Data processing method and system of electronic information technology based on big data

Abstract

The invention discloses a data processing method and system of an electronic information technology based on big data, and relates to the technical field of production data processing. According to the method, the theoretical highest operation temperature and the theoretical allowable abnormal sound value are obtained by calculating the operation duration of the equipment and matching the operation duration with the preset duration value range, the hidden danger index is judged by combining the actual operation evaluation value and the abnormal sound evaluation value, whether the equipment normally operates or not can be accurately determined, and potential safety hazards and early warning equipment can be accurately judged. According to the method, the fault indexes are matched with the pre-constructed database, the hidden danger reasons are calculated and determined based on the historical fault cases and the fault index difference values, and compared with traditional troubleshooting depending on manual experience, the accuracy and efficiency of fault diagnosis are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of production data processing technology, specifically to data processing methods and systems based on big data electronic information technology. Background Technology

[0002] In the manufacturing industry, the continuous expansion of production scale and the increasing complexity of production processes have generated massive amounts of data.

[0003] However, existing data processing methods based on big data in electronic information technology still have the following shortcomings when applied to the production process: The production workshop generates massive amounts of data during the production process, including equipment operating parameters, product quality indicators, and employee performance information. Existing data processing methods lack effective processing and analysis of this data, and most of it is stored and judged based on human experience, which leads to a lack of guarantee for production efficiency and equipment stability.

[0004] To this end, data processing methods and systems based on big data electronic information technology have been developed. Summary of the Invention

[0005] The purpose of this invention is to solve the problems pointed out in the background art by proposing a data processing method and system based on big data electronic information technology.

[0006] The objective of this invention can be achieved through the following technical solution: a data processing method based on big data electronic information technology, comprising: Data Tagging: The system receives operational data from various numbered equipment, product quality data, and employee performance data during the production process in the production workshop. It then tags these data as hot data, warm data, and cold data, respectively, and transmits them to the corresponding storage areas for storage based on the tagging results. Operational data includes runtime, operating temperature, and sound data; product quality data includes product pass rate, defect rate, and production quantity; and performance data includes attendance status, assigned product assembly quantity, completed assembly quantity, and number of incorrectly assembled products. Data processing: Extract and analyze the operating data of each numbered device within the set time window to determine the hidden danger index of each numbered device within the current set time window; set the evaluation deadline for product quality data and employee performance data; after the corresponding evaluation deadline is reached, analyze the product quality data and employee performance data in the production process to determine the output index M and employee performance index Ui of the current production process; where i represents the code of each employee; Results generated: Based on the identified hazard index of each numbered piece of equipment, the corresponding steps are executed to determine the cause of the hazard. Based on the production output index M and the performance index Ui of each employee during the production process, a comprehensive evaluation report of the current production process is generated.

[0007] In a preferred embodiment of the present invention, the hazard index of each numbered device within the current set time window is determined as follows: Obtain the start time of each numbered device and calculate the time difference between it and the end time of the current set time window to obtain the running time of each numbered device. Match the running time of each numbered device with the corresponding preset time range. Each time range corresponds to the theoretical maximum operating temperature and theoretical allowable abnormal noise value of a device. The theoretical maximum operating temperature and theoretical allowable abnormal noise value are marked as fa and fb, respectively. Extract the temperature values ​​of each numbered device at each time point in the current set time window and take the average value as the operating evaluation value ta of each numbered device in the current set time window; calculate the root mean square value of the sound signal of each numbered device in the current set time window and take it as the abnormal noise evaluation value tb of each numbered device in the current set time window. The operating evaluation value ta and abnormal noise evaluation value tb of each numbered device within the current set time window are compared with the corresponding theoretical maximum operating temperature fa and theoretical allowable abnormal noise value fb, respectively. If the comparison of a certain numbered device shows... If the hazard index of a certain numbered device is 0, then the hazard index of that device is determined to be 0; conversely, if the comparison result of a certain numbered device is... One of these groups corresponds to a potential hazard index of 1 for the equipment.

[0008] In a preferred embodiment of the present invention, after the corresponding evaluation deadline is reached, the product quality data during the production process is analyzed to determine the current production output index M, specifically as follows: Obtain the current production quantity of products in the production process, and calculate the proportion of qualified products and defective products in the total production quantity to obtain the product qualification rate and defect rate. The minimum pass rate and allowable defect rate corresponding to the preset product pass rate and defect rate are set; the current product pass rate and defect rate in the production process are marked as p1 and p2 respectively, and the minimum pass rate and allowable defect rate are marked as k1 and k2; According to the formula The current production process yields a weighted average of the product pass rate p1 and the defect rate p2 to determine the current production output index M; whereby... These are the influence weighting factors for product pass rate p1 and defect rate p2, respectively.

[0009] In a preferred embodiment of the present invention, after the corresponding evaluation deadline is reached, the performance data of each employee in the production process are analyzed to determine the performance index Ui of each employee in the current production process, specifically as follows: Obtain the actual number of days each employee worked and divide it by the number of days required for the current production process to get the attendance ratio of each employee in the current production process. Extract the number of completed assemblies and the number of products assigned to each employee in the current production process, and calculate the ratio of the number of completed assemblies to the number of products assigned to each employee to obtain the contribution ratio of each employee in the current production process. Extract the number of assembled products with assembly errors for each employee in the current production process, and calculate the proportion of the number of assembled products with assembly errors to the number of completed assemblies, to obtain the quality ratio of each employee in the current production process. Label each employee's attendance rate, effort rate, and quality rate in the current production process as Gi, Di, and Ni, respectively; and substitute them into the formula. A weighted calculation is performed to obtain the performance index Ui of each employee number in the current production process; where These are the influence weighting factors for attendance ratio Gi, effort ratio Di, and quality ratio Ni, respectively.

[0010] In a preferred embodiment of the present invention, the corresponding steps to determine the cause of the potential problem are as follows: If the hazard index of a certain group of numbered equipment is determined to be 1, and the comparison result is... If the equipment is identified as having a potential safety hazard, its operational assessment value ta and abnormal noise assessment value tb are extracted, and then processed according to the formula... A weighted calculation is performed to obtain the failure index vs of the potentially hazardous equipment; where These are the influence weighting factors for the operational evaluation value ta and the abnormal noise evaluation value tb, respectively. The fault index vs of the potentially hazardous equipment is input into a pre-built potential hazard database for matching. The database stores historical fault cases and fault indices before each fault occurred for each numbered piece of equipment. The difference between the fault index vs of the potentially hazardous equipment and the corresponding historical fault index vs is calculated, and the absolute value is taken. The historical fault index with the smallest difference is identified, and the corresponding historical fault case is extracted as the cause of the potential hazard. The number of the potentially hazardous equipment and the cause of the potential hazard are then sent to the technical personnel.

[0011] In a preferred embodiment of the present invention, the steps of determining the cause of the potential hazard further include: If the hazard index of a certain group of numbered equipment is determined to be 1, and the comparison result is... If a device is identified as a potential early warning device, its fault index (vs) is entered into a pre-built early warning database for matching. The database stores historical fault cases and fault indices at the time of each case. The difference between the fault index (vs) of the early warning device and the corresponding historical fault indices (vs) is calculated, and the absolute value is taken. The historical fault index with the smallest difference is identified, and the corresponding historical fault case is extracted as the cause of the potential hazard of the early warning device. A circle is drawn with the location of the early warning device as the center and the distance as the radius. The technicians who are closest to the early warning device within the circle are selected as the solution personnel. The early warning device number and the cause of the potential hazard are sent to the solution personnel.

[0012] In a preferred embodiment of the present invention, a comprehensive evaluation report of the current production process is generated, specifically as follows: Extract the output index M of the current production process, and define the intervals of the three sets of indices corresponding to the output index M. Each interval of the indices corresponds to a production evaluation level. The production evaluation levels include poor, average, and excellent levels. The production index M in the current production process is matched with the range of the three sets of indices to determine the production evaluation level of the current production process. Set a passing score for each employee's performance index Ui, compare each employee's performance index Ui with the corresponding passing score, and if an employee's performance index Ui is higher than the corresponding passing score, the employee is judged as qualified; otherwise, the employee is judged as abnormal. The production assessment level, qualified employees, and abnormal employees of the current production process are filled into a pre-built report template to obtain a comprehensive assessment report of the current production process.

[0013] Data processing systems based on big data electronic information technology include: Data Acquisition Module: Collects real-time operational data generated by each numbered piece of equipment in the production workshop during the production process, and simultaneously records product quality data and employee performance data during the production process. Storage module: Receives operating data of each numbered device, product quality data, and employee performance data during the production process in the production workshop. It marks the operating data of each numbered device, product quality data, and employee performance data as hot data, warm data, and cold data, respectively, and transmits them to the corresponding storage area for storage according to the marking results. Analysis module: Extracts and analyzes the operating data of each numbered device within a set time window from the thermal data to determine the hazard index of each numbered device within the current set time window; sets the evaluation deadline for product quality data and employee performance data; after the corresponding evaluation deadline is reached, analyzes the product quality data and employee performance data in the production process to determine the output index M and employee performance index Ui of the current production process. Generation module: Based on the determined hidden danger index of each numbered equipment, it executes the corresponding steps to determine the cause of the hidden danger, and generates a comprehensive evaluation report of the current production process based on the output index M and the performance index Ui of each employee in the production process.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention calculates the equipment's operating time and matches it with a preset time range to obtain the theoretical maximum operating temperature and the theoretical allowable abnormal noise value. Combining the actual operating evaluation value and the abnormal noise evaluation value, it determines the hidden danger index, which can accurately determine whether the equipment is operating normally. For equipment with hidden dangers and early warning equipment, the fault index is matched with a pre-built database, and the cause of the hidden danger is determined based on the difference between historical fault cases and the fault index. Compared with the traditional method of relying on manual experience to troubleshoot faults, this invention greatly improves the accuracy and efficiency of fault diagnosis. This invention collects data on the number of products produced, the number of qualified products, and the number of defective products, calculates the product qualification rate and the defect rate, and then calculates the production index through weighted average. This comprehensively and accurately reflects the actual product quality status. Different production evaluation levels are set based on the production index to help enterprises clearly understand the production quality level and then make targeted improvements to the production process to improve product quality. This invention collects multi-dimensional performance data on employee attendance, work completion quantity, and work quality, calculates attendance ratio, effort ratio, and quality ratio, and obtains an employee performance index through weighted calculation. This avoids the subjective bias and one-sidedness of traditional performance evaluation. By comparing the employee performance index with the passing index, qualified employees and abnormal employees can be distinguished, providing an objective basis for enterprise human resource management. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the principle of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 Please see Figure 1 As shown, the data processing methods of electronic information technology based on big data include: Data Acquisition: All equipment in the production workshop is numbered, and various sensors, such as temperature and sound sensors, are deployed. Real-time operational data generated by each numbered piece of equipment during production is collected. This operational data includes runtime, operating temperature, and sound data. Simultaneously, product quality data and employee performance data are recorded during the production process. Product quality data includes product pass rate, defect rate, and production quantity. Performance data includes attendance status, assigned product assembly quantity, completed assembly quantity, and number of incorrectly assembled products. It should be noted that various sensors, such as temperature sensors and sound sensors, are deployed on various equipment in the workshop. These sensors collect the equipment's operating parameters in real time, including the equipment's operating temperature and the amplitude of abnormal mechanical noise. Simultaneously, through the equipment's built-in communication interface, operating status information is obtained, such as the equipment's running time and cumulative number of operations. Multiple quality inspection points are set up on the product production line, and various inspection methods are used to obtain product quality data. For example, image recognition technology is used to collect product appearance images through cameras, analyze whether there are scratches, cracks, defects, etc., and send them to workshop staff for review. After the review is completed, the product is judged as qualified or defective. The attendance system in the production workshop records employee attendance, including get off work hours, leave records, etc., and collects information on employee work assignments and task completion progress. Data tagging: Receives operating data of each numbered piece of equipment, product quality data, and employee performance data during the production process in the production workshop. Tags the operating data of each numbered piece of equipment, product quality data, and employee performance data as hot data, warm data, and cold data, respectively, and transmits them to the corresponding storage area for storage based on the tagging results. It should be noted that hot data is stored using high-speed, low-latency storage devices, such as distributed storage systems built with solid-state drives (SSDs), which can quickly write and read real-time operating data from the devices. The storage of product quality data emphasizes data integrity and security, and uses relational databases such as MySQL to store structured product quality data. Employee performance data storage focuses on long-term data preservation and data mining, using data warehouse technologies such as Hive to integrate and store various types of personnel data; Data processing: Extract and analyze the operating data of each numbered device within a set time window from the thermal data to determine the hazard index of each numbered device within the current set time window; set the evaluation deadline for product quality data and employee performance data; after the corresponding evaluation deadline is reached, analyze the product quality data and employee performance data in the production process to determine the output index M and employee performance index Ui of the current production process; where i represents the code of each employee, i=1,2,...,r, and r is the total number of employees in the production workshop; The hazard index for each numbered device within the current set time window is determined as follows: Obtain the start time of each numbered device and calculate the time difference between it and the end time of the current set time window to obtain the running time of each numbered device. Match the running time of each numbered device with the corresponding preset time range. Each time range corresponds to the theoretical maximum operating temperature and theoretical allowable abnormal noise value of a device. The theoretical maximum operating temperature and theoretical allowable abnormal noise value are marked as fa and fb, respectively. Extract the temperature values ​​of each numbered device at each time point in the current set time window and take the average value as the operating evaluation value ta of each numbered device in the current set time window; calculate the root mean square value of the sound signal of each numbered device in the current set time window and take it as the abnormal noise evaluation value tb of each numbered device in the current set time window. The operating evaluation value ta and abnormal noise evaluation value tb of each numbered device within the current set time window are compared with the corresponding theoretical maximum operating temperature fa and theoretical allowable abnormal noise value fb, respectively. If the comparison of a certain numbered device shows... If the hazard index of a certain numbered device is 0, then the hazard index of that device is determined to be 0; conversely, if the comparison result of a certain numbered device is... One of these groups corresponds to a potential hazard index of 1 for the equipment. It should be noted that by calculating the equipment's operating time and matching it with a preset range of time values ​​to obtain the corresponding theoretical maximum operating temperature and theoretical allowable abnormal noise values, this method takes into account the dynamic changes in the equipment's operating status over time. The normal operating parameters of the equipment differ under different operating times, and judging the equipment status solely based on fixed standards is not accurate enough. For example, the normal operating temperature and allowable abnormal noise levels differ between newly started equipment and equipment that has been running continuously for a long time. Combining the evaluation with the operating time allows for a more accurate determination of whether the equipment is in normal operating condition, avoiding misjudgments and omissions. After reaching the corresponding evaluation deadline, the product quality data during the production process is analyzed to determine the current production output index M, specifically: Obtain the current production quantity of products in the production process, and calculate the proportion of qualified products and defective products in the total production quantity to obtain the product qualification rate and defect rate. Based on the current quality requirements of the manufactured products, the minimum pass rate and the allowable defect rate corresponding to the product pass rate and defect rate are preset; the product pass rate and defect rate in the current production process are marked as p1 and p2 respectively, and the minimum pass rate and the allowable defect rate are marked as k1 and k2; According to the formula The yield index M of the current production process is determined by weighting the product pass rate p1 and defect rate p2. These are the weighting factors affecting the product pass rate p1 and the defect rate p2, respectively. It should be noted that by using big data technology to collect a large amount of product quality data during the production process, including the number of products produced, the number of qualified products, and the number of defective products, and by analyzing this data in detail, the product qualification rate and defect rate can be calculated respectively, which can comprehensively and accurately reflect the actual situation of product quality. After the corresponding evaluation deadline is reached, the performance data of each employee in the production process are analyzed to determine the current performance index Ui of each employee in the production process, specifically: Obtain the actual number of days each employee worked and divide it by the number of days required for the current production process to get the attendance ratio of each employee in the current production process. Extract the number of completed assemblies and the number of products assigned to each employee in the current production process, and calculate the ratio of the number of completed assemblies to the number of products assigned to each employee to obtain the contribution ratio of each employee in the current production process. Extract the number of assembled products with assembly errors for each employee in the current production process, and calculate the proportion of the number of assembled products with assembly errors to the number of completed assemblies, to obtain the quality ratio of each employee in the current production process. Label each employee's attendance rate, effort rate, and quality rate in the current production process as Gi, Di, and Ni, respectively; and substitute them into the formula. A weighted calculation is performed to obtain the performance index Ui of each employee number in the current production process; where These are the influence weighting factors for attendance ratio Gi, effort ratio Di, and quality ratio Ni, respectively. It should be noted that big data technology is used to collect multi-dimensional performance data from employees, including attendance, quantity of work completed, and work quality. By quantitatively analyzing this data and calculating attendance ratio, effort ratio, and quality ratio, the subjective bias and one-sidedness that may exist in traditional performance evaluations are avoided. Compared to evaluating employee performance based on only a single indicator (such as quantity of work), this comprehensive data collection and analysis method can more accurately reflect the actual performance of employees in the production process, providing an objective and reliable basis for employee performance evaluation.

[0019] Results generated: Based on the identified hazard index of each numbered piece of equipment, the corresponding steps are executed to determine the cause of the hazard. Based on the production output index M and the performance index Ui of each employee during the production process, a comprehensive evaluation report of the current production process is generated. The appropriate steps were taken to determine the cause of the potential hazard, specifically: Step 1: If the hazard index of a certain group of numbered equipment is determined to be 1, and the comparison result is... If the equipment is identified as having a potential safety hazard, its operational assessment value ta and abnormal noise assessment value tb are extracted, and then processed according to the formula... A weighted calculation is performed to obtain the failure index vs of the potentially hazardous equipment; where These are the influence weighting factors for the operational evaluation value ta and the abnormal noise evaluation value tb, respectively. The fault index vs of the potentially hazardous equipment is input into a pre-built potential hazard database for matching. The potential hazard database stores historical fault cases and fault indices before each fault occurred for each numbered piece of equipment. The difference between the fault index vs of the potentially hazardous equipment and the corresponding historical fault index vs is calculated, and the absolute value is taken. The historical fault index with the smallest difference is identified, and the corresponding historical fault case is extracted as the cause of the potential hazard. The number of the potentially hazardous equipment and the cause of the potential hazard are sent to the technical personnel. Step Two: If the hazard index of a certain group of numbered equipment is determined to be 1, and the comparison result is... If a device is identified as a potential early warning device, its fault index (vs) is entered into a pre-built early warning database for matching. The database stores historical fault cases and fault indices at the time of each case. The difference between the fault index (vs) of the early warning device and the corresponding historical fault indices (vs) is calculated, and the absolute value is taken. The historical fault index with the smallest difference is identified, and the corresponding historical fault case is extracted as the cause of the potential danger of the early warning device. A circle is drawn with the location of the early warning device as the center and the distance as the radius. The technicians who are closest to the early warning device within the circle are selected as the solution personnel. The number of the early warning device and the cause of the potential danger are sent to the solution personnel. It should be noted that by judging the equipment hazard index, equipment with potential hazards can be quickly identified. For equipment with potential hazards and early warning equipment, the fault index is matched with a pre-built database (hazard database and early warning database). Based on the difference between historical fault cases and fault index, the possible causes of the current equipment hazard are accurately located. Compared with the traditional method of troubleshooting based on manual experience, the accuracy and efficiency of fault diagnosis are greatly improved by big data analysis. Different handling strategies are adopted for different types of potentially hazardous equipment (potentially hazardous equipment and early warning equipment). For potentially hazardous equipment, the equipment number and cause of the potential hazard are sent directly to the technical personnel so that they can take further action after assessment. For early warning equipment, the nearest technical personnel are selected based on the equipment location to handle the issue, ensuring that a response can be made as soon as a potential problem occurs. This rapid response mechanism can effectively reduce equipment downtime and reduce production losses caused by equipment failure. For example, in manufacturing, every minute of equipment downtime can result in huge economic losses, and timely maintenance response can minimize the occurrence of such situations. Generate a comprehensive evaluation report of the current production process, specifically as follows: Extract the output index M of the current production process, and define the intervals of the three sets of indices corresponding to the output index M. Each interval of the indices corresponds to a production evaluation level. The production evaluation levels include poor, average, and excellent levels. The production index M in the current production process is matched with the range of the three sets of indices to determine the production evaluation level of the current production process. Set a passing score for each employee's performance index Ui; this is set by managers based on each employee's length of service and performance; compare each employee's performance index Ui with the corresponding passing score. If an employee's performance index Ui is higher than the corresponding passing score, they are considered a qualified employee; otherwise, they are considered an abnormal employee. The production assessment level, qualified employees, and abnormal employees of the current production process are filled into a pre-built report template to obtain a comprehensive assessment report of the current production process. It should be noted that by filling in information such as production assessment levels, qualified employees, and abnormal employees into a pre-built report template, a comprehensive assessment report is generated, providing enterprise management with comprehensive and systematic information. Management can use this report to understand the overall status of production and employees, and make more informed decisions, such as developing production plans, adjusting staffing, and optimizing resource allocation.

[0020] Example 2 Please see Figure 2 As shown, based on the data processing method of electronic information technology based on big data provided in Embodiment 1 of this application, Embodiment 2 of this application proposes a data processing system of electronic information technology based on big data. Embodiment 2 is merely a preferred embodiment of Embodiment 1, and the implementation of Embodiment 2 will not affect the separate implementation of Embodiment 1.

[0021] Specifically, the data processing system based on big data electronic information technology provided in Embodiment 2 of this application differs in that it includes: The data acquisition module is used to collect real-time operational data generated by each numbered piece of equipment in the production workshop during the production process, and to record product quality data and employee performance data during the production process. The storage module is used to receive the operating data of each numbered equipment, product quality data and employee performance data during the production process in the production workshop. It marks the operating data of each numbered equipment, product quality data and employee performance data as hot data, warm data and cold data respectively, and transmits them to the corresponding storage area for storage according to the marking results. The analysis module is used to extract the operating data of each numbered device within a set time window from the thermal data for analysis, and to determine the hidden danger index of each numbered device within the current set time window; it sets the evaluation deadline for product quality data and employee performance data, and after the corresponding evaluation deadline is reached, it analyzes the product quality data and employee performance data in the production process to determine the output index M and employee performance index Ui of the current production process. The generation module is used to determine the cause of potential hazards based on the identified hazard index of each numbered piece of equipment, and to generate a comprehensive evaluation report of the current production process based on the output index M and the performance index Ui of each employee. The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data processing method based on big data electronic information technology, characterized in that, include: Data tagging: Receives operating data of each numbered piece of equipment, product quality data, and employee performance data during the production process in the production workshop. Tags the operating data of each numbered piece of equipment, product quality data, and employee performance data as hot data, warm data, and cold data, respectively, and transmits them to the corresponding storage area for storage based on the tagging results. The operational data includes runtime, operating temperature, and sound data; the product quality data includes product pass rate, defect rate, and production quantity; and the performance data includes attendance status, assigned product assembly quantity, completed assembly quantity, and number of incorrectly assembled products. Data processing: Extract and analyze the operating data of each numbered device within the set time window to determine the hidden danger index of each numbered device within the current set time window; set the evaluation deadline for product quality data and employee performance data; after the corresponding evaluation deadline is reached, analyze the product quality data and employee performance data in the production process to determine the output index M and employee performance index Ui of the current production process; where i represents the code of each employee; Results generated: Based on the identified hazard index of each numbered piece of equipment, the corresponding steps are executed to determine the cause of the hazard. Based on the production output index M and the performance index Ui of each employee during the production process, a comprehensive evaluation report of the current production process is generated.

2. The data processing method based on big data electronic information technology according to claim 1, characterized in that, The hazard index for each numbered device within the current set time window is determined as follows: Obtain the start time of each numbered device and calculate the time difference between it and the end time of the current set time window to obtain the running time of each numbered device. Match the running time of each numbered device with the corresponding preset time range. Each time range corresponds to the theoretical maximum operating temperature and theoretical allowable abnormal noise value of a device. The theoretical maximum operating temperature and theoretical allowable abnormal noise value are marked as fa and fb, respectively. Extract the temperature values ​​of each numbered device at each time point in the current set time window and take the average value as the operating evaluation value ta of each numbered device in the current set time window; calculate the root mean square value of the sound signal of each numbered device in the current set time window and take it as the abnormal noise evaluation value tb of each numbered device in the current set time window. The operating evaluation value ta and abnormal noise evaluation value tb of each numbered device within the current set time window are compared with the corresponding theoretical maximum operating temperature fa and theoretical allowable abnormal noise value fb, respectively. If the comparison of a certain numbered device shows... If the hazard index of a certain numbered device is 0, then the hazard index of that device is determined to be 0; conversely, if the comparison result of a certain numbered device is... One of these groups corresponds to a potential hazard index of 1 for the equipment.

3. The data processing method based on big data electronic information technology according to claim 2, characterized in that, After reaching the corresponding evaluation deadline, the product quality data during the production process is analyzed to determine the current production output index M, specifically: Obtain the current production quantity of products in the production process, and calculate the proportion of qualified products and defective products in the total production quantity to obtain the product qualification rate and defect rate. The minimum pass rate and allowable defect rate corresponding to the preset product pass rate and defect rate are set; the current product pass rate and defect rate in the production process are marked as p1 and p2 respectively, and the minimum pass rate and allowable defect rate are marked as k1 and k2; According to the formula The current production process yields a weighted average of the product pass rate p1 and the defect rate p2 to determine the current production output index M; whereby... These are the influence weighting factors for product pass rate p1 and defect rate p2, respectively.

4. The data processing method based on big data electronic information technology according to claim 3, characterized in that, After the corresponding evaluation deadline is reached, the performance data of each employee in the production process are analyzed to determine the current performance index Ui of each employee in the production process, specifically: Obtain the actual number of days each employee worked and divide it by the number of days required for the current production process to get the attendance ratio of each employee in the current production process. Extract the number of completed assemblies and the number of products assigned to each employee in the current production process, and calculate the ratio of the number of completed assemblies to the number of products assigned to each employee to obtain the contribution ratio of each employee in the current production process. Extract the number of assembled products with assembly errors for each employee in the current production process, and calculate the proportion of the number of assembled products with assembly errors to the number of completed assemblies, to obtain the quality ratio of each employee in the current production process. Label each employee's attendance rate, effort rate, and quality rate in the current production process as Gi, Di, and Ni, respectively; and substitute them into the formula. A weighted calculation is performed to obtain the performance index Ui of each employee number in the current production process; where These are the influence weighting factors for attendance ratio Gi, effort ratio Di, and quality ratio Ni, respectively.

5. The data processing method based on big data electronic information technology according to claim 4, characterized in that, The appropriate steps were taken to determine the cause of the potential hazard, specifically: If the hazard index of a certain group of numbered equipment is determined to be 1, and the comparison result is... If the equipment is identified as having a potential hazard, its operational assessment value ta and abnormal noise assessment value tb are extracted, and then processed according to the formula... A weighted calculation is performed to obtain the failure index vs of the potentially hazardous equipment; where These are the influence weighting factors for the operational evaluation value ta and the abnormal noise evaluation value tb, respectively. The fault index vs of the potentially hazardous equipment is input into a pre-built potential hazard database for matching. The database stores historical fault cases and fault indices before each fault occurred for each numbered piece of equipment. The difference between the fault index vs of the potentially hazardous equipment and the corresponding historical fault index vs is calculated, and the absolute value is taken. The historical fault index with the smallest difference is identified, and the corresponding historical fault case is extracted as the cause of the potential hazard. The number of the potentially hazardous equipment and the cause of the potential hazard are then sent to the technical personnel.

6. The data processing method based on big data electronic information technology according to claim 5, characterized in that, To determine the cause of any potential problems, the corresponding steps include: If the hazard index of a certain group of numbered equipment is determined to be 1, and the comparison result is... If a device is identified as a potential early warning device, its fault index (vs) is entered into a pre-built early warning database for matching. The database stores historical fault cases and fault indices at the time of each case. The difference between the fault index (vs) of the early warning device and the corresponding historical fault indices (vs) is calculated, and the absolute value is taken. The historical fault index with the smallest difference is identified, and the corresponding historical fault case is extracted as the cause of the potential hazard of the early warning device. A circle is drawn with the location of the early warning device as the center and the distance as the radius. The technicians who are closest to the early warning device within the circle are selected as the solution personnel. The early warning device number and the cause of the potential hazard are sent to the solution personnel.

7. The data processing method based on big data electronic information technology according to claim 6, characterized in that, Generate a comprehensive evaluation report of the current production process, specifically as follows: Extract the output index M of the current production process, and define the intervals of the three sets of indices corresponding to the output index M. Each interval of the indices corresponds to a production evaluation level. The production evaluation levels include poor, average, and excellent levels. The production index M in the current production process is matched with the range of the three sets of indices to determine the production evaluation level of the current production process. Set a passing score for each employee's performance index Ui, compare each employee's performance index Ui with the corresponding passing score, and if an employee's performance index Ui is higher than the corresponding passing score, the employee is judged as qualified; otherwise, the employee is judged as abnormal. The production assessment level, qualified employees, and abnormal employees of the current production process are filled into a pre-built report template to obtain a comprehensive assessment report of the current production process.

8. A data processing system based on big data electronic information technology, applied to the data processing method based on big data electronic information technology proposed in any one of claims 1-7, characterized in that, include: Data Acquisition Module: Collects real-time operational data generated by each numbered piece of equipment in the production workshop during the production process, and simultaneously records product quality data and employee performance data during the production process. Storage module: Receives operating data of each numbered device, product quality data, and employee performance data during the production process in the production workshop. It marks the operating data of each numbered device, product quality data, and employee performance data as hot data, warm data, and cold data, respectively, and transmits them to the corresponding storage area for storage according to the marking results. Analysis module: Extracts and analyzes the operating data of each numbered device within a set time window from the thermal data to determine the hazard index of each numbered device within the current set time window; sets the evaluation deadline for product quality data and employee performance data; after the corresponding evaluation deadline is reached, analyzes the product quality data and employee performance data in the production process to determine the output index M and employee performance index Ui of the current production process. Generation module: Based on the determined hidden danger index of each numbered equipment, it executes the corresponding steps to determine the cause of the hidden danger, and generates a comprehensive evaluation report of the current production process based on the output index M and the performance index Ui of each employee in the production process.

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