Large-scale industrial refrigeration system technology full-process digital technology service platform

Abstract

The invention belongs to the technical field of large-scale industrial refrigeration systems, and particularly relates to a full-process digital technology service platform of a large-scale industrial refrigeration system technology. A complete full-process digital solution supported by a large-scale industrial refrigeration system technology is established, a large amount of work is scientifically, accurately and intelligently completed with little manual intervention, digital result output is carried out, the system technology digital solution is used for replacing a traditional working mode with manual input and manual calculation as a main mode, and the working efficiency is greatly improved. And big data analysis and extraction of historical items are carried out, and dynamic iteration is carried out to a solution, so that the digital intelligent service platform of the large-scale industrial refrigeration system technology has a certain error correction function and a deep learning function.

Description

Technical Field

[0001] The present invention belongs to the technical field of large industrial refrigeration systems, and particularly relates to a full-process digital technology service platform for large industrial refrigeration system technology. Background Art

[0002] In the current global industrial refrigeration field, the design and implementation of large industrial refrigeration systems are technically intensive and highly complex tasks. However, currently, the design process of large industrial refrigeration systems still largely relies on manual operations. From the calculation of refrigeration load to equipment selection, and then to pipe system simulation and material calculation, most of these links are completed through manual input and advancement, and full automation and intelligence have not been achieved. This not only increases the risk of human errors but also leads to an extended design cycle and increased costs.

[0003] More critically, there are obvious deficiencies in data processing and sharing in the existing technical system. There is a lack of an effective interconnection and transfer mechanism for data between various links, resulting in a serious information island phenomenon. This not only makes designers frequently switch between different links and repeatedly input data, causing unnecessary work burdens and resource waste, but also restricts the further improvement of the overall system performance.

[0004] At the same time, due to the lack of digital basic support, there are also obvious shortcomings in the intelligent service of the existing technical system. It is unable to perform dynamic adjustment and optimization based on real-time data, nor can it provide intelligent decision-making support and operation and maintenance management for users. This not only reduces the flexibility and response speed of the system but also makes it difficult to meet the growing demand of users for efficient and convenient services. Summary of the Invention

[0005] In order to overcome the problems in the existing technology, the present invention proposes a full-process digital technology service platform for large industrial refrigeration system technology.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] The present invention provides a full-process digital technology service platform for large industrial refrigeration system technology, including:

[0008] A refrigeration load calculation module for calculating the refrigeration load;

[0009] An equipment intelligent selection module for selecting the equipment model based on the calculated refrigeration load and according to the equipment selection conditions;

[0010] A refrigeration machine room equipment layout module for calculating the layout and size of the refrigeration machine room equipment according to the project land use plan, equipment model, and machine room type selection;

[0011] Engineering material specification intelligent matching module, used to calculate engineering material specifications based on refrigeration load and evaporation temperature parameters. The engineering material specifications include pipe diameter, valve model and wire and cable specifications;

[0012] Intelligent calculation module for engineering material usage, used for engineering material specifications and calculation of engineering material usage;

[0013] The intelligent quotation generation module generates quotations based on the selected equipment model and engineering material usage.

[0014] Furthermore, it also includes a refrigeration system main piping simulation module, which is used to import the piping diameter parameters into the piping simulation system after calculating the amount of engineering materials and before generating a quotation, and provide simulation results and pipe diameter correction suggestions through fluid mechanics and thermodynamics calculations.

[0015] Furthermore, it also includes a technical solution intelligent generation module, which is used to generate a technical solution document based on the calculated refrigeration load and equipment model, and implant an introduction to the principles, parameters and characteristics of large-scale refrigeration systems and equipment.

[0016] Furthermore, it also includes a big data analysis module, which is used to summarize the technical analysis of completed projects, establish a data center for segmented industries, compare actual construction costs with quotations, analyze technical indicators and costs, and feed back to each module to achieve dynamic iteration, thereby giving the platform deep learning capabilities.

[0017] Furthermore, it also includes an intelligent fast estimation module, which is an independent functional module. The module inputs key technical parameters and conditions and combines the results of big data analysis to complete the equipment selection and the rough estimate of the refrigeration system.

[0018] Furthermore, the equipment selection conditions include selecting a compressor according to compressor selection conditions: determining the required cooling capacity based on the calculated refrigeration load; considering the cooling capacity of each model of compressor under corresponding working conditions, and the cooling capacity of each model of compressor under corresponding working conditions is stored in a static database; based on the required cooling capacity and considering the number of compressors, filtering qualified compressor models from a preset static database.

[0019] Furthermore, the equipment selection conditions include selecting a condenser according to condenser selection conditions: selecting the condenser according to parameters of the selected compressor, heat rejection coefficient of each operating condition, and the number of condensers; wherein the parameters of the compressor include cooling capacity and shaft power.

[0020] Furthermore, the equipment selection conditions include selecting the barrel pump unit according to the barrel pump unit calculation rules: intelligently selecting the barrel pump unit according to the selected compressor parameters, the gas-liquid separation diameter calculation formula, the static database of nominal diameters of various types of barrel pump units and the number of barrel pump units.

[0021] Furthermore, the equipment selection conditions include selecting an evaporator according to the evaporator heat transfer coefficient: selecting the evaporator according to the refrigeration load calculation result, the heat transfer coefficient of each working condition, the number of evaporators and other conditions.

[0022] Furthermore, the engineering material specifications are calculated based on the refrigeration load and evaporation temperature parameters, including: calculating the pipe diameter of each equipment pipe system through the pipe diameter flow rate calculation formula according to the refrigeration load, evaporation temperature and an established static database, and after determining the pipe diameter of the pipe system, selecting the valve model and wire and cable specifications for each pipe system; wherein the static database includes economic flow rate information of various working fluids at different refrigeration loads and evaporation temperatures.

[0023] Compared with the prior art, the present invention has the following technical effects:

[0024] The present invention has built a complete full-process digital solution for technical support of large-scale industrial refrigeration systems. With a small amount of manual intervention, a large amount of work can be completed intelligently and scientifically and accurately, and digital results can be output. The traditional working mode based on manual input and manual calculation is replaced by a digital solution of system technology. Big data analysis and refinement of historical projects are carried out, and dynamically iterated into the solution, so that this large-scale industrial refrigeration system technology digital intelligent service platform has certain error correction and deep learning functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 It is a schematic diagram of the overall structure of the system of the present invention. DETAILED DESCRIPTION

[0027] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the technical solutions proposed by the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. The specific features, structures or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by technicians in the technical field of the present invention.

[0028] The refrigeration unit of a large refrigeration system mainly includes a compressor, a condenser, a barrel pump unit and an evaporator. The compressor is the heart of the refrigeration system, responsible for compressing the low-temperature and low-pressure refrigerant gas into a high-temperature and high-pressure gas to provide power for the refrigeration cycle. The main function of the condenser is to condense the high-temperature and high-pressure gas discharged from the compressor into a low-temperature and high-pressure liquid, while releasing heat to the surrounding environment. The barrel pump unit is mainly used to transport low-temperature refrigerant to the evaporator to ensure that the evaporator can continue to perform refrigeration work. The evaporator is the cold output device of the refrigeration system, which absorbs the heat of the refrigerant by evaporation to achieve the purpose of refrigeration.

[0029] In one embodiment of the present invention, referring to Figure 1 , provides a full-process digital technology service platform for large-scale industrial refrigeration system technology, including refrigeration load calculation module, equipment intelligent selection module, technical solution intelligent generation module, refrigeration room equipment layout intelligent design module, engineering material specification intelligent matching module, engineering material usage intelligent calculation module, quotation intelligent generation module, refrigeration system main piping simulation module, bidding assistance module, big data analysis module;

[0030] The cooling load calculation module is used to calculate the cooling load, including calculating the cooling load based on heat flow parameters, estimating the cooling load based on big data analysis experience, and allowing manual intervention and adjustment;

[0031] The equipment intelligent selection module is used to select the equipment model based on the calculated refrigeration load and according to the equipment selection conditions; the equipment includes a compressor, a condenser, a barrel pump unit and an evaporator;

[0032] The technical solution intelligent generation module is used to generate a technical solution document based on the calculated refrigeration load and equipment model, and to introduce the principles, parameters and characteristics of large-scale refrigeration systems and equipment;

[0033] The refrigeration room equipment layout module is used to calculate the layout and size of the refrigeration room equipment according to the project land planning, equipment model and room type selection, and automatically calculate the emergency exhaust requirements;

[0034] Intelligent calculation module for engineering material usage, which is used for engineering material specifications, equipment layout and size in the computer room, and calculation of engineering material usage. The engineering material specifications include pipe diameter, valve model and wire and cable specifications;

[0035] The engineering material usage intelligent calculation module is used for engineering material specifications and calculating engineering material usage;

[0036] The intelligent quotation generation module generates a quotation based on the selected equipment model and the amount of engineering materials used;

[0037] The main piping simulation module of the refrigeration system is used to import the pipe diameter parameters of the piping system into the piping simulation system after calculating the amount of engineering materials and before generating a quotation, and to provide simulation results and pipe diameter correction suggestions through fluid mechanics and thermodynamics calculations;

[0038] The bidding assistance module is used to assist bidding; the big data analysis module is used to summarize the technical analysis of completed projects, establish a segmented industry data center, compare the actual construction cost with the quotation, analyze technical indicators, costs, etc., and feed back to the functional module to achieve dynamic iteration, giving the platform deep learning capabilities.

[0039] The following is a detailed description of each of the above modules:

[0040] In this embodiment, the refrigeration load calculation module is used to calculate the refrigeration load, which includes calculating the refrigeration load based on heat flow parameters, estimating the refrigeration load based on big data analysis experience, and allowing manual intervention and adjustment.

[0041] In a specific embodiment, the refrigeration load calculation module includes a detailed load calculation submodule and a load experience estimation submodule.

[0042] The detailed load calculation submodule automatically calculates the refrigeration load through embedded rules based on the heat flow parameters and combined with the default values ​​of industry experience, and generates a load calculation book. Specifically, according to the heat flow of the cold room enclosure structure, the heat flow of the goods in the cold room, the heat flow of the ventilation and ventilation of the cold room, the heat flow of the motor operation in the cold room, the heat flow of the operation in the cold room and the calculation coefficient of the application cold scene, the calculation module provides a large number of industry experience default values ​​of the application scene calculation coefficients that match the input basic parameters, which can be manually intervened, and the automatic calculation of the refrigeration load with a small amount of manual intervention can be realized through the embedded calculation rules and formulas, and the load calculation book can be generated.

[0043] The embedded calculation rules include:

[0044] Cold room cooling equipment load Q s= Heat transfer rate Q1 of cold storage enclosure + Coefficient P of cold processing load of goods in cold storage * Heat transfer rate Q2 of goods in cold storage + Heat transfer rate Q3 of ventilation and air change in cold storage + Heat transfer rate Q4 of motor operation in cold storage + Heat transfer rate Q5 of operation in cold storage;

[0045] Mechanical load Q of cold storage s = (Seasonal correction coefficient N1 of heat transfer rate of cold storage enclosure * ∑Heat transfer rate Q1 of cold storage enclosure + Reduction coefficient N2 of heat transfer rate of goods * ∑Heat transfer rate Q2 of goods in cold storage + Number of ventilation and air change times N3 * ∑Heat transfer rate Q3 of ventilation and air change in cold storage + Coefficient N4 of synchronous operation of motors in cold storage * ∑Heat transfer rate Q4 of motor operation in cold storage + Coefficient N5 of synchronous operation in cold storage * ∑Heat transfer rate Q5 of operation in cold storage) * Compensation coefficient R of cold loss of refrigeration equipment and pipelines.

[0046] The load empirical estimation sub-module matches similar projects based on big data analysis, extracts the average value of unit refrigeration load as the estimated value, and supports manual adjustment and optimization. Specifically, conduct big data analysis on the completed projects, and intelligently match the same type of project with the highest similarity to the current project according to conditions such as the attributes of the project, the type of goods, and the cold processing time; extract the sample average value of the unit refrigeration load from the matched projects as the estimated value of the current project to achieve fast and relatively accurate calculation; also allow manual intervention so that users can adjust and optimize the estimation results according to the actual situation.

[0047] In this embodiment, the equipment intelligent selection module is used to select the equipment model based on the calculated refrigeration load and according to the equipment selection conditions. The equipment selection conditions include compressor selection conditions, condenser selection conditions, calculation rules of barrel pump units, and heat transfer coefficient of evaporators.

[0048] Specifically, select the compressor according to the compressor selection conditions, including: determining the required refrigeration capacity based on the calculated refrigeration load; considering the refrigeration capacity of each model of compressor under the corresponding working conditions, and the working conditions include evaporation temperature, condensation temperature, ambient temperature, etc., where the refrigeration capacity of each model of compressor under the corresponding working conditions is stored in the static database; screening the compressor models that meet the conditions from the static database according to the required refrigeration capacity and considering conditions such as the number of compressors. To ensure the stable operation of the system, the principle of "matching the equipment model upward according to the calculation result" should be followed during selection, that is, select the compressor model with a refrigeration capacity slightly larger than the calculation result to cope with possible load fluctuations and uncertainties.

[0049] Select a condenser according to the condenser selection conditions, including: intelligent selection of the condenser based on parameters of the selected compressor, heat rejection coefficients under various operating conditions, the number of condensers, etc. The parameters of the compressor include refrigerating capacity and shaft power. Among them, according to the refrigerating capacity of the selected compressor, determine the heat that the condenser needs to handle. The larger the refrigerating capacity, the larger the heat dissipation area and heat dissipation capacity of the condenser need to be increased accordingly; the larger the shaft power of the compressor, it means that the heat and heat dissipation pressure that the condenser needs to handle are also larger. Considering the heat rejection coefficients under operating conditions includes evaporation temperature, condensation temperature, and ambient temperature; the evaporation temperature affects the circulation state of the refrigerant and the heat dissipation effect of the condenser. The higher the evaporation temperature, the relatively smaller the heat dissipation pressure of the condenser; the condensation temperature is the temperature of the refrigerant at the outlet of the condenser, which directly affects the heat dissipation capacity and efficiency of the condenser; the ambient temperature has a significant impact on the heat dissipation effect of the condenser. In a high-temperature environment, the heat dissipation efficiency of the condenser will decrease, so a condenser with stronger heat dissipation capacity needs to be selected; calculate the heat rejection coefficient according to the operating conditions to evaluate the heat dissipation capacity of the condenser. The larger the heat rejection coefficient, the stronger the heat dissipation capacity of the condenser. Determine the number of required condensers according to the overall requirements and load calculation of the refrigeration system. Insufficient quantity will lead to insufficient heat dissipation, and excessive quantity will cause waste of resources.

[0050] Select a barrel pump unit according to the barrel pump unit calculation rules, including: intelligent selection of the barrel pump unit based on parameters of the selected compressor, gas-liquid separation diameter calculation formula, static database of the nominal diameter of each type of barrel pump unit, and the number of barrel pump units, etc. Determine the compressor parameters. According to the total suction volume of the selected compressor, determine the refrigerant flow rate that the condenser and barrel pump unit need to handle; the larger the total suction volume, it means that the refrigerant flow rate that the condenser and barrel pump unit need to handle is also larger. According to parameters such as the type of refrigeration working medium, compressor suction volume, gas flow velocity in the barrel, section coefficient, and number of air inlets, use the gas-liquid separation diameter calculation formula to determine the required gas-liquid separation diameter. Gas-liquid separation diameter = f(type of refrigeration working medium, compressor suction volume, gas flow velocity in the barrel, section coefficient, number of air inlets). Obtain parameters such as the type of refrigeration working medium and compressor suction volume from the compressor manufacturer or relevant technical documents; determine parameters such as the gas flow velocity in the barrel, section coefficient, and number of air inlets according to system design and operation experience, and substitute these parameters into the gas-liquid separation diameter calculation formula for calculation.

[0051] Select the evaporator according to the heat transfer coefficient of the evaporator, including: intelligent selection of the evaporator (excluding the cooling fan) based on the calculation results of the refrigeration load, heat transfer coefficients under various working conditions, the number of evaporators, etc. The number of devices needs to be manually input, and the matching result is calibrated and prompted with the calculated value, allowing for manual intervention. The working conditions include the analysis of the operating pressure, temperature, material properties, and environmental conditions and their influence on the selection of the evaporator. The operating pressure affects the boiling point, pressure loss, and heat transfer efficiency of the evaporator, and the temperature conditions affect the heat transfer coefficient of the evaporator and the state of the material. Consider the properties of the material such as viscosity, foaming property, thermal sensitivity, corrosiveness, etc., and select a suitable type of evaporator. For example, for materials with high viscosity, it is advisable to choose a forced circulation type, rotary type, or falling film type evaporator; for thermally sensitive materials, a single-pass film type evaporator with a short residence time should be selected. Analyze the influence of environmental conditions such as wind speed and humidity on the heat dissipation performance of the evaporator. When purchasing, it is necessary to pay attention to parameters such as the temperature range and refrigeration medium of the evaporator to ensure that it adapts to the temperature range of the environment where it is located.

[0052] In this embodiment, the technical solution intelligent generation module is used to intelligently generate technical solution documents in three versions: standard version, simplified version, and English version based on the calculation results of the refrigeration load, system division, equipment selection, etc., and automatically implant the selected system and equipment principles, parameters, and feature introductions into the technical solutions without manual intervention.

[0053] In this embodiment, the refrigeration machine room equipment layout module is used to calculate the layout and dimensions of the refrigeration machine room equipment based on the project land use plan, equipment model, and machine room type selection, and automatically calculate the accident exhaust requirements.

[0054] Specifically, the selection based on the project land use plan, equipment selection, and machine room type includes: obtaining information on the project land use plan, which includes the land area, shape, surrounding environment, etc.; obtaining the equipment selection results, which include the equipment model, quantity, external dimensions, etc.; initially setting five types of machine rooms according to the equipment layout method, including compact type, dispersed type, modular type, integrated type, extended type, etc.; and selecting the most suitable machine room type based on the project land use plan and equipment selection results.

[0055] The calculation of the layout and dimensions of the refrigeration machine room equipment includes: arranging the equipment in sequence from one side of the machine room according to the order of the compressor, liquid storage tank, barrel pump unit, etc.; considering factors such as the spacing between equipment, operating space, and maintenance channels to ensure reasonable and compact equipment layout; determining the length and width dimensions of the machine room based on the maximum dimensions of the equipment in the length and width directions, plus the necessary spacing and operating space; and considering the height of the machine room to ensure that the installation, operation, and maintenance requirements of the equipment are met.

[0056] The automatic calculation of the accident exhaust air requirements includes: selecting a suitable refrigeration refrigerant according to the project requirements and the equipment selection results; determining the exhaust air volume per unit time according to the requirements of the cold storage design standard; considering factors such as the properties of the refrigeration refrigerant, the number and layout of the equipment in the machine room, etc., and calculating the accident exhaust air requirements of the machine room.

[0057] In this embodiment, the intelligent matching module for engineering material specifications is used to automatically calculate the pipe diameter, valve model, and wire and cable specifications based on the calculated refrigeration load and evaporation temperature parameters to ensure the accurate matching of the engineering materials for the refrigeration system.

[0058] Specifically, the automatic calculation of the pipe diameter, valve model, and wire and cable specifications based on the load and evaporation temperature parameters of each device includes: collecting key parameters such as the load and evaporation temperature of each device; using the established static database, which contains the economic flow velocity information of various refrigerants at different cooling capacities and evaporation temperatures; according to these parameters and the information in the database, through the pipe diameter flow velocity calculation formula, the pipe diameter of the pipe system of each device can be automatically calculated.

[0059] After determining the pipe diameter, it is also necessary to select a suitable valve model for each pipeline. This step also depends on the information in the database, and the most suitable valve model can be matched from the database according to factors such as the pipe diameter, fluid characteristics, and working pressure.

[0060] In addition, according to the results of the intelligent equipment selection and the automatic calculation results of the engineering materials for the refrigeration system, the required wire and cable specifications can be further determined. This includes considering factors such as the power demand of the equipment, the current load, and the electrical conductivity, insulation level, and safety standards of the wire and cable.

[0061] In this embodiment, the intelligent calculation module for engineering material usage is used to determine the pipe specification quantity, valve type quantity, insulation material usage, profile usage, refrigerant filling quantity, and refrigerant oil filling quantity by integrating parameters such as equipment positioning, interface specifications, and equipment division, providing accurate material demand calculation for the refrigeration project.

[0062] Specifically, the determination of the pipe specification and quantity: input the equipment space positioning information, including the location, height, spacing, etc. of the equipment; enter the interface specifications, such as the diameter, wall thickness, connection method, etc. of the pipeline; according to the evaporation temperature system division, divide the system into different temperature intervals; combine the above information, and calculate and determine the pipe specifications and quantities within each temperature interval.

[0063] Determination of valve type and quantity: Input the type of equipment, such as compressors, condensers, evaporators, etc. Enter the interface specifications, which match the pipeline interfaces; Analyze the functional requirements of each valve according to parameters such as heat transfer capacity, evaporation temperature system division, liquid supply method, and defrosting method; Match and determine the required valve type and quantity.

[0064] Determination of the quantity of pipeline insulation materials and external protection materials for insulation: According to the pipeline specifications and quantity, determine the thickness and material of the insulation layer; Enter the evaporation temperature system division information to consider the performance requirements of insulation materials in different temperature ranges; Input the climate information of the province where the project is located to adjust the selection of insulation materials. Calculate and determine the quantity of insulation materials and external protection materials for insulation.

[0065] Determination of the quantity of profiles: Input the size information of the refrigeration machine room, including length, width, height, etc.; Enter the equipment space positioning information to plan the layout of profiles; Preset the specifications and models of support and hanger profiles; Calculate and determine the required quantity of profiles.

[0066] Determination of the refrigerant charge: Input the internal volume information of the equipment and pipelines; Enter the quantity of equipment and pipelines; Preset the refrigerant charge ratio, considering the operation efficiency and safety of the system; Calculate and determine the required refrigerant charge.

[0067] Determination of the lubricating oil charge: Input the equipment model and quantity information; Preset the single-unit lubricating oil consumption of each model of equipment; Calculate and determine the required lubricating oil charge.

[0068] In this embodiment, the intelligent quotation generation module is used to establish a price database of equipment and engineering materials, and based on the selected equipment and material requirements, achieve a comprehensive pricing of equipment, materials, and project fees, and generate a quotation.

[0069] Specifically, establish a static price database of equipment and engineering materials; After the intelligent equipment selection is completed, generate a quotation for the equipment part based on the selected equipment; Based on the engineering material quantity results calculated by the intelligent engineering material quantity calculation module, automatically calculate the refrigeration materials and electrical materials and generate a quotation for the engineering material part in combination with the static price database of engineering materials; According to the main equipment material details and equipment parameters of the refrigeration system, automatically calculate, classify, summarize, and price the equipment, engineering materials, and project fees systematically, and generate four versions of quotations: standard version, simplified version, comprehensive unit price version, and English version.

[0070] The implementation methods for the comprehensive pricing of the project regulatory fees include: determining the pipeline inspection fees based on the pipeline specifications and quantities, and the preset inspection fees per unit length for each specification of the pipeline; determining the pipeline flaw detection and radiography fees based on the pipeline specifications and quantities, the evaporation temperature system division, the types of refrigerants in the pipeline, and the preset number of radiographs per unit length under different temperature conditions for each refrigerant; determining the project installation fees based on the equipment model quantities, the consumption of engineering materials such as pipe profiles, and the preset fee charging standards for the installation of each model of equipment and various types of engineering materials.

[0071] In this embodiment, the main pipe system simulation module of the refrigeration system is used to import the key pipe system parameters into the pipe system simulation system after the material calculation is completed and before entering the pricing link, and through fluid mechanics and thermodynamics calculations, give the simulation results and pipe diameter correction suggestions.

[0072] Specifically, for the refrigeration systems using refrigerants such as R717, R744, R507, and R23, the relevant parameters of the five key pipe systems, namely the two-phase flow return pipe, the single-phase flow return pipe, the single-phase flow exhaust pipe, the single-phase flow high-pressure liquid supply pipe, and the single-phase flow pump-out liquid pipe, that is, pipe length, pipe fittings, pipe diameter, load, refrigerant, etc., are substituted into the pipe system simulation system for relevant fluid mechanics and thermodynamics calculations, and the simulation results in four aspects of compressor cooling capacity attenuation, compressor additional power consumption, flash evaporation amount of the high-pressure liquid supply pipe, and pump head are given, and suggestions on whether to correct the main pipe diameters are given.

[0073] In this embodiment, the tendering assistance module is used to assist in tendering.

[0074] Specifically, the tendering assistance module consists of three parts: the qualification bid sub-module, the commercial bid sub-module, and the technical sub-bid module. Among them, the qualification sub-module is used to establish an updatable static database of all qualification documents, and clicking will output it to the area to be synthesized; the commercial sub-module substitutes the data from the output version generated by the quotation system and performs the conversion of the specified tendering template; the technical sub-module establishes a complete static database, combines the generated dynamic technical parameters of the equipment for automatic synthesis, generates a complete technical parameter file, and generates other documents such as construction organization according to different refrigerants and system types. The system sets the tendering template, and after manual selection, the system automatically performs the synthesis of the qualification, commercial, and technical parts of the documents and the optimization of the template. For large databases and mathematical models for tendering systems in various industries, clicking will complete the rapid generation of tendering documents; a language model is built, keywords are set, and intelligent description of tendering projects is realized.

[0075] In this embodiment, the big data analysis module is used to summarize the technical analysis of the completed projects, establish a data center for the subdivision industry, compare the actual construction costs with the quotations, analyze technical indicators, costs, etc., and feedback to the functional module to achieve dynamic iteration, endowing the platform with deep learning capabilities.

[0076] Conduct various technical analyses on completed projects, summarize and collate the technical analyses of each project to establish a big data analysis center for the sub-industry, and realize the comparative analysis between the actual construction cost of the project and the quotation system; the main analysis contents include: horizontal industry technical indicators, budgetary estimate indicators, energy consumption indicators, cost analysis, etc. for a single project; and comprehensive comparison indicators for multiple projects longitudinally. Some analysis indicators are fed back to functional modules such as industry accumulation, load calculation, equipment selection, project estimation, and quick calculation to achieve dynamic iteration, enabling the platform to have a certain degree of deep learning function.

[0077] In this embodiment, the intelligent quick estimation module is an independent functional module. A mathematical model for automatic equipment calculation is built. After selecting the project category according to the fuzzy definition of the project and manually inputting the key technical parameters and conditions, combined with the big data analysis results, quick and relatively accurate equipment selection and refrigeration system budgetary estimate are realized, meeting the technical and budgetary work requirements in the early stage of the project.

[0078] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A full-process digital technology service platform for large-scale industrial refrigeration system technology, characterized by: include: A cooling load calculation module is used to calculate the cooling load; Intelligent equipment selection module, used to select equipment model based on the calculated cooling load and equipment selection conditions; Refrigeration room equipment layout module, used to calculate the layout and size of refrigeration room equipment according to project land planning, equipment model and room type selection; Engineering material specification intelligent matching module, used to calculate engineering material specifications based on refrigeration load and evaporation temperature parameters. The engineering material specifications include pipe diameter, valve model and wire and cable specifications; Intelligent calculation module for engineering material usage, used for engineering material specifications and calculation of engineering material usage; The intelligent quotation generation module generates quotations based on the selected equipment model and engineering material usage.

2. According to claim 1, a large-scale industrial refrigeration system technology full-process digital technology service platform is characterized in that: It also includes a refrigeration system main piping simulation module, which is used to import the piping diameter parameters into the piping simulation system after calculating the amount of engineering materials and before generating a quotation, and provide simulation results and pipe diameter correction suggestions through fluid mechanics and thermodynamics calculations.

3. According to claim 1, a large-scale industrial refrigeration system technology full-process digital technology service platform is characterized in that: It also includes a technical solution intelligent generation module, which is used to generate a technical solution document based on the calculated refrigeration load and equipment model, and implant an introduction to the principles, parameters and characteristics of large-scale refrigeration systems and equipment.

4. A large-scale industrial refrigeration system technology full-process digital technology service platform according to claim 1, characterized in that: It also includes a big data analysis module, which is used to summarize the technical analysis of completed projects, establish a data center for segmented industries, compare actual construction costs with quotations, analyze technical indicators and costs, and provide feedback to each module to achieve dynamic iteration, giving the platform deep learning capabilities.

5. A large-scale industrial refrigeration system technology full-process digital technology service platform according to claim 4, characterized in that: It also includes an intelligent quick estimation module, which is an independent functional module. The module inputs key technical parameters and conditions and combines the results of big data analysis to complete the equipment selection and the rough estimate of the refrigeration system.

6. A large-scale industrial refrigeration system technology full-process digital technology service platform according to claim 1, characterized in that: The selecting a compressor according to the equipment selection conditions includes: determining the required refrigeration capacity based on the calculated refrigeration load; Considering the refrigeration capacity of each type of compressor under corresponding working conditions, the refrigeration capacity of each type of compressor under corresponding working conditions is stored in a static database; according to the required refrigeration capacity and considering the number of compressors, the compressor models that meet the conditions are screened from the preset static database.

7. A large-scale industrial refrigeration system technology full-process digital technology service platform according to claim 1, characterized in that: The equipment selection conditions include selecting a condenser according to the condenser selection conditions: selecting the condenser according to the parameters of the selected compressor, the heat rejection coefficient of each working condition, and the number of condensers; wherein the parameters of the compressor include cooling capacity and shaft power.

8. A large-scale industrial refrigeration system technology full-process digital technology service platform according to claim 1, characterized in that: The equipment selection conditions include selecting the barrel pump unit according to the barrel pump unit calculation rules: intelligently selecting the barrel pump unit according to the selected compressor parameters, the gas-liquid separation diameter calculation formula, the static database of the nominal diameters of various types of barrel pump units and the number of barrel pump units.

9. A large-scale industrial refrigeration system technology full-process digital technology service platform according to claim 1, characterized in that: The equipment selection conditions include selecting the evaporator according to the evaporator heat transfer coefficient: selecting the evaporator according to the refrigeration load calculation results, the heat transfer coefficient of each working condition, the number of evaporators and other conditions.

10. A large-scale industrial refrigeration system technology full-process digital technology service platform according to claim 1, characterized in that: The calculation of engineering material specifications based on refrigeration load and evaporation temperature parameters includes: calculating the pipe diameter of each equipment pipe system through a pipe diameter flow rate calculation formula based on the refrigeration load, evaporation temperature and an established static database, and after determining the pipe diameter of the pipe system, selecting a valve model and wire and cable specifications for each pipe system; wherein the static database includes economic flow rate information of various working fluids at different refrigeration loads and evaporation temperatures.

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