection robot performs inspection according to the inspection path data to realize monitoring of the industrial equipment;

[0089] The inspection data acquisition module 33 is used to acquire inspection data, wherein the inspection data is used to represent the data obtained by the inspection robot;

[0090] The monitoring module 34 is used to perform industrial monitoring processing on the inspection data based on equipment feature extraction to obtain monitoring result data.

[0091] In some optional embodiments of the present application, a data processing device for industrial monitoring is proposed. Figure 4A schematic diagram of another data processing device for industrial monitoring provided by the present application, such as Figure 4 As shown, the device comprises:

[0092] An image recognition module 41 is used to perform recognition processing on the inspection data to obtain inspection image data, wherein the inspection image data is image data used to represent the inspection of industrial equipment;

[0093] A preprocessing module 42, used for performing preprocessing on the inspection image data based on image cropping to obtain process inspection image data;

[0094] A chart recognition module 43 is used to perform chart recognition processing based on deep learning on the process inspection image data to obtain process monitoring result data, wherein the process monitoring result data is chart data used to represent industrial equipment;

[0095] The multi-sample evaluation module 44 is used to perform multi-sample evaluation processing on the process monitoring result data to obtain monitoring result data.

[0096] The specific manner of executing the operation of each unit in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0097] In summary, in the present application, monitoring demand data is obtained, wherein the monitoring demand data is relevant data for representing the demand for industrial monitoring; patrol robot path planning processing is performed according to the monitoring demand data to obtain patrol path data, and the patrol robot performs patrol according to the patrol path data to realize monitoring of industrial equipment; patrol data is obtained, wherein the patrol data is data for representing the patrol robot's patrol; the patrol data is subjected to industrial monitoring processing based on equipment feature extraction to obtain monitoring result data. By planning the robot patrol route according to the industrial equipment monitoring demand, the robot conducts patrol according to the patrol route, collects and identifies the operation chart data of the industrial equipment, and outputs the identification result, the problem of low accuracy in industrial equipment monitoring in the prior art is solved, and the technical effect of improving the accuracy and efficiency of industrial equipment monitoring is achieved.

[0098] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0099] Obviously, those skilled in the art should understand that the above-mentioned units or steps of the present application can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0100] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method for industrial monitoring, characterized in that: Applied to an inspection robot to enable the inspection robot to monitor industrial equipment, the method includes: Acquiring monitoring demand data, wherein the monitoring demand data is relevant data used to indicate demand for industrial monitoring; Performing inspection robot path planning processing according to the monitoring demand data to obtain inspection path data, and the inspection robot performs inspection according to the inspection path data to realize monitoring of industrial equipment; Acquiring inspection data, wherein the inspection data is used to represent data obtained by the inspection robot; The inspection data is subjected to industrial monitoring processing based on equipment feature extraction to obtain monitoring result data.

2. The data processing method according to claim 1, characterized in that: The inspection data is subjected to industrial monitoring processing based on equipment feature extraction, and the monitoring result data obtained includes: Performing recognition processing on the inspection data to obtain inspection image data, wherein the inspection image data is image data used to represent the inspection of industrial equipment; Performing preprocessing based on image cropping on the inspection image data to obtain process inspection image data; Performing deep learning-based chart recognition processing on the process inspection image data to obtain process monitoring result data, wherein the process monitoring result data is chart data for representing industrial equipment; Performing multi-sample evaluation processing on the process monitoring result data to obtain the monitoring result data.

3. The data processing method according to claim 2, characterized in that: The process inspection image data is subjected to chart recognition processing based on deep learning to obtain process monitoring result data including: Performing a recognition process based on chart features on the process inspection image to obtain first chart feature data and second chart feature data, wherein the first chart feature data is feature data for indicating a chart type in the process inspection image data, and the second chart feature data is feature data for indicating a chart data indication in the process inspection image data; Matching a template chart corresponding to the first chart feature data to obtain template chart data, wherein the template chart data is data for representing a template chart corresponding to a chart type in the process inspection data image data; The second chart feature data and the template chart data are calculated and processed to obtain the process monitoring result data.

4. The data processing method according to claim 2, characterized in that: The process inspection image data is subjected to chart recognition processing based on deep learning to obtain process monitoring result data including: Performing position feature-based recognition processing on the process inspection image data to obtain position feature data, wherein the position feature data is used to represent the position feature data of the industrial equipment corresponding to the process inspection image; Matching the industrial equipment corresponding to the position feature data in a preset industrial equipment model to obtain the industrial equipment data to be tested, wherein the industrial equipment data to be tested includes equipment chart template data; The process inspection image data and the equipment chart template data are subjected to chart recognition processing based on a preset algorithm to obtain the process monitoring result data.

5. The data processing method according to claim 2, characterized in that: The process monitoring result data is subjected to multi-sample evaluation processing to obtain the monitoring result data including: Performing evaluation processing on the process monitoring result data based on a preset number of samples to obtain multi-sample evaluation data; The multiple sample evaluation data are judged to determine whether the multiple sample evaluation data meet the preset evaluation rules, If the multi-sample evaluation data meets the preset evaluation rules, the monitoring result data is obtained; If the multi-sample evaluation data does not satisfy the preset evaluation rules, the monitoring abnormality prompt data is output.

6. The data processing method according to claim 1, characterized in that: According to the monitoring demand data, the inspection robot path planning process is performed to obtain the inspection path data including: Extracting characteristic points from the monitoring demand data to obtain characteristic point data, wherein the characteristic point data is data used to represent the location of the industrial equipment to be monitored; Acquiring robot operation data, wherein the robot operation data includes operation position data of the inspection robot; Path planning processing is performed on the operating position data and the characteristic point data to obtain the inspection path data.

7. A data processing device for industrial monitoring, characterized in that: Applied to inspection robots to enable them to monitor industrial equipment, including: A demand acquisition module, used to acquire monitoring demand data, wherein the monitoring demand data is relevant data used to represent demand industrial monitoring; A path planning module is used to perform path planning processing of the inspection robot according to the monitoring demand data to obtain inspection path data, and the inspection robot performs inspection according to the inspection path data to realize monitoring of industrial equipment; An inspection data acquisition module, used to acquire inspection data, wherein the inspection data is used to represent data obtained by the inspection robot; The monitoring module is used to perform industrial monitoring processing on the inspection data based on equipment feature extraction to obtain monitoring result data.

8. The data processing device according to claim 7, characterized in that: Monitoring modules, including: An image recognition module, used to perform recognition processing on the inspection data to obtain inspection image data, wherein the inspection image data is image data used to represent the inspection of industrial equipment; A preprocessing module, used for performing preprocessing on the inspection image data based on image cropping to obtain process inspection image data; A chart recognition module, used for performing chart recognition processing based on deep learning on the process inspection image data to obtain process monitoring result data, wherein the process monitoring result data is chart data for representing industrial equipment; The multi-sample evaluation module is used to perform multi-sample evaluation processing on the process monitoring result data to obtain the monitoring result data.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the data processing method for industrial monitoring according to any one of claims 1 to 6.

10. An electronic device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the data processing method for industrial monitoring described in any one of claims 1-6.

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