Electric automatic processing method and system based on data analysis

Abstract

The invention discloses an electrical automatic processing method and system based on data analysis, and the method comprises the steps: installing sensors on equipment in a processing workshop, which are used for collecting automatic processing parameter information, and the types of the sensors comprise a power sensor, a temperature sensor and the like. The optimal operation state analysis model is established by using the information in the historical database and combining the feedforward neural network, the optimal operation state of the electrical automation system can be predicted and evaluated based on the historical data, a basis is provided for real-time adjustment, and by calculating the load rate and the working efficiency of the electrical automation system, the optimal operation state of the electrical automation system can be evaluated. The real-time processing parameters and the standard electrical data are compared with the preset standard or historical data, the efficiency problem in system operation can be found in time, a direction is provided for optimization, and according to the similarity calculation result of the real-time processing parameters and the standard electrical data, the system can automatically judge whether adjustment is needed or not and take corresponding control measures, so that the control efficiency is improved. And the automatic processing system is always kept in the optimal operation state.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical automation processing, and in particular, to an electrical automation processing method and system based on data analysis. Background Art

[0002] Electrical automation processing is the application of electrical automation technology in the field of industrial production and processing. It uses means such as electronic technology, computer technology, automatic control theory, and information technology to automatically detect, control, optimize, and manage various parameters during the processing process, so as to achieve the automation, intelligence, and high efficiency of the processing process.

[0003] However, when the existing electrical automation processing system analyzes the equipment parameters during the processing process, it mainly relies on the experience of the staff to make judgments, and corresponding measures are taken based on the judgments. However, this method is, on the one hand, not intelligent enough, and on the other hand, if the staff makes a misjudgment, it will cause the electrical automation processing system to be difficult to operate in the best state, resulting in low processing efficiency. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides an electrical automation processing method and system based on data analysis. This technical solution solves the problem that when the existing electrical automation processing system analyzes the equipment parameters during the processing process, it mainly relies on the experience of the staff to make judgments, and corresponding measures are taken based on the judgments. However, this method is, on the one hand, not intelligent enough, and on the other hand, if the staff makes a misjudgment, it will cause the electrical automation processing system to be difficult to operate in the best state, resulting in low processing efficiency as mentioned in the above background art.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows: In the first aspect of the present invention, an electrical automation processing method based on data analysis is provided, including: Install sensors on the equipment in the processing workshop to collect information on automation processing parameters. The types of the sensors include power sensors, temperature sensors, etc.; Deduplicate and fill in the missing values of the collected automation processing parameter information to obtain complete processing parameter information with analytical significance; Retrieve historical processing parameter information and the operating states of the electrical automation system under different parameters from the historical database to form a historical electrical data set, and establish an optimal operating state analysis model based on a feedforward neural network; Retrieve real-time processing load power information, rated power information, no-load loss information, and power factor information from the complete processing parameter information, and calculate the load rate and working efficiency of the electrical automation system; Compare the calculated work efficiency to obtain the comparison result and the feedback on the result; Retrieve the electrical parameter data corresponding to the optimal operating state, denoted as the standard electrical data. Calculate the similarity between the real-time processing parameter data and the standard electrical data. If the similarity value is too high, no adjustment is required. If the similarity value is too low, control the electrical system to make adjustments to bring the automated processing system to the optimal operating state.

[0006] Preferably, the deduplication of the collected automated processing parameter information specifically includes the following steps: Arrange all the automated processing parameter information coordinates in a column; Retrieve the repeatability of the first coordinate and the subsequent coordinates. If there is a repeat, delete the subsequent coordinates; After the retrieval is complete, retrieve the repeatability of the second coordinate and the subsequent coordinates. If there is a repeat, delete the subsequent coordinates; Repeat the above steps until all the automated processing parameter information coordinates have been detected.

[0007] Preferably, the calculation formula for missing value filling is:

[0008] In the formula, is the filled data; i is the number of filled data; are different types of automated processing parameters; n is the number of different types of automated processing parameters.

[0009] Preferably, the steps for retrieving the historical processing parameter information from the historical database and the operating states of the electrical automation system under different parameters to form a historical electrical data set specifically include the following: Number the historical processing parameter information sequentially, denoted as A 1 、A 2 、A 3 ……An; Retrieve the corresponding operating states of the electrical automation system according to the numbered order of the historical processing parameter information, denoted as; B 1 、B 2 、B 3 ……Bn ; Based on the historical processing parameter information and the operating states of the electrical automation system in the numbered order, form a historical electrical data set. The historical electrical data set is { 、 }、{ 、 }、{ 、 }.......{ , }

[0010] Preferably, the establishment of the optimal operating state analysis model based on the feedforward neural network specifically includes the following steps: Construct an optimal operating state analysis model based on the feedforward neural network; Based on machine learning, label and divide multiple historical electrical data sets to obtain a training set, a validation set, and a test set; Use the training set, the validation set, and the test set to supervise the training, validation, and testing of the optimal operating state analysis model, and obtain the optimal operating state analysis model with an accuracy rate meeting the preset accuracy rate requirements.

[0011] Preferably, the calculation formula for the load rate of the electrical automation system is:

[0012] In the formula, is the load rate of the electrical automation system; is the load power; is the rated power; is the power factor; The calculation formula for the working efficiency of the electrical automation system is:

[0013] In the formula, is the working efficiency of the electrical automation system; is the load rate of the electrical automation system; is the no-load loss; is the rated power.

[0014] Preferably, the comparison of the calculated working efficiency to obtain the comparison result and the feedback on the result specifically includes the following steps: Compare the calculated working efficiency with a preset threshold; If the calculated working efficiency is greater than or equal to the preset threshold, the electrical automation processing system does not need to adjust parameters; If the calculated working efficiency is less than the preset threshold, the corresponding parameters at this time need to be analyzed again, and the secondary analysis is similarity calculation.

[0015] Preferably, the formula for calculating the similarity between the real-time processing parameter data and the standard electrical data is:

[0016] In the formula, is the similarity between real-time processing parameter data and the i-th feature of standard electrical data is the j-th feature index value of the i-th feature of real-time processing parameter data is the j-th feature index value of the i-th feature of standard electrical data is the total number of the i-th feature index values

[0017] In the second aspect of the present invention, there is also provided an electrical automation processing system based on data analysis, including: An installation module, which is used to install sensors on equipment in a processing workshop for collecting automated processing parameter information. The types of the sensors include power sensors, temperature sensors, etc.; A data processing module, which is used to remove duplicates and fill in missing values for the collected automated processing parameter information to obtain complete processing parameter information with analytical significance; A construction module, which is used to retrieve historical processing parameter information and the operating states of the electrical automation system under different parameters from a historical database to form a historical electrical data set, and establish an optimal operating state analysis model based on a feedforward neural network; A calculation module, which is used to retrieve real-time processing load power information, rated power information, no-load loss information, and power factor information from the complete processing parameter information, and calculate the load rate and working efficiency of the electrical automation system; A comparison module, which is used to compare the calculated working efficiency to obtain a comparison result and feedback on the result; An adjustment module, which is used to retrieve the electrical parameter data corresponding to the optimal operating state, denoted as standard electrical data, calculate the similarity between the real-time processing parameter data and the standard electrical data. If the similarity value is too high, no adjustment is required. If the similarity value is too low, the electrical system is controlled to be adjusted so that the automated processing system reaches the optimal operating state.

[0018] In the third aspect of the present invention, there is also provided an electronic device. The electronic device has at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of the first aspect of the present invention.

[0019] The beneficial effects of the present invention are as follows: By utilizing the information in the historical database and combining with the feedforward neural network, an optimal operating state analysis model is established, which can predict and evaluate the optimal operating state of the electrical automation system based on historical data, providing a basis for real-time adjustment. By calculating the load rate and working efficiency of the electrical automation system and comparing them with the preset standards or historical data, the efficiency problems in the system operation can be detected in a timely manner, providing a direction for optimization. According to the calculation result of the similarity between the real-time processing parameters and the standard electrical data, the system can automatically determine whether adjustment is needed and take corresponding control measures to keep the automated processing system always in the optimal operating state. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the electrical automation processing method based on data analysis in the present invention; Figure 2 It is a schematic diagram of the method for removing duplicates from the collected automated processing parameter information in the present invention; Figure 3 It is a schematic diagram of the method for constructing the historical electrical data set in the present invention; Figure 4 It is a schematic diagram of the method for establishing an optimal operating state analysis model based on the feedforward neural network in the present invention; Figure 5 The block diagram of an exemplary electronic device capable of implementing the embodiments of the present invention is shown; Among them, 500 is the electronic device, 501 is the computing unit, 502 is the ROM, 503 is the RAM, 504 is the bus, 505 is the I / O interface, 506 is the input unit, 507 is the output unit, 508 is the storage unit, and 509 is the communication unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0022] Please refer to Figure 1 As shown, an electrical automation processing method based on data analysis. In the first aspect of the present invention, an electrical automation processing method based on data analysis is provided, including: S101. Install sensors on the equipment in the processing workshop to collect automated processing parameter information. The types of sensors include power sensors, temperature sensors, etc.; S102. Remove duplicates and fill in missing values for the collected automated processing parameter information to obtain complete processing parameter information with analytical significance; S103. Retrieve historical processing parameter information and the operating states of the electrical automation system under different parameters from the historical database, form a historical electrical data set, and establish an optimal operating state analysis model based on a feedforward neural network; S104. Retrieve real-time processing load power information, rated power information, no-load loss information, and power factor information from the complete processing parameter information, and calculate the load rate and operating efficiency of the electrical automation system; S105. Compare the calculated operating efficiency, obtain the comparison result and the feedback on the result; S106. Retrieve the electrical parameter data corresponding to the optimal operating state, denoted as standard electrical data, calculate the similarity between the real-time processing parameter data and the standard electrical data. If the similarity value is too high, no adjustment is required. If the similarity value is too low, control the electrical system to make adjustments so that the automated processing system reaches the optimal operating state.

[0023] Those skilled in the art can understand that by installing sensors on the equipment, the present invention can collect key parameter information such as power and temperature in the automated processing process in real time, providing a data basis for subsequent analysis and optimization. Duplicate data removal and missing value filling are performed on the collected data to ensure the integrity and accuracy of the data, avoiding analysis errors caused by data problems. Using the information in the historical database and combining with a feedforward neural network, an optimal operating state analysis model is established, which can predict and evaluate the optimal operating state of the electrical automation system based on historical data, providing a basis for real-time adjustment. By calculating the load rate and operating efficiency of the electrical automation system and comparing them with preset standards or historical data, efficiency problems in the system operation can be detected in a timely manner, providing a direction for optimization. According to the similarity calculation result between the real-time processing parameters and the standard electrical data, the system can automatically determine whether adjustment is required and take corresponding control measures to keep the automated processing system always in the optimal operating state.

[0024] Please refer to Figure 2 As shown, the specific steps for duplicate data removal of the collected automated processing parameter information are as follows: S201. Arrange all the coordinates of the automated processing parameter information in a column; S202. Retrieve the repeatability of the first coordinate and the subsequent coordinates. If they are repeated, delete the subsequent coordinates; S203. After the retrieval is completed, retrieve the repeatability of the second coordinate and the subsequent coordinates. If they are repeated, delete the subsequent coordinates; S204. Repeat the above steps until all the coordinates of the automated processing parameter information are detected.

[0025] The calculation formula for missing value filling is:

[0026] In the formula, is the filling data; i is the number of filling data; is the automated processing parameters of different types; n is the number of different types of automated processing parameters.

[0027] Retrieve the historical processing parameter information from the historical database and the operating status of the electrical automation system under different parameters to form a historical electrical data set, which specifically includes the following steps: Number the historical processing parameter information sequentially, denoted as A 1 、A 2 、A 3 ……An; Retrieve the corresponding operating status of the electrical automation system according to the numbering order of the historical processing parameter information, denoted as; B 1 、B 2 、B 3 ……Bn ; Based on the historical processing parameter information and the operating status of the electrical automation system in the numbering order, form a historical electrical data set, and the historical electrical data set is { 、 }, { 、 }, { 、 }.......{ 、 }.

[0028] Please refer to Figure 3 as shown, and establish an optimal operating state analysis model based on the feedforward neural network, which specifically includes the following steps: S301. Based on the feedforward neural network, construct an optimal operating state analysis model; S302. Based on machine learning, label and divide multiple historical electrical data sets to obtain a training set, a validation set, and a test set; S303. Use the training set, the validation set, and the test set to perform supervised training, validation, and testing on the optimal operating state analysis model to obtain an optimal operating state analysis model with an accuracy rate meeting the preset accuracy rate requirements.

[0029] The calculation formula for the load rate of the electrical automation system is:

[0030] In the formula, is the load rate of the electrical automation system; is the load power; is the rated power; is the power factor; The working efficiency calculation formula of the electrical automation system is:

[0031] In the formula, is the working efficiency of the electrical automation system; is the load rate of the electrical automation system; is the no-load loss; is the rated power.

[0032] Please refer to Figure 4 shown in, compare the calculated working efficiency, and the specific steps for obtaining the comparison result and the feedback on the result are as follows: S401. Compare the calculated working efficiency with a preset threshold; S402. If the calculated working efficiency is greater than or equal to the preset threshold, the electrical automation processing system does not need to make parameter adjustments; S403. If the calculated working efficiency is less than the preset threshold, the corresponding parameters at this time need to be analyzed again, and the secondary analysis is similarity calculation.

[0033] The formula for calculating the similarity between the real-time processing parameter data and the standard electrical data is:

[0034] In the formula, is the similarity between the real-time processing parameter data and the i-th feature of the standard electrical data, is the j-th feature index value of the i-th feature of the real-time processing parameter data, is the j-th feature index value of the i-th feature of the standard electrical data, is the total number of the i-th feature index values.

[0035] In the second aspect of the present invention, an electrical automation processing system based on data analysis is further provided, including: An installation module, which is used to install sensors on the equipment in the processing workshop to collect automated processing parameter information. The types of sensors include power sensors, temperature sensors, etc.; A data processing module, which is used to remove duplicates and fill in missing values for the collected automated processing parameter information to obtain complete processing parameter information with analytical significance; A construction module, which is used to retrieve historical processing parameter information and the operating states of the electrical automation system under different parameters from the historical database to form a historical electrical data set, and establish an optimal operating state analysis model based on a feedforward neural network; A calculation module, which is used to retrieve real-time processing load power information, rated power information, no-load loss information, and power factor information from the complete processing parameter information, and calculate the load rate and working efficiency of the electrical automation system; A comparison module, which is used to compare the calculated working efficiency, obtain the comparison result and the feedback on the result; An adjustment module, which is used to retrieve the electrical parameter data corresponding to the optimal operating state, denoted as standard electrical data, calculate the similarity between the real-time processing parameter data and the standard electrical data. If the similarity value is too high, no adjustment is required. If the similarity value is too low, control the electrical system to make adjustments so that the automated processing system reaches the optimal operating state.

[0036] In the third aspect of the present invention, an electronic device is further provided.

[0037] Figure 5 A schematic block diagram of an electronic device 500 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0038] The electronic device 500 includes a computing unit 501, which can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 502 or the computer program loaded from the storage unit 508 into the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0039] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disc, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0040] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 executes the various methods and processes described above, such as methods S100 to S600. For example, in some embodiments, methods S101 to S106 can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the methods S101 to S106 described above can be executed. Alternatively, in other embodiments, the computing unit 501 can be configured to execute methods S101 to S106 in any other suitable manner (e.g., by means of firmware).

[0041] The various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0042] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0043] In the context of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0044] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0045] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0046] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, a server of a distributed system, or a server incorporating blockchain.

[0047] In summary, by installing sensors on the device, the present invention can collect key parameter information in the automated processing process in real time, such as power and temperature, providing a data basis for subsequent analysis and optimization. Duplicate data removal and missing value filling are performed on the collected data to ensure the integrity and accuracy of the data, avoiding analysis errors caused by data problems. Using the information in the historical database and combining with the feedforward neural network, an optimal operating state analysis model is established, which can predict and evaluate the optimal operating state of the electrical automation system based on historical data, providing a basis for real-time adjustment. By calculating the load rate and working efficiency of the electrical automation system and comparing them with preset standards or historical data, efficiency problems in the system operation can be timely detected, providing a direction for optimization. According to the calculation result of the similarity between the real-time processing parameters and the standard electrical data, the system can automatically determine whether adjustment is needed and take corresponding control measures, so that the automated processing system always maintains the optimal operating state.

[0048] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the principles described in the specification are only the principles of the present invention. Without departing from the spirit and scope of the present invention, various changes and improvements will occur to the present invention, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. An electrical automation processing method based on data analysis, characterized in that ,include: Install sensors on the equipment in the processing workshop to collect automated processing parameter information, and the types of sensors include power sensors and temperature sensors; Remove duplicates and fill missing values ​​from the collected automated processing parameter information to obtain complete processing parameter information with analytical significance; Retrieve historical processing parameter information and the operating status of the electrical automation system under different parameters from the historical database to form a historical electrical data set, and establish an optimal operating status analysis model based on a feedforward neural network; Retrieve real-time processing load power information, rated power information, no-load loss information and power factor information from the complete processing parameter information to calculate the load rate and work efficiency of the electrical automation system; Compare the calculated work efficiency, obtain the comparison results and feedback on the results; The electrical parameter data corresponding to the optimal operating state is retrieved and recorded as standard electrical data. The similarity between the real-time processing parameter data and the standard electrical data is calculated. If the similarity value is too high, no adjustment is required. If the similarity value is too low, the electrical system is controlled to be adjusted so that the automated processing system reaches the optimal operating state.

2. The electrical automation processing method based on data analysis according to claim 1 is characterized in that ,The deduplication of the collected automated processing parameter information specifically includes the following steps: Arrange all automated processing parameter information coordinates into one column; Retrieve the duplication of the first coordinate and subsequent coordinates, and if there is duplication, delete the subsequent coordinates; After the search is completed, the second coordinate and the subsequent coordinates are searched for duplication. If there is duplication, the subsequent coordinates are deleted; Repeat the above steps until all automated processing parameter information coordinates are detected.

3. The electrical automation processing method based on data analysis according to claim 2 is characterized in that ,The calculation formula for filling missing values ​​is: In the formula, is the filling data; i is the number of filling data; are different types of automated processing parameters; and n is the number of different types of automated processing parameters.

4. The electrical automation processing method based on data analysis according to claim 3 is characterized in that ,The ,retrieving historical processing parameter information and the operation status of the electrical ,automation system under different parameters from the historical database to form a ,historical electrical data set specifically includes the following steps: The historical processing parameter information is numbered sequentially, recorded as A1, A2, A3...An; According to the serial number sequence of the historical processing parameter information, the corresponding electrical automation system operation status is retrieved, which is recorded as B1, B2, B3...Bn; The historical processing parameter information based on the serial number and the operating status of the electrical automation system constitute a historical electrical data set, and the historical electrical data set is { , }、{ , }、{ , }.......{ , }.

5. The electrical automation processing method based on data analysis according to claim 4 is characterized in that ,The optimal operation state analysis model based on the feedforward neural network specifically includes the following steps: Based on the feedforward neural network, the optimal operation status analysis model is constructed; Based on machine learning, multiple historical electrical data sets are labeled and divided to obtain training sets, validation sets, and test sets; The optimal operating status analysis model is supervisedly trained, verified and tested using a training set, a validation set and a test set to obtain the optimal operating status analysis model whose accuracy meets the preset accuracy requirements.

6. The electrical automation processing method based on data analysis according to claim 5 is characterized in that ,The calculation formula of the load rate of the electrical automation system is: ; In the formula, is the load rate of the electrical automation system; is the load power; is the rated power; is the power factor; The working efficiency calculation formula of the electrical automation system is: ; In the formula, To improve the working efficiency of electrical automation system; is the load rate of the electrical automation system; is the no-load loss; is the rated power.

7. The electrical automation processing method based on data analysis according to claim 6 is characterized in that , the steps of comparing the calculated work efficiency, obtaining the comparison results and providing feedback on the results specifically include the following steps: Compare the calculated work efficiency with a preset threshold; If the calculated work efficiency is greater than or equal to the preset threshold, the electrical automation processing system does not need to make parameter adjustments; If the calculated work efficiency is less than a preset threshold, it is necessary to perform a secondary analysis on the corresponding parameters at this time, and the secondary analysis is a similarity calculation.

8. The electrical automation processing method based on data analysis according to claim 7 is characterized in that ,The formula for calculating the similarity between the real-time processing parameter data and the standard electrical data is: ; In the formula, is the real-time processing parameter data and the similarity of the i-th feature with the standard electrical data, is the jth feature index value of the i-th feature of the real-time processing parameter data, is the jth characteristic index value of the i-th characteristic of the standard electrical data, is the total number of characteristic index values ​​of the ith feature.

9. An electrical automation processing system based on data analysis, used to implement an electrical automation processing method based on data analysis as claimed in any one of claims 1 to 8, characterized in that ,include: An installation module, the installation module is used to install sensors on the equipment in the processing workshop to collect automated processing parameter information, and the types of sensors include power sensors and temperature sensors, etc.; A data processing module, which is used to remove duplicates and fill missing values ​​in the collected automated processing parameter information to obtain complete processing parameter information with analytical significance; A construction module, wherein the construction module is used to retrieve historical processing parameter information and the operating status of the electrical automation system under different parameters from a historical database to form a historical electrical data set, and to establish an optimal operating status analysis model based on a feedforward neural network; A calculation module, which is used to retrieve real-time processing load power information, rated power information, no-load loss information and power factor information from the complete processing parameter information, and calculate the load rate and work efficiency of the electrical automation system; A comparison module, which is used to compare the calculated work efficiency, obtain the comparison result and provide feedback on the result; The adjustment module is used to retrieve the electrical parameter data corresponding to the optimal operating state, record it as standard electrical data, and calculate the similarity between the real-time processing parameter data and the standard electrical data. If the similarity value is too high, no adjustment is required. If the similarity value is too low, the electrical system is controlled to be adjusted so that the automated processing system reaches the optimal operating state.

10. An electronic device comprising at least one processor; and a memory connected to the at least one processor in communication; characterized in that: The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

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