Meat production and marketing docking data analysis platform integrating multi-source data

Abstract

The invention relates to the technical field of agricultural product production and marketing, in particular to a meat production and marketing docking data analysis platform integrating multi-source data, in which the daily remaining amount of meat products at a single point of sale is recorded, whether the stockpiling amount of the meat products at the current point of sale is qualified or not is preliminarily determined according to the remaining amount of the products, and the more the remaining amount of the products is, the larger the stockpiling amount is. In the prior art, the more likely the meat products go bad due to long storage time, the higher the purchase frequency is, and the faster the meat products at the current point of sale are updated, the stockpiling condition of the meat products at the current point of sale is comprehensively analyzed according to the remaining amount of the products and the purchase time interval, and the control precision of the stockpiling condition of the meat products at the current point of sale is improved; and according to the analysis result, the stock quantity is reduced or the meat products hoarded at the current point of sale are conveyed to the adjacent point of sale for sale, so that the unmarketable time of the meat products is shortened, and the meat products are prevented from going bad due to overlong hoarding time.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product production and marketing, and in particular to a meat production and marketing docking data analysis platform integrating multi-source data. Background Art

[0002] Since meat has a short shelf life and is easily perishable, storage conditions need to be strictly controlled. In actual use, it is mostly produced and sold within a short distance. Existing technologies select target distributors through the preferred values ​​of each distributor, and screen target cooperative distributors based on the annual agricultural product output, reducing the possibility of unsalable agricultural products. However, meat products may lack real-time monitoring during the production and distribution process, resulting in the inability to promptly discover and deal with problems, such as improper temperature control or expired products, and excessive stockpiling of meat products at various sales outlets, resulting in expired products and waste.

[0003] Chinese Patent Publication No.: CN116384768A discloses a production and marketing decision-making system for agricultural products based on big data, including a shared device end, a data collection module, a data preprocessing module, a data analysis module, a data decision module and a user device end. It can sort the preferred values ​​corresponding to each distributor from large to small by comparing them, compare the preferred values ​​of each distributor, and then select the distributor with the maximum preferred value as the target distributor of the current year, so as to facilitate the screening of the target cooperative distributors according to the agricultural product output of the current year, and to facilitate the accurate judgment of the target distributors to be cooperated this year based on some data of the cooperative distributors in previous years, so as to minimize the possibility of unsalable agricultural products. When the preferred values ​​of multiple distributors are the same, the distributors can be selected by comparing the average of their transportation costs, and the distributors with the smaller average transportation cost are selected and marked as target distributors, which is conducive to cost saving.

[0004] However, the prior art still has the following problems:

[0005] The lack of real-time monitoring of meat products during production and distribution results in excessive stockpiling of meat products at points of sale, leading to product expiration and waste. Summary of the invention

[0006] To this end, the present invention provides a meat production and marketing docking data analysis platform that integrates multi-source data to overcome the problem in the prior art that due to the lack of real-time monitoring of meat products during the production and distribution process, there is excessive stockpiling of meat products at sales points, resulting in product expiration and waste.

[0007] To achieve the above objectives, the present invention provides a meat production and marketing docking data analysis platform that integrates multi-source data. It includes:

[0008] A recording unit, which is used to record the target daily remaining quantity, the target single input quantity and the target input time of a plurality of target output points;

[0009] An image acquisition unit, which is used to acquire surface images of a single batch of meat samples and grayscale the surface images of the meat samples;

[0010] a data preprocessing unit connected to the recording unit, for calculating a first target day remaining quantity mean value based on the target day remaining quantity of a single target output point within the recorded period, calculating a target input average time interval based on the target input moment of the single target output point within the recorded period, and calculating a production and sales balance state representation value for the target output point based on the first target day remaining quantity mean value and the target input average time interval;

[0011] An analysis unit, which is connected to the recording unit, the image acquisition unit and the data preprocessing unit, is used to determine the target production and marketing balance state category of the current target output point based on the production and marketing balance state characterization value, and determine the target scheduling method for the current target output point according to the determination result, including:

[0012] Calculate the time domain discreteness of the target single input quantity of the current target output point, calculate the average of the second target day remaining quantity of the adjacent target output points, determine the scheduling tendency characterization value for the current target output point based on the time domain discreteness and the average of the second target day remaining quantity, analyze whether the current target output point meets the scheduling standard based on the scheduling tendency characterization value, adjust the target single input quantity according to the target production and marketing balance state characterization value, determine the freshness of the meat according to the grayscale surface image of the meat sample, and determine whether the target of the batch meets the delivery to the adjacent target output point based on the freshness;

[0013] Or, maintain the target single input amount and input frequency of the current target output point;

[0014] The adjacent target output point is the target output point closest to the current target output point.

[0015] Furthermore, the data preprocessing unit calculates the production and marketing balance state representation value for the target output point, including:

[0016] Solve the ratio of the mean value of the first target day remaining quantity of the current target output point in the periodic segment to the preset target day remaining quantity mean standard threshold value to obtain the first day remaining quantity feature;

[0017] Solve the ratio of the preset target input average time interval standard threshold value of the current target output point in the periodic segment to the target input average time interval to obtain a second input average time interval feature;

[0018] The first day's remaining quantity feature and the second input average time interval feature are weighted and summed to obtain the production and sales balance state characterization value.

[0019] Furthermore, the analysis unit determines the target production and sales balance state category of the current target output point based on the production and sales balance state characterization value, including:

[0020] If the production-sales balance state characterization value is greater than or equal to the preset production-sales balance state characterization value standard threshold, the analysis unit determines that the current target output point is in the target production-sales weak balance state;

[0021] If the production and marketing balance state characterization value is less than the preset production and marketing balance state characterization value standard threshold, the analysis unit determines that the current target output point is in the target production and marketing strong balance state.

[0022] Furthermore, the analysis unit determines a target scheduling method for the current target output point according to the determination result, including:

[0023] If the current target output point is in a weak balance state of target production and sales, the analysis unit calculates the time domain discreteness of the target single input quantity of the current target output point, calculates the average of the target daily remaining quantities of adjacent target output points, determines the scheduling tendency characterization value for the current target output point based on the time domain discreteness and the average of the target daily remaining quantities, analyzes whether the current target output point meets the scheduling standard based on the scheduling tendency characterization value, adjusts the target single input quantity according to the target production and sales balance state characterization value, determines the freshness of the target according to the grayscale target sample surface image, and determines whether the batch of targets meets the requirements for delivery to adjacent target output points based on the freshness;

[0024] If the current target output point is in a target production and sales strong balance state, the analysis unit determines the target single input quantity and input frequency for maintaining the current target output point.

[0025] Furthermore, the analysis unit calculates the time domain discreteness of the target single input amount of the current target output point, including:

[0026] Determine the target single input amount at each target input time within the period,

[0027] Calculate the variance of each target single input.

[0028] Furthermore, the analysis unit is used to determine the scheduling tendency characterization value for the current target output point based on the time domain discreteness and the second target day remaining quantity average, including:

[0029] The first time domain discreteness feature is obtained based on the ratio of the time domain discreteness of the target single input amount of the current target output point to the preset time domain discreteness standard threshold.

[0030] The second target day remaining quantity feature is obtained based on the ratio of the preset target day remaining quantity mean standard threshold value of the adjacent target output points to the second target day remaining quantity mean value,

[0031] It is used to perform weighted summation on the first time domain discreteness feature and the second target day remaining quantity feature to obtain the dispatch tendency representation value.

[0032] Furthermore, the analysis unit analyzes whether the current target output point meets the scheduling standard based on the scheduling tendency characterization value, including:

[0033] If the scheduling tendency characterization value is greater than or equal to a preset scheduling tendency characterization value standard threshold, the analysis unit determines that the current target output point does not meet the scheduling standard;

[0034] If the scheduling tendency characterization value is less than the preset scheduling tendency characterization value standard threshold, the analysis unit determines that the current target output point meets the scheduling standard.

[0035] Furthermore, the analysis unit reduces the target single input quantity of the current target output point that does not meet the scheduling standard according to the target production and sales balance state characterization value, wherein:

[0036] The reduction in the target single input quantity is positively correlated with the target production and sales balance state representation value.

[0037] Furthermore, the analysis unit is also used to determine the freshness of the meat according to the prediction results of the model for the test set, wherein the model is constructed based on color features and texture features of the grayscale surface image of the meat sample.

[0038] Further, the analysis unit determines whether the target of the batch meets the requirement of being delivered to an adjacent target output point based on the freshness, including:

[0039] If the freshness of the meat samples of the batch is greater than or equal to a preset freshness standard threshold, the analysis unit determines that the target of the batch meets the delivery standard for delivery to an adjacent target output point;

[0040] If the freshness of the meat samples of the batch is less than the preset freshness standard threshold, the analysis unit determines that the target of the batch does not meet the delivery standard for delivery to the adjacent target output point.

[0041] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention records the daily remaining amount of meat products at a single sales point, and preliminarily determines whether the stocking amount of meat products at the current sales point is qualified based on the remaining amount of the products. The more the remaining amount of the products, the easier it is to deteriorate due to long storage time. The higher the purchase frequency, the faster the meat products at the current sales point are updated. The present invention comprehensively analyzes the stocking situation of meat products at the current sales point based on the remaining amount of the products and the purchase time interval, improves the control accuracy of the stocking situation of meat products at the current sales point, and reduces the purchase amount or transports the meat products stocked at the current sales point to a neighboring sales point for sale based on the analysis results, so as to shorten the unsalable time of meat products and avoid the deterioration of meat products due to excessive storage time.

[0042] In particular, the present invention takes into account the sudden increase in the purchase volume and sales volume of meat products caused by special time periods, such as the Spring Festival. If the analysis is based on the data of this special time period, it is easy to affect the analysis results. Therefore, whether the time period of data collection is suitable for analyzing the situation of the current sales point is analyzed according to the discrete degree of the purchase volume data. In actual application, if there is a shortage of goods at a sales point close to the current sales point, or the stockpile of meat products is small, it is possible to consider transporting the meat products of the current sales point to the adjacent sales point for product scheduling, so as to avoid the meat products from being unsalable and stockpiled for too long at the current sales point. The present invention comprehensively analyzes whether the current sales point can be scheduled based on the time domain discreteness of the single input amount of meat products and the remaining amount of products at the adjacent sales points, thereby improving the control accuracy of the product output situation of the current sales point.

[0043] In particular, the present invention takes into account that meat products have a short shelf life. In actual use, temperature changes often occur during the transportation and transfer of products. Meat products can easily deteriorate due to temperature changes. The present invention analyzes the quality (i.e., freshness) of the meat products based on their surface color. Meat products with high freshness can be transported to nearby sales points to avoid deterioration of the transported products due to temperature changes during transportation, thereby causing waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A structural block diagram of a meat production and marketing docking data analysis platform that integrates multi-source data according to the present invention;

[0045] Figure 2 A flow chart for determining the target production and sales balance state category of the current target output point;

[0046] Figure 3 A decision flow chart for determining a target scheduling method for a current target output point;

[0047] Figure 4A flow chart for analyzing whether the current target output point meets the scheduling standards. DETAILED DESCRIPTION

[0048] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] It should be pointed out that the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical data of the system of the present invention in the six months before this determination and the corresponding historical determination results. It can be understood by those skilled in the art that the determination method of the system of the present invention for a single parameter mentioned above can be to select the value with the highest proportion as the preset standard parameter according to the data distribution, use weighted summation to use the obtained value as the preset standard parameter, substitute each historical data into a specific formula and use the value obtained by the formula as the preset standard parameter or other selection methods, as long as the system of the present invention can clearly define different specific situations in the single determination process through the obtained values.

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0051] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0052] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] See also Figure 1 As shown, it is a structural block diagram of the meat production and marketing docking data analysis platform that integrates multi-source data of the present invention.

[0054] A recording unit, which is used to record the target daily remaining quantity, the target single input quantity and the target input time of a plurality of target output points;

[0055] An image acquisition unit, which is used to acquire surface images of a single batch of meat samples and grayscale the surface images of the meat samples;

[0056] a data preprocessing unit connected to the recording unit, for calculating a first target day remaining quantity mean value based on the target day remaining quantity of a single target output point within the recorded period, calculating a target input average time interval based on the target input moment of the single target output point within the recorded period, and calculating a production and sales balance state representation value for the target output point based on the first target day remaining quantity mean value and the target input average time interval;

[0057] An analysis unit, which is connected to the recording unit, the image acquisition unit and the data preprocessing unit, is used to determine the target production and marketing balance state category of the current target output point based on the production and marketing balance state characterization value, and determine the target scheduling method for the current target output point according to the determination result, including:

[0058] Calculate the time domain discreteness of the target single input quantity of the current target output point, calculate the average of the second target day remaining quantity of the adjacent target output points, determine the scheduling tendency characterization value for the current target output point based on the time domain discreteness and the average of the second target day remaining quantity, analyze whether the current target output point meets the scheduling standard based on the scheduling tendency characterization value, adjust the target single input quantity according to the target production and marketing balance state characterization value, determine the freshness of the meat according to the grayscale surface image of the meat sample, and determine whether the target of the batch meets the delivery to the adjacent target output point based on the freshness;

[0059] Or, maintain the target single input amount and input frequency of the current target output point;

[0060] The adjacent target output point is the target output point closest to the current target output point.

[0061] Specifically, there is no limitation on the specific structures of the recording unit and the image acquisition unit, as long as they can realize the corresponding functions. For example, the image acquisition unit can adopt a scanner or a camera, which will not be described in detail.

[0062] Specifically, there is no limitation on the specific structure of the data preprocessing unit and the analysis unit, which may be composed of logic components, including a field programmable processor, a computer, and a microprocessor in the computer.

[0063] Specifically, the data preprocessing unit calculates the production and marketing balance state representation value for the target output point, including:

[0064] Solve the ratio of the mean value of the first target day remaining quantity of the current target output point in the periodic segment to the preset target day remaining quantity mean standard threshold value to obtain the first day remaining quantity feature;

[0065] Solve the ratio of the preset target input average time interval standard threshold value of the current target output point in the periodic segment to the target input average time interval to obtain a second input average time interval feature;

[0066] The first day's remaining quantity feature and the second input average time interval feature are weighted and summed to obtain the production and sales balance state characterization value.

[0067] Specifically, in this embodiment, the preset standard threshold value of the target day remaining amount mean is obtained by pre-determination, and the target day remaining amount of several target output points within the cycle period is recorded to solve the target day remaining amount mean. The preset standard threshold value of the target day remaining amount mean is 0.75 to 0.85 times the target day remaining amount mean.

[0068] Specifically, in this embodiment, the preset target input average time interval standard threshold is obtained by pre-determination, and the target input moments of several target output points within the cycle segment are recorded to solve the target input average time interval. The preset target input average time interval standard threshold is 0.7 to 0.8 times the target input average time interval.

[0069] See also Figure 2 As shown, it is a determination flow chart for determining the target production and sales balance state category of the current target output point.

[0070] Specifically, the analysis unit determines the target production and sales balance state category of the current target output point based on the production and sales balance state characterization value, including:

[0071] If the production-sales balance state characterization value is greater than or equal to the preset production-sales balance state characterization value standard threshold, the analysis unit determines that the current target output point is in the target production-sales weak balance state;

[0072] If the production and marketing balance state characterization value is less than the preset production and marketing balance state characterization value standard threshold, the analysis unit determines that the current target output point is in the target production and marketing strong balance state.

[0073] Specifically, in this embodiment, the preset standard threshold value of the production and sales balance status characterization value is selected between the interval [1.2, 1.4].

[0074] See also Figure 3 As shown, it is a decision flow chart for determining the target scheduling method for the current target output point.

[0075] Specifically, the analysis unit determines the target scheduling mode for the current target output point according to the determination result, including:

[0076] If the current target output point is in a weak balance state of target production and sales, the analysis unit calculates the time domain discreteness of the target single input quantity of the current target output point, calculates the average of the target daily remaining quantities of adjacent target output points, determines the scheduling tendency characterization value for the current target output point based on the time domain discreteness and the average of the target daily remaining quantities, analyzes whether the current target output point meets the scheduling standard based on the scheduling tendency characterization value, adjusts the target single input quantity according to the target production and sales balance state characterization value, determines the freshness of the target according to the grayscale target sample surface image, and determines whether the batch of targets meets the requirements for delivery to adjacent target output points based on the freshness;

[0077] If the current target output point is in a target production and sales strong balance state, the analysis unit determines the target single input quantity and input frequency for maintaining the current target output point.

[0078] Specifically, the analysis unit calculates the time domain discreteness of the target single input amount of the current target output point, including:

[0079] Determine the target single input amount at each target input time within the period,

[0080] Calculate the variance of each target single input.

[0081] Specifically, the analysis unit is used to determine the scheduling tendency representation value for the current target output point based on the time domain discreteness and the second target day remaining quantity average, including:

[0082] The first time domain discreteness feature is obtained based on the ratio of the time domain discreteness of the target single input amount of the current target output point to the preset time domain discreteness standard threshold.

[0083] The second target day remaining quantity feature is obtained based on the ratio of the preset target day remaining quantity mean standard threshold value of the adjacent target output points to the second target day remaining quantity mean value,

[0084] It is used to perform weighted summation on the first time domain discreteness feature and the second target day remaining quantity feature to obtain the dispatch tendency representation value.

[0085] Specifically, in this embodiment, the preset time domain discreteness standard threshold is obtained by pre-measurement, the target single input amount of several target output points is recorded, the variance of each single target output point is calculated, and the mean of each variance is calculated.

[0086] See also Figure 4 As shown, it is a determination flow chart for analyzing whether the current target output point meets the scheduling standards.

[0087] Specifically, the analyzing unit analyzes whether the current target output point meets the scheduling standard based on the scheduling tendency characterization value, including:

[0088] If the scheduling tendency characterization value is greater than or equal to a preset scheduling tendency characterization value standard threshold, the analysis unit determines that the current target output point does not meet the scheduling standard;

[0089] If the scheduling tendency characterization value is less than the preset scheduling tendency characterization value standard threshold, the analysis unit determines that the current target output point meets the scheduling standard.

[0090] Specifically, in this embodiment, the preset scheduling tendency characterization value standard threshold is selected in the interval [1.3, 1.45].

[0091] Specifically, the analysis unit reduces the target single input quantity of the current target output point that does not meet the scheduling standard according to the target production and sales balance state characterization value, wherein:

[0092] The reduction in the target single input quantity is positively correlated with the target production and sales balance state representation value.

[0093] In this embodiment, optionally,

[0094] The target production and marketing balance state characterization value is compared with the preset first preset target production and marketing balance state characterization value and the second preset target production and marketing balance state characterization value,

[0095] If the target production and marketing balance state characterization value is less than or equal to the first preset target production and marketing balance state characterization value, then the first target single input amount is reduced, and the first target single input amount is 0.2 times the initial target single input amount;

[0096] If the target production and marketing balance state characterization value is greater than the first preset target production and marketing balance state characterization value and less than or equal to the second preset target production and marketing balance state characterization value, then reduce the second target single input amount, and the second target single input amount is 0.25 times the initial target single input amount;

[0097] If the target production and marketing balance state characterization value is greater than the second preset target production and marketing balance state characterization value, the third target single input amount is reduced, and the third target single input amount is 0.3 times the initial target single input amount;

[0098] In this embodiment, the preset target production and sales balance state characterization value is obtained in advance, the target production and sales balance state characterization values ​​of several sales points are calculated, and the average of the target production and sales balance state characterization values ​​is solved. The first preset target production and sales balance state characterization value is 1.1 to 1.2 times the average of the target production and sales balance state characterization values, and the second preset target production and sales balance state characterization value is 1.3 to 0.35 times the average of the target production and sales balance state characterization values.

[0099] Specifically, the analysis unit is also used to determine the freshness of the meat according to the prediction results of the model for the test set, wherein the model is constructed based on the color features and texture features of the grayscale surface image of the meat sample.

[0100] Specifically, the analyzing unit determines the freshness of the meat according to the grayscale surface image of the meat sample, including:

[0101] The grayscale meat sample surface image is subjected to denoising and background segmentation.

[0102] Extract color and texture features from the surface image of meat samples,

[0103] The prediction model is established by using the extracted color and texture features combined with machine learning algorithms.

[0104] The prediction model is trained using the training set and validated using the test set.

[0105] Determine the freshness level of the meat based on the model prediction results.

[0106] Specifically, in this embodiment, the freshness level interval is [1, 10], and the higher the freshness level of the meat sample, the fresher the meat sample.

[0107] Specifically, the analyzing unit determines whether the target of the batch meets the requirement of being delivered to an adjacent target output point based on the freshness, including:

[0108] If the freshness of the meat samples of the batch is greater than or equal to a preset freshness standard threshold, the analysis unit determines that the target of the batch meets the delivery standard for delivery to an adjacent target output point;

[0109] If the freshness of the meat samples of the batch is less than the preset freshness standard threshold, the analysis unit determines that the target of the batch does not meet the delivery standard for delivery to the adjacent target output point.

[0110] Specifically, in this embodiment, the preset freshness standard threshold is selected in the interval [1.1, 1.3].

[0111] Specifically, in this embodiment, the adjacent delivery points are delivery points within a 5-kilometer range with the current delivery point as the center.

[0112] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A meat production and marketing docking data analysis platform integrating multi-source data, characterized in that: include: A recording unit, which is used to record the target daily remaining quantity, the target single input quantity and the target input time of a plurality of target output points; An image acquisition unit, which is used to acquire surface images of meat samples of a single batch and convert the surface images of the meat samples into grayscale; a data preprocessing unit connected to the recording unit, for calculating a first target day remaining quantity mean value based on the target day remaining quantity of a single target output point within the recorded period, calculating a target input average time interval based on the target input moment of the single target output point within the recorded period, and calculating a production and sales balance state representation value for the target output point based on the first target day remaining quantity mean value and the target input average time interval; An analysis unit, which is connected to the recording unit, the image acquisition unit and the data preprocessing unit, is used to determine the target production and marketing balance state category of the current target output point based on the production and marketing balance state characterization value, and determine the target scheduling method for the current target output point according to the determination result, including: Calculate the time domain discreteness of the target single input quantity of the current target output point, calculate the average of the second target day remaining quantity of the adjacent target output points, determine the scheduling tendency characterization value for the current target output point based on the time domain discreteness and the average of the second target day remaining quantity, analyze whether the current target output point meets the scheduling standard based on the scheduling tendency characterization value, adjust the target single input quantity according to the target production and marketing balance state characterization value, determine the freshness of the meat according to the grayscale surface image of the meat sample, and determine whether the target of the batch meets the delivery to the adjacent target output point based on the freshness; Or, maintain the target single input amount and input frequency of the current target output point; The adjacent target output point is the target output point closest to the current target output point.

2. The meat production and marketing docking data analysis platform integrating multi-source data according to claim 1 is characterized in that: The data preprocessing unit calculates the production and marketing balance state representation value for the target output point, including: Solve the ratio of the mean value of the first target day remaining quantity of the current target output point in the periodic segment to the preset target day remaining quantity mean standard threshold value to obtain the first day remaining quantity feature; Solve the ratio of the preset target input average time interval standard threshold value of the current target output point in the periodic segment to the target input average time interval to obtain a second input average time interval feature; The first day's remaining quantity feature and the second input average time interval feature are weighted and summed to obtain the production and sales balance state characterization value.

3. The meat production and marketing docking data analysis platform integrating multi-source data according to claim 1 is characterized in that: The analyzing unit determines the target production and sales balance state category of the current target output point based on the production and sales balance state characterization value, including: If the production-sales balance state characterization value is greater than or equal to the preset production-sales balance state characterization value standard threshold, the analysis unit determines that the current target output point is in the target production-sales weak balance state; If the production and marketing balance state characterization value is less than the preset production and marketing balance state characterization value standard threshold, the analysis unit determines that the current target output point is in the target production and marketing strong balance state.

4. The meat production and marketing docking data analysis platform integrating multi-source data according to claim 1 is characterized in that: The analysis unit determines a target scheduling method for the current target output point according to the determination result, including: If the current target output point is in a weak balance state of target production and sales, the analysis unit calculates the time domain discreteness of the target single input quantity of the current target output point, calculates the average of the target daily remaining quantities of adjacent target output points, determines the scheduling tendency characterization value for the current target output point based on the time domain discreteness and the average of the target daily remaining quantities, analyzes whether the current target output point meets the scheduling standard based on the scheduling tendency characterization value, adjusts the target single input quantity according to the target production and sales balance state characterization value, determines the freshness of the target according to the grayscale target sample surface image, and determines whether the batch of targets meets the requirements for delivery to adjacent target output points based on the freshness; If the current target output point is in a target production and sales strong balance state, the analysis unit determines the target single input quantity and input frequency for maintaining the current target output point.

5. The meat production and marketing docking data analysis platform integrating multi-source data according to claim 1 is characterized in that: The analysis unit calculates the time domain discreteness of the target single input amount of the current target output point, including: Determine the target single input amount at each target input time within the period, Calculate the variance of each target single input.

6. The meat production and marketing docking data analysis platform integrating multi-source data according to claim 1 is characterized in that: The analysis unit is used to determine the scheduling tendency representation value for the current target output point based on the time domain discreteness and the second target day remaining quantity average, including: The first time domain discreteness feature is obtained based on the ratio of the time domain discreteness of the target single input amount of the current target output point to the preset time domain discreteness standard threshold. The second target day remaining quantity feature is obtained based on the ratio of the preset target day remaining quantity mean standard threshold value of the adjacent target output points to the second target day remaining quantity mean value, It is used to perform weighted summation on the first time domain discreteness feature and the second target day remaining quantity feature to obtain the dispatch tendency representation value.

7. The meat production and marketing docking data analysis platform integrating multi-source data according to claim 1 is characterized in that: The analyzing unit analyzes whether the current target output point meets the scheduling standard based on the scheduling tendency characterization value, including: If the scheduling tendency characterization value is greater than or equal to a preset scheduling tendency characterization value standard threshold, the analysis unit determines that the current target output point does not meet the scheduling standard; If the scheduling tendency characterization value is less than the preset scheduling tendency characterization value standard threshold, the analysis unit determines that the current target output point meets the scheduling standard.

8. The meat production and marketing docking data analysis platform integrating multi-source data according to claim 1 is characterized in that: The analysis unit reduces the target single input quantity of the current target output point that does not meet the scheduling standard according to the target production and sales balance state characterization value, wherein: The reduction in the target single input quantity is positively correlated with the target production and sales balance state representation value.

9. The meat production and marketing docking data analysis platform integrating multi-source data according to claim 1 is characterized in that: The analysis unit is also used to determine the freshness of the meat according to the prediction results of the model for the test set, wherein the model is constructed based on the color features and texture features of the grayscale surface image of the meat sample.

10. The meat production and marketing docking data analysis platform integrating multi-source data according to claim 1, characterized in that: The analyzing unit determines whether the target of the batch meets the requirement of being delivered to an adjacent target output point based on the freshness, including: If the freshness of the meat samples of the batch is greater than or equal to a preset freshness standard threshold, the analysis unit determines that the target of the batch meets the delivery standard for delivery to an adjacent target output point; If the freshness of the meat samples of the batch is less than the preset freshness standard threshold, the analysis unit determines that the target of the batch does not meet the delivery standard for delivery to the adjacent target output point.

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