An enterprise data management system based on the Internet of Things

Abstract

The invention discloses an enterprise data management system based on the Internet of Things, and relates to the technical field of data management. The invention comprises: an enterprise environment monitoring module, a vacuum packaging bag leakage detection module, a memory particle detection and processing unit and a local database. The invention monitors the storage environment of the memory particles in real time through sensors, and controls an intelligent robot to monitor the vacuum packaging bags of the enterprise at regular intervals, so as to timely discover the leakage problem of the vacuum packaging bags, thereby ensuring the performance of the memory particles. After the vacuum packaging bag has a leakage problem, the quality level of each memory particle inside the vacuum packaging bag is re-evaluated, thereby ensuring the quality of the enterprise's production products. At the same time, the memory particles with lower quality levels are allocated to the testing department as consumable samples, thereby reducing the discard rate of the memory particles.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an enterprise data management system based on the Internet of Things. Background Art

[0002] Nowadays, there are more and more high-tech enterprises, many of which need to use memory particles. Memory particles are important components that determine whether the company's products can be used normally. However, many companies do not manage memory particles reasonably, which causes the memory particles to be worn during storage, thereby reducing the quality of memory particles. The Internet of Things can monitor various data of memory particles in real time. Therefore, it is necessary to manage the data of the company's memory particles through the Internet of Things.

[0003] Prior art, such as the invention patent application with announcement number: CN116932556A, discloses an enterprise data management system based on the Internet of Things, which system includes: the central control system is used to centrally manage all data running in the enterprise data management system, and all data specifically include transmission data and management data. The specific content of the centralized management includes transmission management, compression management and security management. This invention realizes personalized data display, thereby facilitating management personnel to carry out targeted management, improve production efficiency and production quality, and fully ensure the stability, security, effectiveness and integration of data.

[0004] Existing technology, such as the invention patent application with announcement number: CN117521969A, discloses a smart park operation index calculation system based on digital twins, which includes: obtaining data from the archive platform and the data middle platform, providing unified data classification and coding standards, unified interface standards and unified database structure standards; park comprehensive operation service management system. The smart park operation index calculation system based on digital twins provided by this invention realizes comprehensive perception of the park by aggregating and analyzing relevant data of the park, and improves the park's intelligent digital operation service management capabilities.

[0005] From the above scheme, it can be seen that the current enterprise data management system lacks certain attention to the intelligent leakage monitoring of the vacuum packaging bags used by the enterprise to store memory particles. In order to ensure the performance of the memory particles, they are often required to be stored in vacuum packaging bags. In actual applications, the vacuum packaging bags are prone to problems of not being able to be completely sealed during the vacuuming process. After being stored for a period of time, the vacuum packaging bags are prone to leakage, causing the memory particles to be easily affected by environmental factors, thereby reducing the performance of the memory particles. At the same time, there is a lack of attention to the re-evaluation of the quality of the memory particles after the vacuum packaging bags have leakage problems. When the vacuum packaging bags leak and the memory particles are in contact with the air for a long time, the silk screen is prone to fuzzy and the solder balls are worn, resulting in a reduction in the quality grade of the memory particles and reducing the quality of the company's products. Summary of the invention

[0006] The purpose of the present invention is to provide an enterprise data management system based on the Internet of Things, which solves the problems existing in the background technology.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an enterprise data management system based on the Internet of Things, including: an enterprise environmental monitoring module, used to obtain the characteristic values ​​of various monitoring data belonging to the enterprise in real time, and the various monitoring data include: the characteristic values ​​of various environmental data of each vacuum packaging bag storing memory particles, and marking each vacuum packaging bag storing memory particles belonging to the enterprise as a target vacuum packaging bag.

[0008] The vacuum packaging bag leakage detection module is used to obtain the monitoring points of each target vacuum packaging bag belonging to the enterprise, control the intelligent robot to obtain the target plane coordinates and measured distance values ​​of each monitoring point of each target vacuum packaging bag belonging to the enterprise at the preset monitoring time point, and screen the leaking vacuum packaging bags belonging to the enterprise.

[0009] The memory particle detection and processing unit is used to control the downward pressure of the suction cup of the intelligent robot to suck up each memory particle in each leaking vacuum packaging bag belonging to the enterprise one by one, analyze the quality wear coefficient of each memory particle in each leaking vacuum packaging bag belonging to the enterprise, evaluate the quality grade of each memory particle in each leaking vacuum packaging bag belonging to the enterprise, and process each memory particle in each leaking vacuum packaging bag belonging to the enterprise.

[0010] The memory particle detection and processing unit includes: a leaking memory particle information acquisition module, a memory particle quality monitoring module, and a memory particle processing module.

[0011] The leaking memory particle information acquisition module is used to control the intelligent robot to dismantle each leaking vacuum packaging bag belonging to the enterprise, so as to obtain each memory particle in each leaking vacuum packaging bag belonging to the enterprise, calculate the suction cup downward pressure applied by the intelligent robot when sucking up the memory particles in each leaking vacuum packaging bag belonging to the enterprise, and control the suction cup of the intelligent robot to press down, so as to suck up each memory particle in each leaking vacuum packaging bag belonging to the enterprise one by one, and obtain the surface silk-screen image of each memory particle and the surface image of each solder ball in each leaking vacuum packaging bag belonging to the enterprise.

[0012] The memory particle quality monitoring module is used to analyze the quality wear coefficient of each memory particle in each leaking vacuum packaging bag belonging to the enterprise, and evaluate the quality grade of each memory particle in each leaking vacuum packaging bag belonging to the enterprise.

[0013] The memory particle processing module is used to load memory particles of the same quality grade and model into the same vacuum packaging bag and perform vacuum processing, so as to obtain the quality grade of each vacuum packaging bag belonging to the enterprise and the memory particles stored therein, obtain the target quality grade of the memory particles of each department belonging to the enterprise from the local database, and allocate the memory particles in each vacuum packaging bag belonging to the enterprise to each department according to the quality grade. If the quality grade of the memory particles stored in a vacuum packaging bag belonging to the enterprise is the fourth quality grade, the memory particles stored in the vacuum packaging bag are allocated to the testing departments belonging to the enterprise as consumable samples.

[0014] The beneficial effects of the present invention are: (1) The enterprise environment monitoring module of the present invention monitors the storage environment of memory particles in real time through sensors, thereby facilitating subsequent analysis.

[0015] (2) The vacuum packaging bag leakage detection module of the present invention controls the intelligent robot to monitor the vacuum packaging bags of the enterprise at regular intervals, thereby timely discovering the leakage problem of the vacuum packaging bags, reducing the harm of environmental factors to the memory particles in the leaking vacuum packaging bags of the enterprise, thereby ensuring the performance of the memory particles.

[0016] (3) The memory particle detection and processing unit of the present invention re-evaluates the quality level of each memory particle inside the vacuum packaging bag after leakage occurs, thereby reducing the occurrence rate of the problem that the quality level of memory particles is reduced due to leakage and these memory particles are provided to departments with higher quality requirements, thereby ensuring the quality of the company's production products. At the same time, memory particles with lower quality levels are allocated to the testing department as consumable samples, reducing the discard rate of memory particles. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1 As shown, the present invention provides an enterprise data management system based on the Internet of Things, including: an enterprise environment monitoring module, a vacuum packaging bag leakage detection module, a memory particle detection and processing unit and a local database.

[0021] It should be noted that the enterprise environment monitoring module is connected to the vacuum packaging bag leakage detection module, the vacuum packaging bag leakage detection module is connected to the memory particle detection and processing unit, and the local database is connected to the vacuum packaging bag leakage detection module and the memory particle detection and processing unit.

[0022] It should also be noted that the local database is used to store the initial plane coordinates of each monitoring point of each target vacuum packaging bag belonging to the enterprise, the allowable coordinate offset distance value, the leakage hazard coefficient threshold, the initial measured distance value of each monitoring point of each target vacuum packaging bag belonging to the enterprise, the target quality grade of memory particles of each department belonging to the enterprise, the model of memory particles stored in each leaking vacuum packaging bag belonging to the enterprise, the solder ball diameter of each model of memory particles, the initial suction cup downward pressure corresponding to each solder ball diameter interval, the suction cup downward pressure force drop value corresponding to each environmental threat coefficient interval, the suitable temperature value of each model of memory particles, the characteristic value of the temperature of each leaking vacuum packaging bag belonging to the enterprise at each monitoring time point during the leakage time period, the initial surface silk screen image of each model of memory particles, the quality wear coefficient silk screen adjustment parameter value corresponding to each similarity interval, the ideal surface image of the solder ball, the solder ball wear coefficient adjustment parameter value corresponding to each solder ball of each model of memory particles, the quality grade of the memory particles in each leaking vacuum packaging bag belonging to the enterprise before the intelligent robot detection, the quality high quality coefficient corresponding to each quality grade, and the quality grade corresponding to each quality hazard coefficient interval.

[0023] The enterprise environmental monitoring module is used to obtain the characteristic values ​​of each monitoring data belonging to the enterprise in real time. The monitoring data include: the characteristic values ​​of each environmental data of each vacuum packaging bag storing memory particles, and marking each vacuum packaging bag storing memory particles belonging to the enterprise as a target vacuum packaging bag.

[0024] It should be noted that the memory particles of the present invention are stored in a disk-like manner. After the memory particles are stacked in disks, they are placed in a vacuum packaging bag for vacuum treatment.

[0025] In a specific embodiment, the characteristic values ​​of each monitoring data belonging to the enterprise are obtained by a specific method: the characteristic values ​​of each environmental data of each vacuum packaging bag storing memory particles belonging to the enterprise are obtained through a temperature sensor, a humidity sensor and a dust concentration measuring instrument.

[0026] It should be noted that the environmental data include temperature, humidity and dust concentration.

[0027] The enterprise environment monitoring module of the present invention monitors the storage environment of memory particles in real time through sensors, thereby facilitating subsequent analysis.

[0028] The vacuum packaging bag leakage detection module is used to obtain the monitoring points of each target vacuum packaging bag belonging to the enterprise, control the intelligent robot to obtain the target plane coordinates and measured distance values ​​of each monitoring point of each target vacuum packaging bag belonging to the enterprise at a preset monitoring time point, and screen the leaking vacuum packaging bags belonging to the enterprise.

[0029] It should be noted that the calculated distance value is the distance value between each monitoring point of each target vacuum packaging bag belonging to the enterprise and the infrared ranging sensor of the intelligent robot.

[0030] In a specific embodiment, the monitoring points of each target vacuum packaging bag belonging to the enterprise are obtained by a specific method: when each target vacuum packaging bag belonging to the enterprise is put into storage, the monitoring points are marked by an intelligent robot, thereby obtaining the monitoring points of each target vacuum packaging bag belonging to the enterprise.

[0031] In a specific embodiment, the target plane coordinates and measured distance values ​​of each monitoring point of each target vacuum packaging bag belonging to the enterprise are obtained by a specific method: the target plane coordinates of each monitoring point of each target vacuum packaging bag belonging to the enterprise are obtained by a camera carried by an intelligent robot, and the measured distance values ​​of each monitoring point of each target vacuum packaging bag belonging to the enterprise are obtained by an infrared ranging sensor carried by the intelligent robot.

[0032] In a specific embodiment of the present invention, the specific screening method for screening the leaking vacuum packaging bags belonging to the enterprise is as follows: obtaining the initial plane coordinates of each monitoring point of each target vacuum packaging bag belonging to the enterprise from the local database, and calculating the distance value between the target plane coordinates of each monitoring point of each target vacuum packaging bag belonging to the enterprise and the initial plane coordinates according to the target plane coordinates of each monitoring point of each target vacuum packaging bag belonging to the enterprise. , where x represents the number of each target vacuum packaging bag, , y is a positive integer greater than 2, n is the number of each monitoring point, , m is a positive integer greater than 2.

[0033] Obtain the allowable coordinate offset distance value A from the local database and calculate the coordinate offset hazard coefficient of each monitoring point of each target vacuum packaging bag of the enterprise , where e is a natural constant, and the coordinate offset hazard coefficient of each target vacuum packaging bag of the enterprise is calculated.

[0034] According to the measured distance values ​​of each monitoring point of each target vacuum packaging bag of the enterprise, the leakage hazard coefficient of each target vacuum packaging bag of the enterprise is calculated.

[0035] The leakage hazard coefficient threshold is obtained from the local database. If the leakage hazard coefficient of a target vacuum packaging bag belonging to the enterprise is greater than the leakage hazard coefficient threshold, the target vacuum packaging bag is marked as a leaking vacuum packaging bag.

[0036] In a specific embodiment of the present invention, the specific calculation method for calculating the leakage hazard coefficient of each target vacuum packaging bag of the enterprise is as follows: obtaining the initial measured distance value of each monitoring point of each target vacuum packaging bag of the enterprise from the local database .

[0037] According to the calculated distance value of each monitoring point of each target vacuum packaging bag of the enterprise , calculate the distance offset leakage coefficient of each monitoring point of each target vacuum packaging bag of the enterprise , calculate the distance deviation leakage coefficient of each target vacuum packaging bag of the enterprise , and according to the coordinate deviation hazard coefficient of each target vacuum packaging bag of the enterprise , calculate the leakage hazard coefficient of each target vacuum packaging bag of the enterprise .

[0038] The vacuum packaging bag leakage detection module of the present invention controls the intelligent robot to monitor the vacuum packaging bags of the enterprise at regular intervals, thereby timely discovering the leakage problems of the vacuum packaging bags, reducing the harm of environmental factors to the memory particles in the leaking vacuum packaging bags of the enterprise, thereby ensuring the performance of the memory particles.

[0039] The memory particle detection and processing unit is used to control the suction cup of the intelligent robot to press down, suck up each memory particle in each leaking vacuum packaging bag belonging to the enterprise one by one, analyze the quality wear coefficient of each memory particle in each leaking vacuum packaging bag belonging to the enterprise, evaluate the quality grade of each memory particle in each leaking vacuum packaging bag belonging to the enterprise, and process each memory particle in each leaking vacuum packaging bag belonging to the enterprise.

[0040] It should be noted that the quality levels include: quality level 1, quality level 2, quality level 3 and quality level 4. The quality of memory particles of different quality levels is ranked from best to worst as follows: quality level 1, quality level 2, quality level 3, quality level 4.

[0041] In a specific embodiment of the present invention, the memory particle detection and processing unit includes: a leaking memory particle information acquisition module, a memory particle quality monitoring module, and a memory particle processing module.

[0042] The leaking memory particle information acquisition module is used to control the intelligent robot to dismantle each leaking vacuum packaging bag belonging to the enterprise, so as to obtain each memory particle in each leaking vacuum packaging bag belonging to the enterprise, calculate the suction cup downward pressure applied by the intelligent robot when sucking up the memory particles in each leaking vacuum packaging bag belonging to the enterprise, and control the suction cup of the intelligent robot to press down, so as to suck up each memory particle in each leaking vacuum packaging bag belonging to the enterprise one by one, and obtain the surface silk-screen image of each memory particle and the surface image of each solder ball in each leaking vacuum packaging bag belonging to the enterprise.

[0043] The memory particle quality monitoring module is used to analyze the quality wear coefficient of each memory particle in each leaking vacuum packaging bag belonging to the enterprise, and evaluate the quality grade of each memory particle in each leaking vacuum packaging bag belonging to the enterprise.

[0044] The memory particle processing module is used to load memory particles of the same quality grade and model into the same vacuum packaging bag and perform vacuum processing, so as to obtain the quality grade of each vacuum packaging bag belonging to the enterprise and the memory particles stored therein, obtain the target quality grade of the memory particles of each department belonging to the enterprise from the local database, and allocate the memory particles in each vacuum packaging bag belonging to the enterprise to each department according to the quality grade. If the quality grade of the memory particles stored in a vacuum packaging bag belonging to the enterprise is the fourth quality grade, the memory particles stored in the vacuum packaging bag are allocated to the testing departments belonging to the enterprise as consumable samples.

[0045] In a specific embodiment of the present invention, the calculation method of the downward pressure exerted by the suction cup of the intelligent robot when sucking up the memory particles in each leaking vacuum packaging bag belonging to the enterprise is as follows: obtaining the model of the memory particles stored in each leaking vacuum packaging bag belonging to the enterprise and the solder ball diameter of each model of memory particles from the local database, and mapping to obtain the solder ball diameter of the memory particles stored in each leaking vacuum packaging bag belonging to the enterprise.

[0046] The initial suction cup downward pressure force corresponding to each solder ball diameter range is obtained from the local database, and the initial suction cup downward pressure force applied when the intelligent robot sucks the memory particles in each leaking vacuum packaging bag of the enterprise is mapped. , where i represents the number of each leaking vacuum packaging bag, , j is a positive integer greater than 2, and the environmental threat coefficient of the internal particles in each leaking vacuum packaging bag of the enterprise is calculated.

[0047] Obtain the suction cup downward pressure drop value corresponding to each environmental threat coefficient interval from the local database, and map it to obtain the suction cup downward pressure drop value when the intelligent robot sucks the memory particles in each leaking vacuum packaging bag of the enterprise .

[0048] Calculate the downward pressure applied by the suction cup of the intelligent robot when it sucks the memory particles in the leaking vacuum packaging bags of the enterprise .

[0049] It should be noted that the greater the environmental threat coefficient, the greater the drop in the suction cup's downward pressure.

[0050] In a specific embodiment, the surface silk-screen images of each memory particle and the surface images of each solder ball in each leaking vacuum packaging bag of the enterprise are obtained by a specific method: the surface silk-screen images of each memory particle and the surface images of each solder ball in each leaking vacuum packaging bag of the enterprise are obtained by a camera carried by an intelligent robot.

[0051] In a specific embodiment of the present invention, the environmental threat coefficient of the memory particles in each leaky vacuum packaging bag of the enterprise is calculated by: obtaining the suitable temperature value of each type of memory particles from the local database, mapping to obtain the suitable temperature value of the memory particles stored in each leaky vacuum packaging bag of the enterprise .

[0052] Obtain the characteristic values ​​of the temperature of each leaking vacuum packaging bag of the enterprise at each monitoring time point during the leakage period from the local database , where p represents the number of each monitoring time point, , q is a positive integer greater than 2, and the temperature threat coefficient of the internal particles of each leaking vacuum packaging bag of the enterprise at each monitoring time point is calculated .

[0053] Similarly, calculate the humidity threat coefficient of the internal particles of each leaky vacuum packaging bag of the enterprise at each monitoring time point , Dust threat factor .

[0054] Calculate the environmental threat coefficient of the particles stored in each leaky vacuum packaging bag of the enterprise .

[0055] It should be noted that the leakage time period refers to the time from the last time the intelligent robot inspected the leaking vacuum packaging bags of the enterprise to the time when the intelligent robot inspected the leaking vacuum packaging bags of the enterprise this time.

[0056] In a specific embodiment of the present invention, the mass wear coefficient of each memory particle of each leaky vacuum packaging bag of the enterprise is analyzed, and the specific analysis method is: according to the surface silk screen image of each memory particle of each leaky vacuum packaging bag of the enterprise, the silk screen adjustment parameter value of the mass wear coefficient of each memory particle of each leaky vacuum packaging bag of the enterprise is calculated. , where t represents the number of each memory particle, , s is a positive integer greater than 2.

[0057] According to the surface image of each solder ball of each memory particle in each leaky vacuum packaging bag of the enterprise, the solder ball wear coefficient of each memory particle in each leaky vacuum packaging bag of the enterprise is calculated. .

[0058] According to the environmental threat coefficient of the particles stored in each leaky vacuum packaging bag of the enterprise , analyze the mass wear coefficient of each memory particle in each leaky vacuum packaging bag of the enterprise .

[0059] In a specific embodiment of the present invention, the silk-screen adjustment parameter value of the mass wear coefficient of each memory particle in each leaking vacuum packaging bag belonging to the enterprise is calculated by: obtaining the initial surface silk-screen image of each model of memory particles from the local database, and extracting the initial surface silk-screen image of the memory particles stored in each leaking vacuum packaging bag belonging to the enterprise according to the model of the memory particles stored in each leaking vacuum packaging bag belonging to the enterprise.

[0060] By using image comparison technology, the surface silk-screen images of each memory particle of each leaking vacuum packaging bag belonging to the enterprise are compared with the initial surface silk-screen images, so as to obtain the similarity between the surface silk-screen images of each memory particle of each leaking vacuum packaging bag belonging to the enterprise and the initial surface silk-screen images.

[0061] The silk-screen adjustment parameter value of the mass wear coefficient corresponding to each similarity interval is obtained from the local database, and the silk-screen adjustment parameter value of the mass wear coefficient of each memory particle of each leaking vacuum packaging bag of the enterprise is mapped.

[0062] It should be noted that the lower the similarity, the larger the silk-screen adjustment parameter value of the mass wear coefficient.

[0063] In a specific embodiment, the similarity between the surface silk-screen image of each memory particle of each leaking vacuum packaging bag of the enterprise and the initial surface silk-screen image is obtained by the following specific method: the existing image comparison technology is relatively mature, and the similarity between the surface silk-screen image of each memory particle of each leaking vacuum packaging bag of the enterprise and the initial surface silk-screen image can be obtained by the existing image comparison technology.

[0064] In a specific embodiment of the present invention, the calculation method of the solder ball wear coefficient of each memory particle in each leaky vacuum packaging bag of the enterprise is as follows: an ideal surface image of the solder ball is obtained from a local database, and the gray value of each pixel point of the ideal surface image of the solder ball is obtained. , where N represents the number of each pixel. , M is a positive integer greater than 2.

[0065] According to the surface images of each solder ball of each memory particle in each leaking vacuum packaging bag of the enterprise, the gray value of each pixel point of the surface image of each solder ball of each memory particle in each leaking vacuum packaging bag of the enterprise is obtained. , where K represents the number of each solder ball, , L is a positive integer greater than 2.

[0066] Obtain the solder ball wear coefficient adjustment parameter value corresponding to each solder ball of each type of memory particle from the local database, and map it to obtain the solder ball wear coefficient adjustment parameter value of each solder ball of each memory particle in each leaky vacuum packaging bag of the enterprise .

[0067] Calculate the solder ball wear coefficient of each memory particle in each leaky vacuum packaging bag of the enterprise .

[0068] It should be noted that the solder ball wear coefficient adjustment parameter value corresponding to each solder ball of the memory particle is divided according to the function of each solder ball, such as: if the function of a solder ball is an empty connection, the solder ball wear coefficient adjustment parameter value of the solder ball is 1, if the function of a solder ball is signal output, the solder ball wear coefficient adjustment parameter value of the solder ball is 1.1, if the function of a solder ball is power input, the solder ball wear coefficient adjustment parameter value of the solder ball is 1.2, and so on. In actual applications, the power input function of the solder ball is more important than the signal output function, and the signal output function is more important than the empty connection function.

[0069] In a specific embodiment of the present invention, the quality grade of each memory particle in each leaking vacuum packaging bag of the enterprise is evaluated, and the specific evaluation method is: the quality grade of the memory particles in each leaking vacuum packaging bag of the enterprise before the intelligent robot detection and the quality coefficient corresponding to each quality grade are obtained from the local database, and the quality coefficient of each memory particle in each leaking vacuum packaging bag of the enterprise is mapped. .

[0070] According to the quality wear coefficient of each memory particle of each leaky vacuum packaging bag of the enterprise , calculate the quality hazard coefficient of each particle in each leaky vacuum packaging bag of the enterprise .

[0071] The quality grade corresponding to each quality hazard coefficient interval is obtained from the local database, and the quality grade of each memory particle in each leaky vacuum packaging bag of the enterprise is mapped.

[0072] The memory particle detection and processing unit of the present invention re-evaluates the quality level of each memory particle inside the vacuum packaging bag after leakage occurs, thereby reducing the occurrence rate of the problem that the quality level of memory particles is reduced due to leakage, but these memory particles are provided to departments with higher quality level requirements, thereby ensuring the quality of the company's production products. At the same time, memory particles with lower quality levels are allocated to the testing department as consumable samples, reducing the discard rate of memory particles.

[0073] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An enterprise data management system based on the Internet of Things, characterized in that: include: The enterprise environment monitoring module is used to obtain the characteristic values ​​of each monitoring data belonging to the enterprise in real time, wherein each monitoring data includes: the characteristic values ​​of each environmental data of each vacuum packaging bag storing memory particles, and mark each vacuum packaging bag storing memory particles belonging to the enterprise as each target vacuum packaging bag; The vacuum packaging bag leakage detection module is used to obtain the monitoring points of each target vacuum packaging bag of the enterprise, control the intelligent robot to obtain the target plane coordinates and measured distance values ​​of each monitoring point of each target vacuum packaging bag of the enterprise at the preset monitoring time point, and screen the leaking vacuum packaging bags of the enterprise; The specific screening method for the leaky vacuum packaging bags of the screening enterprise is as follows: Obtain the initial plane coordinates of each monitoring point of each target vacuum packaging bag of the enterprise from the local database, and calculate the distance between the target plane coordinates of each monitoring point of each target vacuum packaging bag of the enterprise and the initial plane coordinates according to the target plane coordinates of each monitoring point of each target vacuum packaging bag of the enterprise , where x represents the number of each target vacuum packaging bag, , y is a positive integer greater than 2, n is the number of each monitoring point, , m is a positive integer greater than 2; Obtain the allowable coordinate offset distance value A from the local database and calculate the coordinate offset hazard coefficient of each monitoring point of each target vacuum packaging bag of the enterprise , where e is a natural constant, and the coordinate offset hazard coefficient of each target vacuum packaging bag of the enterprise is calculated; According to the measured distance values ​​of each monitoring point of each target vacuum packaging bag of the enterprise, the leakage hazard coefficient of each target vacuum packaging bag of the enterprise is calculated; Obtaining a leakage hazard coefficient threshold from a local database, if the leakage hazard coefficient of a target vacuum packaging bag of an enterprise is greater than the leakage hazard coefficient threshold, marking the target vacuum packaging bag as a leaking vacuum packaging bag; The specific calculation method for calculating the leakage hazard coefficient of each target vacuum packaging bag of the enterprise is as follows: Obtain the initial calculated distance value of each monitoring point of each target vacuum packaging bag of the enterprise from the local database ; According to the calculated distance value of each monitoring point of each target vacuum packaging bag of the enterprise , calculate the distance offset leakage coefficient of each monitoring point of each target vacuum packaging bag of the enterprise , calculate the distance deviation leakage coefficient of each target vacuum packaging bag of the enterprise , and according to the coordinate deviation hazard coefficient of each target vacuum packaging bag of the enterprise , calculate the leakage hazard coefficient of each target vacuum packaging bag of the enterprise ; The memory particle detection and processing unit is used to control the suction cup of the intelligent robot to press down, suck up each memory particle in each leaking vacuum packaging bag of the enterprise one by one, analyze the quality wear coefficient of each memory particle in each leaking vacuum packaging bag of the enterprise, evaluate the quality grade of each memory particle in each leaking vacuum packaging bag of the enterprise, and process each memory particle in each leaking vacuum packaging bag of the enterprise; The memory particle detection and processing unit includes: a leaking memory particle information acquisition module, a memory particle quality monitoring module, and a memory particle processing module; The leaking memory particle information acquisition module is used to control the intelligent robot to remove each leaking vacuum packaging bag belonging to the enterprise, so as to obtain each memory particle in each leaking vacuum packaging bag belonging to the enterprise, calculate the suction cup downward pressure applied force when the intelligent robot absorbs the memory particles in each leaking vacuum packaging bag belonging to the enterprise, and control the suction cup of the intelligent robot to press down, and after sucking up each memory particle in each leaking vacuum packaging bag belonging to the enterprise one by one, obtain the surface silk screen image of each memory particle and the surface image of each solder ball in each leaking vacuum packaging bag belonging to the enterprise; The memory particle quality monitoring module is used to analyze the quality wear coefficient of each memory particle of each leaking vacuum packaging bag belonging to the enterprise, and evaluate the quality grade of each memory particle of each leaking vacuum packaging bag belonging to the enterprise; The memory particle processing module is used to load memory particles of the same quality grade and the same model into the same vacuum packaging bag, and perform vacuum processing, so as to obtain the quality grade of each vacuum packaging bag belonging to the enterprise and the memory particles stored therein, obtain the target quality grade of the memory particles of each department belonging to the enterprise from the local database, and distribute the memory particles of each vacuum packaging bag belonging to the enterprise to each department according to the quality grade. If the quality grade of the memory particles stored in a vacuum packaging bag belonging to the enterprise is the fourth quality grade, the memory particles stored in the vacuum packaging bag are distributed to each testing department belonging to the enterprise as consumable samples; The specific analysis method of the mass wear coefficient of each internal particle of each leaky vacuum packaging bag of the analysis enterprise is as follows: According to the surface silk-screen images of each memory particle of each leaky vacuum packaging bag of the enterprise, the silk-screen adjustment parameter value of the mass wear coefficient of each memory particle of each leaky vacuum packaging bag of the enterprise is calculated. , where i represents the number of each leaking vacuum packaging bag, , j is a positive integer greater than 2, t represents the number of each memory particle, , s is a positive integer greater than 2; According to the surface image of each solder ball of each memory particle in each leaky vacuum packaging bag of the enterprise, the solder ball wear coefficient of each memory particle in each leaky vacuum packaging bag of the enterprise is calculated. ; According to the environmental threat coefficient of the particles stored in each leaky vacuum packaging bag of the enterprise , analyze the mass wear coefficient of each memory particle in each leaky vacuum packaging bag of the enterprise .

2. According to the enterprise data management system based on the Internet of Things according to claim 1, it is characterized in that: The specific calculation method of the downward pressure exerted by the suction cup of the computing intelligent robot when sucking the memory particles in each leaking vacuum packaging bag of the enterprise is: The model of memory particles stored in each leaky vacuum packaging bag of the enterprise and the solder ball diameter of each model of memory particles are obtained from the local database, and the solder ball diameter of the memory particles stored in each leaky vacuum packaging bag of the enterprise is mapped; The initial suction cup downward pressure force corresponding to each solder ball diameter range is obtained from the local database, and the initial suction cup downward pressure force applied when the intelligent robot sucks the memory particles in each leaking vacuum packaging bag of the enterprise is mapped. , and calculate the environmental threat coefficient of the particles stored in each leaky vacuum packaging bag of the enterprise; Obtain the suction cup downward pressure drop value corresponding to each environmental threat coefficient interval from the local database, and map it to obtain the suction cup downward pressure drop value when the intelligent robot sucks the memory particles in each leaking vacuum packaging bag of the enterprise ; Calculate the downward pressure applied by the suction cup of the intelligent robot when it sucks the memory particles in the leaking vacuum packaging bags of the enterprise .

3. The enterprise data management system based on the Internet of Things according to claim 2 is characterized in that: The specific calculation method for calculating the environmental threat coefficient of the internal particles of each leaking vacuum packaging bag of the enterprise is: Obtain the appropriate temperature values ​​of each type of memory chip from the local database, and map them to obtain the appropriate temperature values ​​of the memory chips stored in each leaky vacuum packaging bag of the enterprise. ; Obtain the characteristic values ​​of the temperature of each leaking vacuum packaging bag of the enterprise at each monitoring time point during the leakage period from the local database , where p represents the number of each monitoring time point, , q is a positive integer greater than 2, and the temperature threat coefficient of the internal particles of each leaking vacuum packaging bag of the enterprise at each monitoring time point is calculated ； Similarly, calculate the humidity threat coefficient of the internal particles of each leaky vacuum packaging bag of the enterprise at each monitoring time point , Dust threat factor ; Calculate the environmental threat coefficient of the particles stored in each leaky vacuum packaging bag of the enterprise .

4. The enterprise data management system based on the Internet of Things according to claim 3 is characterized in that: The specific calculation method of the silk-screen adjustment parameter value of the mass wear coefficient of each internal particle of each leaking vacuum packaging bag of the enterprise is as follows: Obtain the initial surface silk-screen images of each model of memory particles from the local database, and extract the initial surface silk-screen images of the memory particles stored in each leaky vacuum packaging bag of the enterprise according to the model of the memory particles stored in each leaky vacuum packaging bag of the enterprise; By using image comparison technology, the surface silk-screen image of each memory particle of each leaky vacuum packaging bag of the enterprise is compared with the initial surface silk-screen image, so as to obtain the similarity between the surface silk-screen image of each memory particle of each leaky vacuum packaging bag of the enterprise and the initial surface silk-screen image; The silk-screen adjustment parameter value of the mass wear coefficient corresponding to each similarity interval is obtained from the local database, and the silk-screen adjustment parameter value of the mass wear coefficient of each memory particle of each leaking vacuum packaging bag of the enterprise is mapped.

5. The enterprise data management system based on the Internet of Things according to claim 3 is characterized in that: The specific calculation method of the solder ball wear coefficient of each memory particle of each leaky vacuum packaging bag of the enterprise is as follows: Get the ideal surface image of the solder ball from the local database, and obtain the gray value of each pixel of the ideal surface image of the solder ball , where N represents the number of each pixel. , M is a positive integer greater than 2; According to the surface images of each solder ball of each memory particle in each leaking vacuum packaging bag of the enterprise, the gray value of each pixel point of the surface image of each solder ball of each memory particle in each leaking vacuum packaging bag of the enterprise is obtained. , where K represents the number of each solder ball, , L is a positive integer greater than 2; Obtain the solder ball wear coefficient adjustment parameter value corresponding to each solder ball of each type of memory particle from the local database, and map it to obtain the solder ball wear coefficient adjustment parameter value of each solder ball of each memory particle in each leaky vacuum packaging bag of the enterprise ; Calculate the solder ball wear coefficient of each memory particle in each leaky vacuum packaging bag of the enterprise 。 6. The enterprise data management system based on the Internet of Things according to claim 3 is characterized in that: The specific evaluation method for evaluating the quality grade of each internal particle of each leaky vacuum packaging bag of the evaluation enterprise is as follows: The quality grade of the memory particles in each leaking vacuum packaging bag of the enterprise before the intelligent robot detection and the quality coefficient corresponding to each quality grade are obtained from the local database, and the quality coefficient of each memory particle in each leaking vacuum packaging bag of the enterprise is mapped. ; According to the quality wear coefficient of each memory particle of each leaky vacuum packaging bag of the enterprise , calculate the quality hazard coefficient of each particle in each leaky vacuum packaging bag of the enterprise ; The quality grade corresponding to each quality hazard coefficient interval is obtained from the local database, and the quality grade of each memory particle in each leaky vacuum packaging bag of the enterprise is mapped.

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