Data analysis system and method for egg intelligent storage

Abstract

The application discloses an egg intelligent warehouse data analysis system and method, and relates to the technical field of warehouse regulation and control, which comprises the following steps: obtaining the Halls unit, eggshell strength, eggshell thickness and egg shape index of different batches of eggs in the egg warehouse, and classifying according to the Halls unit; performing weight distribution processing, and establishing an egg body strength scoring mechanism using the eggshell thickness and the egg shape index; obtaining the breakage data of eggs with different strength scores on automatic guided vehicles at different speeds, and performing speed breakage analysis processing; obtaining the maximum allowable speed of the automatic guided vehicle according to the basic attribute analysis of the eggs to be transported, and adjusting; the application is used to solve the problem that the existing warehouse AGV regulation and control technology cannot accurately obtain the strength condition of the eggs in a non-destructive manner when controlling the speed in the egg warehouse scene, and to adjust and improve the transportation speed of the automatic guided vehicle while ensuring a low breakage rate of the eggs.

Description

Technical Field

[0001] This invention relates to the field of warehouse control technology, specifically to an intelligent data analysis system and method for egg products. Background Technology

[0002] Warehouse control technology refers to a technical system that uses intelligent algorithms, control strategies, and data analysis to dynamically control and collaboratively manage automated guided vehicles (AGVs) in a warehouse environment. Its core objective is to achieve efficient and safe operation of AGVs in warehouse scenarios, maximizing warehouse efficiency and reducing operating costs.

[0003] In egg storage scenarios, the speed of automated guided vehicles (AGVs) is closely related to the egg breakage rate. When the AGV accelerates or stops abruptly, the eggs will slide or tilt due to inertia. When the speed exceeds a certain level, the impact of a sudden stop can subject the eggs inside the box to pressure several times their own weight, causing the eggshells to crack. Moreover, the higher the speed, the greater the AGV's vibration on uneven ground or at seams, and the higher the breakage rate. Therefore, to control the egg breakage rate, the speed of automated guided vehicles transporting eggs is often set very low. Although this can control costs, it affects the eggs' health. The efficiency of egg storage and scheduling is crucial; however, the breakage rate of eggs is also related to the strength of their shells. If the speed of the AGV is adjusted by detecting the shell strength, the existing low-cost egg shell strength detection method will damage the eggs themselves. Although it can control the breakage rate of eggs during transportation, it will greatly increase the detection cost, resulting in low economic benefits. Therefore, the existing AGV control technology for egg storage cannot accurately obtain the strength of eggs in a non-destructive way when controlling the speed of the AGV in the egg storage scenario, thus failing to adjust and increase the transportation speed of the AGV while ensuring a low breakage rate. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It obtains basic attribute data of eggs by acquiring Haugh units, shell strength, shell thickness, and egg shape index of different batches of eggs in egg storage, classifying them according to Haugh units, and then performing weighted allocation processing to establish an egg strength scoring mechanism. It also acquires breakage data of eggs with different strength scores on automated guided vehicles (AGVs) at different speeds and performs speed-based breakage analysis. Based on the basic attribute analysis of the eggs to be transported, it obtains the maximum permissible speed of the AGV and adjusts it accordingly. This addresses the problem that existing AGV control technology in egg storage scenarios cannot accurately obtain the strength of eggs in a non-destructive manner when controlling vehicle speed, thus ensuring a low breakage rate while adjusting and increasing the transport speed of the AGV.

[0005] To achieve the above objectives, in the first aspect, this application provides an intelligent data analysis system for egg storage, including a basic data acquisition module, a weighting intensity module, a breakage analysis module, and a speed control module;

[0006] The basic data acquisition module is used to acquire Haugh units, eggshell strength, eggshell thickness and egg shape index of different batches of eggs in the egg storage, and classify them according to Haugh units to obtain basic attribute data of eggs.

[0007] The weight strength module includes a weight allocation unit and a strength scoring unit. The weight allocation unit performs weight allocation processing based on the basic attribute data of the egg, and the strength scoring unit establishes an egg body strength scoring mechanism using eggshell thickness and egg shape index.

[0008] The breakage analysis module is used to acquire breakage data of eggs with different strength ratings on automated guided vehicles at different speeds, and to perform speed-based breakage analysis to obtain speed-based breakage reference data.

[0009] The speed control module analyzes and obtains the maximum permissible speed of the automated guided vehicle based on the basic properties of the eggs to be transported and the vehicle speed and breakage reference data, and then makes adjustments accordingly.

[0010] Furthermore, the basic data acquisition module is configured with basic data acquisition strategies, which include:

[0011] Randomly select k1 eggs from the egg storage, denoted as sampled eggs; obtain the Haugh unit of each sampled egg; and arrange the Haugh units of each egg in descending order, denoted as the sampled Haugh unit sequence; where k1 is the set number of eggs to select.

[0012] The Haugh unit sequence is divided into k2 Haugh unit clusters using a density clustering algorithm, and these clusters are named the 1st Haugh unit cluster, the 2nd Haugh unit cluster, ..., the k1th Haugh unit cluster according to their order in the Haugh unit sequence.

[0013] Furthermore, the basic data collection strategy also includes:

[0014] Designate any one sampled egg as the first sampled egg, measure the major and minor diameters of the first sampled egg, and calculate the egg shape index of the first sampled egg; at the same time, obtain the eggshell strength and eggshell thickness of the first sampled egg; and record the information of the first egg batch to which the first sampled egg belongs and the corresponding Haugh unit cluster; record these as the basic attribute data of the first sampled egg.

[0015] The basic attribute data of eggs is obtained by repeatedly obtaining the basic attribute data of all sampled eggs.

[0016] Furthermore, the weight allocation unit is configured with a weight allocation strategy, which includes:

[0017] All sampled eggs are divided into groups according to the HAF unit cluster to which the corresponding HAF unit belongs, and these groups are called sampled egg sets. The basic attribute data of any sampled egg set is called the first basic data.

[0018] All egg shape indices, eggshell strength, and eggshell thickness in the first basic data were standardized and then randomly and evenly divided into two parts according to the corresponding sampled eggs, which were denoted as Reference Data 1 and Reference Data 2 respectively.

[0019] An eggshell strength prediction model is constructed based on a multiple linear regression equation. The eggshell strength prediction model is as follows: ES = u0 + u1 * GX1 + u2 * GX2 + v, where ES represents eggshell strength, GX1 and GX2 represent eggshell strength and egg shape index respectively, u0, u1 and u2 represent the parameters of the model, and v represents the error term.

[0020] The eggshell strength prediction model was trained and fitted using reference data 1 and reference data 2 respectively. After completion, strength prediction model 1 and strength prediction model 2 were obtained in sequence.

[0021] Furthermore, the weight allocation strategy also includes:

[0022] For intensity prediction model 1 and intensity prediction model 2, they are tested using their respective untrained reference data, and their root mean square errors are calculated and denoted as RM1 and RM2 respectively.

[0023] In intensity prediction model 1, u1 and u2 are denoted as QA1 and QA2 respectively; u1 and u2 in intensity prediction model 1 are denoted as QB1 and QB2 respectively; and QC1 and QC2 are calculated, where QC1 = [RM2 / (RM1+RM2)]*QA1 + [RM1 / (RM1+RM2)]*QB1; QC2 = [RM2 / (RM1+RM2)]*QA2 + [RM1 / (RM1+RM2)]*QB2;

[0024] Then calculate TQ and SQ, where TQ = QC1 / (QC1+QC2) and SQ = QC2 / (QC1+QC2); record TQ and SQ in order as the thickness weight and egg shape weight of the corresponding Haugh unit cluster, respectively; and repeatedly obtain the thickness weight and egg shape weight of all Haugh unit clusters.

[0025] Furthermore, the intensity scoring unit is configured by an intensity scoring strategy, which includes:

[0026] Obtain the theoretical maximum thickness MT and theoretical minimum thickness LT of the egg; denote [LT, MT] as the theoretical thickness range; divide [LT, MT] evenly into 100 sub-intervals, and assign all sub-intervals in ascending order to scores from 1 to 100, which are recorded as eggshell thickness scores;

[0027] Obtain the theoretical optimal range of the egg shape index, denoted as [LS0, MS0], and obtain the theoretical maximum egg shape index MS1 and the theoretical minimum egg shape index LS1. Divide [LS1, (LS0+MS0 / 2)] evenly into 100 sub-intervals, and assign all sub-intervals to scores from 1 to 100 in ascending order. At the same time, divide [(LS0+MS0 / 2), MS1] evenly into 100 sub-intervals, and assign all sub-intervals to scores from 1 to 100 in descending order. Record all of these as the egg shape index score.

[0028] For an egg, obtain the corresponding eggshell thickness score TF and egg shape index score SF based on the eggshell thickness and egg shape index; obtain the Haugh unit cluster where the egg belongs, and then calculate the egg body strength score STR based on the corresponding thickness weight TQ and egg shape weight SQ, STR=TQ*TF+SQ*SF.

[0029] Furthermore, the damage analysis module is configured with damage analysis strategies, which include:

[0030] Obtain the theoretical speed range of the automated guided vehicle, and divide the theoretical speed range into k2 speed levels evenly; denote any speed level as the first speed level, where k2 is the set number of divisions;

[0031] When the automated guided vehicle transports undamaged eggs at the first speed level, after each transport is completed, eggs that are broken but intact are counted and recorded as slightly broken eggs; the egg body strength score of each slightly broken egg is obtained; the number of slightly broken eggs is classified according to the corresponding speed level and egg body strength score; and the number of eggs under each speed level and egg body strength score is divided by the number of counts to record the number of single breaks.

[0032] Repeatedly count the number of slightly broken eggs during the automated guided vehicle (AGV) transport process and calculate and update the corresponding number of single breaks in real time; mark the number of single breaks under all speed levels and egg body strength scores as egg breakage data.

[0033] Furthermore, damage analysis strategies also include:

[0034] Record any egg body strength score as the first strength score; sort the number of single breaks corresponding to all first strength scores in the egg breakage data according to the corresponding speed level from small to large, and record it as the first speed breakage sequence;

[0035] Find the position of the maximum value in the first velocity damage sequence, denoted as position n. Remove the data after position n to obtain the second velocity damage sequence. Find the first non-zero position in the second velocity damage sequence, denoted as position i.

[0036] Repeatedly acquire the data from position i-1 to position i+j in the second velocity damage sequence and denot it as the j-th damage fitting sequence, j = {1, 2, ..., k3}, where k3 is the number of positions to be set, and i+k3>n;

[0037] For any one of the failure fitting sequences, we denote it as the first fitting sequence; we perform linear fitting on the number of single failures and the corresponding speed level in the first fitting sequence to obtain the linear relationship between the number of single failures and the speed level, and substitute the number of single failures that is 0 into the corresponding linear relationship to obtain the corresponding speed level, which is denoted as the reference speed level.

[0038] Repeatedly obtain the reference velocity level corresponding to all the damaged fitted sequences and calculate the average value to obtain the reference average velocity level. Mark the velocity corresponding to the reference average velocity level in the theoretical velocity range as the maximum zero-loss velocity of the first strength score.

[0039] Repeatedly obtain the maximum zero-damage speed of all egg body strength scores and record it as the reference data for vehicle speed damage.

[0040] Furthermore, the speed control module is configured with a speed control strategy, which includes:

[0041] For the eggs to be transported, k4 eggs are randomly selected and recorded as the sampled eggs. The HAF unit, egg shape index and shell thickness of each sampled egg are collected; where k4 is the set number of samples.

[0042] Based on the Haugh unit cluster to which each sampled egg belongs, the corresponding thickness weight and egg shape weight are obtained, and the egg body strength score of each sampled egg is obtained based on the corresponding egg shape index and eggshell thickness.

[0043] Arrange the egg body strength scores of all sampled eggs in ascending order, and record it as the first score sequence; obtain the data from the first digit to the k5th percentile of the first score sequence, and record it as the second score sequence;

[0044] Calculate the difference between any two adjacent egg strength scores in the second scoring sequence, denoted as the scoring difference; obtain the maximum scoring difference and the second-largest scoring difference, denoted as AC and BC in sequence; denote the smaller of the two egg strength scores corresponding to AC as the suspected abnormal score.

[0045] If the position of the suspected abnormal score is not in the range from the 1st to the q1*k4-th position in the second scoring sequence, then determine that the suspected abnormal score has no abnormality, and mark the smallest egg strength score in the second scoring sequence as the minimum strength score; where q1 is the set proportionality coefficient.

[0046] If the position of the suspected abnormal score is in the range from the 1st to the q1*k4-th position in the second scoring sequence, and AC < q2*BC, then mark the suspected abnormal score as having no abnormality, and mark the smallest egg strength score in the second scoring sequence as the minimum strength score; where q2 is the set proportionality coefficient.

[0047] If the position of the suspected abnormal score is in the range from the 1st to the q1*k4-th position in the second scoring sequence, and AC ≥ q2*BC, then mark the suspected abnormal score as abnormal, and mark all egg strength scores smaller than the suspected abnormal score in the second scoring sequence as abnormal; then mark the smallest egg strength score without an abnormal mark in the second scoring sequence as the minimum strength score.

[0048] Obtain the maximum zero-loss speed corresponding to the minimum strength score according to the vehicle speed breakage reference data, denoted as the first zero-loss speed, and adjust the vehicle speed of the automatic guided vehicle transporting the first batch of eggs to be transported to the first zero-loss speed.

[0049] In a second aspect, the present application provides an analysis method for egg product intelligent warehousing data, including the following steps:

[0050] Obtain the Haugh unit, eggshell strength, eggshell thickness, and egg shape index of eggs in different batches in the egg warehouse, and classify them according to the Haugh unit to obtain the basic attribute data of eggs. <​​​​​​​​​​The beneficial effects of this invention are as follows: This invention obtains basic attribute data of eggs by acquiring Haugh units, shell strength, shell thickness, and egg shape index of different batches of eggs in egg storage, and classifying them according to Haugh units; it performs weight allocation processing based on the basic attribute data of eggs, and establishes an egg body strength scoring mechanism using shell thickness and egg shape index; it acquires breakage data of eggs with different strength scores on automated guided vehicles (AGVs) at different speeds, and performs speed-based breakage analysis to obtain speed-based breakage reference data; it analyzes and obtains the maximum allowable speed of the AGV based on the basic attributes of the eggs to be transported and the speed-based breakage reference data, and then adjusts it accordingly; it can accurately obtain the strength of eggs in a non-destructive manner, and while ensuring a low breakage rate, it adjusts and increases the transportation speed of the AGV, thereby improving the scheduling efficiency of egg storage;

[0055] This invention constructs a multiple linear regression prediction model using eggshell thickness and egg shape index, and obtains corresponding thickness weights and egg shape weights through cross-validation and root mean square error weighting. This ensures the rationality and accuracy of the weights, guaranteeing the accuracy and robustness of the egg strength score. By statistically analyzing the number of micro-breakages of eggs for each strength score in real time and determining the maximum zero-loss speed through multiple linear fitting algorithms, it avoids misjudgments caused by single-data errors and can find an accurate maximum zero-loss speed for each strength score. By filtering out outliers in the first score sequence, the minimum strength score and the corresponding first zero-loss speed are determined and adjusted. This eliminates interference from outliers in the sampled eggs, reducing the breakage rate of transported eggs while improving the efficiency of egg transportation. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the system of the present invention;

[0057] Figure 2 This is a flowchart of the steps of the method of the present invention;

[0058] Figure 3 This is a flowchart of the speed control strategy of the present invention;

[0059] Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0061] Example 1, please refer to Figure 1 As shown, this application provides an intelligent egg storage data analysis system, including a basic data acquisition module, a weighting intensity module, a breakage analysis module, and a speed control module;

[0062] The basic data acquisition module is used to obtain the Haugh unit, shell strength, shell thickness and egg shape index of different batches of eggs in the egg storage, and classify them according to the Haugh unit to obtain the basic attribute data of the eggs.

[0063] The basic data acquisition module is configured with a basic data acquisition strategy, which includes: randomly selecting k1 eggs from the egg storage, denoted as sampled eggs; obtaining the Haugh unit of each sampled egg; and arranging the Haugh units of each egg in descending order, denoted as the sampled Haugh unit sequence; where k1 is the set number of eggs to be selected; k1 can be set according to the actual application scenario, and should not be too small. In this embodiment, k1 = 2000.

[0064] The Haugh unit sequence is divided into k2 Haugh unit clusters using a density clustering algorithm, and these clusters are named the 1st Haugh unit cluster, the 2nd Haugh unit cluster, ..., the k1st Haugh unit cluster according to their order in the Haugh unit sequence; in this embodiment, k2 = 5. The continuous HU values ​​are discretized into several quality levels to facilitate subsequent classification processing. The division of Haugh units can also be done without using a density clustering algorithm, directly referring to relevant standards. Using a density clustering algorithm can make the divided Haugh units more consistent with the distribution of Haugh units in the corresponding egg storage.

[0065] Designate any one sampled egg as the first sampled egg, measure the major and minor diameters of the first sampled egg, and calculate the egg shape index of the first sampled egg; egg shape index = (minor diameter / major diameter) * 100%; at the same time, obtain the shell strength and shell thickness of the first sampled egg; and record the information of the first egg batch and the corresponding Haugh unit cluster; record these as the basic attribute data of the first sampled egg.

[0066] Eggshell strength refers to the eggshell's ability to resist external forces. Traditional methods measure eggshell strength by applying vertical pressure to the eggshell and recording the maximum pressure at the moment of breakage, which can damage the egg. Egg shape index reflects the egg's geometry and directly affects the stress distribution that allows it to withstand breakage. Eggshell thickness directly affects the egg's resistance to breakage during transportation, and eggshell thickness can be obtained non-destructively using ultrasonic methods. Both egg shape index and eggshell thickness directly affect eggshell strength.

[0067] The basic attribute data of eggs is obtained by repeatedly retrieving the basic attribute data of all sampled eggs;

[0068] In practice, Haugh units reflect protein quality, which is mainly affected by egg freshness, hen health, and storage conditions. Haugh units are not directly related to eggshell strength, but they do reflect the hen's physiological state to some extent. Healthy hens can produce eggs with both high protein content and high eggshell strength, while older or sick hens may exhibit both decreased protein content and reduced eggshell strength. Therefore, there is an indirect relationship between Haugh units and eggshell strength. This indirect relationship can affect subsequent weighting to some extent, so dividing eggs according to Haugh units can improve the accuracy of weighting to a certain degree.

[0069] The weight strength module includes a weight allocation unit and a strength scoring unit. The weight allocation unit performs weight allocation processing based on the basic attribute data of eggs, and the strength scoring unit establishes an egg body strength scoring mechanism using eggshell thickness and egg shape index.

[0070] The weight allocation unit is configured with a weight allocation strategy, which includes: dividing all sampled eggs according to the Hash unit cluster where the corresponding Hash unit is located, and calling them sampled egg sets; and calling the basic attribute data of any sampled egg set as the first basic data.

[0071] All egg shape indices, eggshell strength, and eggshell thickness in the first basic data were standardized to ensure that the dimensions of each index were consistent and to avoid model errors caused by differences in numerical values. The sampled eggs were then randomly and evenly divided into two parts, which were denoted as Reference Data 1 and Reference Data 2, respectively, for subsequent cross-validation.

[0072] An eggshell strength prediction model is constructed based on a multiple linear regression equation. The eggshell strength prediction model is as follows: ES = u0 + u1*GX1 + u2*GX2 + v, where ES represents eggshell strength, GX1 and GX2 represent eggshell strength and egg shape index respectively, u0, u1 and u2 represent the model parameters, and v represents the error term. The influence of eggshell thickness and egg shape index on eggshell strength is quantified, and u1 and u2 serve as the basis for subsequent weight allocation.

[0073] The eggshell strength prediction model was trained and fitted using reference data 1 and reference data 2 respectively. After completion, strength prediction model 1 and strength prediction model 2 were obtained in sequence.

[0074] For intensity prediction model 1 and intensity prediction model 2, they are tested using their respective untrained reference data, and their root mean square errors are calculated and denoted as RM1 and RM2 respectively. For example, intensity prediction model 1 is trained using reference data 1, and intensity prediction model 1 is tested using reference data 2. Dual model training and cross-testing can not only improve the robustness of the model, but also determine which set of parameters is more reliable by comparing the errors.

[0075] In intensity prediction model 1, u1 and u2 are denoted as QA1 and QA2 respectively; u1 and u2 in intensity prediction model 1 are denoted as QB1 and QB2 respectively; and QC1 and QC2 are calculated, where QC1 = [RM2 / (RM1+RM2)]*QA1 + [RM1 / (RM1+RM2)]*QB1; QC2 = [RM2 / (RM1+RM2)]*QA2 + [RM1 / (RM1+RM2)]*QB2;

[0076] Next, calculate TQ and SQ, where TQ = QC1 / (QC1+QC2) and SQ = QC2 / (QC1+QC2). Record TQ and SQ in order as the thickness weight and egg shape weight of the corresponding Haugh unit cluster, respectively. Repeat the process to obtain the thickness weight and egg shape weight corresponding to all Haugh unit clusters. Use the inverse ratio of the errors of the two models as the weight to ensure that the corresponding parameters of the model with smaller error have a higher proportion in the final weight. The optimal weights for eggshell thickness and egg shape index are obtained by weighted synthesis.

[0077] The strength scoring unit configuration is based on a strength scoring strategy, which includes: obtaining the theoretical maximum thickness MT and theoretical minimum thickness LT of the egg; denoteing [LT, MT] as the theoretical thickness range; that is, the maximum and minimum eggshell thickness visible in egg storage; uniformly dividing [LT, MT] into 100 sub-intervals, and assigning all sub-intervals to scores from 1 to 100 in ascending order, which are recorded as eggshell thickness scores; for example, if the eggshell thickness of an egg is located in the first sub-interval, then the eggshell thickness score is 1 point;

[0078] Obtain the theoretical optimal range of the egg shape index, denoted as [LS0, MS0], and obtain the theoretical maximum egg shape index MS1 and the theoretical minimum egg shape index LS1; that is, the maximum and minimum egg shape index that can be seen in egg storage. A higher egg shape index is not necessarily better; an excessively high or low index will affect the compressive strength of the eggshell. The theoretical optimal range for the egg shape index of ordinary eggs is [72%, 76%], which can be adjusted according to the actual application scenario. Divide [LS1, (LS0+MS0 / 2)] evenly into 100 sub-intervals, and assign all sub-intervals to scores from 1 to 100 in ascending order. Simultaneously, divide [(LS0+MS0 / 2), MS1] evenly into 100 sub-intervals, and assign all sub-intervals to scores from 1 to 100 in descending order; these are collectively recorded as the egg shape index score. An excessively high or low egg shape index will result in a low egg shape index score.

[0079] For an egg, the eggshell thickness score (TF) and egg shape index (SF) are obtained based on the eggshell thickness and egg shape index. The Haugh unit cluster of the egg is then determined, and the egg body strength score (STR) is calculated based on the corresponding thickness weight (TQ) and egg shape weight (SQ): STR = TQ*TF + SQ*SF. The egg body strength score reflects the egg's overall resistance to breakage. This score can be obtained non-destructively, significantly reducing egg breakage during testing compared to directly measuring shell strength.

[0080] In the specific implementation process, the thickness weight and egg shape weight are assigned based on the model, which can avoid the error caused by subjective setting; by training and fitting two sets of data to each other, the bias caused by the randomness of the samples in a single model can be reduced; error weighting is equivalent to weighting the two estimates, which can effectively suppress the influence of outliers or differences in sample division on the final weight.

[0081] The breakage analysis module is used to acquire breakage data of eggs with different strength ratings on automated guided vehicles at different speeds, and to perform speed-based breakage analysis to obtain speed-based breakage reference data.

[0082] The breakage analysis module is configured with a breakage analysis strategy, which includes: obtaining the theoretical speed range of the automated guided vehicle, i.e., the speed range allowed in egg storage; uniformly dividing the theoretical speed range into k2 speed levels; and designating any speed level as the first speed level, where k2 is the set number of divisions; k2 can be divided according to the actual application scenario, and in this embodiment, k2 = 100.

[0083] When the automated guided vehicle transports undamaged eggs at the first speed level, after each transport, eggs that are broken but intact (i.e., cracked eggs) are counted and recorded as slightly broken eggs. The egg strength score for each slightly broken egg is obtained. The number of slightly broken eggs is then categorized according to the corresponding speed level and egg strength score. The number of eggs at each speed level and egg strength score is divided by the number of counts to obtain the single breakage count. Slightly broken eggs indicate that the impact of the speed on the egg exceeds the critical point of egg strength, but not by a significant margin.

[0084] Repeatedly count the number of slightly broken eggs during the transport of automated guided vehicles, and calculate and update the corresponding number of single breaks in real time; mark the number of single breaks under all speed levels and egg body strength scores as egg breakage data;

[0085] Record any egg body strength score as the first strength score; sort the number of single breaks corresponding to all first strength scores in the egg breakage data according to the corresponding speed level from small to large, and record it as the first speed breakage sequence;

[0086] Find the position of the maximum value in the first speed breakage sequence, denoted as position n, and remove the data after position n; the data after position n represents that the speed is too high and the egg is severely broken, which is not of reference value for finding the speed that makes the egg break without any damage, so it is removed; thus, the second speed breakage sequence is obtained; find the first non-zero position in the second speed breakage sequence, denoted as position i;

[0087] The data from position i-1 to position i+j in the second velocity failure sequence are repeatedly acquired and denoted as the j-th failure fitting sequence, j = {1, 2, ..., k3}, where k3 is the number of positions to be set, i+k3>n; in this embodiment, k3 = 4; that is, there are 4 failure fitting sequences;

[0088] For any one of the failure fitting sequences, we denote it as the first fitting sequence; we perform linear fitting on the number of single failures and the corresponding speed level in the first fitting sequence to obtain the linear relationship between the number of single failures and the speed level, and substitute the number of single failures that is 0 into the corresponding linear relationship to obtain the corresponding speed level, which is denoted as the reference speed level.

[0089] Repeatedly obtain the reference speed level corresponding to all the broken fitted sequences and calculate the average value to obtain the reference average speed level. Mark the speed corresponding to the reference average speed level in the theoretical speed range as the maximum zero-loss speed of the first strength score; that is, the maximum speed at which the automatic guide vehicle can prevent the egg from breaking during transportation.

[0090] Repeatedly obtain the maximum zero-loss speed of all egg body strength scores and record it as the reference data for vehicle speed breakage;

[0091] In practice, the speed and the number of single breaks may not necessarily show a linear change overall, but they can be regarded as linear changes in local locations. By changing the endpoint of the local location, selecting multiple sets of data with different endpoints, fitting them separately, and then averaging them, we can more accurately capture the trend of the breakage initiation, further smooth out random errors, and even if a certain subsequence is affected by random fluctuations, other sequences can still provide effective information, improving the accuracy and representativeness of the maximum zero-loss speed.

[0092] The speed control module analyzes the basic properties of the eggs to be transported and the speed and breakage reference data to obtain the maximum permissible speed of the automated guided vehicle and makes adjustments accordingly.

[0093] The speed control module is configured with a speed control strategy, which includes: for the eggs to be transported, randomly select k4 eggs, which are called sampled eggs, and collect the Haugh unit, egg shape index and eggshell thickness of each sampled egg; where k4 is the set number of samples; k4 can be set according to the number of eggs to be transported.

[0094] Based on the Haugh unit cluster to which each sampled egg belongs, the corresponding thickness weight and egg shape weight are obtained. Based on the corresponding egg shape index and eggshell thickness, the egg body strength score of each sampled egg is obtained; this accurately reflects the breakage resistance of eggs in the current batch and provides a quantitative reference for subsequent speed decisions.

[0095] Please see Figure 3 As shown, the egg strength scores of all sampled eggs are arranged in ascending order and recorded as the first score sequence; the data from the first digit to the k5th percentile of the first score sequence are obtained and recorded as the second score sequence; in this embodiment, k5 = 40, that is, the first 40% of the data are obtained, focusing on the batch of eggs with the lowest score, which has the highest risk of breakage and is the key data for determining the maximum permissible vehicle speed.

[0096] Calculate the difference between any two adjacent egg strength scores in the second scoring sequence and record it as the score difference; obtain the largest score difference and the second largest score difference, and record them as AC and BC respectively in order; record the smaller of the two egg strength scores corresponding to AC as the suspected abnormal score.

[0097] If the suspected abnormal score is not located in the first to q1*k4th position in the second scoring sequence, that is, the abnormal score is not located in the edge region of the sequence, it indicates that the largest score difference is caused by the stratification of egg body strength scores; then it is determined that the suspected abnormal score is not abnormal, and the smallest egg body strength score in the second scoring sequence is marked as the minimum strength score; where q1 is the set proportional coefficient; in this embodiment, q1 is 0.1.

[0098] If the position of the suspected abnormal score is from the 1st to the q1*k4-th in the second score sequence, and AC < q2*BC, that is, the position of the abnormal score is in the edge position area of the sequence, but the difference from the adjacent data is not abnormal, indicating normal data fluctuation; then mark the suspected abnormal score as normal, and mark the minimum egg strength score in the second score sequence as the minimum strength score; in this embodiment, q2 is 1.5;

[0099] If the position of the suspected abnormal score is from the 1st to the q1*k4-th in the second score sequence, and AC ≥ q2*BC, that is, the position of the abnormal score is in the edge position area of the sequence, and the difference from the adjacent data is too large, indicating that there are individual eggs with abnormally low strength among the eggs to be transported; then mark the suspected abnormal score as abnormal, and mark all the egg strength scores in the second score sequence that are less than the suspected abnormal score as abnormal; then mark the minimum egg strength score without abnormal mark in the second score sequence as the minimum strength score; If we want to keep the individual eggs with abnormally low strength intact, we must greatly reduce the speed of the automatic guided vehicle, which seriously affects the transportation efficiency of egg storage. Considering from the economic benefit, the cost performance is not high. Therefore, we can give up keeping the individual eggs with abnormally low strength intact, and in turn换取更高的运输效率,以提高经济效益;

[0100] Obtain the maximum zero-loss speed corresponding to the minimum strength score according to the vehicle speed breakage reference data, denote it as the first zero-loss speed, and adjust the vehicle speed of the automatic guided vehicle transporting the first batch of eggs to be transported to the first zero-loss speed;

[0101] In the specific implementation process, relevant data needs to be sampled and collected for the eggs to be stored in the egg storage, while for the eggs to be出库或者调整转运的鸡蛋,可以直接使用入库时采集的数据,无需重复抽样采集。

[0102] Example 2, please refer to Figure 2 As shown, this application provides an analysis method for egg intelligent storage data, including the following steps:

[0103] Step S1, obtain the Haugh unit, eggshell strength, eggshell thickness, and egg shape index of eggs in different batches in the egg storage, and classify them according to the Haugh unit to obtain the basic attribute data of eggs; Step S1 includes the following sub-steps:

[0104] Step S101, randomly select k1 eggs from the egg storage, denote them as sampled eggs; obtain the Haugh unit of each sampled egg; and arrange the Haugh units of each egg in descending order, denote it as the sampled Haugh unit sequence; where k1 is the set number of selected eggs;

[0105] It should be noted that there is an unclear expression "出库或者调整转运的鸡蛋" in the original text, which may need to be further clarified for a more accurate translation. Also, the part "进而换取更高的运输效率,以提高经济效益" in the translation of seems to be an incorrect or incomplete translation. It should be something like "in exchange for higher transportation efficiency to improve economic benefits".Step S102: Divide the Haugh unit sequence into k2 Haugh unit clusters using a density clustering algorithm, and label them as the 1st Haugh unit cluster, the 2nd Haugh unit cluster, ..., the k1th Haugh unit cluster according to their order in the Haugh unit sequence.

[0106] Step S103: Designate any one of the sampled eggs as the first sampled egg, measure the major and minor diameters of the first sampled egg, and calculate the egg shape index of the first sampled egg; at the same time, obtain the shell strength and shell thickness of the first sampled egg; and record the information of the first batch of eggs to which the first sampled egg belongs and the corresponding Haugh unit cluster; record these as the basic attribute data of the first sampled egg.

[0107] Step S104: Repeatedly obtain the basic attribute data of all sampled eggs to obtain the basic attribute data of eggs.

[0108] Step S2 involves weighting the eggs based on their basic attribute data and establishing an egg strength scoring mechanism using eggshell thickness and egg shape index. Step S2 includes the following sub-steps:

[0109] Step S201: Divide all sampled eggs into clusters of Hash units according to the corresponding Hash units, and denote them as sampled egg sets. Denote the basic attribute data of any sampled egg set as the first basic data.

[0110] Step S202: Standardize all egg shape index, eggshell strength and eggshell thickness in the first basic data, and randomly and evenly divide them into two parts according to the corresponding sampled eggs, which are recorded as reference data 1 and reference data 2 respectively.

[0111] Step S203: Construct an eggshell strength prediction model based on the multiple linear regression equation. The eggshell strength prediction model is as follows: ES = u0 + u1 * GX1 + u2 * GX2 + v, where ES represents eggshell strength, GX1 and GX2 represent eggshell strength and egg shape index respectively, u0, u1 and u2 represent the parameters of the model, and v represents the error term.

[0112] Step S204: The eggshell strength prediction model is trained and fitted using reference data 1 and reference data 2 respectively. After completion, strength prediction model 1 and strength prediction model 2 are obtained in sequence.

[0113] Step S205: For intensity prediction model 1 and intensity prediction model 2, test them using their respective untrained reference data and calculate their respective root mean square errors, which are denoted as RM1 and RM2 in order.

[0114] Step S206: In intensity prediction model 1, u1 and u2 are sequentially denoted as QA1 and QA2, respectively; in intensity prediction model 1, u1 and u2 are sequentially denoted as QB1 and QB2, respectively; and QC1 and QC2 are calculated, where QC1 = [RM2 / (RM1+RM2)]*QA1 + [RM1 / (RM1+RM2)]*QB1; QC2 = [RM2 / (RM1+RM2)]*QA2 + [RM1 / (RM1+RM2)]*QB2;

[0115] Step S207: Calculate TQ and SQ, where TQ = QC1 / (QC1+QC2) and SQ = QC2 / (QC1+QC2); Record TQ and SQ in order as the thickness weight and egg-shaped weight of the corresponding Hatch unit clusters respectively; and repeatedly obtain the thickness weight and egg-shaped weight of all Hatch unit clusters.

[0116] Step S208: Obtain the theoretical maximum thickness MT and theoretical minimum thickness LT of the egg; denote [LT, MT] as the theoretical thickness range; divide [LT, MT] evenly into 100 sub-intervals, and assign all sub-intervals in ascending order to scores from 1 to 100, which are recorded as eggshell thickness scores.

[0117] Step S209: Obtain the theoretical optimal range of egg shape index, denoted as [LS0, MS0], and obtain the theoretical maximum egg shape index MS1 and the theoretical minimum egg shape index LS1; divide [LS1, (LS0+MS0 / 2)] evenly into 100 sub-intervals, and assign all sub-intervals to scores from 1 to 100 in ascending order; at the same time, divide [(LS0+MS0 / 2), MS1] evenly into 100 sub-intervals, and assign all sub-intervals to scores from 1 to 100 in descending order; record all of these as the egg shape index score.

[0118] Step S210: For an egg, obtain the corresponding eggshell thickness score TF and egg shape index score SF based on the eggshell thickness and egg shape index; and obtain the Haugh unit cluster to which the egg belongs in Haugh units.

[0119] Then, based on the corresponding thickness weight TQ and egg shape weight SQ, the egg body strength score STR is calculated, STR = TQ*TF + SQ*SF.

[0120] Step S3 involves acquiring breakage data of eggs with different strength ratings on automated guided vehicles at different speeds, and performing speed-based breakage analysis to obtain speed-based breakage reference data. Step S3 includes the following sub-steps:

[0121] Step S301: Obtain the theoretical speed range of the automated guided vehicle, and divide the theoretical speed range into k2 speed levels evenly; denot any speed level as the first speed level, where k2 is the set number of divisions;

[0122] Step S302: When the automated guided vehicle transports undamaged eggs at the first speed level, after each transport is completed, eggs that are damaged but intact are counted and recorded as slightly damaged eggs; the egg body strength score of each slightly damaged egg is obtained; the number of slightly damaged eggs is classified according to the corresponding speed level and egg body strength score; and the number of eggs under each speed level and egg body strength score is divided by the number of counts and recorded as the number of single breaks.

[0123] Step S303: Repeatedly count the number of slightly broken eggs that occur during the transportation process of the automated guided vehicle, and calculate and update the corresponding number of single breaks in real time; mark the number of single breaks under all speed levels and egg body strength scores as egg breakage data;

[0124] Step S304: Record any egg body strength score as the first strength score; sort the number of single breaks corresponding to all first strength scores in the egg breakage data according to the corresponding speed level from small to large, and record it as the first speed breakage sequence.

[0125] Step S305: Obtain the position of the maximum value in the first velocity damage sequence, denoted as position n; remove the data after position n to obtain the second velocity damage sequence; obtain the first non-zero position in the second velocity damage sequence, denoted as position i;

[0126] Step S306: Repeatedly acquire the data from position i-1 to position i+j in the second velocity damage sequence and record it as the j-th damage fitting sequence, j = {1, 2, ..., k3}, where k3 is the number of positions to be set, and i+k3>n;

[0127] Step S307: For any one of the damage fitting sequences, denoted as the first fitting sequence; perform linear fitting on the number of single damages and the corresponding speed level in the first fitting sequence to obtain the linear relationship between the number of single damages and the speed level, and substitute the number of single damages that is 0 into the corresponding linear relationship to obtain the corresponding speed level, denoted as the reference speed level.

[0128] Step S308: Repeatedly obtain the reference velocity level corresponding to all the damaged fitted sequences and calculate the average value to obtain the reference average velocity level. Mark the velocity corresponding to the reference average velocity level in the theoretical velocity range as the maximum zero-loss velocity of the first strength score.

[0129] Step S309: Repeatedly obtain the maximum zero-loss speed of all egg body strength scores and record it as the reference data for vehicle speed damage.

[0130] Step S4: Obtain the maximum allowable vehicle speed of the automated guided vehicle based on the basic attributes of the eggs to be transported and the vehicle speed damage reference data, and make adjustments. Step S4 includes the following sub-steps:

[0131] Step S401: For the eggs to be transported, randomly select k4 eggs, denoted as sampled eggs, and collect the Haugh unit, egg shape index, and eggshell thickness of each sampled egg. Here, k4 is the set number of sampled eggs.

[0132] Step S402: According to the Haugh unit cluster to which the Haugh unit of each sampled egg belongs, obtain the corresponding thickness weight and egg shape weight, and obtain the egg body strength score of each sampled egg based on the corresponding egg shape index and eggshell thickness.

[0133] Step S403: Arrange the egg body strength scores of all sampled eggs in ascending order, denoted as the first score sequence. Obtain the data from the first position to the k5th percentile of the first score sequence, denoted as the second score sequence.

[0134] Step S404: Calculate the difference between any two adjacent egg body strength scores in the second score sequence, denoted as the score difference. Obtain the largest score difference and the second largest score difference, and denote them as AC and BC in order. Denote the smaller of the two egg body strength scores corresponding to AC as the suspected abnormal score.

[0135] Step S405: If the position of the suspected abnormal score is not in the range from the first position to the q1*k4th position in the second score sequence, then determine that the suspected abnormal score has no abnormality, and mark the smallest egg body strength score in the second score sequence as the minimum strength score.

[0136] Step S406: If the position of the suspected abnormal score is in the range from the first position to the q1*k4th position in the second score sequence, and AC < q2*BC, then mark the suspected abnormal score as having no abnormality, and mark the smallest egg body strength score in the second score sequence as the minimum strength score.

[0137] Step S407: If the position of the suspected abnormal score is in the range from the first position to the q1*k4th position in the second score sequence, and AC ≥ q2*BC, then mark the suspected abnormal score as abnormal, and mark all egg body strength scores smaller than the suspected abnormal score in the second score sequence as abnormal. Then mark the smallest non-abnormal marked egg body strength score in the second score sequence as the minimum strength score. <opposite text="0000287">Step S408: Obtain the maximum zero-damage speed corresponding to the minimum strength score according to the vehicle speed damage reference data, denoted as the first zero-damage speed, and adjust the vehicle speed of the automated guided vehicle transporting the first batch of eggs to be transported to the first zero-damage speed.

[0139] Example 3, please refer to Figure 4 As shown, Figure 4 A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, steps such as those in the analysis method for intelligent egg storage data are performed to achieve the following functions: obtaining the Haugh unit, eggshell strength, eggshell thickness, and egg shape index of different batches of eggs in the egg storage, and classifying them according to the Haugh unit to obtain basic egg attribute data; performing weight allocation processing based on the basic egg attribute data, and establishing an egg body strength scoring mechanism using eggshell thickness and egg shape index; obtaining breakage data of eggs with different strength scores on automated guided vehicles at different speeds, and performing speed-based breakage analysis to obtain speed-based breakage reference data; and analyzing the basic attributes of the eggs to be transported and the speed-based breakage reference data to obtain the maximum permissible speed of the automated guided vehicle and making adjustments accordingly.

[0140] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0141] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps in the above-mentioned intelligent egg storage data analysis method to achieve the following functions: obtaining the Haugh unit, eggshell strength, eggshell thickness, and egg shape index of different batches of eggs in the egg storage, and classifying them according to the Haugh unit to obtain basic egg attribute data; performing weight allocation processing based on the basic egg attribute data, and establishing an egg body strength scoring mechanism using eggshell thickness and egg shape index; obtaining breakage data of eggs with different strength scores on automated guided vehicles at different speeds, and performing speed breakage analysis processing to obtain speed breakage reference data; and analyzing and obtaining the maximum allowable speed of the automated guided vehicle based on the basic attributes of the eggs to be transported and the speed breakage reference data, and making adjustments accordingly.

[0142] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the technical solutions described above, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products can be stored in computer-readable storage media, such as ROM / RAM, magnetic disks, optical disks, etc., and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0143] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An egg smart warehousing data analysis system, characterized in that, The system comprises a basic acquisition module, a weight intensity module, a damage analysis module and a speed control module. The basic acquisition module is configured to obtain the Haugh units, shell strength, shell thickness and egg shape index of different batches of eggs in the egg storage, and classify the eggs according to the Haugh units to obtain the basic attribute data of the eggs. The weight intensity module comprises a weight distribution unit and an intensity scoring unit, the weight distribution unit is configured to perform weight distribution processing based on the basic attribute data of the eggs, and the intensity scoring unit is configured to establish an egg body intensity scoring mechanism using the shell thickness and the egg shape index. The damage analysis module is configured to obtain the damage data of eggs with different intensity scores on the automatic guided vehicle at different speeds, and perform speed damage analysis processing to obtain speed damage reference data. The speed control module is configured to obtain the maximum allowable speed of the automatic guided vehicle according to the basic attributes of the eggs to be transported and the speed damage reference data, and perform adjustment.

2. The system for analysis of egg intelligence warehousing data according to claim 1, wherein, The basic acquisition module is configured with a basic acquisition strategy, which comprises: Randomly selecting k1 eggs from the egg storage, denoted as sample eggs; obtaining the Haugh unit of each sample egg; and arranging the Haugh unit of each egg in descending order, denoted as a sample Haugh unit sequence; wherein k1 is the number of selected eggs; The density clustering algorithm is used to divide the Haugh unit sequence into k2 Haugh unit clusters, and the first Haugh unit cluster, the second Haugh unit cluster, …, and the k1 Haugh unit cluster are recorded in order in the Haugh unit sequence.

3. The system for analysis of egg intelligence warehousing data according to claim 2, wherein, The basic acquisition strategy further comprises: Any one of the sample eggs is denoted as a first sample egg, the length and the short diameter of the first sample egg are measured, and the egg shape index of the first sample egg is calculated; at the same time, the shell strength and the shell thickness of the first sample egg are obtained; and the first egg batch information of the first sample egg and the Haugh unit cluster where the corresponding Haugh unit is located are recorded; denoted as the basic attribute data of the first sample egg; The basic attribute data of all sample eggs is obtained to obtain the basic attribute data of the eggs.

4. The system for analysis of egg intelligence warehousing data according to claim 3, wherein, The weight distribution unit is configured with a weight distribution strategy, which comprises: All sample eggs are divided according to the Haugh unit cluster where the corresponding Haugh unit is located, denoted as a sample egg set, and the basic attribute data of any sample egg set is denoted as first basic data; The egg shape index, shell strength and shell thickness in the first basic data are respectively standardized, and are randomly and uniformly divided into two parts according to the corresponding sample eggs, denoted as reference data 1 and reference data 2 respectively; A shell strength prediction model is constructed based on a multiple linear regression equation, and the shell strength prediction model is as follows: where ES represents the shell strength, GX1 and GX2 represent the shell strength and the egg shape index in order, u0, u1 and u2 represent the parameters of the model, and v represents an error term. The reference data 1 and the reference data 2 are respectively used to train and fit the shell strength prediction model, and the strength prediction model 1 and the strength prediction model 2 are respectively obtained in order after completion.

5. The system for analysis of egg intelligence warehousing data according to claim 4, wherein, The weight distribution strategy further comprises: For the strength prediction model 1 and the strength prediction model 2, the reference data not used for training is respectively used for testing, and the root mean square error of each is respectively calculated, denoted as RM1 and RM2 in order. Let u1 and u2 in the strength prediction model 1 be denoted as QA1 and QA2 in sequence respectively; let u1 and u2 in the strength prediction model 2 be denoted as QB1 and QB2 in sequence respectively; and calculate QC1 and QC2, wherein QC1 = [RM2 / (RM1+RM2)]*QA1 + [RM1 / (RM1+RM2)]*QB1; QC2 = [RM2 / (RM1+RM2)]*QA2 + [RM1 / (RM1+RM2)]*QB2; Then calculate TQ and SQ, wherein TQ = QC1 / (QC1+QC2), SQ = QC2 / (QC1+QC2); let TQ and SQ be denoted as the thickness weight and the egg shape weight of the corresponding Hounsfield unit cluster in sequence respectively; and repeat obtaining the thickness weight and the egg shape weight of all Hounsfield unit clusters.

6. The system for analysis of egg intelligence warehousing data according to claim 5, wherein, The strength scoring unit is configured with a strength scoring strategy, and the strength scoring strategy comprises: Obtain the theoretical maximum thickness MT and the theoretical minimum thickness LT of the egg; let [LT, MT] be denoted as the theoretical thickness range; divide [LT, MT] into 100 subintervals uniformly, and let all the subintervals correspond to 1 to 100 points in the order from small to large, denoted as eggshell thickness score; Obtain the theoretical best range of the egg shape index, denoted as [LS0, MS0], and obtain the theoretical maximum egg shape index MS1 and the theoretical minimum egg shape index LS1 of the egg; divide [LS1, (LS0+MS0 / 2)] into 100 subintervals uniformly, and let all the subintervals correspond to 1 to 100 points in the order from small to large; at the same time, divide [(LS0+MS0 / 2), MS1] into 100 subintervals uniformly, and let all the subintervals correspond to 1 to 100 points in the order from large to small; denoted as egg shape index score together; For an egg, obtain the corresponding eggshell thickness score TF and egg shape index score SF according to the eggshell thickness and the egg shape index of the egg; and obtain the Hounsfield unit cluster in which the Hounsfield unit of the egg is located, Then, according to the corresponding thickness weight TQ and the egg shape weight SQ, the egg body strength score STR of the egg is calculated, STR = TQ*TF + SQ*SF.

7. The system for analysis of egg intelligence warehousing data according to claim 6, wherein, The breakage analysis module is configured with a breakage analysis strategy, and the breakage analysis strategy comprises: Obtain the theoretical speed range of the automatic guided vehicle, and divide the theoretical speed range into k2 speed levels uniformly; let any one speed level be denoted as the first speed level, wherein k2 is the number of divisions set; When the automatic guided vehicle transports the eggs without breakage at the first speed level, count the eggs with breakage but complete shape among the transported eggs after each transportation is completed, denoted as micro-breakage eggs; obtain the egg body strength score of each micro-breakage egg; and classify the number of micro-breakage eggs according to the corresponding speed level and the egg body strength score; and divide the number at each speed level and the egg body strength score by the number of statistics, denoted as the single breakage number; The number of micro-damaged eggs appearing in the automatic guided vehicle transportation process is counted repeatedly, and the corresponding single damage number is calculated and updated in real time; the single damage number under all speed levels and egg body strength scores is marked as the egg damage data.

8. The system for analysis of egg intelligence warehousing data according to claim 7, wherein, The damage analysis strategy also includes: Any one egg body strength score is recorded as a first strength score; the single damage number corresponding to all first strength scores in the egg damage data is sorted in ascending order according to the corresponding speed level, and is recorded as a first speed damage sequence; The position of the maximum value in the first speed damage sequence is obtained, which is recorded as position n, and the data after position n is removed to obtain a second speed damage sequence; the first non-zero position in the second speed damage sequence is obtained, which is recorded as position i; The data from position i-1 to position i+j in the second speed damage sequence is repeatedly obtained and recorded as the jth damage fitting sequence, j={1, 2, …, k3}, wherein k3 is the set number of positions, i+k3>n; For any one damage fitting sequence, record it as a first fitting sequence; the single damage number in the first fitting sequence and the corresponding speed level are linearly fitted to obtain the linear relationship between the corresponding single damage number and the speed level, and the single damage number of 0 is substituted into the corresponding linear relationship to obtain the corresponding speed level, which is recorded as the reference speed level; The reference speed levels corresponding to all damage fitting sequences are repeatedly obtained and averaged to obtain the reference average speed level, and the speed corresponding to the reference average speed level in the theoretical speed range is marked as the maximum zero-loss speed of the first strength score; The maximum zero-loss speed of all egg body strength scores is repeatedly obtained and recorded as the vehicle speed damage reference data.

9. The system for analysis of egg intelligence warehousing data according to claim 8, wherein, The speed control module is configured with a speed control strategy, and the speed control strategy includes: For the eggs to be transported, randomly select k4 eggs from them, which are recorded as the sampled eggs; collect the Hounsfield unit, egg shape index and eggshell thickness of each sampled egg; wherein k4 is the set number of samples; According to the Hounsfield unit cluster to which each sampled egg's Hounsfield unit belongs, the corresponding thickness weight and egg shape weight are obtained, and according to the corresponding egg shape index and eggshell thickness, the egg body strength score of each sampled egg is obtained; Arrange all the egg body strength scores of the sampled eggs in ascending order, and record them as a first score sequence; obtain the data from the first to the k5th percentile of the first score sequence, and record it as a second score sequence, k5=40; Calculate the difference between any two adjacent egg body strength scores in the second score sequence, and record it as the score difference; obtain the maximum score difference and the second maximum score difference, and record them in order as AC and BC respectively; record the smaller one of the two egg body strength scores corresponding to AC as the suspected abnormal score, If the position of the suspected abnormal score is not located in the 1st to q1*k4th positions in the second score sequence, it is judged that the suspected abnormal score is not abnormal, and the smallest egg body strength score in the second score sequence is marked as the minimum strength score; wherein q1 is a set proportion coefficient; If the position of the suspected abnormal score is located at the 1st to q1*k4th in the second score sequence, and AC<q2*BC, the suspected abnormal score is marked as normal, and the smallest egg body intensity score in the second score sequence is marked as the minimum intensity score; wherein q2 is a set proportion coefficient; If the position of the suspected abnormal score is located at the 1st to q1*k4th in the second score sequence, and AC≥q2*BC, the suspected abnormal score is marked as abnormal, and all the egg body intensity scores less than the suspected abnormal score in the second score sequence are marked as abnormal; and the smallest egg body intensity score without abnormal mark in the second score sequence is marked as the minimum intensity score; According to the vehicle speed damage reference data, the maximum zero damage speed corresponding to the minimum intensity score is obtained, denoted as the first zero damage speed, and the vehicle speed of the automatic guided vehicle transporting the first to-be-transported eggs is adjusted to the first zero damage speed.

10. The method for analyzing the data of the egg intelligent storage system according to any one of claims 1-9, characterized in that, The method comprises the following steps: Obtaining the Hounsfield unit, eggshell strength, eggshell thickness and egg shape index of different batches of eggs in the egg storage, and classifying according to the Hounsfield unit to obtain the basic attribute data of the eggs; Based on the basic attribute data of the eggs, weight distribution processing is performed, and an egg body intensity scoring mechanism is established using the eggshell thickness and the egg shape index; Obtaining the damage data of eggs with different intensity scores on the automatic guided vehicle at different vehicle speeds, and performing vehicle speed damage analysis processing to obtain vehicle speed damage reference data; According to the basic attributes of the to-be-transported eggs and the vehicle speed damage reference data analysis, the maximum allowable speed of the automatic guided vehicle is obtained, and the adjustment is performed.

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