Intelligent Operation and Maintenance Monitoring Method and System for Sensor-based Busbar Trunking Processing Equipment

Abstract

The present application relates to the technical field of intelligent monitoring of equipment, and provides an intelligent operation and maintenance monitoring method and system for busbar trunking processing equipment based on sensors, including: determining busbar trunking processing cycle information; constructing a set of operating characteristics of busbar trunking processing equipment; using the set of operating characteristics as identification information to perform initial data identification in the processing cycle information; determining the initial installation positions of sensors based on the initial data identification results; installing M sensors to collect real-time operating sensing information; adjusting and optimizing the initial installation positions of the sensors through the real-time operating sensing information, and performing intelligent operation and maintenance data monitoring according to the optimized positions of the sensors. It can solve the technical problem of low accuracy of operation and maintenance monitoring results due to unreasonable setting of the installation positions of monitoring sensors during the operation and maintenance monitoring of busbar trunking processing equipment. By optimizing the installation positions of monitoring sensors, the accuracy of operation and maintenance monitoring results can be improved.

Description

Technical Field

[0001] This application relates to the technical field of intelligent hotel management, and specifically relates to an intelligent operation and maintenance monitoring method and system for busbar trunking processing equipment based on sensors. Background Art

[0002] A busbar trunking is an enclosed metal device composed of copper and aluminum busbars. As a new circuit method, it can be used to replace wire and cable for power supply. Since the production requirements of busbar trunking products are relatively strict, it is necessary to monitor the operating status of busbar trunking processing equipment. Traditional equipment monitoring methods usually set multiple sensors at fixed positions for operating status monitoring, without considering the influence of the surrounding environment on the sensor acquisition results, resulting in a low accuracy rate of the sensor monitoring data acquisition results.

[0003] In summary, there is a technical problem in the prior art that the accuracy rate of the operation and maintenance monitoring results is low due to the unreasonable layout of the monitoring sensors during the operation and maintenance monitoring of busbar trunking processing equipment. Summary of the Invention

[0004] Based on this, it is necessary to provide an intelligent operation and maintenance monitoring method and system for busbar trunking processing equipment based on sensors for the above technical problems.

[0005] An intelligent operation and maintenance monitoring method for busbar trunking processing equipment based on sensors, the method includes: determining the busbar trunking processing cycle information by comparing with the parameters of the busbar trunking processing equipment; connecting to the busbar trunking processing equipment, performing the operation data interaction of the busbar trunking processing equipment, and constructing a set of operation characteristics of the busbar trunking processing equipment; using the set of operation characteristics of the busbar trunking processing equipment as identification information, performing initial data identification in the busbar trunking processing cycle information to obtain an initial data identification result; based on the initial data identification result, arranging the positions of N sensors to determine the initial arrangement positions of the N sensors, where N is a positive integer greater than 0; according to the initial arrangement positions of the N sensors, arranging M sensors, controlling the M sensors to collect real-time operation sensing information of the busbar trunking processing equipment during operation, and obtaining the real-time operation sensing information of the busbar trunking processing equipment, where M is a positive integer greater than or equal to N; adjusting and optimizing the initial arrangement positions of the N sensors through the real-time operation sensing information, and performing intelligent operation and maintenance data monitoring on the busbar trunking processing equipment according to the optimized positions of P sensors.

[0006] An intelligent operation and maintenance monitoring system for busbar trunking processing equipment based on sensors, including:

[0007] Busbar trunking processing cycle information determination module, which is used to determine the busbar trunking processing cycle information by comparing the busbar trunking processing equipment parameters;

[0008] Operation data interaction module, which is used to connect to the busbar trunking processing equipment, execute the operation data interaction of the busbar trunking processing equipment, and construct a set of operation characteristics of the busbar trunking processing equipment;

[0009] Initial data identification module, which is used to use the set of operation characteristics of the busbar trunking processing equipment as identification information, perform initial data identification in the busbar trunking processing cycle information, and obtain an initial data identification result;

[0010] Initial layout position determination module, which is used to perform the position layout of N sensors based on the initial data identification result, and determine the initial layout positions of the N sensors, where N is a positive integer greater than 0;

[0011] Real-time operation sensing information acquisition module, which is used to layout M sensors according to the initial layout positions of the N sensors, control the M sensors to collect real-time operation sensing information of the busbar trunking processing equipment during operation, and obtain the real-time operation sensing information of the busbar trunking processing equipment, where M is a positive integer greater than or equal to N;

[0012] Intelligent operation and maintenance data monitoring module, which is used to adjust and optimize the initial layout positions of the N sensors through the real-time operation sensing information, and monitor the intelligent operation and maintenance data of the busbar trunking processing equipment according to the optimized positions of P sensors.

[0013] The above-mentioned intelligent operation and maintenance monitoring method and system for busbar processing equipment based on sensors can solve the technical problem of low accuracy of operation and maintenance monitoring results due to unreasonable arrangement of monitoring sensor positions during the operation and maintenance monitoring of busbar processing equipment. First, obtain the busbar processing cycle information; obtain the set of operating characteristics of the busbar processing equipment; perform initial data identification within the processing cycle according to the set of operating characteristics; determine the position arrangement conditions based on the environmental information around the busbar processing equipment, and judge the initial data identification results according to the position arrangement conditions to determine the initial arrangement positions of N sensors; then arrange M sensors according to the initial arrangement positions, and collect real-time operation sensing information of the busbar processing equipment during operation through the M sensors; adjust and optimize the initial arrangement positions of the N sensors according to the real-time operation sensing information to obtain the optimized positions of the sensors, and set sensors based on the optimized positions of the sensors to perform data monitoring for the intelligent operation and maintenance of the busbar processing equipment. By optimizing the arrangement positions of the monitoring sensors, the accuracy of the operation and maintenance monitoring results of the busbar processing equipment can be improved.

[0014] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically given below. Brief Description of the Drawings

[0015] Figure 1 It is a schematic flow chart of an intelligent operation and maintenance monitoring method for busbar processing equipment based on sensors provided by the present application;

[0016] Figure 2 It is a schematic flow chart of obtaining the initial data identification result in the intelligent operation and maintenance monitoring method for busbar processing equipment based on sensors provided by the present application;

[0017] Figure 3 It is a schematic flow chart of determining the initial arrangement positions of N sensors in the intelligent operation and maintenance monitoring method for busbar processing equipment based on sensors provided by the present application;

[0018] Figure 4 It is a schematic structural diagram of an intelligent operation and maintenance monitoring system for busbar processing equipment based on sensors provided by the present application.

[0019] Description of the reference numerals: busbar processing cycle information determination module 1, operation data interaction module 2, initial data identification module 3, initial arrangement position determination module 4, real-time operation sensing information acquisition module 5, intelligent operation and maintenance data monitoring module 6. Detailed Description of the Embodiments

[0020] In order to make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0021] As Figure 1 shown, the present application provides an intelligent operation and maintenance monitoring method for a busbar trunking processing device based on sensors, including:

[0022] Step S100: Determine the busbar trunking processing cycle information by comparing the parameters of the busbar trunking processing device;

[0023] Step S200: Connect the busbar trunking processing device, perform the operation data interaction of the busbar trunking processing device, and construct a set of operation characteristics of the busbar trunking processing device;

[0024] Specifically, obtain the operation control parameters of the target busbar trunking processing device, where the target busbar trunking processing device refers to the busbar trunking processing device to be subjected to intelligent operation and maintenance monitoring. Obtain the busbar trunking product processing cycle information based on the operation control parameters, and the processing cycle information refers to the time required for the busbar trunking processing device to produce one busbar trunking product. Connect the busbar trunking processing device, and extract the operation data of the busbar trunking processing device. The operation data refers to the operation control parameters during the busbar trunking processing, such as: assembly control parameters, riveting control parameters, fitting control parameters, etc. The riveting control parameters include parameters such as riveting angle, temperature, humidity, time, etc. Obtain a set of operation characteristics of the busbar trunking processing device according to the operation data. By obtaining the set of operation characteristics of the busbar trunking processing device, it provides data support for obtaining the identification information in the next step.

[0025] Step S300: Use the set of operation characteristics of the busbar trunking processing device as identification information, and perform initial data identification in the busbar trunking processing cycle information to obtain an initial data identification result;

[0026] As Figure 2 shown, in one embodiment, step S300 of the present application further includes:

[0027] Step S310: Perform operation-related analysis based on the set of operation characteristics of the busbar trunking processing device and the busbar trunking processing cycle information to generate Z correlation coefficients;

[0028] In one embodiment, step S310 of the present application further includes:

[0029] Step S311: The calculation formula for generating Z correlation coefficients is as follows:

[0030]

[0031] Step S312: where ρ xy is the Z correlation coefficients, x is the set of operating characteristics of the busbar processing equipment, y is the busbar processing cycle information, Cov(x, y) is the covariance of x and y, and D(x), D(y) are the variances of x and y.

[0032] Specifically, construct Z correlation coefficient calculation formulas: where ρ xy is the Z correlation coefficients, x is the set of operating characteristics of the busbar processing equipment, y is the busbar processing cycle information, Cov(x, y) is the covariance of x and y, and D(x), D(y) are the variances of x and y. By constructing the correlation coefficient calculation formulas, it provides support for the correlation analysis of the set of operating characteristics of the busbar processing equipment and the busbar processing cycle information, and can more intuitively represent the correlation degree between each operating characteristic in the set of operating characteristics of the busbar processing equipment and the busbar processing cycle information. At the same time, it can improve the accuracy of obtaining the correlation coefficients. Calculate the correlation coefficients of the set of operating characteristics of the busbar processing equipment and the busbar processing cycle information according to the Z correlation coefficient calculation formulas to obtain Z correlation coefficients. By obtaining the Z correlation coefficients, it provides support for obtaining the initial weights of the operating characteristics of the busbar processing equipment in the next step.

[0033] Step S320: Configure the initial weights according to the Z correlation coefficients and output Z initial weights;

[0034] Step S330: Perform weight training on the set of operating characteristics of the busbar processing equipment according to the Z initial weights and output identification information.

[0035] Specifically, perform initial weight allocation according to the Z correlation coefficients. The initial weight allocation refers to allocating corresponding weights according to the magnitudes of the correlation coefficients. Those skilled in the art can customize the weight allocation rules based on actual situations. For example, when the correlation coefficient is 1, the weight value is set to 1; when the correlation coefficient is 3, the weight value is set to 3; to obtain Z initial weights. Embed the Z initial weights into the corresponding operating characteristics of the set of operating characteristics of the busbar processing equipment and output identification information. The identification information is the set of operating characteristics with initial weights. Then use the set of operating characteristics of the busbar processing equipment as the identification information to perform initial data identification in the busbar processing cycle. The initial data identification refers to identifying according to the processing nodes corresponding to the operating characteristics of the busbar processing equipment in the busbar processing cycle to obtain the initial data identification result. By obtaining the initial data identification result, it provides support for determining the initial layout positions of the sensors in the next step.

[0036] Step S400: Based on the initial data identification result, arrange the positions of N sensors, and determine the initial arrangement positions of the N sensors, where N is a positive integer greater than 0;

[0037] As Figure 3 shown, in one embodiment, step S400 of the present application further includes:

[0038] Step S410: Collect the environmental information of the busbar processing equipment and determine the position arrangement conditions;

[0039] Step S420: Determine whether the initial data identification result meets the position arrangement conditions;

[0040] Step S430: If it is satisfied, integrate the satisfied positions, and use the integrated position information as the initial arrangement positions of the N sensors.

[0041] Specifically, collect the environmental information of the area around the busbar processing equipment. The environmental information includes the spatial information of the surrounding area, which refers to the surrounding area constraint conditions. The surrounding area constraint conditions refer to the influence of the surrounding environment on the sensor accuracy. For example, assuming that the surrounding environment is under direct sunlight, arranging a temperature sensor here will cause the monitoring result of the temperature sensor to be too high, thus affecting the accuracy of the later monitoring data. By analyzing the environmental information of the surrounding area, determine the position arrangement conditions. The position arrangement conditions refer to multiple position areas that can meet the sensor requirements.

[0042] Judge the initial data identification result according to the position arrangement conditions. When the area in the initial data identification result meets the position arrangement conditions, use the multiple position areas that meet the position arrangement conditions as the initial arrangement positions of N sensors, where N is a positive integer greater than 0. The specific value of N can be custom-set by those skilled in the art based on the actual situation, for example: 10. By obtaining the initial arrangement positions of the N sensors, it provides support for the next step of sensor arrangement.

[0043] Step S500: According to the initial arrangement positions of the N sensors, arrange M sensors, and control the M sensors to collect real-time operation sensing information of the busbar processing equipment during operation, and obtain the real-time operation sensing information of the busbar processing equipment, where M is a positive integer greater than or equal to N;

[0044] In one embodiment, step S500 of the present application further includes:

[0045] Step S510: Associate the initial arrangement positions of the N sensors with the busbar processing equipment to determine the association index;

[0046] Step S520: Traverse the correlation index, and successively compare the correlation index of the initial layout positions of the N sensors with a preset correlation threshold to obtain a set of correlation positions;

[0047] Step S530: Arrange the M sensors based on the set of correlation positions.

[0048] Specifically, arrange the M sensors according to the initial layout positions of the N sensors, where M is a positive integer greater than N, and the value of M is determined based on the actual situation. Associate the initial layout positions of the N sensors with the busbar processing equipment. The association means determining how many busbar processing equipment need to be monitored simultaneously at the initial layout position of each sensor, and determining the correlation index, where the correlation index is the number of busbar processing equipment monitored simultaneously.

[0049] Traverse the correlation index, and successively compare the correlation index of the initial layout positions of the N sensors with a preset correlation threshold. Those skilled in the art can customize the preset correlation threshold based on the actual situation. For example: 3. When the correlation index is greater than the preset correlation threshold, the initial layout position corresponding to the correlation index is used as the correlation position to obtain a set of correlation positions. Arrange the M sensors according to the set of correlation positions, where M is a positive integer greater than or equal to 2, and the specific value of M can be customized based on the specific value of the correlation index, and set the minimum number of sensors on the basis of meeting the monitoring conditions. For example: when the correlation index is greater than the correlation threshold and less than 2 times the correlation threshold, then M is set to 2; when the correlation index is greater than 2 times the correlation threshold and less than 3 times the correlation threshold, then M is set to 3.

[0050] Collect real-time operation sensing information of the busbar processing equipment during operation through the M sensors to obtain the real-time operation sensing information of the busbar processing equipment, where the real-time operation sensing information includes the operation control parameters of the busbar processing equipment. By obtaining the real-time operation sensing information, it provides support for the next step of optimizing and adjusting the initial layout positions of the sensors.

[0051] Step S600: Adjust and optimize the initial layout positions of the N sensors through the real-time operation sensing information, and perform data monitoring for intelligent operation and maintenance of the busbar processing equipment according to the optimized positions of the P sensors.

[0052] In one embodiment, step S600 of the present application further includes:

[0053] Step S610: Interact with the busbar processing equipment to read the sensing information within the sensing time node and control execution node of the busbar processing equipment;

[0054] Step S620: Perform an adaptation analysis on the sensing information and the initial deployment positions of the N sensors;

[0055] Step S630: If the M sensors among the initial deployment positions of the N sensors cannot obtain the sensing information, update the initial deployment positions of the N sensors to obtain the optimized positions of the P sensors.

[0056] Specifically, the initial deployment positions of the N sensors are adjusted and optimized through the real-time running sensing information. First, connect the busbar processing equipment and read the sensing information within the sensing time nodes and control execution nodes of the busbar processing equipment. The sensing time nodes can be custom-set based on the sensor characteristics. For example, every 1 minute is one sensing time node. The control execution node refers to the time periods of multiple processing links within the busbar processing cycle. Obtain the sensing information, which is the operation control parameters of the busbar processing equipment. Then, perform an adaptation analysis on the sensing information and the initial deployment positions of the N sensors. The adaptation analysis means judging whether the sensing information obtained by the sensors meets the preset sensing information acquisition requirements. When the M sensors among the initial deployment positions of the N sensors cannot meet the preset sensing information acquisition requirements, for example, the sensors at the initial deployment positions do not receive the real-time sensing information or the received real-time sensing information is incomplete and there are data gaps, then the initial deployment positions of the N sensors are updated. The position update method is to take the initial deployment position as the center point and plan a circular area with a diameter of 1 meter. Perform position adjustment within the circular area according to the preset moving unit until complete real-time sensing information can be received. The preset moving unit can be custom-set, for example: 100 square centimeters. Obtain the optimized positions of the P sensors, where P is an integer greater than or equal to 0. Perform position adjustment on the sensors that need to be optimized according to the optimized positions of the P sensors, and then perform intelligent operation and maintenance data monitoring on the busbar processing equipment according to the set sensors. By optimizing the deployment positions of the monitoring sensors, the accuracy of the operation and maintenance monitoring results of the busbar processing equipment can be improved.

[0057] In one embodiment, step S600 of the present application further includes:

[0058] Step S640: Perform a trigger statistics on the abnormal operation and maintenance of the busbar processing equipment, where the abnormal operation and maintenance include abnormal data monitoring values;

[0059] Step S650: Construct an abnormal file of the busbar processing equipment according to the statistical frequency and the abnormal data monitoring values;

[0060] Step S660: Perform operation and maintenance management of the busbar processing equipment based on the abnormal file.

[0061] Specifically, set the abnormal data monitoring value, which can be customized based on the actual situation. For example, the standard operating temperature is 60 degrees Celsius, and the temperature abnormal monitoring value is set to 80 degrees Celsius. Trigger statistics for abnormal operation and maintenance of the busbar processing equipment according to the abnormal data monitoring value. The trigger means that the real-time monitoring data of the busbar processing equipment is greater than the abnormal data monitoring value, and obtain the statistical frequency, which refers to the number of trigger times of abnormal monitoring. Construct the abnormal file of the busbar processing equipment according to the statistical frequency and the abnormal data monitoring value. The abnormal file is used to record the abnormal state of the busbar processing equipment during operation. Finally, perform operation and maintenance management of the busbar processing equipment according to the abnormal file. For example, an abnormal trigger frequency threshold and an abnormal data monitoring limit value can be set. When the statistical frequency meets the abnormal trigger frequency threshold or the abnormal data monitoring value meets the abnormal data monitoring limit value, a warning instruction is generated, and the busbar processing equipment is maintained and serviced according to the warning instruction. By constructing the abnormal file of the busbar processing equipment, abnormal situations during busbar processing can be detected in a timely manner, avoiding major losses to production.

[0062] In one embodiment, as Figure 4 shown, a smart operation and maintenance monitoring system for busbar processing equipment based on sensors is provided, including: a busbar processing cycle information determination module 1, an operation data interaction module 2, an initial data identification module 3, an initial layout position determination module 4, a real-time operation sensing information acquisition module 5, a smart operation and maintenance data monitoring module 6. Among them:

[0063] The busbar processing cycle information determination module 1 is used to determine the busbar processing cycle information by comparing the parameters of the busbar processing equipment;

[0064] The operation data interaction module 2 is used to connect to the busbar processing equipment, perform the operation data interaction of the busbar processing equipment, and construct a set of operation characteristics of the busbar processing equipment;

[0065] The initial data identification module 3 is used to use the set of operation characteristics of the busbar processing equipment as identification information, perform initial data identification in the busbar processing cycle information, and obtain the initial data identification result;

[0066] The initial layout position determination module 4 is used to perform the position layout of N sensors based on the initial data identification result, and determine the initial layout positions of the N sensors, where N is a positive integer greater than 0;

[0067] A real-time operation sensing information acquisition module 5, which is configured to deploy M sensors according to the initial deployment positions of the N sensors, control the M sensors to collect real-time operation sensing information during the operation of the busbar processing equipment, and obtain the real-time operation sensing information of the busbar processing equipment, where M is a positive integer greater than or equal to N;

[0068] An intelligent operation and maintenance data monitoring module 6, which is configured to adjust and optimize the initial deployment positions of the N sensors through the real-time operation sensing information, and monitor the intelligent operation and maintenance data of the busbar processing equipment according to the optimized positions of the P sensors.

[0069] In one embodiment, the system further includes:

[0070] A correlation coefficient generation module, which is configured to perform operation correlation analysis based on the busbar processing equipment operation feature set and the busbar processing cycle information, and generate Z correlation coefficients;

[0071] An initialization weight output module, which is configured to perform initialization weight configuration according to the Z correlation coefficients and output Z initialization weights;

[0072] An identification information output module, which is configured to perform weight training on the busbar processing equipment operation feature set according to the Z initialization weights and output identification information.

[0073] In one embodiment, the system further includes:

[0074] A correlation coefficient generation calculation formula module, which refers to the calculation formula for generating Z correlation coefficients as follows:

[0075]

[0076] A formula information summarization module, which refers to where ρ xy is the Z correlation coefficients, x is the busbar processing equipment operation feature set, y is the busbar processing cycle information, Cov(x, y) is the covariance of x and y, and D(x), D(y) are the variances of x and y.

[0077] In one embodiment, the system further includes:

[0078] An association index determination module, which is used to associate the initial deployment positions of the N sensors with the busbar processing equipment and determine the association index;

[0079] An associated position set acquisition module, which is used to traverse the association index, compare the association index of the initial deployment positions of the N sensors with a preset association threshold in sequence, and acquire an associated position set;

[0080] A sensor deployment module, which is used to deploy M sensors based on the associated position set.

[0081] In one embodiment, the system further includes:

[0082] A position deployment condition determination module, which is used to collect the environmental information of the busbar processing equipment and determine the position deployment conditions;

[0083] An initial data identification result judgment module, which is used to judge whether the initial data identification result meets the position deployment conditions;

[0084] A position integration module, which is used to, if it is satisfied, integrate the satisfied positions and use the integrated position information as the initial deployment positions of the N sensors.

[0085] In one embodiment, the system further includes:

[0086] A sensing information reading module, which is used to interact with the busbar processing equipment and read the sensing information in the sensing time node and the control execution node of the busbar processing equipment;

[0087] An adaptation analysis module, which is used to perform adaptation analysis on the sensing information and the initial deployment positions of the N sensors;

[0088] A sensor optimized position acquisition module, which is used to update the initial deployment positions of the N sensors and acquire the P sensor optimized positions when the M sensors among the initial deployment positions of the N sensors cannot acquire the sensing information.

[0089] In one embodiment, the system further includes:

[0090] A trigger statistics module, which is used to perform trigger statistics on the abnormal operation and maintenance of the busbar processing equipment, where the abnormal operation and maintenance include abnormal data monitoring values;

[0091] Anomaly file construction module, which is used to construct an anomaly file of the busbar processing equipment according to the statistical frequency and the anomaly data monitoring value;

[0092] Operation and maintenance management module, which is used to perform operation and maintenance management of the busbar processing equipment based on the anomaly file.

[0093] In summary, the present application provides an intelligent operation and maintenance monitoring method and system for busbar processing equipment based on sensors, having the following technical effects:

[0094] 1. Solved the technical problem of low accuracy of operation and maintenance monitoring results due to unreasonable arrangement of monitoring sensors during the operation and maintenance monitoring of busbar processing equipment. By optimizing the arrangement of monitoring sensors, the accuracy of operation and maintenance monitoring results of busbar processing equipment can be improved.

[0095] 2. By constructing a correlation coefficient calculation formula, it provides support for the correlation analysis of the operation characteristic set of the busbar processing equipment and the busbar processing cycle information, can more intuitively represent the correlation degree between each operation characteristic in the operation characteristic set of the busbar processing equipment and the busbar processing cycle information, and at the same time can improve the accuracy of obtaining the correlation coefficient.

[0096] 3. By constructing an anomaly file of the busbar processing equipment, abnormal situations during busbar processing can be detected in time, avoiding major losses to production.

[0097] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0098] The above-described embodiments only represent several implementation manners of the present application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A sensor-based intelligent operation and maintenance monitoring method for busbar trunking processing equipment, characterized in that The method includes: Determining the busbar processing cycle information by comparing the parameters of the busbar processing equipment; Connecting the busbar processing equipment, performing the operation data interaction of the busbar processing equipment, and constructing a set of operation characteristics of the busbar processing equipment; Using the set of operation characteristics of the busbar processing equipment as identification information, performing initial data identification in the busbar processing cycle information, and obtaining an initial data identification result; The initial data identification refers to identifying the processing nodes corresponding to the operation characteristics of the busbar processing equipment in the busbar processing cycle to obtain an initial data identification result; Based on the initial data identification result, arranging the positions of N sensors, and determining the initial arrangement positions of the N sensors, including: Collecting the environmental information of the busbar processing equipment and determining the position arrangement conditions; Judging whether the initial data identification result meets the position arrangement conditions; If it meets, integrating the satisfied positions, and using the integrated position information as the initial arrangement positions of the N sensors; where N is a positive integer greater than 0; According to the initial arrangement positions of the N sensors, arranging M sensors, and controlling the M sensors to collect real-time operation sensing information of the busbar processing equipment during operation, and obtaining the real-time operation sensing information of the busbar processing equipment, where M is a positive integer greater than or equal to N; Adjusting and optimizing the initial arrangement positions of the N sensors through the real-time operation sensing information, and performing data monitoring for the intelligent operation and maintenance of the busbar processing equipment according to the optimized positions of P sensors.

2. The method according to claim 1, wherein After obtaining the initial data identification result, the method further includes: Performing operation-related analysis based on the set of operation characteristics of the busbar processing equipment and the busbar processing cycle information to generate Z correlation coefficients; Performing initial weight configuration according to the Z correlation coefficients and outputting Z initial weights; Performing weight training on the set of operation characteristics of the busbar processing equipment according to the Z initial weights and outputting identification information.

3. The method according to claim 2, wherein The calculation formula for generating Z correlation coefficients is as follows: Among them, ρ xy is the Z correlation coefficients, x is the set of operating characteristics of the busbar processing equipment, y is the busbar processing cycle information, Cov(x, y) is the covariance of x and y, and D(x) and D(y) are the variances of x and y.

4. The method according to claim 1, characterized in that When arranging the M sensors, the method further includes: Associating the initial arrangement positions of the N sensors with the busbar processing equipment to determine an association index; Traversing the association index, comparing the association index of the initial arrangement positions of the N sensors with a preset association threshold in sequence, and obtaining an association position set; Arranging the M sensors based on the association position set.

5. The method according to claim 1, characterized in that, When obtaining the optimized positions of P sensors, the method further includes: Interacting with the busbar processing equipment to read the sensing time nodes and the sensing information in the control execution nodes of the busbar processing equipment; Performing adaptation analysis on the sensing information and the initial arrangement positions of the N sensors; If the M sensors among the initial arrangement positions of the N sensors cannot obtain the sensing information, updating the initial arrangement positions of the N sensors to obtain the optimized positions of P sensors.

6. The method according to claim 1, wherein The method further includes: Trigger statistics for abnormal operation and maintenance of the busway processing equipment, where the abnormal operation and maintenance includes abnormal data monitoring values; Construct an abnormal file of the busway processing equipment according to the statistical frequency and the abnormal data monitoring values; Perform operation and maintenance management of the busway processing equipment based on the abnormal file.

7. Sensor-based intelligent operation and maintenance monitoring system for busbar trunking processing equipment, characterized in that, The system is used to execute the method according to claims 1 to 6, and the system includes: A busway processing cycle information determination module, which is used to determine busway processing cycle information by comparing busway processing equipment parameters; An operation data interaction module, which is used to connect to the busway processing equipment, execute the operation data interaction of the busway processing equipment, and construct a set of operation characteristics of the busway processing equipment; An initial data identification module, which is used to use the set of operation characteristics of the busway processing equipment as identification information and perform initial data identification in the busway processing cycle information to obtain an initial data identification result; An initial layout position determination module, which is used to perform the layout of the positions of N sensors based on the initial data identification result to determine the initial layout positions of the N sensors, where N is a positive integer greater than 0; A real-time operation sensing information acquisition module, which is used to arrange M sensors according to the initial layout positions of the N sensors, control the M sensors to collect real-time operation sensing information of the busway processing equipment during operation, and obtain the real-time operation sensing information of the busway processing equipment, where M is a positive integer greater than or equal to N; An intelligent operation and maintenance data monitoring module, which is used to adjust and optimize the initial layout positions of the N sensors through the real-time operation sensing information, and perform intelligent operation and maintenance data monitoring of the busway processing equipment according to the optimized positions of P sensors.

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