A cloud-based digital supply chain service management system and method

Abstract

The present invention discloses a cloud-based digital supply chain service management system and method, which specifically relates to the field of supply chain management, including a management area division module: used for determining target supply chain data as a target monitoring area, and dividing it into monitoring sub-areas according to an equal time division method, and numbering each sub-area in the target monitoring area as 1, 2, ..., n in sequence; a data acquisition module: used for collecting natural environment data in the target monitoring area through Internet of Things devices, ERP systems, and CRM systems; the present invention adopts a cloud-based deployment mode, which can quickly respond to changes in business needs; by analyzing and processing operation data and market data, it is ensured that the system is always kept in the optimal operating state, thereby greatly improving the flexibility and adaptability of the system.

Description

Technical Field

[0001] The present invention relates to the field of supply chain management technology, and more specifically, to a cloud-based digital supply chain service management system and method. Background Art

[0002] The supply chain service management system covers the flow management of goods, information and funds in the entire supply chain process from raw material procurement to product delivery to the end consumer. It has the characteristics of integration, real-time, flexibility, intelligent decision-making support, risk management capabilities, scalability, customer orientation and cost-effectiveness. Therefore, building a cloud-based digital supply chain service management system has become an important direction for the digital transformation of enterprises.

[0003] The traditional supply chain service management system includes a data acquisition module, an intelligent processing module, a collaborative management module, and a decision support module. The data acquisition module collects data from all links of the supply chain, such as inventory, logistics, orders, etc., in real time through Internet of Things technology, RFID, and GPS equipment, providing data support for intelligent optimization and decision-making; the intelligent processing module processes and analyzes data based on big data analysis and artificial intelligence technology. This module conducts in-depth mining and analysis of the collected data in the supply chain system, and provides functions such as market trend forecasting, inventory optimization, and risk warning; through intelligent algorithms, the response speed and accuracy of the supply chain are improved and operating costs are reduced; the collaborative management module realizes seamless collaboration between upstream and downstream companies in the supply chain, supports collaborative operations in order management, production scheduling, logistics distribution and other links, and improves the flexibility and adaptability of the supply chain; the decision support module provides decision support for corporate management based on data analysis results.

[0004] However, in actual use, it still has some shortcomings, such as insufficient flexibility. Traditional supply chain service management systems often need to be deployed locally, which means that they may not be able to adapt to changes in business needs as quickly as cloud systems; insufficient data accuracy. Supply chain service management systems are usually deployed on local servers, and their data processing and storage capabilities are limited by hardware resources. Data between different systems cannot be effectively integrated and shared, facing the problem of data silos, resulting in insufficient data accuracy; incomplete data collection. Traditional supply chain service management systems are limited by their architectural design, making it difficult to achieve full integration with different systems and different data sources, and are unable to obtain complete and comprehensive supply chain data.

[0005] Therefore, there is an urgent need to provide a cloud-based digital supply chain service management system and method to solve the problems of insufficient flexibility, insufficient data accuracy, and incomplete data collection in existing supply chain service management systems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a cloud-based digital supply chain service management system and method, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a cloud-based digital supply chain service management system and method, comprising:

[0008] Management area division module: used to determine the target supply chain data as the target monitoring area, and divide it into each monitoring sub-area according to the equal time division method, and number each sub-area in the target monitoring area as 1, 2, ..., n in sequence;

[0009] Data collection module: used to collect natural environment data in the target monitoring area through IoT devices, ERP system, and CRM system, and transmit the collected operation data to the operation data processing and analysis module, and the collected market data to the market data processing and analysis module;

[0010] Operational data processing and analysis module: used to analyze and process the operational data collected in the data acquisition module to obtain the transaction data impact coefficient, inventory data impact coefficient and logistics data impact coefficient, and transmit them to the supply chain service rationality index calculation module;

[0011] Market data processing and analysis module: used to analyze and process the market data collected in the data acquisition module to obtain the market demand data impact coefficient and the sales data impact coefficient, and transmit them to the supply chain service rationality index calculation module;

[0012] Supply chain service rationality index calculation module: used to import transaction data impact coefficient value, inventory data impact coefficient value, logistics data impact coefficient value, market demand data impact coefficient value and sales data impact coefficient value into the supply chain service rationality index mathematical model, obtain the supply chain service rationality index value, and transmit it to the supply chain service rationality judgment module;

[0013] Supply chain service rationality judgment module: used to compare the supply chain service rationality index value with the preset supply chain service rationality index value, calculate the difference between the supply chain service rationality index value and the preset video security storage rationality index value, and when the difference is less than the preset difference, transmit the difference and supply chain service data to the data interaction transmission module;

[0014] Data interaction and transmission module: used to transmit the difference value calculated by the supply chain service rationality judgment module and the supply chain service data to the data cloud, and display the real-time status, historical data, and analysis results of the supply chain to facilitate managers to make decisions and optimization.

[0015] Preferably, the operational data processing and analysis module includes a transaction data impact coefficient calculation unit, an inventory data impact coefficient calculation unit and a logistics data impact coefficient calculation unit; the market data processing and analysis module includes a market demand data impact coefficient calculation unit and a sales data impact coefficient calculation unit.

[0016] Preferably, the operational data includes transaction data influencing parameters, inventory data influencing parameters and logistics data influencing parameters; the market data includes market demand data influencing parameters and sales data influencing parameters.

[0017] Preferably, the transaction data influencing parameters include order quantity, denoted as q; total order amount, denoted as V; order completion rate, denoted as R; order response time, denoted as t; inventory data influencing parameters include order quantity, denoted as q; inventory volume, denoted as Q; inventory turnover rate, denoted as ITO; out-of-stock rate, denoted as η; logistics data influencing parameters include order quantity, denoted as q; total order amount, denoted as V; transportation time, denoted as T; logistics cost, denoted as O; transportation damage rate, denoted as μ; market demand data influencing parameters include market demand volume, denoted as D; product market share, denoted as Ms; market growth rate, denoted as MGR; market consumption rate, denoted as Qc; sales data influencing parameters include sales volume, denoted as S; promotion investment return rate, denoted as ROI; sales return rate, denoted as RR.

[0018] Preferably, the transaction data impact coefficient calculation unit is used to import the transaction data impact parameters into the transaction data impact coefficient mathematical model to obtain the transaction data impact coefficient value; the inventory data impact coefficient calculation unit is used to import the inventory data impact parameters into the inventory data impact coefficient mathematical model to obtain the inventory data impact coefficient value; the logistics data impact coefficient calculation unit is used to import the logistics data impact parameters into the logistics data impact coefficient mathematical model to obtain the logistics data impact coefficient value.

[0019] Preferably, the transaction data impact coefficient mathematical model is specifically: ,

[0020] The mathematical model of inventory data impact coefficient is as follows: ,

[0021] The mathematical model of the logistics data impact coefficient is as follows: ,

[0022] where q i represents the number of orders in the i-th time period, V i represents the total amount of orders in the i-th time period; R i represents the order completion rate in the i-th time period; represents the average order quantity, represents the average order completion rate; represents the average order response time in the i-th time period; Q i Represents the inventory in the i-th time period; ITO i represents the inventory turnover rate in the i-th time period; η i represents the out-of-stock rate in the i-th time period.

[0023] Preferably, the market demand data impact coefficient calculation unit is used to import the market demand data impact parameters into the market demand data impact coefficient mathematical model to obtain the market demand data impact coefficient value; the sales data impact coefficient calculation unit is used to import the sales data impact parameters into the sales data impact coefficient mathematical model to obtain the sales data impact coefficient value.

[0024] Preferably, the market demand data impact coefficient mathematical model is specifically: ,

[0025] The mathematical model of sales data impact coefficient is as follows: ,

[0026] Where D i-1 represents the market demand in the i-1th time period, D i represents the market demand in the i-th time period, S i-1 represents the sales volume in the i-1th time period, D i represents the sales volume in the i-th time period.

[0027] Preferably, the mathematical model of the supply chain service rationality index is specifically: ,

[0028] where δ i represents the sales data impact coefficient of the i-th time period, σ i Represents the impact coefficient of market demand data in the i-th time period.

[0029] Preferably, a cloud-based digital supply chain service management method comprises the following steps:

[0030] Step 1: Management area division: used to determine the target supply chain data as the target monitoring area, and divide it into each monitoring sub-area according to the equal time division method, and number each sub-area in the target monitoring area as 1, 2, ..., n in sequence;

[0031] Step 2: Data collection: It is used to collect natural environment data in the target monitoring area through IoT devices, ERP system, and CRM system, and transmit the collected operation data to step 3, and the collected market data to step 4;

[0032] Step 3: Operational data processing and analysis: used to analyze and process the operational data collected in step 2 to obtain transaction data impact coefficients, inventory data impact coefficients, and logistics data impact coefficients, and transmit them to step 5;

[0033] Step 4: Market data processing and analysis: used to analyze and process the market data collected in step 2 to obtain the market demand data impact coefficient and the sales data impact coefficient, and transmit them to step 5;

[0034] Step 5: Calculation of supply chain service rationality index: used to import the transaction data impact coefficient value, inventory data impact coefficient value, logistics data impact coefficient value, market demand data impact coefficient value and sales data impact coefficient value into the supply chain service rationality index mathematical model, obtain the supply chain service rationality index value, and transmit it to step 6;

[0035] Step 6: Supply chain service rationality judgment: used to compare the supply chain service rationality index value with the preset supply chain service rationality index value, calculate the difference between the supply chain service rationality index value and the preset video security storage rationality index value, and when the difference is less than the preset difference, transmit the difference and the supply chain service data to step 7;

[0036] Step 7: Data interaction and transmission: used to transmit the difference value calculated in step 6 and the supply chain service data to the data cloud, and display the real-time status, historical data, and analysis results of the supply chain to facilitate managers to make decisions and optimization.

[0037] Technical effects and advantages of the present invention:

[0038] 1. The present invention adopts a cloud deployment mode, which can quickly respond to changes in business needs; by analyzing and processing operational data and market data, it ensures that the system always remains in the optimal operating state, thereby greatly improving the flexibility and adaptability of the system;

[0039] 2. The present invention collects, integrates and analyzes supply chain data from all links in real time through standardized API interfaces and integration frameworks, achieving seamless connection and efficient integration between different systems and data sources, and providing accurate support for decision-making;

[0040] 3. The present invention ensures comprehensive coverage and real-time updating of supply chain data through standardized API interfaces and integration frameworks, breaks the data silo phenomenon, and ensures the comprehensiveness of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0042] Figure 2It is a schematic diagram of the structure of the method of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] As attached Figure 1 A cloud-based digital supply chain service management system and method is shown, including a management area division module; a data collection module, an operation data processing and analysis module, a market data processing and analysis module, a supply chain service rationality index calculation module, a supply chain service rationality judgment module, and a data interaction and transmission module; the operation data processing and analysis module includes a transaction data impact coefficient calculation unit, an inventory data impact coefficient calculation unit, and a logistics data impact coefficient calculation unit; the market data processing and analysis module includes a market demand data impact coefficient calculation unit and a sales data impact coefficient calculation unit.

[0045] The output end of the management area division module is telecommunication connected to the input end of the data acquisition module, the output end of the data acquisition module is telecommunication connected to the input end of the video operation data processing and analysis module, the output end of the video operation data processing and analysis module is telecommunication connected to the input end of the supply chain service rationality index calculation module, the output end of the data acquisition module is telecommunication connected to the input end of the market data processing and analysis module, the output end of the market data processing and analysis module is telecommunication connected to the input end of the supply chain service rationality index calculation module, the output end of the supply chain service rationality index calculation module is telecommunication connected to the input end of the supply chain service rationality judgment module, and the output end of the supply chain service rationality judgment module is telecommunication connected to the input end of the data interaction and transmission module.

[0046] The management area division module is used to determine the target supply chain data as a target monitoring area, and divide it into monitoring sub-areas according to an equal time division method, and number each sub-area in the target monitoring area as 1, 2, ..., n in sequence.

[0047] In this embodiment, it is specifically necessary to explain that the management area division module first determines the need to collect supply chain data within the target monitoring area, calculates the total time length covered by the target monitoring area, and evenly divides the target monitoring area into a number of monitoring sub-areas of equal time length according to 6 hours, and numbers each monitoring sub-area in sequence, starting from 1 and numbering consecutive integers up to n.

[0048] The data acquisition module is used to collect natural environment data in the target monitoring area through Internet of Things devices, ERP system, and CRM system, and transmit the collected operation data to the operation data processing and analysis module, and the collected market data to the market data processing and analysis module.

[0049] In this embodiment, it should be specifically explained that the operation data includes transaction data influencing parameters, inventory data influencing parameters and logistics data influencing parameters; the market data includes market demand data influencing parameters and sales data influencing parameters.

[0050] In this embodiment, it is specifically necessary to explain that the transaction data influencing parameters include the order quantity, recorded as q; the total order amount, recorded as V; the order completion rate, recorded as R; the order response time, recorded as t; the inventory data influencing parameters include the order quantity, recorded as q; the inventory amount, recorded as Q; the inventory turnover rate, recorded as ITO; the out-of-stock rate, recorded as η; the logistics data influencing parameters include the order quantity, recorded as q; the total order amount, recorded as V; the transportation time, recorded as T; the logistics cost, recorded as O; the transportation damage rate, recorded as μ; the market demand data influencing parameters include the market demand, recorded as D; the product market share, recorded as Ms; the market growth rate, recorded as MGR; the market consumption rate, recorded as Qc; the sales data influencing parameters include sales, recorded as S; the promotion investment return rate, recorded as ROI; the sales return rate, recorded as RR;

[0051] In this embodiment, it is specifically necessary to explain that the data collection module establishes an integrated ERP system at the local level and regularly pulls transaction data influencing parameters through the API interface; connects to the WMS to obtain inventory data influencing parameters in real time; uses the API of the integrated TMS and logistics service provider to obtain logistics details of logistics data influencing parameters; uses industry reports and data analysis platforms, combined with public data such as social media and e-commerce platforms, to regularly update market data, and conducts internal data analysis to calculate market demand data influencing parameters; obtains sales data from the POS system, and obtains sales data influencing parameters in combination with the financial records of promotional activities.

[0052] The operation data processing and analysis module is used to analyze and process the operation data collected in the data acquisition module to obtain the transaction data impact coefficient, inventory data impact coefficient and logistics data impact coefficient, and transmit them to the supply chain service rationality index calculation module.

[0053] The transaction data impact coefficient calculation unit is used to import the transaction data impact parameters into the transaction data impact coefficient mathematical model to obtain the transaction data impact coefficient value; the inventory data impact coefficient calculation unit is used to import the inventory data impact parameters into the inventory data impact coefficient mathematical model to obtain the inventory data impact coefficient value; the logistics data impact coefficient calculation unit is used to import the logistics data impact parameters into the logistics data impact coefficient mathematical model to obtain the logistics data impact coefficient value.

[0054] In this embodiment, it should be specifically explained that the mathematical model of the transaction data impact coefficient is: ,

[0055] The mathematical model of inventory data impact coefficient is as follows: ,

[0056] The mathematical model of the logistics data impact coefficient is as follows: ,

[0057] where q i represents the number of orders in the i-th time period, V i represents the total amount of orders in the i-th time period; R i represents the order completion rate in the i-th time period; represents the average order quantity, represents the average order completion rate; represents the average order response time in the i-th time period; Q i Represents the inventory in the i-th time period; ITO i represents the inventory turnover rate in the i-th time period; η i represents the out-of-stock rate in the i-th time period.

[0058] The market data processing and analysis module is used to analyze and process the market data collected in the data acquisition module to obtain the market demand data impact coefficient and the sales data impact coefficient, and transmit them to the supply chain service rationality index calculation module.

[0059] The market demand data impact coefficient calculation unit is used to import the market demand data impact parameters into the market demand data impact coefficient mathematical model to obtain the market demand data impact coefficient value; the sales data impact coefficient calculation unit is used to import the sales data impact parameters into the sales data impact coefficient mathematical model to obtain the sales data impact coefficient value.

[0060] In this embodiment, it should be specifically noted that the mathematical model of the market demand data influence coefficient is: ,

[0061] The mathematical model of sales data impact coefficient is as follows: ,

[0062] Where D i-1 represents the market demand in the i-1th time period, D i represents the market demand in the i-th time period, S i-1 represents the sales volume in the i-1th time period, D i represents the sales volume in the i-th time period.

[0063] The supply chain service rationality index calculation module is used to import the transaction data influence coefficient value, inventory data influence coefficient value, logistics data influence coefficient value, market demand data influence coefficient value and sales data influence coefficient value into the supply chain service rationality index mathematical model, obtain the supply chain service rationality index value, and transmit it to the supply chain service rationality judgment module.

[0064] In this embodiment, it should be specifically explained that the mathematical model of the supply chain service rationality index is: ,

[0065] where δ i represents the sales data impact coefficient of the i-th time period, σ i Represents the impact coefficient of market demand data in the i-th time period.

[0066] The supply chain service rationality judgment module is used to compare the supply chain service rationality index value with the preset supply chain service rationality index value, calculate the difference between the supply chain service rationality index value and the preset video security storage rationality index value, and when the difference value is less than the preset difference value, transmit the difference value and the supply chain service data to the data interaction transmission module.

[0067] In this embodiment, it should be specifically explained that the preset supply chain service rationality index value is the average of the historical supply chain service rationality index values, and the preset difference value is the minimum difference between the historical supply chain service rationality index value and the preset supply chain service rationality index value.

[0068] The data interaction and transmission module is used to transmit the difference value calculated by the supply chain service rationality judgment module and the supply chain service data to the data cloud, and display the real-time status, historical data, and analysis result information of the supply chain to facilitate managers to make decisions and optimizations.

[0069] In this embodiment, it is specifically necessary to explain that the data cloud supports a multi-tenant architecture, can provide services to multiple enterprise users at the same time, provide a cross-regional and cross-departmental collaborative working environment, support information sharing and real-time communication between upstream and downstream enterprises in the supply chain, and realize seamless collaboration of the supply chain.

[0070] refer to Figure 2 , a cloud-based digital supply chain service management method, the specific steps include:

[0071] Step 1: Management area division: used to determine the target supply chain data as the target monitoring area, and divide it into each monitoring sub-area according to the equal time division method, and number each sub-area in the target monitoring area as 1, 2, ..., n in sequence;

[0072] Step 2: Data collection: It is used to collect natural environment data in the target monitoring area through IoT devices, ERP system, and CRM system, and transmit the collected operation data to step 3, and the collected market data to step 4;

[0073] Step 3: Operational data processing and analysis: used to analyze and process the operational data collected in step 2 to obtain transaction data impact coefficients, inventory data impact coefficients, and logistics data impact coefficients, and transmit them to step 5;

[0074] Step 4: Market data processing and analysis: used to analyze and process the market data collected in step 2 to obtain the market demand data impact coefficient and the sales data impact coefficient, and transmit them to step 5;

[0075] Step 5: Calculation of supply chain service rationality index: used to import the transaction data impact coefficient value, inventory data impact coefficient value, logistics data impact coefficient value, market demand data impact coefficient value and sales data impact coefficient value into the supply chain service rationality index mathematical model, obtain the supply chain service rationality index value, and transmit it to step 6;

[0076] Step 6: Supply chain service rationality judgment: used to compare the supply chain service rationality index value with the preset supply chain service rationality index value, calculate the difference between the supply chain service rationality index value and the preset video security storage rationality index value, and when the difference is less than the preset difference, transmit the difference and the supply chain service data to step 7;

[0077] Step 7: Data interaction and transmission: used to transmit the difference value calculated in step 6 and the supply chain service data to the data cloud, and display the real-time status, historical data, and analysis results of the supply chain to facilitate managers to make decisions and optimization.

[0078] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0079] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A cloud-based digital supply chain service management system, characterized in that: include: Management area division module: used to determine the target supply chain data as the target monitoring area, and divide it into each monitoring sub-area according to the equal time division method, and number each sub-area in the target monitoring area as 1, 2, ..., n in sequence; Data collection module: used to collect natural environment data in the target monitoring area through IoT devices, ERP system, and CRM system, and transmit the collected operation data to the operation data processing and analysis module, and the collected market data to the market data processing and analysis module; The operational data includes transaction data influencing parameters, inventory data influencing parameters and logistics data influencing parameters; the market data includes market demand data influencing parameters and sales data influencing parameters; The transaction data influencing parameters include the order quantity, denoted as q; the total order amount, denoted as V; Order completion rate, denoted as R; Order response time, denoted as t; inventory data influencing parameters include order quantity, denoted as q; Inventory volume, denoted as Q; inventory turnover rate, denoted as ITO; out-of-stock rate, denoted as η; logistics data influencing parameters include order quantity, denoted as q; total order amount, denoted as V; transportation time, denoted as T; logistics cost, denoted as O; transportation damage rate, denoted as μ; market demand data influencing parameters include market demand, denoted as D; product market share, denoted as Ms; market growth rate, denoted as MGR; market consumption rate, denoted as Qc; sales data influencing parameters include sales volume, denoted as S; promotion investment return rate, denoted as ROI; sales return rate, denoted as RR; Operational data processing and analysis module: used to analyze and process the operational data collected in the data acquisition module to obtain the transaction data impact coefficient, inventory data impact coefficient and logistics data impact coefficient, and transmit them to the supply chain service rationality index calculation module; The operation data processing and analysis module includes a transaction data impact coefficient calculation unit, an inventory data impact coefficient calculation unit, and a logistics data impact coefficient calculation unit; The transaction data impact coefficient calculation unit is used to import the transaction data impact parameters into the transaction data impact coefficient mathematical model to obtain the transaction data impact coefficient value; the inventory data impact coefficient calculation unit is used to import the inventory data impact parameters into the inventory data impact coefficient mathematical model to obtain the inventory data impact coefficient value; the logistics data impact coefficient calculation unit is used to import the logistics data impact parameters into the logistics data impact coefficient mathematical model to obtain the logistics data impact coefficient value; The mathematical model of the transaction data impact coefficient is specifically: , The mathematical model of inventory data impact coefficient is as follows: , The mathematical model of the logistics data impact coefficient is as follows: , where q i represents the number of orders in the i-th time period, V i Represents the total amount of orders in the i-th time period; R i represents the order completion rate in the i-th time period; represents the average order quantity, represents the average order completion rate; represents the average order response time in the i-th time period; Q i represents the inventory in the i-th time period; ITO i represents the inventory turnover rate in the i-th time period; η i represents the out-of-stock rate in the i-th time period; Market data processing and analysis module: used to analyze and process the market data collected in the data acquisition module to obtain the market demand data impact coefficient and the sales data impact coefficient, and transmit them to the supply chain service rationality index calculation module; The market data processing and analysis module includes a market demand data impact coefficient calculation unit and a sales data impact coefficient calculation unit; The market demand data impact coefficient calculation unit is used to import the market demand data impact parameter into the market demand data impact coefficient mathematical model to obtain the market demand data impact coefficient value; the sales data impact coefficient calculation unit is used to import the sales data impact parameter into the sales data impact coefficient mathematical model to obtain the sales data impact coefficient value; The mathematical model of the market demand data impact coefficient is specifically: , The mathematical model of sales data impact coefficient is as follows: , Where D i-1 represents the market demand in the i-1th time period, D i represents the market demand in the i-th time period, S i-1 represents the sales volume in the i-1th time period, D i represents the sales volume in the i-th time period; Supply chain service rationality index calculation module: used to import transaction data impact coefficient value, inventory data impact coefficient value, logistics data impact coefficient value, market demand data impact coefficient value and sales data impact coefficient value into the supply chain service rationality index mathematical model, obtain the supply chain service rationality index value, and transmit it to the supply chain service rationality judgment module; The mathematical model of the supply chain service rationality index is specifically: , where δ i represents the sales data impact coefficient of the i-th time period, σ i represents the impact coefficient of market demand data in the i-th time period; Supply chain service rationality judgment module: used to compare the supply chain service rationality index value with the preset supply chain service rationality index value, calculate the difference between the supply chain service rationality index value and the preset video security storage rationality index value, and when the difference is less than the preset difference, transmit the difference and supply chain service data to the data interaction transmission module; Data interaction and transmission module: used to transmit the difference value calculated by the supply chain service rationality judgment module and the supply chain service data to the data cloud, and display the real-time status, historical data, and analysis results of the supply chain to facilitate managers to make decisions and optimization.

2. A cloud-based digital supply chain service management method, used to use the cloud-based digital supply chain service management system described in claim 1, characterized in that: The following steps are involved: Step 1: Management area division: used to determine the target supply chain data as the target monitoring area, and divide it into each monitoring sub-area according to the equal time division method, and number each sub-area in the target monitoring area as 1, 2, ..., n in sequence; Step 2: Data collection: It is used to collect natural environment data in the target monitoring area through IoT devices, ERP system, and CRM system, and transmit the collected operation data to step 3, and the collected market data to step 4; Step 3: Operational data processing and analysis: used to analyze and process the operational data collected in step 2 to obtain transaction data impact coefficients, inventory data impact coefficients, and logistics data impact coefficients, and transmit them to step 5; Step 4: Market data processing and analysis: used to analyze and process the market data collected in step 2 to obtain the market demand data impact coefficient and the sales data impact coefficient, and transmit them to step 5; Step 5: Calculation of supply chain service rationality index: used to import the transaction data impact coefficient value, inventory data impact coefficient value, logistics data impact coefficient value, market demand data impact coefficient value and sales data impact coefficient value into the supply chain service rationality index mathematical model, obtain the supply chain service rationality index value, and transmit it to step 6; Step 6: Supply chain service rationality judgment: used to compare the supply chain service rationality index value with the preset supply chain service rationality index value, calculate the difference between the supply chain service rationality index value and the preset video security storage rationality index value, and when the difference is less than the preset difference, transmit the difference and the supply chain service data to step 7; Step 7: Data interaction and transmission: used to transmit the difference value calculated in step 6 and the supply chain service data to the data cloud, and display the real-time status, historical data, and analysis results of the supply chain to facilitate managers to make decisions and optimization.

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