Block chain technology-based manufacturing cost transparent management method and system for production technical improvement project

Abstract

The invention discloses a manufacturing cost transparent management method and system of a production technical improvement project based on a block chain technology, and relates to the technical field of manufacturing cost management of the production technical improvement project, and the method comprises the following steps: obtaining equipment manufacturing cost abnormal data in the production technical improvement project, the equipment cost abnormal data comprises production efficiency abnormal data of the equipment and internal abnormal data of the equipment; setting a block chain platform and storing the equipment cost data of each project, setting an abnormal smart contract in each block, and generating an equipment cost anomaly evaluation coefficient based on a preset abnormal smart contract according to the equipment cost anomaly data; and judging whether the equipment of the production technical improvement project has manufacturing cost abnormity or not according to the equipment manufacturing cost abnormity evaluation coefficient, reminding the equipment of the project with the manufacturing cost abnormity and informing personnel of technical improvement. According to the invention, through the block chain technology, the problem that potential risks are neglected due to non-transparent cost and lack of systematic risk assessment in a technical improvement process is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of transparent cost management of production technology transformation projects, and more specifically, to a method and system for transparent cost management of production technology transformation projects based on blockchain technology. Background Art

[0002] Cost management of production technology transformation projects refers to comprehensive and systematic cost control and management activities for technology transformation projects, aiming to ensure that the actual investment does not exceed the planned investment while meeting the quality and progress requirements. This includes management activities during the entire project implementation phase, ensuring the scientific nature of project investment decisions and maximizing project investment benefits through accurate budget preparation and strict cost control.

[0003] In the cost management of traditional production technology transformation projects, common drawbacks include lack of systematic evaluation, inaccurate project budgets, and neglect of potential risks, which lead to cost overruns and inefficiency. In addition, information is not transparent, which leads to a lack of effective information sharing between project departments, resulting in the inability to timely and accurately transmit cost data, affecting decision-making.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a cost transparency management method and system for production technical transformation projects based on blockchain technology. Through blockchain technology, the problems of cost opacity and lack of systematic risk assessment during the technical transformation process, which lead to potential risks being ignored, are solved.

[0006] To achieve the above object, the present invention provides the following technical solutions: A cost transparency management method and system for production technology transformation projects based on blockchain technology obtains abnormal equipment cost data in production technology transformation projects, wherein the abnormal equipment cost data includes abnormal production efficiency data of equipment and internal abnormal data of equipment; a blockchain platform is set up and data is stored in the equipment cost data of each project, and an abnormal smart contract is set up in each block, and an abnormal equipment cost evaluation coefficient is generated based on the abnormal equipment cost data and the preset abnormal smart contract; a judgment is made based on the abnormal equipment cost evaluation coefficient to determine whether the equipment in the production technology transformation project has cost abnormalities, and the equipment of the abnormal cost project is reminded and the personnel are notified to make technical improvements.

[0007] In a preferred embodiment, the specific method for obtaining the abnormal equipment cost assessment coefficient is as follows: set up an abnormal smart contract and clarify the goals and functions of the abnormal smart contract, that is, define and calculate the abnormal cost assessment coefficient; use the Solidity contract programming language to write a smart contract, and define the execution process, conditions and logical calculation formula for calculating the abnormal cost assessment coefficient in the contract, and the conditions are the abnormal equipment cost data; deploy the set abnormal smart contract to the corresponding production technology transformation project blockchain; send instructions to the smart contract by inputting the abnormal equipment cost data to execute the logical calculation formula in the contract; generate the abnormal equipment cost assessment coefficient based on the abnormal smart contract.

[0008] In a preferred embodiment, the specific method for obtaining the abnormal production efficiency data is as follows: Real-time monitoring is used to obtain the first data of the equipment during the production process, wherein the first data includes the working time, labor cost and production cost of the equipment; the equipment's ergonomic data is obtained based on the equipment's service life and the equipment's working efficiency; the resource consumption data is obtained through the resource input and resource consumption factor of the equipment during production and use; the production efficiency abnormality data is calculated based on the resource consumption data, the equipment's ergonomic data and the first data based on a preset production efficiency abnormality formula.

[0009] In a preferred embodiment, the internal abnormal data of the device includes voltage abnormal data and device load abnormal data.

[0010] In a preferred embodiment, the specific method for obtaining the equipment load abnormality data is as follows: by monitoring the movement state of the equipment under the drive of the motor, the load capacity of the equipment is obtained; and the efficiency and friction coefficient of the equipment transmission coefficient are calculated to obtain the load torque of the equipment; according to the load inertia and load torque, the equipment load abnormality data of the equipment is obtained based on a preset equipment load abnormality calculation formula.

[0011] In a preferred embodiment, the specific method for acquiring the voltage abnormality data is as follows: by collecting voltage data, using an adaptive variant bat algorithm to process the voltage data to obtain local abnormal data of the voltage data, then clustering the local abnormal data, and finally classifying and detecting the clustered data to obtain abnormal voltage data.

[0012] In a preferred embodiment, the determination is made based on the equipment cost abnormality assessment coefficient, and the specific steps are as follows: if the equipment cost abnormality assessment coefficient is greater than 1 or equal to 1, it means that the equipment production of the project is in an unprofitable state, and it is determined which project has a higher risk of equipment abnormality, and priority technical improvement processing is performed; if the equipment cost abnormality assessment coefficient is less than 1, it means that the equipment production of the project is in a normal profitable state; if the equipment cost abnormality assessment coefficient is getting closer and closer to 1, it is necessary to inspect and maintain the equipment to avoid the risks that may exist in the equipment in advance.

[0013] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through blockchain technology, transparent management of data storage is achieved, which effectively solves the problem of information opacity in existing technologies, resulting in a lack of effective information sharing among project departments, and the inability to transmit cost data in a timely and accurate manner, affecting decision-making.

[0014] 2. Through the smart contract technology in the blockchain, the evaluation of abnormal equipment costs in the process of production technology transformation projects is realized, which effectively solves the problems of lack of systematic evaluation, inaccurate project budgets, and neglect of potential risks in existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the structure of a cost transparency management method for a production technology transformation project based on blockchain technology provided in an embodiment of the present application.

[0016] Figure 2 A schematic diagram of the structure of a cost transparency management system for production technology transformation projects based on blockchain technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] 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.

[0018] Embodiment 1, Figure 1 A schematic diagram of the structure of the cost transparency management method for production technology transformation projects based on blockchain technology is given in the present invention, which includes the following steps: S1, obtaining abnormal equipment cost data in the production technology transformation project, wherein the abnormal equipment cost data includes abnormal production efficiency data of the equipment and internal abnormal data of the equipment.

[0019] In this example, the abnormal production efficiency data of the equipment refers to various data indicators that show abnormal production efficiency compared with normal production levels, reflecting problems in the production process or results. These abnormal data can usually help identify potential problems, optimize production processes, and improve the overall efficiency of equipment.

[0020] The acquisition and analysis of production efficiency abnormality data has the following advantages: Improve production efficiency: By analyzing the efficiency abnormality data of production equipment, bottlenecks and problems in the production process can be discovered in a timely manner, and then these problems can be solved through technical transformation. For example, through the analysis of equipment operation data, it was found that the failure rate of a key equipment in a specific process was high. After improvement, the failure rate of the equipment decreased by 30%, thereby improving production efficiency.

[0021] Reduce production costs: Technological transformation can gradually eliminate backward production processes and equipment, further improve the comprehensive utilization rate of resources, and achieve full and reasonable use of resources. Through technological transformation, enterprises are encouraged to upgrade equipment and replace manual labor with machines, thereby achieving energy conservation and consumption reduction and improving the economic benefits of enterprises.

[0022] ‌Improve product quality and consistency‌: The automated production line ensures a high degree of consistency in the production process through sophisticated sensors, control systems, and robotic operations. Each production step is strictly executed in accordance with the set standards, reducing errors caused by human operations, thereby improving the consistency of product quality‌.

[0023] ‌Reduced labor costs‌: Companies can reduce their reliance on workers and reduce labor costs. At the same time, companies can develop new products and processes more quickly, and conduct simulations and tests, thereby achieving rapid response and customized production to meet the personalized needs of the market.

[0024] The specific method for obtaining the abnormal production efficiency data is as follows: Real-time monitoring and acquisition of first data of the equipment during the production process, wherein the first data includes the working time, labor cost and production cost of the equipment; Obtain equipment ergonomics data based on the equipment's service life and its working efficiency; Obtain resource consumption data through resource input and resource consumption factors of equipment during production and use; The production efficiency abnormality data is calculated based on the resource consumption data, the equipment's work efficiency data and the first data based on a preset production efficiency abnormality formula.

[0025] The specific calculation formula for the ergonomic data of the equipment is as follows:

[0026] The specific calculation formula of the resource consumption data is as follows:

[0027] The specific calculation formula for the abnormal production efficiency data is as follows:

[0028] In the formula, S is the abnormal data of production efficiency, T is the working time, re is the labor cost, and C is the production cost. The ergonomic data of the equipment, is the output of production equipment, is the production equipment intake, a is the index coefficient, w is the reduced energy efficiency after maintenance, is the number of maintenance times, t is the service life, Y is the resource consumption data, is the j-th resource input in the resource consumption process, F is the number of resource inputs, where F=1,2,...,R, R is an integer, is the resource utilization rate, is the amount of resource loss, is the resource consumption factor.

[0029] It should be noted that abnormal production efficiency data is affected by the equipment's work efficiency data, resource consumption data, working hours, and labor costs. When these data become larger and larger, the abnormal production efficiency data in the production process will become higher and higher, and possible hidden dangers will become more and more obvious.

[0030] In this example, the internal abnormal data of the device refers to abnormalities in the operation of the device that lead to high costs. The internal abnormal data of the device includes voltage abnormal data and device load abnormal data.

[0031] The load of the equipment affects the performance stability and energy efficiency of the equipment. Excessive load will reduce the energy efficiency of the equipment. Analyzing the abnormal load data of the equipment has the following advantages: Failure prediction and prevention: By monitoring load anomalies, early signs of impending equipment failure can be detected, so that preventive measures can be taken to avoid downtime. Timely identification of abnormal load conditions can reduce unplanned downtime due to equipment failure, thereby improving production continuity.

[0032] Improve equipment utilization: Optimizing operating conditions and analyzing load data can help determine the optimal operating load of equipment, thereby improving overall equipment utilization. By identifying and adjusting equipment with uneven loads, resources can be better allocated to avoid overloading some equipment while others are idle.

[0033] Reduce maintenance costs: Implement preventive maintenance. Through load data analysis, enterprises can formulate more effective maintenance plans, conduct targeted preventive maintenance, and reduce maintenance costs. Reduce emergency repairs. By monitoring load anomalies, small problems can be handled in a timely manner, reducing the high emergency repair costs caused by untimely repairs.

[0034] Improve production efficiency: Optimize the production process. Analyzing abnormal load data can help identify bottlenecks in production and adjust the process to improve production efficiency. Enhance flexibility. By analyzing load changes, companies can more flexibly respond to changes in market demand and adjust production plans.

[0035] Improve product quality: Consistency control. Equipment under abnormal load may cause unstable product quality. By analyzing these data, the equipment operation status can be adjusted in time to maintain product quality consistency. Reduce defective products. Early identification of load abnormalities can reduce defective products caused by equipment problems and improve the qualified rate of final products.

[0036] Enhanced decision support: Data-driven decision making. By analyzing equipment load data, companies can obtain more accurate operating information and make more scientific decisions. Identifying abnormal load conditions can help companies better assess and manage risks in the production process.

[0037] Promote technological improvement: By analyzing load data, we can find deficiencies in equipment and production processes, providing a basis for technological improvement. Implement new technologies. Based on load data analysis, we can try new technologies and processes to improve production efficiency and quality.

[0038] The specific method for obtaining the abnormal load data of the device is as follows: By monitoring the motion state of the equipment driven by the motor, the load capacity of the equipment can be obtained; The efficiency and friction coefficient of the equipment transmission coefficient are calculated to obtain the load torque of the equipment; The equipment load abnormality data of the equipment is obtained based on the preset equipment load abnormality calculation formula according to the load inertia and the load torque.

[0039] The specific calculation formula of the load torque of the equipment is as follows:

[0040] The specific calculation formula of the equipment load abnormality data is as follows:

[0041] In the formula, f is the load abnormality data, mr is the load capacity, and N is the load torque of the equipment. is the load capacity, is the friction coefficient, v is the efficiency of the equipment transmission coefficient, and P is the efficiency of the equipment transmission coefficient.

[0042] It should be noted that load torque refers to the torque generated when a system or device is subjected to an external load. Data analysis of load torque and load capacity is of great significance for obtaining abnormal load data of the equipment.

[0043] The voltage abnormality data refers to the unstable operation state of the equipment caused by the abnormal voltage during the operation of the equipment. Analyzing the voltage abnormality data has the following advantages: High power system stability: Prevent failures. By monitoring and analyzing voltage anomalies, potential failures can be discovered in time, so that measures can be taken to prevent equipment damage or system outages.

[0044] Optimized operation: Analysis of voltage fluctuations helps to formulate more reasonable operation strategies to maintain the stability and reliability of the power system.

[0045] Support equipment maintenance and management: Accurate maintenance, through the analysis of voltage anomaly data, can formulate more targeted maintenance plans and reduce equipment failure rate. Analyzing voltage anomalies helps to understand the use status of equipment, thereby optimizing the life cycle management of equipment.

[0046] Optimize energy use: Reduce energy waste. By identifying and resolving voltage anomalies, energy efficiency can be improved and operating costs can be reduced. Renewable energy integration: When renewable energy is connected to the grid, analyzing voltage anomalies can help balance supply and demand and ensure the effective use of renewable energy.

[0047] The specific method for obtaining the voltage abnormality data is as follows: By collecting voltage data and using the adaptive mutation bat algorithm to process the voltage data, local abnormal data of the voltage data is obtained, and then the local abnormal data is clustered. Finally, the clustered data is classified and detected to obtain abnormal voltage data.

[0048] The specific calculation formula of the equipment voltage abnormality data is as follows:

[0049] In the formula. is the voltage abnormality data, is the voltage in the first period of time, n is the time node, where n=1,2...., R, R is an integer, is the average voltage, is the voltage deviation.

[0050] S2, sets up a blockchain platform and stores the abnormal equipment cost data of each project, sets up an abnormal smart contract in each block, and generates an abnormal equipment cost assessment coefficient based on the preset abnormal smart contract according to the abnormal equipment cost data.

[0051] In this example, the abnormal smart contract refers to a computer program executed on the blockchain for operating and managing data. Writing a smart contract to define the storage and operation rules of data is a necessary step. After completing the writing of the smart contract, it needs to be deployed on the selected blockchain platform.

[0052] The specific method for obtaining the equipment cost abnormal assessment coefficient is as follows: Set up an abnormal smart contract and clarify the objectives and functions of the abnormal smart contract, that is, define and calculate the abnormal cost assessment coefficient; Use the Solidity contract programming language to write a smart contract, and define the execution process, conditions and logical calculation formula for calculating the abnormal cost assessment coefficient in the contract, where the condition is the abnormal equipment cost data; Deploy the completed abnormal smart contract to the corresponding production technology transformation project blockchain; By inputting abnormal equipment cost data, instructions are sent to the smart contract to execute the logical calculation formula in the contract; Generate equipment cost abnormal assessment coefficient based on abnormal smart contract.

[0053] The specific calculation formula based on the logic calculation formula on the abnormal smart contract is as follows:

[0054] In the formula, L is the cost abnormality assessment coefficient, S is the production efficiency abnormality data, and x is the cost spent in the production project. is the abnormal data of equipment voltage, f is the abnormal data of equipment load, It is the cost in the production process.

[0055] It should be noted that when the abnormal cost assessment coefficient is greater than 1, it means that the abnormal value of the project equipment at this time is higher than the cost, and there is a continuous loss. When the abnormal cost assessment coefficient is less than 1, it means that the abnormal value of the project equipment at this time is lower than the cost and is profitable. When the abnormal cost assessment coefficient is equal to 1, it means that there is neither loss nor profit at this time.

[0056] S3, based on the equipment cost abnormality assessment coefficient, determine whether the equipment in the production technical transformation project has cost abnormalities, remind the equipment of the project with abnormal costs and notify the personnel to make technical improvements. The specific steps are as follows: If the equipment cost abnormality assessment coefficient is greater than 1 or equal to 1, it means that the equipment production of the project is in an unprofitable state. At this time, the cost abnormality warning device set on each blockchain will sound an alarm in the system and remind the staff. Because the transparent management technology of the blockchain is used, the staff can easily observe which project's equipment has abnormal costs, and use the equipment cost abnormality assessment coefficient to determine which project has a higher risk of equipment abnormality, and perform priority technical improvement processing.

[0057] If the equipment cost abnormal assessment coefficient is less than 1, it means that the equipment production of the project is in a normal profit state, and the risk of equipment cost abnormality is relatively small. However, if it gets closer and closer to 1, it is necessary to inspect and maintain the equipment to avoid risks that may exist in the equipment in advance.

[0058] Embodiment 2, Figure 2 This is a schematic diagram of the structure of the cost transparency management system for production technology transformation projects based on blockchain technology in the present invention, including a data acquisition module, a data processing module, and a data analysis module: A data acquisition module, used to acquire abnormal equipment cost data in the production technology transformation project, wherein the abnormal equipment cost data includes abnormal production efficiency data of the equipment and internal abnormal data of the equipment; The data processing module is used to set up the blockchain platform and store the equipment cost data of each project, and set up an abnormal smart contract in each block to generate an abnormal equipment cost assessment coefficient based on the preset abnormal smart contract according to the abnormal equipment cost data; The data analysis module determines whether the equipment in the production technical transformation project has cost abnormalities based on the equipment cost abnormality assessment coefficient, reminds the equipment in the project with abnormal costs and notifies the personnel to make technical improvements.

[0059] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0060] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0061] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0062] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0063] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0064] 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 cost transparency management method for production technology transformation projects based on blockchain technology, characterized in that: The steps include: Acquire abnormal equipment cost data in the production technology transformation project, wherein the abnormal equipment cost data includes abnormal production efficiency data of the equipment and internal abnormal data of the equipment; Set up a blockchain platform and store the equipment cost data of each project, and set up an abnormal smart contract in each block to generate an abnormal equipment cost assessment coefficient based on the preset abnormal smart contract according to the abnormal equipment cost data; Based on the equipment cost abnormality assessment coefficient, it is determined whether there are cost abnormalities in the equipment of the production technical transformation project. The equipment of the project with abnormal costs will be reminded and personnel will be notified to make technical improvements.

2. The cost transparency management method for production technology transformation projects based on blockchain technology according to claim 1 is characterized in that: The specific method for obtaining the equipment cost abnormal assessment coefficient is as follows: Set up an abnormal smart contract and clarify the objectives and functions of the abnormal smart contract, that is, define and calculate the abnormal cost assessment coefficient; Use the Solidity contract programming language to write a smart contract, and define the execution process, conditions and logical calculation formula for calculating the abnormal cost assessment coefficient in the contract, where the condition is the abnormal equipment cost data; Deploy the completed abnormal smart contract to the corresponding production technology transformation project blockchain; By inputting abnormal equipment cost data, instructions are sent to the smart contract to execute the logical calculation formula in the contract; Generate equipment cost abnormal assessment coefficient based on abnormal smart contract.

3. The cost transparency management method for production technology transformation projects based on blockchain technology according to claim 1 is characterized in that: The specific method for obtaining the abnormal production efficiency data is as follows: Real-time monitoring and acquisition of first data of the equipment during the production process, wherein the first data includes the working time, labor cost and production cost of the equipment; Obtain equipment ergonomics data based on the equipment's service life and its working efficiency; Obtain resource consumption data through resource input and resource consumption factors of equipment during production and use; The production efficiency abnormality data is calculated based on the resource consumption data, the equipment's work efficiency data and the first data based on a preset production efficiency abnormality formula.

4. The cost transparency management method for production technology transformation projects based on blockchain technology according to claim 1 is characterized in that: The internal abnormal data of the device includes voltage abnormal data and device load abnormal data.

5. The cost transparency management method for production technology transformation projects based on blockchain technology according to claim 4 is characterized in that: The specific method for obtaining the abnormal load data of the device is as follows: By monitoring the motion state of the equipment driven by the motor, the load capacity of the equipment can be obtained; The efficiency and friction coefficient of the equipment transmission coefficient are calculated to obtain the load torque of the equipment; The equipment load abnormality data of the equipment is obtained based on the preset equipment load abnormality calculation formula according to the load inertia and the load torque.

6. The cost transparency management method for production technology transformation projects based on blockchain technology according to claim 4 is characterized in that: The specific method for obtaining the voltage abnormality data is as follows: By collecting voltage data and using the adaptive mutation bat algorithm to process the voltage data, local abnormal data of the voltage data is obtained, and then the local abnormal data is clustered. Finally, the clustered data is classified and detected to obtain abnormal voltage data.

7. The cost transparency management method for production technology transformation projects based on blockchain technology according to claim 1 is characterized in that: The specific steps for determining according to the abnormal evaluation coefficient of equipment cost are as follows: If the equipment cost abnormality assessment coefficient is greater than 1 or equal to 1, it means that the equipment production of the project is unprofitable, and it is determined which project has a higher risk of equipment abnormality, and priority technical improvement is carried out; If the equipment cost abnormal assessment coefficient is less than 1, it means that the equipment production of the project is in a normal profit state; If the abnormal assessment coefficient of equipment cost gets closer and closer to 1, it is necessary to inspect and maintain the equipment to avoid risks that may exist in the equipment in advance.

8. The cost transparency management method for production technology transformation projects based on blockchain technology according to claim 3 is characterized in that: The specific calculation formula for the ergonomic data of the equipment is as follows: The specific calculation formula for the resource consumption data is as follows: The specific calculation formula for the abnormal production efficiency data is as follows: In the formula, S is the abnormal data of production efficiency, T is the working time, re is the labor cost, and C is the production cost. The ergonomic data of the equipment, is the output of production equipment, is the production equipment intake, a is the index coefficient, w is the reduced energy efficiency after maintenance, is the number of maintenance times, t is the service life, Y is the resource consumption data, is the j-th resource input in the resource consumption process, F is the number of resource inputs, where F=1,2,...,R, R is an integer, is the resource utilization rate, is the amount of resource loss, is the resource consumption factor.

9. The cost transparency management method for production technology transformation projects based on blockchain technology according to claim 2 is characterized in that: The specific calculation formula based on the logic calculation formula on the abnormal smart contract is as follows: In the formula, L is the cost abnormality assessment coefficient, S is the production efficiency abnormality data, and x is the cost spent in the production project. is the abnormal data of equipment voltage, f is the abnormal data of equipment load, It is the cost in the production process.

10. A system using the cost transparency management method of production technology transformation projects based on blockchain technology as described in any one of claims 1 to 9, comprising a data acquisition module, a data processing module, and a data analysis module, and there is a connection between the modules; A data acquisition module, used to acquire abnormal equipment cost data in the production technology transformation project, wherein the abnormal equipment cost data includes abnormal production efficiency data of the equipment and internal abnormal data of the equipment; The data processing module is used to set up the blockchain platform and store the equipment cost data of each project, and set up an abnormal smart contract in each block to generate an abnormal equipment cost assessment coefficient based on the preset abnormal smart contract according to the abnormal equipment cost data; The data analysis module determines whether the equipment in the production technical transformation project has cost abnormalities based on the equipment cost abnormality assessment coefficient, reminds the equipment in the project with abnormal costs and notifies the personnel to make technical improvements.

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