An intelligent monitoring system for the production and preparation of electronic products

Abstract

The present invention discloses an intelligent monitoring system in the production and preparation of electronic products, including an environmental information collection module, a welding information collection module, an employee information collection module, an equipment information collection module, a comprehensive processing module and an information transmission module; the environmental information collection module is used to collect environmental temperature information and environmental humidity information in the production and preparation of electronic products; the welding information collection module is used to collect welding information in the production and preparation of electronic products; the employee information collection module is used to collect employee information; the comprehensive processing module is used to process the collected environmental temperature information, environmental humidity information, welding information, employee information and mechanical equipment information to obtain a first error probability, a second error probability and a third error probability. The present invention can accurately obtain the comprehensive error probability, accurately judge the yield rate of electronic products, reduce the workload of manual sampling, and more intelligently monitor the production and preparation process of electronic products.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring, and in particular to an intelligent monitoring system in the production and preparation of electronic products. Background Art

[0002] By processing and analyzing the collected information, the system can calculate the first, second and third error probabilities, which respectively reflect the possible adverse effects of the environment, employees and mechanical equipment on the production process. By further processing these error probabilities, the system can generate electronic product preparation information, including welding quality, employee performance, mechanical equipment status and overall preparation status;

[0003] By real-time monitoring and analysis of key parameters in the production process, the system can help managers identify potential problems in a timely manner and take corresponding measures to prevent and correct them, thereby avoiding production interruptions and product defects and improving production efficiency and product quality.

[0004] The existing monitoring system is unable to monitor and analyze the key parameters in the production process in real time, resulting in the system being unable to help managers discover potential problems in a timely manner. The existing monitoring system is not intelligent enough, resulting in a large degree of manual intervention, which has a certain impact on the use of the monitoring system. Therefore, an intelligent monitoring system for the production and preparation of electronic products is proposed. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides an intelligent monitoring system in the production and preparation of electronic products.

[0006] The present invention solves the above technical problems through the following technical solutions, which include an environmental information collection module, a welding information collection module, an employee information collection module, an equipment information collection module, a comprehensive processing module and an information transmission module;

[0007] The environmental information collection module is used to collect environmental temperature information and environmental humidity information during the production and preparation of electronic products;

[0008] The welding information collection module is used to collect welding information in the production and preparation of electronic products;

[0009] The employee information collection module is used to collect employee information;

[0010] The equipment information collection module is used to collect mechanical equipment information;

[0011] The comprehensive processing module is used to process the collected ambient temperature information, ambient humidity information, welding information, employee information and mechanical equipment information to obtain a first error probability, a second error probability and a third error probability;

[0012] Then, the first error probability, the second error probability and the third error probability are processed by a comprehensive processing module to obtain electronic product preparation information, where the electronic product preparation information includes welding quality information, employee scoring information, mechanical equipment status information and electronic product qualification rate information;

[0013] The information transmission module is used to transmit the acquired electronic product preparation information to the administrator terminal.

[0014] Further, the specific process of obtaining the first error probability is as follows: the ambient temperature information and the ambient humidity information in the production and preparation of the electronic product within the preset time T are processed, the ambient temperature information in the production and preparation of the electronic product within the preset time T is recorded as f(t), and the ambient humidity information in the production and preparation of the electronic product is recorded as g(t), and the environmental parameters are obtained;

[0015] The welding information in the production and preparation of electronic products is processed. The welding information in the production and preparation of electronic products includes welding temperature information recorded as h and welding time information recorded as j, and welding parameters are obtained. The environmental parameters and welding parameters are comprehensively processed to obtain the first error probability. The first error probability is processed to obtain welding quality information.

[0016] Further, the specific process of comprehensive processing of environmental parameters and welding parameters is as follows:

[0017] By formula Calculate the average environmental temperature f during the production and preparation of electronic products within a preset time T;

[0018] By formula Calculate the average humidity value g of the environment during the production and preparation of electronic products within a preset time T;

[0019] The environmental parameter W is calculated by the formula W=f*w1+g*w2, where w1 is the weight of f and w2 is the weight of g, w1+w2=1, w1>w2;

[0020] By formula Obtain welding parameter information k, where a and b are proportional parameters with different values ​​and both greater than zero, and a and b are proportional;

[0021] The first error probability L1 is obtained through the formula 1-(1-W)*(1-k)=L1.

[0022] Further, the specific process of obtaining the welding quality information is as follows: the welding quality information includes abnormal welding information and normal welding information, the first error probability is collected once every preset time, and the collection is performed p times continuously, and the p first error probabilities collected are calculated to obtain the first error rate average value, wherein p is a positive integer, and p≥100;

[0023] Subtract the p first error probabilities from the average of the first error probabilities to obtain p first error probability differences;

[0024] When any one of the p first error probability differences is greater than or equal to a preset difference, welding abnormality information is generated;

[0025] When the p first error probability differences are all smaller than the preset difference, normal welding information is generated.

[0026] Furthermore, the specific process of obtaining the second error probability is as follows: the employee information is the professional knowledge test scores of N employees, that is, the test content is sent to N employees at random time, the employees complete the test within the specified time and obtain the test scores, the professional knowledge test scores of the N employees are processed, the professional knowledge test scores of the N employees are recorded as A1, A2, ..., AN, the first employee evaluation parameters are obtained, the electronic products assembled by the employees are manually sampled, the second employee evaluation parameters are obtained, the first employee evaluation parameters and the second employee evaluation parameters are comprehensively processed, the second error probability is obtained, the second error probability is processed, and the employee scoring information is obtained.

[0027] Further, the specific process of comprehensively processing the first employee evaluation parameter and the second employee evaluation parameter is as follows:

[0028] Pass first Calculate the mean test score D of N employees;

[0029] Then through Calculate the mean variance E of the test scores of N employees;

[0030] Last passed Obtain the first employee evaluation parameter M;

[0031] Where N is the total number of employees, Ai represents the test score of the i-th employee;

[0032] The number of electronic products assembled by employees for manual sampling is preset as U, where the number of qualified electronic products assembled by employees is recorded as G, and the second employee evaluation parameter V=G / U;

[0033] By formula Obtain a second error probability L2, where w3 is the weight of M and w4 is the weight of V.

[0034] Further, the specific process of obtaining the employee rating information is as follows: the employee rating information includes employee evaluation abnormal information and employee evaluation normal information, the second error probability is collected once every preset time, and the collection is performed q times in succession, and the q collected second error probabilities are calculated to obtain the second error probability average value, wherein q is a positive integer, and q≥5;

[0035] Subtract the q second error probabilities from the second error probability average to obtain q second error probability differences;

[0036] When any one of the q second error probability differences is greater than or equal to a preset difference, employee evaluation abnormality information is generated;

[0037] When the q second error probability differences are all smaller than the preset difference, normal employee evaluation information is generated.

[0038] Further, the specific process of obtaining the third error probability is as follows: the mechanical equipment information includes the mechanical equipment spindle deformation amount recorded as α, the mechanical equipment spindle wear amount recorded as β and the mechanical equipment spindle corrosion area γ, and the mechanical equipment status information includes the mechanical equipment normal information and the mechanical equipment abnormal information;

[0039] By formula Obtain the third error parameter λ, where α_max is the maximum value of the allowable deformation of the mechanical equipment spindle, β_max is the maximum value of the allowable wear of the mechanical equipment spindle, γ_max is the maximum value of the allowable corrosion area of ​​the mechanical equipment spindle, w5, w6 and w7 are proportional parameters greater than zero, w5+w6+w7=1;

[0040] When λ<0.01, the third error probability L3=λ, and normal information of mechanical equipment is generated. When λ≥0.01, the third error probability L3=1, and abnormal information of mechanical equipment is generated.

[0041] Further, the specific process of obtaining the electronic product qualification rate information is as follows: the electronic product qualification rate information includes an abnormal electronic product qualification rate and a normal electronic product qualification rate;

[0042] Extract a first error probability, a second error probability, and a third error probability, mark the first error probability as S1, the second error probability as S2, and the third error probability as S3;

[0043] First, calculate the sum of the first error probability, the second error probability and the third error probability through S1+S2+S3=X;

[0044] Then, by using S1*S2+S1*S3+S2*S3=Y, the sum of the products of any two error probabilities is calculated to avoid repeated calculations;

[0045] Finally, through XY=S, the total error probability S is obtained;

[0046] When S≥0.01, the qualified rate of electronic products is abnormal, and when S<0.01, the qualified rate of electronic products is normal.

[0047] The beneficial effects of the present invention are embodied in that the intelligent monitoring system in the production and preparation of electronic products can reduce product defects and errors, thereby reducing the scrap rate and thus reducing production costs. At the same time, optimizing the production process can also reduce energy consumption and maintenance costs, making the system more worthy of popularization and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0049] Figure 1 is a system block diagram of the present invention;

[0050] Figure 2 It is an operation flow chart of the present invention;

[0051] Figure 3 It is a specific process flow chart for obtaining welding quality information;

[0052] Figure 4 It is a specific process flow chart for obtaining employee rating information. DETAILED DESCRIPTION

[0053] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0054] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0055] like Figure 1 to Figure 4 As shown, this embodiment provides a technical solution: an intelligent monitoring system in the production and preparation of electronic products, including an environmental information collection module, a welding information collection module, an employee information collection module, an equipment information collection module, a comprehensive processing module and an information transmission module;

[0056] The environmental information collection module is used to collect environmental temperature information and environmental humidity information during the production and preparation of electronic products;

[0057] The welding information collection module is used to collect welding information in the production and preparation of electronic products;

[0058] The employee information collection module is used to collect employee information;

[0059] The equipment information collection module is used to collect mechanical equipment information;

[0060] The comprehensive processing module is used to process the collected ambient temperature information, ambient humidity information, welding information, employee information and mechanical equipment information to obtain a first error probability, a second error probability and a third error probability;

[0061] Then, the first error probability, the second error probability and the third error probability are processed by a comprehensive processing module to obtain electronic product preparation information, where the electronic product preparation information includes welding quality information, employee scoring information, mechanical equipment status information and electronic product qualification rate information;

[0062] The information transmission module is used to transmit the acquired electronic product preparation information to the administrator terminal.

[0063] The specific process of obtaining the first error probability is as follows: processing the ambient temperature information and ambient humidity information in the production and preparation of electronic products within a preset time T, recording the ambient temperature information in the production and preparation of electronic products within the preset time T as f(t), and recording the ambient humidity information in the production and preparation of electronic products as g(t), and obtaining environmental parameters;

[0064] The welding information in the production and preparation of electronic products is processed. The welding information in the production and preparation of electronic products includes welding temperature information recorded as h and welding time information recorded as j, and welding parameters are obtained. The environmental parameters and welding parameters are comprehensively processed to obtain the first error probability. The first error probability is processed to obtain welding quality information.

[0065] By real-time monitoring of environmental parameters and welding parameters and timely adjustments, product defects caused by environmental changes or improper welding can be reduced, thereby improving product quality; reducing product defects and errors can reduce scrap rates, thereby reducing production costs. At the same time, optimizing the production process can also reduce energy consumption and maintenance costs. Through the comprehensive processing of environmental parameters and welding parameters, as well as the analysis of the first error probability, the production process becomes more predictable and controllable, which helps to achieve the goals of lean production and continuous improvement.

[0066] The specific process of comprehensive processing of environmental parameters and welding parameters is as follows:

[0067] By formula Calculate the average environmental temperature f during the production and preparation of electronic products within a preset time T;

[0068] By formula Calculate the average humidity value g of the environment during the production and preparation of electronic products within a preset time T;

[0069] The environmental parameter W is calculated by the formula W=f*w1+g*w2, where w1 is the weight of f and w2 is the weight of g, w1+w2=1, w1>w2;

[0070] By formula Obtain welding parameter information k, where a and b are proportional parameters with different values ​​and both greater than zero, and a and b are proportional;

[0071] The first error probability L1 is obtained through the formula 1-(1-W)*(1-k)=L1.

[0072] By using formulas for calculation, it is possible to ensure that the processing of environmental parameters and welding parameters is more accurate and objective, reducing the subjectivity and errors of human judgment. Through the comprehensive processing of environmental parameters and welding parameters, as well as the calculation of the first error probability, the quality control capability of the production process is enhanced. This helps to reduce product defects and errors, and improve product quality and customer satisfaction.

[0073] The specific process of obtaining the welding quality information is as follows: the welding quality information includes abnormal welding information and normal welding information, the first error probability is collected once every preset time, and the collection is performed p times continuously, and the p first error probabilities collected are calculated to obtain the first error rate average value, wherein p is a positive integer, and p≥100;

[0074] Subtract the p first error probabilities from the average of the first error probabilities to obtain p first error probability differences;

[0075] When any one of the p first error probability differences is greater than or equal to a preset difference, welding abnormality information is generated;

[0076] When the p first error probability differences are all smaller than the preset difference, normal welding information is generated.

[0077] Through real-time monitoring and computational analysis, abnormal conditions in the welding process can be discovered in time, and measures can be taken quickly to deal with them, thereby avoiding the problem from expanding or affecting product quality. Timely discovery and handling of welding problems can reduce rework and downtime and improve production efficiency. At the same time, ensuring welding quality can improve the overall quality and reliability of the product. Through real-time monitoring and analysis of welding quality, the production process becomes more controllable. Production personnel can adjust production parameters or take other measures in time based on welding quality information to ensure the stability and consistency of the production process.

[0078] The specific process of obtaining the second error probability is as follows: the employee information is the professional knowledge test scores of N employees, that is, the test content is sent to N employees at random time, and the employees complete the test within the specified time to obtain the test scores, the professional knowledge test scores of the N employees are processed, and the professional knowledge test scores of the N employees are recorded as A1, A2, ..., AN, the first employee evaluation parameters are obtained, the electronic products assembled by the employees are manually sampled, the second employee evaluation parameters are obtained, the first employee evaluation parameters and the second employee evaluation parameters are comprehensively processed, the second error probability is obtained, the second error probability is processed, and the employee scoring information is obtained.

[0079] By evaluating employees' professional knowledge and practical operational capabilities, we can promptly discover employees' skill deficiencies and take measures to improve them, thereby improving product quality. Through a comprehensive evaluation of employee performance, we can more reasonably allocate human resources, assign employees to positions that are more suitable for them, and improve overall work efficiency. Through regular evaluation and feedback, employees can understand their work performance and identify directions for improvement. At the same time, companies can also give corresponding rewards and incentives based on employee performance to improve employee work enthusiasm and satisfaction.

[0080] The specific process of comprehensively processing the first employee evaluation parameter and the second employee evaluation parameter is as follows:

[0081] Pass first Calculate the mean test score D of N employees;

[0082] Then through Calculate the mean variance E of the test scores of N employees;

[0083] Last passed Obtain the first employee evaluation parameter M;

[0084] Where N is the total number of employees, Ai represents the test score of the i-th employee;

[0085] The number of electronic products assembled by employees for manual sampling is preset as U, where the number of qualified electronic products assembled by employees is recorded as G, and the second employee evaluation parameter V=G / U;

[0086] By formula Obtain a second error probability L2, where w3 is the weight of M and w4 is the weight of V.

[0087] The evaluation results calculated by the formula are relatively objective and fair, reducing the impact of human factors, improving the accuracy and credibility of the evaluation, and comprehensively considering the employee's performance in both professional knowledge and actual operation, avoiding the one-sidedness that may be caused by a single evaluation, making the evaluation results more comprehensive and accurate. Through regular evaluation and feedback, employees can understand their work performance, clarify the direction of improvement, and stimulate the motivation and enthusiasm for self-improvement.

[0088] The specific process of obtaining the employee rating information is as follows: the employee rating information includes employee evaluation abnormal information and employee evaluation normal information, the second error probability is collected once every preset time, and the collection is continuous for q times, and the q collected second error probabilities are calculated to obtain the second error probability average value, where q is a positive integer, q≥5;

[0089] Subtract the q second error probabilities from the second error probability average to obtain q second error probability differences;

[0090] When any one of the q second error probability differences is greater than or equal to a preset difference, employee evaluation abnormality information is generated;

[0091] When the q second error probability differences are all smaller than the preset difference, normal employee evaluation information is generated.

[0092] Through real-time monitoring and computational analysis, problems encountered by employees at work can be quickly discovered, and timely measures can be taken to intervene and correct them, thereby avoiding the expansion of problems or affecting overall production. Timely discovery and handling of employee problems can help employees improve their working methods and skill levels, thereby improving work efficiency and product quality. Regular evaluation and feedback can allow employees to more clearly understand their work performance and existing problems, thereby stimulating the motivation and enthusiasm for self-improvement.

[0093] The specific process of obtaining the third error probability is as follows: the mechanical equipment information includes the mechanical equipment spindle deformation amount recorded as α, the mechanical equipment spindle wear amount recorded as β and the mechanical equipment spindle corrosion area γ, and the mechanical equipment status information includes the mechanical equipment normal information and the mechanical equipment abnormal information;

[0094] By formula Obtain the third error parameter λ, where α_max is the maximum value of the allowable deformation of the mechanical equipment spindle, β_max is the maximum value of the allowable wear of the mechanical equipment spindle, γ_max is the maximum value of the allowable corrosion area of ​​the mechanical equipment spindle, w5, w6 and w7 are proportional parameters greater than zero, w5+w6+w7=1;

[0095] When λ<0.01, the third error probability L3=λ, and normal information of mechanical equipment is generated. When λ≥0.01, the third error probability L3=1, and abnormal information of mechanical equipment is generated.

[0096] By promptly discovering and handling abnormalities in mechanical equipment, we can reduce production accidents and safety hazards caused by equipment failures and improve production safety. By promptly discovering and handling problems such as wear and corrosion of mechanical equipment, we can slow down the aging of equipment and extend its service life. By continuously monitoring and evaluating the status of mechanical equipment, we can formulate reasonable repair and maintenance plans and reduce unnecessary repair costs and downtime.

[0097] The specific process of obtaining the electronic product qualification rate information is as follows: the electronic product qualification rate information includes the electronic product qualification rate abnormality and the electronic product qualification rate normal;

[0098] Extract a first error probability, a second error probability, and a third error probability, mark the first error probability as S1, the second error probability as S2, and the third error probability as S3;

[0099] First, calculate the sum of the first error probability, the second error probability and the third error probability through S1+S2+S3=X;

[0100] Then, by using S1*S2+S1*S3+S2*S3=Y, the sum of the products of any two error probabilities is calculated to avoid repeated calculations;

[0101] Finally, through XY=S, the total error probability S is obtained;

[0102] When S≥0.01, the qualified rate of electronic products is abnormal, and when S<0.01, the qualified rate of electronic products is normal.

[0103] By comprehensively considering multiple error probabilities, the reasons for the decline in the qualified rate of electronic products can be diagnosed more accurately, which helps to formulate targeted improvement measures. According to the feedback of the total error probability S, the management can timely adjust the production process, strengthen employee training or perform equipment maintenance, etc., to improve the efficiency and qualified rate of electronic product preparation. By timely discovering and handling production problems, the scrap rate, rework rate and downtime can be reduced, thereby reducing production costs and risks.

[0104] In summary, when the present invention is used, reducing product defects and errors can reduce the scrap rate, thereby reducing production costs, while optimizing the production process can also reduce energy consumption and maintenance costs.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An intelligent monitoring system for the production and preparation of electronic products, characterized in that: It includes environment information collection module, welding information collection module, employee information collection module, equipment information collection module, comprehensive processing module and information transmission module; The environmental information collection module is used to collect environmental temperature information and environmental humidity information during the production and preparation of electronic products; The welding information collection module is used to collect welding information in the production and preparation of electronic products; The employee information collection module is used to collect employee information; The equipment information collection module is used to collect mechanical equipment information; The comprehensive processing module is used to process the collected ambient temperature information, ambient humidity information, welding information, employee information and mechanical equipment information to obtain a first error probability, a second error probability and a third error probability; Then, the first error probability, the second error probability and the third error probability are processed by a comprehensive processing module to obtain electronic product preparation information, where the electronic product preparation information includes welding quality information, employee scoring information, mechanical equipment status information and electronic product qualification rate information; The information transmission module is used to transmit the acquired electronic product preparation information to the administrator terminal; The specific process of obtaining the first error probability is as follows: processing the ambient temperature information and ambient humidity information in the production and preparation of electronic products within a preset time T, recording the ambient temperature information in the production and preparation of electronic products within the preset time T as f(t), and recording the ambient humidity information in the production and preparation of electronic products as g(t), and obtaining environmental parameters; Processing welding information in the production and preparation of electronic products, including welding temperature information recorded as h and welding time information recorded as j, obtaining welding parameters, comprehensively processing environmental parameters and welding parameters, obtaining a first error probability, processing the first error probability, and obtaining welding quality information; The specific process of comprehensive processing of environmental parameters and welding parameters is as follows: By formula Calculate the average environmental temperature f during the production and preparation of electronic products within a preset time T; By formula Calculate the average humidity value g of the environment during the production and preparation of electronic products within a preset time T; The environmental parameter W is calculated by the formula W=f*w1+g*w2, where w1 is the weight of f, w2 is the weight of g, w1+w2=1, w1>w2; By formula Obtain welding parameter information k, where a and b are proportional parameters with different values ​​and both greater than zero, and a and b are proportional; The first error probability L1 is obtained by the formula 1-(1-W)*(1-k)=L1; The specific process of obtaining the second error probability is as follows: the employee information is the professional knowledge test scores of N employees, that is, the test content is sent to N employees at random time, and the employees complete the test within the specified time to obtain the test scores, the professional knowledge test scores of the N employees are processed, and the professional knowledge test scores of the N employees are recorded as A1, A2, ..., AN, and the first employee evaluation parameters are obtained. The electronic products assembled by the employees are manually inspected to obtain the second employee evaluation parameters, the first employee evaluation parameters and the second employee evaluation parameters are comprehensively processed to obtain the second error probability, the second error probability is processed, and the employee scoring information is obtained; The specific process of obtaining the third error probability is as follows: the mechanical equipment information includes the mechanical equipment spindle deformation amount recorded as α, the mechanical equipment spindle wear amount recorded as β and the mechanical equipment spindle corrosion area γ, and the mechanical equipment status information includes the mechanical equipment normal information and the mechanical equipment abnormal information; By formula Obtain the third error parameter λ, where α_max is the maximum value of the allowable deformation of the mechanical equipment spindle, β_max is the maximum value of the allowable wear of the mechanical equipment spindle, γ_max is the maximum value of the allowable corrosion area of ​​the mechanical equipment spindle, w5, w6 and w7 are proportional parameters greater than zero, w5+w6+w7=1; When λ<0.01, the third error probability L3=λ, and normal information of mechanical equipment is generated. When λ≥0.01, the third error probability L3=1, and abnormal information of mechanical equipment is generated.

2. The intelligent monitoring system for electronic product production and preparation according to claim 1, characterized in that: The specific process of obtaining the welding quality information is as follows: the welding quality information includes abnormal welding information and normal welding information, the first error probability is collected once every preset time, and the collection is performed p times continuously, and the p first error probabilities collected are calculated to obtain the first error rate average value, wherein p is a positive integer, and p≥100; Subtract the p first error probabilities from the average of the first error probabilities to obtain p first error probability differences; When any one of the p first error probability differences is greater than or equal to a preset difference, welding abnormality information is generated; When the p first error probability differences are all smaller than the preset difference, normal welding information is generated.

3. The intelligent monitoring system for electronic product production and preparation according to claim 2, characterized in that: The specific process of comprehensively processing the first employee evaluation parameter and the second employee evaluation parameter is as follows: Pass first Calculate the mean test score D of N employees; Then through Calculate the mean variance E of the test scores of N employees; Last passed Obtain the first employee evaluation parameter M; Where N is the total number of employees, Ai represents the test score of the i-th employee; The number of electronic products assembled by employees for manual sampling is preset as U, where the number of qualified electronic products assembled by employees is recorded as G, and the second employee evaluation parameter V=G / U; By formula Obtain a second error probability L2, where w3 is the weight of M and w4 is the weight of V.

4. The intelligent monitoring system for electronic product production and preparation according to claim 3, characterized in that: The specific process of obtaining the employee rating information is as follows: the employee rating information includes employee evaluation abnormal information and employee evaluation normal information, the second error probability is collected once every preset time, and the collection is continuous for q times, and the q collected second error probabilities are calculated to obtain the second error probability average value, where q is a positive integer, q≥5; Subtract the q second error probabilities from the second error probability average to obtain q second error probability differences; When any one of the q second error probability differences is greater than or equal to a preset difference, employee evaluation abnormality information is generated; When the q second error probability differences are all smaller than the preset difference, normal employee evaluation information is generated.

5. The intelligent monitoring system for electronic product production and preparation according to claim 4, characterized in that: The specific process of obtaining the electronic product qualification rate information is as follows: the electronic product qualification rate information includes the electronic product qualification rate abnormality and the electronic product qualification rate normal; Extract a first error probability, a second error probability, and a third error probability, mark the first error probability as S1, the second error probability as S2, and the third error probability as S3; First, calculate the sum of the first error probability, the second error probability and the third error probability through S1+S2+S3=X; Then, by S1*S2+S1*S3+S2*S3=Y, the sum of the products of any two error probabilities is calculated; Finally, through XY=S, the total error probability S is obtained; The total error probability S is obtained. When S≥0.01, the qualified rate of the electronic products is abnormal. When S<0.01, the qualified rate of the electronic products is normal.

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