Multi-dimensional comprehensive evaluation method, system, medium and equipment for development level of cold chain in agricultural product origin

Abstract

The present application relates to the field of agricultural product origin cold chain logistics development degree, and discloses a kind of multi-dimensional comprehensive evaluation method, system, medium and equipment of agricultural product origin cold chain development level, it includes: obtaining multi-source data, and carry out multi-source data fusion and pretreatment;The multi-source data after pretreatment is input into the key index calculation model constructed, to obtain per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate and agricultural product origin low-temperature processing rate;Based on per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate and agricultural product origin low-temperature processing rate, evaluation index calculation is carried out through dynamic weight configuration mechanism, and scale comparative advantage index and capacity comparative advantage index are obtained as evaluation index, and the evaluation index is visualized output. The present application can comprehensively reflect facility construction, operation efficiency and industrial benefit.

Description

Technical Field

[0001] This invention relates to the technical field of cold chain logistics development at agricultural product origins, and in particular to a multi-dimensional comprehensive evaluation method, system, medium, and equipment for the development level of cold chain logistics at agricultural product origins. Background Technology

[0002] Cold chain logistics for agricultural products is a crucial link in ensuring the quality of fresh agricultural products, reducing distribution losses, and extending the industrial chain. Its development level is directly related to agricultural efficiency, farmers' income, and consumption upgrading. However, existing methods for evaluating the development level of cold chain logistics still have significant shortcomings and are insufficient to meet the needs of high-quality development in the industry.

[0003] The main problems with existing technologies are as follows: First, limited evaluation dimensions and lack of quantitative analysis: Traditional evaluations rely heavily on qualitative descriptions and lack a systematic quantitative indicator system. For example, existing studies often focus on the existing scale of cold chain facilities (such as per capita availability) or single operational indicators (such as low-temperature processing rate), failing to integrate the entire process of facility construction, operational efficiency, and business benefits, and thus failing to fully reflect the actual supporting role of cold chain logistics in the agricultural industry. Second, the indicator design is disconnected from the needs of the agricultural industry: Existing indicators (such as cold chain transportation rate) emphasize technical parameters in the logistics process, failing to fully consider the differences in agricultural product categories (such as differences in preservation processes for fruits, vegetables, meat, and aquatic products) and the actual operating scenarios in production areas, resulting in weak correlation between evaluation results and the actual needs of policy formulation and investment decisions. Third, insufficient data collection and analysis methods: Existing studies often use case studies or localized regional surveys, resulting in limited sample sizes and insufficient representativeness, lacking large-scale national data support. Furthermore, the quantitative analysis methods for micro-level indicators such as the efficiency, effectiveness, and benefits of operating entities are limited, failing to reveal key influencing factors of facility operation effectiveness (such as the impact of entity type and transportation mode on loss rate). Fourth, there is a lack of unified evaluation standards and statistical systems: There is no comprehensive statistical standard covering the stock, operation, and main business of cold chain facilities in production areas, both domestically and internationally. This results in inconsistent data standards and difficulty in comparing data between regions, making it impossible to provide a scientific basis for judging industry development trends and optimizing policies.

[0004] The aforementioned problems prevent existing technologies from accurately assessing the true development level of cold chain logistics in agricultural product production areas. Although the industry recognizes the importance of quantitative evaluation, it still faces technical bottlenecks in areas such as the construction of multi-dimensional indicator systems, cross-regional data integration, and micro-level operational effect analysis. Summary of the Invention

[0005] To address the aforementioned issues, the purpose of this invention is to provide a multi-dimensional comprehensive evaluation method, system, medium, and equipment for the development level of cold chain logistics in agricultural product production areas, which can comprehensively reflect facility construction, operational efficiency, and industrial benefits.

[0006] To achieve the above objectives, in a first aspect, the technical solution adopted by the present invention is as follows: a multi-dimensional comprehensive evaluation method for the development level of cold chain in agricultural product production areas, comprising: acquiring multi-source data and performing multi-source data fusion and preprocessing; the multi-source data includes regional basic and output data, cold chain processing capacity data, and actual low-temperature processing volume data; inputting the preprocessed multi-source data into a constructed key indicator calculation model to obtain per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural product production areas, wherein the low-temperature processing rate of agricultural product production areas includes the low-temperature processing rate of fruits and vegetables, the low-temperature processing rate of meat, and the low-temperature processing rate of aquatic products; based on per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural product production areas, the evaluation indicators are calculated through a dynamic weight configuration mechanism to obtain the scale comparative advantage index and the capacity comparative advantage index as evaluation indicators, and the evaluation indicators are visualized and output.

[0007] Furthermore, multi-source data is acquired and multi-source data fusion and preprocessing are performed. Specifically, the data is compatible with both Excel and database input sources, and heterogeneous data is standardized through a unified data interface. The data is also cleaned to remove outliers.

[0008] Per capita processing capacity is used to reflect the processing resources endowed per capita in a specified area, and to measure the inclusiveness or scale of processing capacity: Per capita processing capacity = Total processing capacity / Total population; The total processing capacity is the maximum processing scale that a designated area can achieve in a given year, expressed in tons per year; the total population is the population of the designated area.

[0009] Furthermore, the cryogenic processing capacity matching degree is used to reflect the fit between total processing capacity and output in this scenario, measuring the degree to which processing capacity supports output or the rationality of resource allocation: Low-temperature processing capacity matching degree = total processing capacity / output The total processing capacity is the maximum processing scale that a designated area can achieve in a given year, expressed in tons per year; the output is the annual output of fruits, vegetables, meat, and aquatic products in the designated area, expressed in tons per year.

[0010] Furthermore, the overall low-temperature treatment rate reflects the comprehensive proportion of fresh agricultural products that have undergone cold chain procedures at the production site. By comparing the actual weight of agricultural products that have undergone low-temperature treatment at the production site with the ideal weight of agricultural products that require low-temperature treatment, the level of cold chain construction and operation can be measured. Overall cryogenic treatment rate = Overall cryogenic treatment volume / (Production) Required processing ratio).

[0011] Furthermore, the low-temperature treatment rate at the agricultural product origin is: .

[0012] Furthermore, the comparative advantage index is calculated as: regional per capita processing capacity / national per capita processing capacity. When the comparative advantage index is greater than 1, it indicates that the region's per capita processing capacity is higher than the national average, and it has a comparative advantage in scale. When the comparative advantage index is 1, it is on par with the national level; When the comparative advantage index is less than 1, it is lower than the national average and is at a relative disadvantage.

[0013] Furthermore, the comparative advantage index is calculated as follows: Regional cryogenic treatment capacity matching degree / National cryogenic treatment capacity matching degree. When the comparative advantage index is greater than 1, it indicates that the region’s low-temperature processing capacity and the matching efficiency of demand / resources are higher than the national average, and it has a comparative advantage in capacity. When the comparative advantage index is 1, it is on par with the national level. When the comparative advantage index is less than 1, the matching efficiency is lower than the national average, indicating a relative disadvantage.

[0014] Secondly, the technical solution adopted by this invention is as follows: a multi-dimensional comprehensive evaluation system for the development level of cold chain in agricultural product production areas, comprising: a multi-source data processing module, which acquires multi-source data and performs multi-source data fusion and preprocessing; the multi-source data includes regional basic and output data, cold chain processing capacity data, and actual low-temperature processing volume data; an indicator calculation module, which inputs the preprocessed multi-source data into a constructed key indicator calculation model to obtain per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural product production areas, including low-temperature processing rate of fruits and vegetables, low-temperature processing rate of meat, and low-temperature processing rate of aquatic products; and a comprehensive evaluation module, which calculates evaluation indicators based on per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural product production areas through a dynamic weight configuration mechanism to obtain a scale comparative advantage index and a capacity comparative advantage index as evaluation indicators, and visualizes the evaluation indicators.

[0015] Thirdly, the technical solution adopted by the present invention is: a computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0016] Fourthly, the technical solution adopted by the present invention is: a computing device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0017] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention achieves a deep integration of cold chain logistics and agricultural industry development, filling the gap in the assessment of existing capacity, efficiency, and benefits. The "total processing capacity" (tons / year) of this invention clearly quantifies the existing cold chain facility data, solving the problem of "unclear existing capacity" in traditional indicators; the "low-temperature processing capacity matching degree" (total processing capacity / output) quantifies operational efficiency through the ratio of capacity to demand; and the "low-temperature processing rate" (actual processing volume / required processing volume) directly reflects the actual effect of the main operation. Together, these three form a full-chain quantitative assessment from facility capacity to operational efficiency to business performance, avoiding a disconnect between evaluation and industry reality.

[0018] 2. This invention breaks through the dimensional limitations of traditional methods, achieving a synergistic analysis of macro-level layout and micro-level benefits. This invention uses the "Scale Comparative Advantage Index" (ratio of regional to national per capita processing capacity) and the "Capacity Comparative Advantage Index" (ratio of regional to national matching degree) to achieve regional comparisons of macro-level facility layout. Simultaneously, it covers micro-level operational efficiency through "per capita processing capacity" (micro-level resource allocation efficiency) and "low-temperature processing rate" (micro-level operational effectiveness), forming a synergistic "macro-micro" analytical framework. For the first time, it incorporates facility construction (total processing capacity), operational efficiency (matching degree, processing rate), and service provider performance (processing rate) into a unified evaluation system, solving the pain point of traditional methods' difficulty in achieving multi-dimensional synergy.

[0019] 3. This invention establishes a standardized statistical system covering all product categories, enhancing the operability of indicators and their correlation with industry promotion. This invention designs category-specific "low-temperature processing rates" for fruits, vegetables, meat, and aquatic products, filling a gap in the evaluation of all agricultural products. The setting of "required low-temperature processing volume at the place of origin" fully considers consumption habits (such as consumption of fresh meat and live fish), distribution models (short-distance immediate harvesting and sales), and economics, significantly improving the operability of the indicators and avoiding the poor operability problems caused by the traditional "one-size-fits-all" approach to cold chain circulation rates. Simultaneously, through the dynamic changes of indicators such as processing rate and matching degree, the actual promoting effect of cold chain facilities on the agricultural industry can be directly reflected (e.g., an increase in processing rate indicates that the facilities effectively support the low-temperature processing of agricultural products, promoting reduced losses and increased efficiency in the industry), achieving precise linkage between evaluation indicators and industrial development.

[0020] In summary, this invention provides a scientific and comprehensive tool for evaluating cold chain logistics through quantitative indicators, multi-dimensional collaboration, full category coverage, and practical optimization, effectively supporting cold chain facility planning, industrial policy formulation, and high-quality development of the agricultural industry. Attached Figure Description

[0021] Figure 1 This is a flowchart of a multi-dimensional comprehensive evaluation method for the development level of cold chain logistics in agricultural product production areas, as described in this invention. Detailed Implementation

[0022] This invention addresses three core problems in existing technologies: ① Existing cold chain logistics evaluation indicators are disconnected from agricultural industry development, resulting in unclear data on cold chain facility inventory, lack of quantitative analysis of operational efficiency, and absence of standards for evaluating the operational effectiveness of entities; ② Traditional evaluation methods struggle to achieve synergistic analysis of macro-level facility layout and micro-level operational benefits, failing to simultaneously cover multiple dimensions such as facility construction, operational efficiency, and service entity performance; ③ The lack of a standardized statistical indicator system covering all categories of agricultural products, coupled with the poor operability of existing indicators such as cold chain circulation rate, prevents the accurate reflection of the facilities' role in promoting the agricultural industry. This invention provides a multi-dimensional comprehensive evaluation method, system, medium, and equipment for the development level of cold chain logistics in agricultural production areas. By constructing a multi-dimensional and quantifiable indicator system, it effectively solves the three core problems of existing technologies, achieving deep integration of cold chain logistics and agricultural industry development. It fills the gaps in inventory, efficiency, and benefit assessment, overcomes the dimensional limitations of traditional methods, enables synergistic analysis of macro-level layout and micro-level benefits, establishes a standardized statistical system covering all categories, and enhances the operability of indicators and their relevance to the industry's promotional effect.

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] In one embodiment of the present invention, a multi-dimensional comprehensive evaluation method for the development level of cold chain logistics in agricultural product production areas is provided. In this embodiment, as shown... Figure 1 As shown, the method includes the following steps: 1) Acquire multi-source data and perform multi-source data fusion and preprocessing; multi-source data includes regional basic and production data, cold chain processing capacity data, and actual low-temperature processing volume data; 2) Input the preprocessed multi-source data into the constructed key indicator calculation model to obtain per capita processing capacity, low temperature processing capacity matching degree, comprehensive low temperature processing rate and low temperature processing rate of agricultural product origin. The low temperature processing rate of agricultural product origin includes the low temperature processing rate of fruits and vegetables, the low temperature processing rate of meat and the low temperature processing rate of aquatic products. 3) Based on per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural product producing areas, an evaluation index is calculated through a dynamic weight allocation mechanism to obtain the scale comparative advantage index and capacity comparative advantage index as evaluation indicators, which are then visualized. The dynamic weight allocation is determined through surveys and expert discussions based on different producing areas and years.

[0026] In step 1) above, multi-source data is acquired and multi-source data fusion and preprocessing are performed. Specifically, the data is compatible with both Excel and database input sources, and heterogeneous data is standardized through a unified data interface. The data is also cleaned and outliers are removed.

[0027] In this embodiment, multi-source data acquisition specifically involves differentiating data based on administrative level. Two data acquisition modes are provided according to the administrative level of the evaluation object (province, city, county vs. village, town) to ensure ease of operation and data accuracy. Provincial, municipal, and county-level evaluation: Users do not need to manually input data. They can directly select the corresponding region through the system's built-in standardized database to retrieve the basic data (such as total population, output of various agricultural products, etc.) and cold chain facility data (such as processing capacity, actual processing volume, etc.) for that level.

[0028] Village and town level evaluations: Users need to manually input local core data, which includes three main categories: Basic and production data: total population; annual production of vegetables, fruits, meat, aquatic products, poultry eggs, and dairy products.

[0029] Cold chain processing capacity data: pre-cooling capacity of fruits and vegetables, cold storage capacity of fruits and vegetables, cooling and freezing capacity of meat, cold storage capacity of eggs, cooling and freezing capacity of aquatic products, and cold storage capacity of aquatic products.

[0030] Actual low-temperature processing data: annual pre-cooling volume of fruits and vegetables, annual storage volume of fruits and vegetables in cold storage, annual cold processing volume of meat, annual storage volume of eggs in cold storage, annual cold processing volume of aquatic products, and annual storage volume of aquatic products in cold storage.

[0031] In this embodiment, specifically, multi-source data fusion is achieved by importing Excel data through the btnLoadX_Click method, which automatically identifies the file format and reads the data from the specified sheet page, then populates it into the DataGridView control; historical data is loaded from the database through the ColdChainForm_Load event, and automatic insertion of the "Total" row is supported.

[0032] The data cleaning rules are as follows: For empty cells, use the logic of `cell == null` followed by `string.Empty :string.IsNullOrWhiteSpace(cell.Value?.ToString())` and `string.Empty :cell.Value.ToString().Trim()` to uniformly fill them with `string.Empty` (covering scenarios where the cell object is null, the cell value is empty / blank characters, etc.). When converting values, if the filling result is `string.Empty`, mark it as 0 directly; if it is a non-empty string, try to convert it to a number using `double.TryParse`. If the conversion fails (e.g., if it is a non-numeric string), mark it as 0 and participate in the calculation.

[0033] In step 2) above, per capita processing capacity reflects the processing resource endowment per capita in a designated area, measuring the inclusiveness or scale of processing capacity. The higher the value, the more abundant the processing resources available to each person on average.

[0034] Processing capacity per capita = Total processing capacity / Total population; The total processing capacity is the maximum processing scale that a designated area (such as the whole country, provinces, cities and counties) can achieve in a certain year, and the unit is tons / year; the total population is the population of the designated area.

[0035] In this embodiment, the low-temperature processing capacity matching degree is used to reflect the fit between the total processing capacity and the output in this scenario, and to measure the degree to which the processing capacity supports the output or the rationality of resource allocation. The higher the value, the more sufficient the total processing capacity is relative to the output (i.e., the more sufficient the processing capacity supports the output); the lower the value, the less sufficient the total processing capacity is relative to the output (i.e., the processing capacity is insufficient to meet the output demand).

[0036] Low-temperature processing capacity matching degree = total processing capacity / output The total processing capacity is the maximum processing scale that a designated region (such as the whole country, provinces, cities and counties) can achieve in a certain year, and the unit is tons / year; the output is the annual output of fruits, vegetables, meat and aquatic products in the designated region, and the unit is tons / year.

[0037] In this embodiment, the comprehensive low-temperature treatment rate is used to reflect the overall proportion of fresh agricultural products that have undergone pre-cooling, refrigeration, and other cold chain processes at the place of origin. By comparing the actual weight of agricultural products that undergo low-temperature treatment at the place of origin with the weight of agricultural products that would ideally require low-temperature treatment, the level of cold chain construction and operation can be measured. Overall cryogenic treatment rate = Overall cryogenic treatment volume / (Production) Required processing ratio).

[0038] In this embodiment, the low-temperature treatment rate at the agricultural product origin is: .

[0039] The actual weight of agricultural products subjected to low-temperature treatment at the production site refers to the weight of agricultural products subjected to low-temperature treatment at the production site, such as the pre-cooling and refrigeration of fruits and vegetables, and the cooling, freezing, and refrigeration of meat. The required amount of low-temperature treatment at the production site is determined by comprehensively considering factors such as residents' consumption habits (e.g., beef and mutton consumption is mainly hot fresh meat, and freshwater fish consumption is mainly live fish), distribution models, and the economics of post-production processing (immediate sales after harvest, and minimizing the use of cold chain in short-distance distribution).

[0040] In this embodiment, the low-temperature treatment rate of fruits and vegetables = the amount of fruits and vegetables treated at low temperatures / (yield) Required processing ratio); Meat cryogenic treatment rate = Meat cryogenic treatment volume / (Production) Required processing ratio); Low-temperature treatment rate of aquatic products = Low-temperature treatment volume of aquatic products / (production volume) Required processing ratio).

[0041] In step 3) above, the comparative advantage index is a quantitative indicator that measures a region’s per capita processing capacity in a specific field relative to the national average. The comparative advantage index = per capita processing capacity of the region / per capita processing capacity of the country.

[0042] Among them, "regional per capita processing capacity" refers to the ratio of the total processing capacity of a certain sector in a region to the region's population, reflecting the average output or efficiency per unit of population within the region; "national per capita processing capacity" is the ratio of the total processing capacity of that sector nationwide to the national population, representing the national average level. The ratio of the two directly reflects the "relative advantage" of a region's per capita processing capacity relative to the national average. When the comparative advantage index is greater than 1, it indicates that the region's per capita processing capacity is higher than the national average, and it has a comparative advantage in scale. When the comparative advantage index is 1, it is on par with the national level; When the comparative advantage index is less than 1, it is lower than the national average and is at a relative disadvantage.

[0043] In step 3) above, the comparative advantage index of capacity = regional cryogenic treatment capacity matching degree / national cryogenic treatment capacity matching degree.

[0044] The Comparative Advantage Index measures a region's "capacity matching efficiency" in the cryogenic treatment field relative to the national average. The "National Cryogenic Treatment Capacity Matching Degree" is the average matching level between cryogenic treatment capacity and corresponding demand or resource input nationwide.

[0045] When the comparative advantage index is greater than 1, it indicates that the region’s low-temperature processing capacity and the matching efficiency of demand / resources are higher than the national average, and it has a comparative advantage in capacity. When the comparative advantage index is 1, it is on par with the national level. When the comparative advantage index is less than 1, the matching efficiency is lower than the national average, indicating a relative disadvantage.

[0046] In step 3) above, the visualization output is as follows: transactional operations are used to achieve batch data updates, and the "DELETE+INSERT" combination statement is executed through SQLiteCommand to avoid data inconsistency caused by partial updates.

[0047] Visualization was optimized using chart configuration: X-axis labels were tilted by -45 degrees, automatic truncation was disabled (TruncatedLabels = false), and staggered display was enabled (IsStaggered = true) to resolve the issue of overlapping labels in multiple regions.

[0048] It provides dual-series data display: bar charts show the indicator values ​​for each region, and red line charts (lineSeries.Color=Color.Red) mark the national average. It supports one-click switching to view 8 indicators (per capita processing capacity, low temperature processing rate, etc.).

[0049] In summary, this invention constructs an evaluation system for the development level of cold chain logistics at the national and provincial levels based on multi-index comprehensive evaluation theory and a weighted index model. First, starting from the agricultural product output structure, it establishes a cold chain facility capacity measurement model and a cold chain low-temperature processing capacity accounting model, classifying and statistically summarizing the refrigeration, freezing, and processing capacities of fruits and vegetables, meat, aquatic products, eggs, and dairy products. Second, it constructs a low-temperature processing rate index system, measuring the low-temperature processing rate by category based on "annual cold chain processing volume / agricultural product output," and forming a comprehensive low-temperature processing rate index through weighted integration. Finally, using standardized processing methods and the comparative advantage index method, it constructs a scale comparative advantage index and a capacity comparative advantage index, forming a comprehensive evaluation model for the regional cold chain logistics development level, enabling horizontal comparison and difference analysis at the national and inter-provincial levels.

[0050] To enhance the adaptability and policy sensitivity of the evaluation system during model design, this invention introduces a low-temperature treatment rate adjustment coefficient (α coefficient) to construct an adjustable weighting mechanism. By embedding configurable weighting factors into the low-temperature treatment rate calculation formula, dynamic adjustments can be made to the importance of different agricultural product categories or policy orientations. This invention employs a parameterized modular design, treating the adjustment coefficient as an independent editable variable. Values ​​are assigned through a front-end input interface or parameter configuration file, and the back-end calculation module calls and updates the results in real time. This supports scenario simulation analysis and multi-scheme comparison calculations, improving the model's flexibility and application value.

[0051] The evaluation system of this invention realizes functions such as cold chain facility scale calculation, low temperature processing capacity calculation, comprehensive index generation and regional comparative analysis, providing a scientific basis for quantitative evaluation and decision optimization of cold chain logistics development level.

[0052] In one embodiment of the present invention, a multi-dimensional comprehensive evaluation system for the development level of cold chain logistics in agricultural product production areas is provided, comprising: The multi-source data processing module acquires multi-source data and performs multi-source data fusion and preprocessing; the multi-source data includes regional basic and production data, cold chain processing capacity data, and actual low-temperature processing volume data; The indicator calculation module inputs the preprocessed multi-source data into the constructed key indicator calculation model to obtain per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural products origin. The low-temperature processing rate of agricultural products origin includes the low-temperature processing rate of fruits and vegetables, the low-temperature processing rate of meat, and the low-temperature processing rate of aquatic products. The comprehensive evaluation module calculates evaluation indicators based on per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural product production areas through a dynamic weight configuration mechanism. It obtains the scale comparative advantage index and capacity comparative advantage index as evaluation indicators and outputs the evaluation indicators in a visual manner.

[0053] In the above embodiments, per capita processing capacity is used to reflect the per capita processing resource endowment in a specified area and to measure the inclusiveness or scale level of processing capacity: per capita processing capacity = total processing capacity / total population; The total processing capacity is the maximum processing scale that a designated area can achieve in a given year, expressed in tons per year; the total population is the population of the designated area.

[0054] In the above embodiments, the low-temperature processing capacity matching degree is used to reflect the fit between the total processing capacity and the output in this scenario, and to measure the degree to which the processing capacity supports the output or the rationality of resource allocation: Low-temperature processing capacity matching degree = total processing capacity / output The total processing capacity is the maximum processing scale that a designated area can achieve in a given year, expressed in tons per year; the output is the annual output of fruits, vegetables, meat, and aquatic products in the designated area, expressed in tons per year.

[0055] In the above embodiments, the comprehensive low-temperature treatment rate is used to reflect the overall proportion of fresh agricultural products that have undergone cold chain links at the place of origin. By comparing the weight of agricultural products that actually undergo low-temperature treatment at the place of origin with the weight of agricultural products that would ideally require low-temperature treatment, the level of cold chain construction and operation can be measured. Overall cryogenic treatment rate = Overall cryogenic treatment volume / (Production) Required processing ratio).

[0056] In the above embodiments, the low-temperature treatment rate at the agricultural product origin is: .

[0057] In the above embodiments, the comparative advantage index = regional per capita processing capacity / national per capita processing capacity; When the comparative advantage index is greater than 1, it indicates that the region's per capita processing capacity is higher than the national average, and it has a comparative advantage in scale. When the comparative advantage index is 1, it is on par with the national level; When the comparative advantage index is less than 1, it is lower than the national average and is at a relative disadvantage.

[0058] In the above embodiments, the comparative advantage index = regional cryogenic treatment capacity matching degree / national cryogenic treatment capacity matching degree; When the comparative advantage index is greater than 1, it indicates that the region’s low-temperature processing capacity and the matching efficiency of demand / resources are higher than the national average, and it has a comparative advantage in capacity. When the comparative advantage index is 1, it is on par with the national level. When the comparative advantage index is less than 1, the matching efficiency is lower than the national average, indicating a relative disadvantage.

[0059] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0060] In one embodiment of the present invention, a computing device is provided. This computing device can be a terminal and may include a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. When the computer programs are executed by the processor, they implement the methods described in the above embodiments. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions stored in the memory.

[0061] 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 the present invention, 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 the present invention. 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.

[0062] In one embodiment of the present invention, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0063] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.

[0064] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0065] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. A multi-dimensional comprehensive evaluation method for the development level of cold chain logistics in agricultural product production areas, characterized in that, include: Acquire multi-source data and perform multi-source data fusion and preprocessing; multi-source data includes regional basic and production data, cold chain processing capacity data, and actual low-temperature processing volume data; The preprocessed multi-source data is input into the constructed key indicator calculation model to obtain per capita processing capacity, low temperature processing capacity matching degree, comprehensive low temperature processing rate and low temperature processing rate of agricultural products origin. The low temperature processing rate of agricultural products origin includes the low temperature processing rate of fruits and vegetables, the low temperature processing rate of meat and the low temperature processing rate of aquatic products. Based on per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural product production areas, evaluation indicators are calculated through a dynamic weighting mechanism to obtain the scale comparative advantage index and capacity comparative advantage index as evaluation indicators, and the evaluation indicators are visualized and output.

2. The multi-dimensional comprehensive evaluation method for the development level of cold chain logistics in agricultural product production areas as described in claim 1, characterized in that, Acquire multi-source data and perform multi-source data fusion and preprocessing, specifically: be compatible with dual input sources of Excel and database, standardize heterogeneous data through a unified data interface; and clean the data to remove outliers. Per capita processing capacity is used to reflect the processing resources endowed per capita in a specified area, and to measure the inclusiveness or scale of processing capacity: Per capita processing capacity = Total processing capacity / Total population; The total processing capacity is the maximum processing scale that a designated area can achieve in a given year, expressed in tons per year; the total population is the population of the designated area.

3. The multi-dimensional comprehensive evaluation method for the development level of cold chain logistics in agricultural product production areas as described in claim 1, characterized in that, Low-temperature processing capacity matching degree is used to reflect the fit between total processing capacity and output in this scenario, and to measure the degree to which processing capacity supports output or the rationality of resource allocation: Low-temperature processing capacity matching degree = total processing capacity / output The total processing capacity is the maximum processing scale that a designated area can achieve in a given year, expressed in tons per year; the output is the annual output of fruits, vegetables, meat, and aquatic products in the designated area, expressed in tons per year.

4. The multi-dimensional comprehensive evaluation method for the development level of cold chain logistics in agricultural product production areas as described in claim 1, characterized in that, The overall low-temperature treatment rate reflects the comprehensive proportion of fresh agricultural products that have undergone cold chain processes at the place of origin. It is measured by comparing the weight of agricultural products that actually undergo low-temperature treatment at the place of origin with the weight of agricultural products that ideally require low-temperature treatment. This can measure the level of cold chain construction and operation. Overall cryogenic treatment rate = Overall cryogenic treatment volume / (Production) Required processing ratio).

5. The multi-dimensional comprehensive evaluation method for the development level of cold chain logistics in agricultural product production areas as described in claim 1, characterized in that, The low-temperature treatment rate of agricultural products at the place of origin is: 。 6. The multi-dimensional comprehensive evaluation method for the development level of cold chain logistics in agricultural product production areas as described in claim 1, characterized in that, Comparative advantage index = Regional per capita processing capacity / National per capita processing capacity; When the comparative advantage index is greater than 1, it indicates that the region's per capita processing capacity is higher than the national average, and it has a comparative advantage in scale. When the comparative advantage index is 1, it is on par with the national level; When the comparative advantage index is less than 1, it is lower than the national average and is at a relative disadvantage.

7. The multi-dimensional comprehensive evaluation method for the development level of cold chain logistics in agricultural product production areas as described in claim 1, characterized in that, Comparative Advantage Index = Regional Cryogenic Treatment Capacity Matching Degree / National Cryogenic Treatment Capacity Matching Degree; When the comparative advantage index is greater than 1, it indicates that the region’s low-temperature processing capacity and the matching efficiency of demand / resources are higher than the national average, and it has a comparative advantage in capacity. When the comparative advantage index is 1, it is on par with the national level. When the comparative advantage index is less than 1, the matching efficiency is lower than the national average, indicating a relative disadvantage.

8. A multi-dimensional comprehensive evaluation system for the development level of cold chain logistics in agricultural product production areas, characterized in that, include: The multi-source data processing module acquires multi-source data and performs multi-source data fusion and preprocessing; the multi-source data includes regional basic and production data, cold chain processing capacity data, and actual low-temperature processing volume data; The indicator calculation module inputs the preprocessed multi-source data into the constructed key indicator calculation model to obtain per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural products origin. The low-temperature processing rate of agricultural products origin includes the low-temperature processing rate of fruits and vegetables, the low-temperature processing rate of meat, and the low-temperature processing rate of aquatic products. The comprehensive evaluation module calculates evaluation indicators based on per capita processing capacity, low-temperature processing capacity matching degree, comprehensive low-temperature processing rate, and low-temperature processing rate of agricultural product production areas through a dynamic weight configuration mechanism. It obtains the scale comparative advantage index and capacity comparative advantage index as evaluation indicators and outputs the evaluation indicators in a visual manner.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 7.

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