Blueberry whole industry chain monitoring method and system, terminal and storage medium

Abstract

The invention relates to the technical field of big data, and particularly provides a blueberry whole industry chain monitoring method and system, a terminal and a storage medium, and the method comprises the steps: obtaining production data, and predicting the total yield of blueberries based on the production data; counting the consumption capability of the processing industry based on the registration information of the processing enterprise; predicting the theoretical sales volume based on the blueberry sales volume over the years and the annual growth of the sales volume; counting the storage capacity based on the warehouse registration information; and matching the total yield, the absorption capacity, the theoretical sales volume and the storage capacity of the blueberries, and identifying defect links. According to the method, data collection and analysis are carried out on the whole industrial chain of blueberries, monitoring of the whole industrial chain is achieved, defect links are recognized based on the monitoring result, and therefore a coping scheme is made through global overall planning.

Description

Technical Field

[0001] The present invention belongs to the field of big data technology, and specifically relates to a method, system, terminal and storage medium for monitoring the entire blueberry industry chain. Background Art

[0002] As a perishable agricultural product, blueberries are particularly sensitive to storage. In the market circulation link, if there is a surge in production and the processing industry's absorption capacity fails to match it in time, it is very easy to cause a backlog and loss of blueberries. Similarly, if the capacity of storage facilities cannot meet actual demand, blueberries will suffer unnecessary losses due to limited storage conditions, which will in turn affect the overall value of the product and the stability of market supply.

[0003] Currently, the sales, processing and storage of blueberries are mostly direct transactions between farmers and enterprises, which makes it impossible to make overall planning. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method, system, terminal and storage medium for monitoring the entire blueberry industry chain to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a method for monitoring the entire blueberry industry chain, comprising: Obtaining production data, and predicting the total blueberry yield based on the production data; Based on the registration information of processing enterprises, the processing industry's consumption capacity is calculated; Predict theoretical sales based on historical blueberry sales and annual sales growth; Count the storage capacity based on warehouse registration information; Match the total blueberry production, consumption capacity, theoretical sales volume and storage capacity to identify defective links.

[0006] In an optional embodiment, obtaining production data and predicting the total blueberry yield based on the production data includes: The factor analysis method is used in advance to screen out the core factors from a variety of influencing factors, and the core factors include ambient temperature and soil moisture; The parameter values ​​of the core factors are collected regularly through sensors, and the parameter values ​​are arranged in chronological order as a data sequence; Inputting the data sequence into a pre-trained LSTM model, wherein the LSTM model predicts the unit yield of blueberries based on the data sequence; The total blueberry production is calculated based on the total area of ​​the blueberry production base and the unit blueberry production.

[0007] In an optional implementation, based on the registration information of the processing enterprises, the consumption capacity of the processing industry is calculated, including: Acquire registration information of the processing enterprise, and extract production line information of the processing enterprise from the registration information; Based on the pre-stored processing parameters of various types of production lines and the production line information of the processing enterprise, the maximum consumption capacity of the processing enterprise is calculated; The sum of the maximum consumption capacities of all processing enterprises is calculated to obtain the consumption capacity of the processing industry.

[0008] In an optional implementation, based on the warehouse registration information, the storage capacity is counted, including: Obtain warehouse registration information, the registration information including warehouse location, warehouse storage environment information, and warehouse capacity information; Extract the candidate warehouse registration information whose warehouse storage environment information meets the standard for storing blueberries; The storage capacity is calculated based on the warehouse capacity information of the candidate warehouse registration information.

[0009] In an optional implementation, the total blueberry production, consumption capacity, theoretical sales volume and storage capacity are matched to identify defective links, including: Construct matching rules: Z=J+d 1 =X+C+d 2 ; Among them, Z is the predicted total blueberry production, J is the consumption capacity, X is the theoretical sales volume, C is the storage capacity, and d 1 is the first difference, d 2 is the second difference; Pre-set a value range of the first difference and a value range of the second difference based on historical data; Calculate the actual value of the first difference. If the actual value of the first difference is within the value range of the first difference, it is confirmed that the current absorption capacity is sufficient; if the actual value of the first difference is lower than the lower limit of the value range of the first difference, it is confirmed that the current absorption capacity is excessive; if the actual value of the first difference is higher than the upper limit of the value range of the first difference, it is confirmed that the current absorption capacity is insufficient; Calculate the actual value of the second difference. If the actual value of the second difference is within the value range of the second difference, no warning is generated. If the actual value of the second difference is lower than the lower limit of the value range of the second difference, it is confirmed that the current storage capacity is in excess, and other goods are allocated to part of the storage capacity. If the actual value of the second difference is higher than the upper limit of the value range of the second difference, it is confirmed that the current storage capacity is insufficient.

[0010] In a second aspect, the present invention provides a blueberry whole industry chain monitoring system, comprising: A yield prediction module, used to obtain production data and predict the total blueberry yield based on the production data; The processing statistics module is used to calculate the processing industry's consumption capacity based on the registration information of processing enterprises; The sales forecast module is used to predict theoretical sales based on the blueberry sales volume and annual sales growth in previous years; The warehouse statistics module is used to count the storage capacity based on warehouse registration information; The link matching module is used to match the total blueberry production, consumption capacity, theoretical sales volume and storage capacity, and identify defective links.

[0011] In an optional embodiment, the yield prediction module includes: A factor analysis unit, used to pre-screen core factors from multiple influencing factors using factor analysis, wherein the core factors include ambient temperature and soil moisture; A parameter collection unit, used to periodically collect parameter values ​​of core factors through sensors, and arrange the parameter values ​​into a data sequence in chronological order; A model prediction unit, used for inputting the data sequence into a pre-trained LSTM model, wherein the LSTM model predicts the unit yield of blueberries based on the data sequence; The yield calculation unit is used to calculate the total yield of blueberries according to the total area of ​​the blueberry production base and the unit yield of blueberries that are pre-stated.

[0012] In an optional embodiment, the processing statistics module includes: An information acquisition unit, used to acquire registration information of a processing enterprise, and extract production line information of the processing enterprise from the registration information; A capacity calculation unit, used to calculate the maximum consumption capacity of the processing enterprise based on pre-stored processing parameters of various types of production lines and production line information of the processing enterprise; The capacity statistics unit is used to calculate the sum of the maximum consumption capacities of all processing enterprises and obtain the consumption capacity of the processing industry.

[0013] In a third aspect, a terminal is provided, including: processor, memory, wherein: The memory is used to store computer programs. The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0014] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0015] The beneficial effect of the present invention is that the blueberry whole industry chain monitoring method, system, terminal and storage medium provided by the present invention realize the monitoring of the whole industry chain by collecting and analyzing data of the whole blueberry industry chain, and identify defective links based on the monitoring results, so as to make a response plan through overall coordination.

[0016] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0018] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0019] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0020] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] The blueberry full industry chain monitoring method provided by the embodiment of the present invention is executed by a computer device, and accordingly, the blueberry full industry chain monitoring system runs in the computer device.

[0024] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1The execution subject may be a blueberry whole industry chain monitoring system. According to different requirements, the order of the steps in the flowchart may be changed, and some may be omitted.

[0025] like Figure 1 As shown, the method includes: Step 110, obtaining production data, and predicting the total blueberry yield based on the production data; Step 120, based on the registration information of the processing enterprises, the consumption capacity of the processing industry is counted; Step 130, predicting theoretical sales volume based on blueberry sales volume and annual sales growth in previous years; Step 140, based on the warehouse registration information, calculate the storage capacity; Step 150, matching the total blueberry production, consumption capacity, theoretical sales volume and storage capacity to identify defective links.

[0026] To facilitate understanding of the present invention, the method for monitoring the entire blueberry industry chain provided by the present invention is further described below based on the principle of the method for monitoring the entire blueberry industry chain of the present invention and the process of monitoring the entire blueberry industry chain in the embodiment.

[0027] Specifically, the monitoring methods for the entire blueberry industry chain include: S1. Obtaining production data, and predicting the total output of blueberries based on the production data.

[0028] (1) The factor analysis method is used in advance to screen out the core factors from a variety of influencing factors, and the core factors include ambient temperature and soil moisture.

[0029] Multiple influencing factors are preset, including: ambient temperature, precipitation, temperature difference between day and night, soil moisture, ambient humidity, light intensity, etc.

[0030] Sample data are collected, including parameter values ​​of various influencing factors and corresponding unit yields of blueberries.

[0031] Kaiser-Meyer-Olkin (KMO) test and Bartlett's sphericity test were performed on the parameter values ​​of various influencing factors. When the KMO value was greater than 0.7 and the P value of the Bartlett test was less than 0.05, it indicated that the variable could be subjected to the next step of factor analysis.

[0032] The optimal comprehensive factor was determined based on the factor eigenvalue, cumulative contribution rate and scree plot.

[0033] Calculate the principal component score coefficients for each variable: ; In the formula, FSC i is the principal component score coefficient of the ith variable, VDR jis the variance contribution rate of the jth comprehensive factor, CDR j is the cumulative contribution rate of the jth comprehensive factor, SC ij is the score of the ith variable in the jth comprehensive factor. By comparing the absolute values ​​of the principal component score coefficients of each variable, the top two variables are determined as core parameters.

[0034] (2) Regularly collect the parameter values ​​of core factors through sensors and arrange the parameter values ​​into a data sequence in chronological order.

[0035] By deploying advanced sensor networks, we can regularly and accurately collect parameter values ​​of core environmental factors that affect blueberry growth and yield, such as soil moisture, light intensity, temperature, etc. These real-time data are then carefully arranged in chronological order to form a continuous and detailed data sequence to fully reflect the changes in environmental conditions during the blueberry growth cycle.

[0036] (3) The data sequence is input into a pre-trained LSTM model, and the LSTM model predicts the unit yield of blueberries based on the data sequence.

[0037] This data sequence is efficiently input into a pre-trained long short-term memory network (LSTM) model. With its unique gating mechanism, the LSTM model is good at capturing long-term dependencies in data and is particularly effective for time series prediction problems. In this scenario, the LSTM model can deeply learn and understand the complex relationship between environmental factors and unit yield of blueberries based on historical data sequences, and then accurately predict the unit yield of blueberries in the future.

[0038] (4) Calculate the total blueberry production based on the total area of ​​the blueberry production base and the unit blueberry production.

[0039] After obtaining the predicted value of blueberry unit yield, combined with the official statistical total area data of blueberry production bases, a simple mathematical operation can be used to quickly and accurately estimate the total blueberry production by multiplying the predicted blueberry unit yield by the total area of ​​the production base.

[0040] S2. Based on the registration information of processing enterprises, the absorption capacity of the processing industry is calculated.

[0041] The registration information of the processing enterprise is obtained, and the production line information of the processing enterprise is extracted from the registration information; based on the pre-stored processing parameters of various types of production lines and the production line information of the processing enterprise, the maximum absorption capacity of the processing enterprise is calculated; the sum of the maximum absorption capacities of all processing enterprises is calculated to obtain the absorption capacity of the processing industry.

[0042] Specifically, the following steps are included: Data collection and collation: First, obtain comprehensive registration information of processing enterprises through official databases, enterprise disclosure platforms or professional information service agencies. This information includes but is not limited to enterprise name, registered address, business scope, legal representative, registered capital, etc. Then, from this rich registration information, extract specific information related to the production line in a targeted manner, such as production line type, quantity, production capacity, main processed products, etc.

[0043] Production line information analysis: Detailed analysis of the extracted production line information to clarify the specific configuration, technical parameters and applicable processing range of each production line. This step is crucial for the subsequent evaluation of the maximum absorption capacity of the processing enterprise, as it is directly related to the actual processing capacity and efficiency of the production line.

[0044] Processing parameter matching and calculation: Based on a pre-stored, extensive and detailed database of standard processing parameters for various types of production lines, the production line information of the processing enterprise is matched with the corresponding processing parameters. These processing parameters may include processing capacity per unit time, raw material conversion rate, energy consumption index, etc. Through scientific algorithms and models, combined with the specific configuration and technical parameters of the production line, the maximum theoretical capacity of each production line is calculated, and then the overall maximum absorption capacity of the processing enterprise is obtained.

[0045] Comprehensive evaluation and summary: After completing the calculation of the maximum consumption capacity of a single processing enterprise, this result is applied to the entire processing industry. By statistically analyzing the maximum consumption capacity data of all processing enterprises and using appropriate methods (such as weighted average, simple summation, etc.), the total consumption capacity of the entire processing industry is calculated. This data not only reflects the overall production potential and capacity boundaries of the processing industry, but also provides an important reference for industrial planning, policy formulation and market regulation.

[0046] Dynamic monitoring and adjustment: It is worth noting that the production line information and processing parameters of processing enterprises may change with factors such as technological progress, equipment updates, and changes in market demand. Therefore, this process should be a continuous and dynamic process, and relevant data and parameters need to be updated and adjusted regularly to ensure that the assessment of the processing industry's consumption capacity remains accurate and timely.

[0047] S3. Predict theoretical sales volume based on historical blueberry sales volume and annual sales growth.

[0048] Filter out the maximum annual growth, calculate the sum of the blueberry sales volume in the previous year and the maximum annual growth, and get the theoretical sales volume.

[0049] S4. Count the storage capacity based on the warehouse registration information.

[0050] The warehouse registration information is obtained, wherein the registration information includes the warehouse location, warehouse storage environment information, and warehouse capacity information; the candidate warehouse registration information whose warehouse storage environment information meets the standard for storing blueberries is extracted; and the storage capacity is calculated based on the warehouse capacity information of the candidate warehouse registration information.

[0051] Specifically, it includes the following: Comprehensive warehouse registration information: First, through official channels, corporate databases or third-party information platforms, comprehensive detailed registration information of all warehouses should be obtained. This information should include but is not limited to the specific location of the warehouse (including address, longitude and latitude, etc., for logistics planning), warehouse storage environment information (such as temperature range, humidity control, ventilation conditions, lighting conditions, dust and insect prevention measures, etc., which are key factors affecting the storage quality of blueberries), and warehouse capacity information (such as the maximum tonnage of blueberries that can be stored, the number of shelves, unit shelf capacity, etc.).

[0052] Screening warehouses that meet blueberry storage standards: After obtaining the registration information of all warehouses, the next step is to extract the registration information of candidate warehouses whose storage environment information can meet or exceed the blueberry storage standards based on the storage requirements of blueberries. This process requires careful comparison of the storage requirements of blueberries (such as the appropriate temperature range, humidity control range, etc.) with the storage environment information of the warehouse to ensure that the selected warehouse can provide the best storage conditions for blueberries, thereby ensuring the quality and freshness of blueberries.

[0053] Calculate the storage capacity of candidate warehouses: For the candidate warehouses that meet the blueberry storage criteria, the next step is to calculate their respective storage capacities based on their warehouse capacity information. This usually involves converting the total capacity of the warehouse (which may be in units of volume or weight) into the specific number of blueberries that can be stored. This step may need to take into account factors such as the packaging method of blueberries, stacking height restrictions, and safe spacing to ensure that the calculation of storage capacity is both accurate and meets practical operational requirements.

[0054] Summarize the overall storage capacity: After calculating the storage capacity of each candidate warehouse, these capacity values ​​can be summarized to obtain the total storage capacity that the entire candidate warehouse set can provide. This data is of great reference value for evaluating the storage capacity of the blueberry supply chain, formulating inventory management strategies, and optimizing logistics and distribution plans.

[0055] S5. Match the total blueberry production, consumption capacity, theoretical sales volume and storage capacity to identify defective links.

[0056] Construct matching rules: Z=J+d 1 =X+C+d 2 ; Among them, Z is the predicted total blueberry production, J is the consumption capacity, X is the theoretical sales volume, C is the storage capacity, and d 1 is the first difference, d 2 is the second difference; Pre-set a value range of the first difference and a value range of the second difference based on historical data; Calculate the actual value of the first difference. If the actual value of the first difference is within the value range of the first difference, it is confirmed that the current absorption capacity is sufficient; if the actual value of the first difference is lower than the lower limit of the value range of the first difference, it is confirmed that the current absorption capacity is excessive; if the actual value of the first difference is higher than the upper limit of the value range of the first difference, it is confirmed that the current absorption capacity is insufficient; Calculate the actual value of the second difference. If the actual value of the second difference is within the value range of the second difference, no warning is generated. If the actual value of the second difference is lower than the lower limit of the value range of the second difference, it is confirmed that the current storage capacity is in excess, and other goods are allocated to part of the storage capacity. If the actual value of the second difference is higher than the upper limit of the value range of the second difference, it is confirmed that the current storage capacity is insufficient.

[0057] In some embodiments, the blueberry full industry chain monitoring system may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the blueberry full industry chain monitoring system may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) Function of monitoring the entire blueberry industry chain.

[0058] In this embodiment, the blueberry whole industry chain monitoring system can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules of the system 200 may include: a production forecasting module 210, a processing statistics module 220, a sales forecasting module 230, a storage statistics module 240 and a link matching module 250. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0059] A yield prediction module, used to obtain production data and predict the total blueberry yield based on the production data; The processing statistics module is used to calculate the processing industry's consumption capacity based on the registration information of processing enterprises; The sales forecast module is used to predict theoretical sales based on the blueberry sales volume and annual sales growth in previous years; The warehouse statistics module is used to count the storage capacity based on warehouse registration information; The link matching module is used to match the total blueberry production, consumption capacity, theoretical sales volume and storage capacity, and identify defective links.

[0060] Optionally, as an embodiment of the present invention, the yield prediction module includes: A factor analysis unit, used to pre-screen core factors from multiple influencing factors using factor analysis, wherein the core factors include ambient temperature and soil moisture; A parameter collection unit, used to periodically collect parameter values ​​of core factors through sensors, and arrange the parameter values ​​into a data sequence in chronological order; A model prediction unit, used for inputting the data sequence into a pre-trained LSTM model, wherein the LSTM model predicts the unit yield of blueberries based on the data sequence; The yield calculation unit is used to calculate the total yield of blueberries according to the total area of ​​the blueberry production base and the unit yield of blueberries that are pre-stated.

[0061] Optionally, as an embodiment of the present invention, the processing statistics module includes: An information acquisition unit, used to acquire registration information of a processing enterprise, and extract production line information of the processing enterprise from the registration information; A capacity calculation unit, used to calculate the maximum consumption capacity of the processing enterprise based on pre-stored processing parameters of various types of production lines and production line information of the processing enterprise; The capacity statistics unit is used to calculate the sum of the maximum consumption capacities of all processing enterprises and obtain the consumption capacity of the processing industry.

[0062] Figure 3 The present invention is a schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the blueberry full industry chain monitoring method provided in an embodiment of the present invention.

[0063] The terminal 300 may include: a processor 310, a memory 320 and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0064] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0065] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0066] The communication unit 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0067] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0068] Therefore, the present invention collects and analyzes data on the entire blueberry industry chain to achieve monitoring of the entire industry chain, and identifies defective links based on the monitoring results, so as to make a response plan through global coordination. The technical effects that can be achieved by this embodiment can be found in the description above and will not be repeated here.

[0069] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0070] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0071] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0072] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0074] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A method for monitoring the entire blueberry industry chain, characterized in that: include: Obtaining production data, and predicting the total blueberry yield based on the production data; Based on the registration information of processing enterprises, the processing industry's consumption capacity is calculated; Predict theoretical sales based on historical blueberry sales and annual sales growth; Count the storage capacity based on warehouse registration information; Match the total blueberry production, consumption capacity, theoretical sales volume and storage capacity to identify defective links.

2. The method according to claim 1, characterized in that Obtaining production data and predicting the total blueberry production based on the production data, including: The factor analysis method is used in advance to screen out the core factors from a variety of influencing factors, and the core factors include ambient temperature and soil moisture; The parameter values ​​of the core factors are collected regularly through sensors, and the parameter values ​​are arranged in chronological order as a data sequence; Inputting the data sequence into a pre-trained LSTM model, wherein the LSTM model predicts the unit yield of blueberries based on the data sequence; The total blueberry production is calculated based on the total area of ​​the blueberry production base and the unit blueberry production.

3. The method according to claim 1, characterized in that: Based on the registration information of processing enterprises, the consumption capacity of the processing industry is calculated, including: Acquire registration information of the processing enterprise, and extract production line information of the processing enterprise from the registration information; Based on the pre-stored processing parameters of various types of production lines and the production line information of the processing enterprise, the maximum consumption capacity of the processing enterprise is calculated; The sum of the maximum consumption capacities of all processing enterprises is calculated to obtain the consumption capacity of the processing industry.

4. The method according to claim 1, characterized in that: Based on warehouse registration information, storage capacity statistics are calculated, including: Obtain warehouse registration information, the registration information including warehouse location, warehouse storage environment information, and warehouse capacity information; Extract the candidate warehouse registration information whose warehouse storage environment information meets the standard for storing blueberries; The storage capacity is calculated based on the warehouse capacity information of the candidate warehouse registration information.

5. The method according to claim 1, characterized in that Match the total blueberry production, consumption capacity, theoretical sales volume and storage capacity to identify deficiencies, including: Construct matching rules: Z=J+d1=X+C+d2; Among them, Z is the predicted total blueberry production, J is the consumption capacity, X is the theoretical sales volume, C is the storage capacity, d1 is the first difference, and d2 is the second difference; Pre-set a value range of the first difference and a value range of the second difference based on historical data; Calculate the actual value of the first difference. If the actual value of the first difference is within the value range of the first difference, it is confirmed that the current absorption capacity is sufficient; if the actual value of the first difference is lower than the lower limit of the value range of the first difference, it is confirmed that the current absorption capacity is excessive; if the actual value of the first difference is higher than the upper limit of the value range of the first difference, it is confirmed that the current absorption capacity is insufficient; Calculate the actual value of the second difference. If the actual value of the second difference is within the value range of the second difference, no warning is generated. If the actual value of the second difference is lower than the lower limit of the value range of the second difference, it is confirmed that the current storage capacity is in excess, and other goods are allocated to part of the storage capacity. If the actual value of the second difference is higher than the upper limit of the value range of the second difference, it is confirmed that the current storage capacity is insufficient.

6. A blueberry whole industry chain monitoring system, characterized in that: include: A yield prediction module, used to obtain production data and predict the total blueberry yield based on the production data; The processing statistics module is used to calculate the processing industry's consumption capacity based on the registration information of processing enterprises; The sales forecast module is used to predict theoretical sales based on the blueberry sales volume and annual sales growth in previous years; The warehouse statistics module is used to count the storage capacity based on warehouse registration information; The link matching module is used to match the total blueberry production, consumption capacity, theoretical sales volume and storage capacity, and identify defective links.

7. The system according to claim 6, characterized in that The production forecasting module comprises: A factor analysis unit, used to pre-screen core factors from multiple influencing factors using factor analysis, wherein the core factors include ambient temperature and soil moisture; A parameter collection unit, used to periodically collect parameter values ​​of core factors through sensors, and arrange the parameter values ​​into a data sequence in chronological order; A model prediction unit, used for inputting the data sequence into a pre-trained LSTM model, wherein the LSTM model predicts the unit yield of blueberries based on the data sequence; The yield calculation unit is used to calculate the total yield of blueberries according to the total area of ​​the blueberry production base and the unit yield of blueberries that are pre-stated.

8. The system according to claim 6, characterized in that The processing statistics module includes: An information acquisition unit, used to acquire registration information of a processing enterprise, and extract production line information of the processing enterprise from the registration information; A capacity calculation unit, used to calculate the maximum consumption capacity of the processing enterprise based on pre-stored processing parameters of various types of production lines and production line information of the processing enterprise; The capacity statistics unit is used to calculate the sum of the maximum consumption capacities of all processing enterprises and obtain the consumption capacity of the processing industry.

9. A terminal, characterized in that: include: A memory device for storing a monitoring program for the entire blueberry industry chain; A processor is used to implement the steps of the blueberry full industry chain monitoring method as described in any one of claims 1 to 5 when executing the blueberry full industry chain monitoring program.

10. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a blueberry full industry chain monitoring program, which, when executed by a processor, implements the steps of the blueberry full industry chain monitoring method according to any one of claims 1 to 5.

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