Enterprise data asset assessment method and system based on AI and data processing

Abstract

The invention relates to the technical field of asset assessment, and particularly discloses an enterprise data asset assessment method and system based on AI and data processing, and the system comprises a data recording module which is used for recording power consumption data and maintenance data of power supply equipment in enterprise production data assets through AI, the value of different power supply devices can be preliminarily evaluated by combining power consumption data and maintenance data of the power supply devices in enterprise production data assets from the beginning of use, and then the residual value influence coefficients of the different power supply devices are calculated by combining the data and data acquired by a data acquisition module, so that the residual value influence coefficients of the different power supply devices are calculated. The states of different power supply devices are analyzed in combination with the residual value influence coefficient threshold, so that the value of the power supply device is further evaluated and analyzed, the quality of diversified data is relatively high, the influence of the external environment is avoided, and the accuracy and credibility of asset evaluation of the power supply device are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of asset evaluation, and in particular to an enterprise data asset evaluation method and system based on AI and data processing. Background Art

[0002] The enterprise data asset evaluation system is an important tool in enterprise management and decision-making. Through the value assessment of data assets, enterprises can clearly understand the advantages and disadvantages of their own data resources, and thus make more accurate and scientific decisions in strategic planning, investment decisions, resource allocation, etc.

[0003] Enterprise data assets cover many aspects. As a power supply production enterprise, the most important enterprise data asset is the power supply equipment status data. This data reflects the production efficiency and value of the power supply equipment. By analyzing and evaluating this data, the production process can be optimized and production efficiency can be improved. The traditional evaluation method is to estimate the equipment's regeneration cost based on equipment specifications, performance, materials, labor and other cost factors, thereby evaluating and analyzing the value of the power supply equipment.

[0004] In the prior art, when analyzing power supply equipment assets in enterprise data assets, the value of the equipment is usually assessed by estimating the regeneration cost based on cost factors such as equipment specifications, performance, materials, and labor. Since part of the data used for evaluation is external parameters, the data quality is poor and is affected by the external environment, which leads to errors in the value assessment of power supply equipment assets in enterprise data assets and affects the accuracy of the evaluation results. Summary of the invention

[0005] The purpose of the present invention is to provide an enterprise data asset evaluation method and system based on AI and data processing to solve the following technical problems:

[0006] How to improve the accuracy of power supply equipment asset assessment in enterprise data assets.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method and system for enterprise data asset evaluation based on AI and data processing, the system comprising:

[0009] The data recording module is used to record the power consumption data and maintenance data of the power supply equipment in the enterprise's production data assets since it was put into use through AI;

[0010] The data collection module is used to collect the daily operating status data of the power supply equipment in the enterprise's production data assets and the daily real-time price of the same power supply equipment;

[0011] The data evaluation module is used to combine the data recorded in the data recording module to calculate the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise's production data assets, and conduct a preliminary evaluation of the value of different power supply equipment;

[0012] The data calculation module is used to combine the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise production data assets with the data collected by the data collection module to calculate the residual value impact coefficient of different power supply equipment;

[0013] The value analysis module is used to analyze the status of different power supply devices in combination with the residual value impact coefficient thresholds of different power supply devices, and further evaluate and analyze their current values.

[0014] Furthermore, the maintenance data recorded by the data recording module includes: the number of maintenance times, maintenance frequency, maintenance difficulty, material costs and maintenance worker costs of any maintenance;

[0015] The daily operation status data of the power supply equipment collected by the data collection module includes: daily startup response time, startup current, number of faults and daily production capacity of the power supply equipment.

[0016] Furthermore, the calculation process in the data evaluation module includes:

[0017] The average hourly power consumption of the ath power supply equipment in the historical data recorded by the data recording module is used to establish the average hourly power consumption change curve of the ath power supply equipment in the enterprise production data assets since it was put into use. a (t);

[0018] And through the formula Calculate the historical power consumption change coefficient r of the ath power supply equipment a ;

[0019] Where a is any power supply device, i is any day in the history of the power supply device, n is the total number of days in the history of the power supply device, t1 is the first day since the ath power supply device was put into use, t2 is the last day in the history of the ath power supply device, dl ai is the power consumption of the ath power supply device on the i-th day in history, For all dl ai The average value, sj ai is the number of working hours on the i-th day in the historical use of the a-th power supply equipment.

[0020] Furthermore, the calculation process in the data evaluation module also includes:

[0021] By formula Calculate the failure influence coefficient s of the ath power supply equipment a ;

[0022] Among them, jx a is the total maintenance time of the ath power supply equipment, jx b For jx a The standard value of pl a is the maintenance frequency of the ath power supply equipment, pl y is the preset inspection frequency, m is any inspection, w is the total number of inspections, cl am The material cost for the mth maintenance of the ath power supply equipment, rg am The labor cost for the mth maintenance of the ath power supply equipment, nd m is the difficulty influence coefficient of the mth maintenance, wx y The preset total maintenance cost.

[0023] Furthermore, the process of performing a preliminary evaluation on the value of the power supply equipment in the data evaluation module includes:

[0024] By calculating the failure influence coefficient s of the ath power supply equipment a and the preset fault influence coefficient threshold s 01 Compare and calculate the power consumption change coefficient r of the ath power supply equipment from the time of use to the i-th day a The preset power consumption change coefficient threshold r 01 Make a comparison;

[0025] If a ≥s 01 And r a ≥r 01 , the system determines that the energy consumption of the ath power supply equipment is high and the depreciation is serious, and determines that the residual value of the power supply equipment is low;

[0026] If a ≥s 01 And r a <r 01 , the system determines that the energy consumption of the ath power supply equipment is low but the depreciation is serious, and determines that the residual value of the power supply equipment is medium;

[0027] If a <s 01 And r a ≥r 01 , the system determines that the energy consumption of the ath power supply equipment is high but the depreciation is not serious, and determines that the residual value of the power supply equipment is medium;

[0028] If a <s 01 And r a <r01 , the system determines that the energy consumption of the ath power supply equipment is low and the depreciation is not serious, and determines that the residual value of the power supply equipment is good.

[0029] Furthermore, the process of data calculation module analysis includes:

[0030] By formula Calculate the residual value impact coefficient μ of the ath power supply equipment in the enterprise's production data assets when obtaining a data evaluation a ;

[0031] Among them, kjx a is the startup response time of the ath power supply device on the day of a data evaluation, kjx y is the preset power-on response time, kl a kl is the startup current of the ath power supply device on the day of a data evaluation, y is the preset startup current, gz a is the number of failures of the ath power supply equipment on the day of a data evaluation, gz y is the preset number of failures, gz b For gz a The standard value of f t (x) is a defined function, if f t (x)>1, then let f t (x) = x, if f t (x)≤1, then let f t (x) = 1, cn a is the capacity of the ath power supply equipment on that day during a data evaluation, cn y is the preset capacity, jg a is the purchase price of the ath power supply equipment on the day of a data evaluation, jg y is the actual purchase price of the ath power supply equipment, jga i is the purchase price of the ath power supply equipment on the i-th day of its historical use, For all jg ai The average value, gz a is the working time of the ath power supply equipment on the day of a data evaluation, gz y The preset working time.

[0032] Furthermore, the analysis process of the value analysis module includes:

[0033] By calculating the residual value impact coefficient μ of the ath power supply equipment in the enterprise's production data assets during a data evaluation a and the preset residual value impact coefficient threshold μ 01 Make a comparison;

[0034] If μ a <μ 01 , the system determines that the residual value of the ath power supply equipment is high;

[0035] If μ a ≥μ 01 , the system determines that the residual value of the ath power supply equipment is low and needs to be replaced.

[0036] An enterprise data asset evaluation method based on AI and data processing, the method comprising:

[0037] S1: The data recording module uses AI to record the power consumption data and maintenance data of the power supply equipment in the enterprise's production data assets since it was put into use;

[0038] S2: The data collection module collects the daily operation status data of the power supply equipment in the enterprise's production data assets and the daily real-time price of the same power supply equipment;

[0039] S3: The data evaluation module is combined with the data recorded in the data recording module to calculate the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise's production data assets, and a preliminary evaluation is performed on the value of different power supply equipment;

[0040] S4: The data calculation module combines the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise production data assets with the data collected by the data collection module to calculate the residual value impact coefficient of different power supply equipment;

[0041] S5: Analyze the status of different power supply devices through the value analysis module in combination with the residual value impact coefficient threshold of different power supply devices, and further evaluate and analyze their current values.

[0042] Beneficial effects of the present invention:

[0043] (1) The present invention can make a preliminary evaluation of the value of different power supply equipment by first combining the power consumption data and maintenance data of the power supply equipment in the enterprise's production data assets since it was put into use, and then combine the data with the data collected by the data acquisition module to calculate the residual value impact coefficient of different power supply equipment, and analyze the status of different power supply equipment in combination with the residual value impact coefficient threshold, so as to further evaluate and analyze the value of the power supply equipment, and the diversified data quality is relatively high and is not affected by the external environment, thereby improving the accuracy and credibility of the power supply equipment asset evaluation.

[0044] (2) The present invention obtains the historical power consumption change coefficient r of the ath power supply device by calculation aThis data reflects the change in the daily electricity consumption data of the ath power supply equipment since it was put into use. The larger the change, the more the power consumption of the power supply equipment itself is gradually increasing with the increase in usage time, which means that the power supply equipment is beginning to age slowly, and its production cost will gradually increase, while its own value will gradually decrease. Through such a setting, accurate data can be provided for the subsequent value assessment of the power supply equipment to ensure the accuracy of the enterprise data asset assessment.

[0045] (3) The present invention calculates the fault influence coefficient s of the ath power supply device by a and the preset fault influence coefficient threshold s 01 Compare and calculate the power consumption change coefficient r of the ath power supply equipment from the time of use to the i-th day a The preset power consumption change coefficient threshold r 01 By comparing, it can be reflected whether the energy consumption of the ath power supply equipment has increased and whether the depreciation is serious in its historical use, so as to make a preliminary analysis of the residual value of the power equipment, and provide data support for the subsequent further evaluation of the residual value of the power supply equipment, so as to ensure the accuracy of the evaluation results.

[0046] (4) The present invention calculates the residual value impact coefficient μ of the ath power supply equipment in the enterprise production data assets during a data evaluation. a and the preset residual value impact coefficient threshold μ 01 For comparison, since this data combines the real-time data collected during a data evaluation and the fault influence coefficient s of the ath power supply equipment a and the historical power consumption change coefficient r of the ath power supply equipment a ,Diversified data can improve the quality of evaluation parameters, thereby improving the accuracy of calculation results, and then make an accurate judgment on the residual value of the a-th power supply equipment, ensuring the accuracy and reliability of the enterprise data asset evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention will be further described below in conjunction with the accompanying drawings.

[0048] Figure 1 It is a schematic block diagram of an enterprise data asset evaluation system based on AI and data processing in the present invention;

[0049] Figure 2 It is a flow chart of an enterprise data asset evaluation method based on AI and data processing in the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] See also Figure 1 As shown, in one embodiment, the present application provides an enterprise data asset evaluation method and system based on AI and data processing, the system comprising:

[0052] The data recording module is used to record the power consumption data and maintenance data of the power supply equipment in the enterprise's production data assets since it was put into use through AI;

[0053] The data collection module is used to collect the daily operating status data of the power supply equipment in the enterprise's production data assets and the daily real-time price of the same power supply equipment;

[0054] The data evaluation module is used to combine the data recorded in the data recording module to calculate the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise's production data assets, and conduct a preliminary evaluation of the value of different power supply equipment;

[0055] The data calculation module is used to combine the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise production data assets with the data collected by the data collection module to calculate the residual value impact coefficient of different power supply equipment;

[0056] The value analysis module is used to analyze the status of different power supply equipment in combination with the residual value impact coefficient threshold of different power supply equipment, and further evaluate and analyze its current value;

[0057] Through the above technical scheme, this embodiment provides a data recording module for recording the power consumption data and maintenance data of the power supply equipment in the enterprise production data assets since it was put into use through AI. When evaluating the power supply equipment assets in the enterprise assets, the daily operation status data of the power supply equipment in the enterprise production data assets and the daily real-time price of the same power supply equipment are first collected through the data acquisition module. Then, the data evaluation module is combined with the data recorded in the data recording module to calculate the historical power consumption change coefficient and the fault impact coefficient of different power supply equipment in the enterprise production data assets, and the value of different power supply equipment is preliminarily evaluated. Subsequently, the data calculation module is combined with the historical power consumption change coefficient and the fault impact coefficient of different power supply equipment in the enterprise production data assets, and the data collected by the data acquisition module, and the residual value impact coefficient of different power supply equipment is calculated, and the value analysis module is combined with the residual value impact coefficient threshold of different power supply equipment to analyze the status of different power supply equipment, and further evaluate and analyze its current value;

[0058] Through such settings, the system can make a preliminary assessment of the value of different power supply equipment by first combining the power consumption data and maintenance data of the power supply equipment in the enterprise's production data assets since it was put into use, and then combine this data with the data collected by the data acquisition module to calculate the residual value impact coefficient of different power supply equipment, and analyze the status of different power supply equipment in combination with the residual value impact coefficient threshold, so as to further evaluate and analyze the value of the power supply equipment, and the diversified data quality is relatively high and is not affected by the external environment, thereby improving the accuracy and credibility of the power supply equipment asset assessment.

[0059] The maintenance data recorded by the data recording module includes: the number of maintenance times, maintenance frequency, maintenance difficulty, material costs and maintenance worker costs for any maintenance;

[0060] The daily operation status data of the power supply equipment collected by the data collection module includes: the daily startup response time, startup current, number of faults and daily production capacity of the power supply equipment;

[0061] Through the above technical scheme, this embodiment provides maintenance data recorded by the data recording module and daily operation status data of the power supply equipment collected by the data acquisition module. The two sets of data can respectively reflect the overall status changes of the power supply equipment and the real-time status of daily operation, thereby providing high-quality data support for subsequent value assessment and ensuring the accuracy of the assessment results.

[0062] The calculation process in the data evaluation module includes:

[0063] The average hourly power consumption of the ath power supply equipment in the historical data recorded by the data recording module is used to establish the average hourly power consumption change curve of the ath power supply equipment in the enterprise production data assets since it was put into use. a (t);

[0064] And through the formula Calculate the historical power consumption change coefficient r of the ath power supply equipment a ;

[0065] Where a is any power supply device, i is any day in the history of the power supply device, n is the total number of days in the history of the power supply device, t1 is the first day since the ath power supply device was put into use, t2 is the last day in the history of the ath power supply device, dl ai is the power consumption of the ath power supply device on the i-th day in history, For all dl ai The average value, sj ai is the number of working hours on the i-th day in the history of use of the a-th power supply equipment;

[0066] Through the above technical solution, this embodiment provides the historical power consumption change coefficient r of the ath power supply device a , can be obtained by formula Calculated, where the formula The fluctuation value of the average hourly power consumption of the a-th power supply device in historical use can be calculated. Therefore, it is obvious that the greater the fluctuation value of the average hourly power consumption of the a-th power supply device in historical use, the greater the historical power consumption change coefficient r of the a-th power supply device. a The larger the data is, the more it reflects the change in the daily electricity consumption data of the ath power supply equipment since it was put into use. The larger the change is, the more the power consumption of the power supply equipment itself is gradually increasing with the increase in usage time, which means that the power supply equipment is beginning to age slowly, and its production cost will gradually increase, while its own value will gradually decrease. Through such a setting, accurate data can be provided for the subsequent value assessment of the power supply equipment to ensure the accuracy of the enterprise data asset assessment.

[0067] The calculation process in the data evaluation module also includes:

[0068] By formula Calculate the failure influence coefficient s of the ath power supply equipment a ;

[0069] Among them, jx a is the total maintenance time of the ath power supply equipment, jx b For jx a The standard value of pl a is the maintenance frequency of the ath power supply equipment, ply is the preset inspection frequency, m is any inspection, w is the total number of inspections, cl am The material cost for the mth maintenance of the ath power supply equipment, rg am The labor cost for the mth maintenance of the ath power supply equipment, nd m is the difficulty influence coefficient of the mth maintenance, wx y is the preset total maintenance cost;

[0070] Through the above technical solution, this embodiment provides the failure influence coefficient s of the ath power supply device a , can be obtained by the formula Obviously, when the total maintenance time of the a-th power supply equipment is longer, and the maintenance frequency, total material cost and total labor cost are higher, then the failure impact coefficient s of the a-th power supply equipment is a The larger the value, the lower the cost of the power supply equipment. On the contrary, the shorter the total maintenance time of the a-th power supply equipment, the lower the maintenance frequency, the total material cost and the total labor cost, the lower the failure impact coefficient s of the a-th power supply equipment. a The smaller it is, it means that the operation of the power supply equipment does not require a lot of cost support, which means that the value of the power supply equipment is relatively high. Through this calculation method, the value of the power supply equipment can be further analyzed, and high-quality data can be provided for the subsequent value assessment of the power supply equipment to ensure the accuracy of the assessment results.

[0071] The process of performing a preliminary evaluation on the value of the power supply equipment in the data evaluation module includes:

[0072] By calculating the failure influence coefficient s of the ath power supply equipment a and the preset fault influence coefficient threshold s 01 Compare and calculate the power consumption change coefficient r of the ath power supply equipment from the time of use to the i-th day a The preset power consumption change coefficient threshold r 01 Make a comparison;

[0073] If a ≥s 01 And r a ≥r 01 , the system determines that the energy consumption of the ath power supply equipment is high and the depreciation is serious, and determines that the residual value of the power supply equipment is low;

[0074] If a ≥s 01 And r a <r 01, the system determines that the energy consumption of the ath power supply equipment is low but the depreciation is serious, and determines that the residual value of the power supply equipment is medium;

[0075] If a 01 And r a ≥r 01 , the system determines that the energy consumption of the ath power supply equipment is high but the depreciation is not serious, and determines that the residual value of the power supply equipment is medium;

[0076] If a 01 And r a <r 01 , the system determines that the energy consumption of the ath power supply equipment is low and the depreciation is not serious, and determines that the residual value of the power supply equipment is good;

[0077] Through the above technical solution, this embodiment calculates the fault influence coefficient s of the ath power supply device a and the preset fault influence coefficient threshold s 01 Compare and calculate the power consumption change coefficient r of the ath power supply equipment from the time of use to the i-th day a The preset power consumption change coefficient threshold r 01 By comparing, it can be reflected whether the energy consumption of the ath power supply equipment has increased and whether the depreciation is serious in its historical use, so as to make a preliminary analysis of the residual value of the power equipment, and provide data support for the subsequent further evaluation of the residual value of the power supply equipment, so as to ensure the accuracy of the evaluation results.

[0078] The process of data calculation module analysis includes:

[0079] By formula Calculate the residual value impact coefficient μ of the ath power supply equipment in the enterprise's production data assets when obtaining a data evaluation a ;

[0080] Among them, kjx a is the startup response time of the ath power supply device on the day of a data evaluation, kjx y is the preset power-on response time, kl a kl is the startup current of the ath power supply device on the day of a data evaluation, y is the preset startup current, gz a is the number of failures of the ath power supply equipment on the day of a data evaluation, gz y is the preset number of failures, gz b For gz a The standard value, f t (x) is a defined function, if f t ​​(x)>1, then let f t (x) = x, if f t (x)≤1, then let f t (x) = 1, cn a is the capacity of the ath power supply equipment on that day during a data evaluation, cn y is the preset capacity, jg a is the purchase price of the ath power supply equipment on the day of a data evaluation, jg y is the actual purchase price of the ath power supply equipment, jga i is the purchase price of the ath power supply equipment on the i-th day of its historical use, For all jg ai The average value, gz a is the working time of the ath power supply equipment on the day of a data evaluation, gz y The preset working hours;

[0081] Through the above technical solution, this embodiment provides a residual value impact coefficient μ of the ath power supply equipment in the enterprise production data assets during a data evaluation. a , can be obtained by formula Obviously, when evaluating data once, the longer the startup response time of the a-th power supply equipment on that day, the higher the startup current and the number of failures, and the lower the production capacity and the purchase price of the new equipment, the greater the residual value impact coefficient μ of the a-th power supply equipment in the enterprise's production data assets. a The higher the value, the greater the impact on the residual value of the a-th power supply equipment. In other words, the residual value of the a-th power supply equipment is lower. On the contrary, when evaluating the data once, the shorter the startup response time of the a-th power supply equipment on that day, the lower the startup current and the number of failures, and the higher the production capacity and the purchase price of the new equipment, the residual value impact coefficient μ of the a-th power supply equipment in the enterprise production data assets is. a The lower it is, the less affected is the residual value of the a-th power supply equipment, and it still has good production efficiency, which means that the residual value of the a-th power supply equipment is higher. Through this calculation method, accurate data can be provided for the subsequent system to judge the residual value of the a-th power supply equipment, thereby ensuring the accuracy of the judgment result.

[0082] The analysis process of the value analysis module includes:

[0083] By calculating the residual value impact coefficient μ of the ath power supply equipment in the enterprise's production data assets during a data evaluation a and the preset residual value impact coefficient threshold μ 01 Make a comparison;

[0084] If μ a <μ 01, the system determines that the residual value of the ath power supply equipment is high;

[0085] If μ a ≥μ 01 , the system determines that the residual value of the ath power supply equipment is low and needs to be replaced;

[0086] Through the above technical solution, this example calculates the residual value impact coefficient μ of the ath power supply equipment in the enterprise's production data assets during a data evaluation. a and the preset residual value impact coefficient threshold μ 01 For comparison, since this data combines the real-time data collected during a data evaluation and the fault influence coefficient s of the ath power supply equipment a and the historical power consumption change coefficient r of the ath power supply equipment a ,Diversified data can improve the quality of evaluation parameters, thereby improving the accuracy of calculation results, and then make an accurate judgment on the residual value of the a-th power supply equipment, ensuring the accuracy and reliability of the enterprise data asset evaluation results.

[0087] See also Figure 2 As shown, a method for evaluating enterprise data assets based on AI and data processing, the method comprising:

[0088] S1: The data recording module uses AI to record the power consumption data and maintenance data of the power supply equipment in the enterprise's production data assets since it was put into use;

[0089] S2: The data collection module collects the daily operation status data of the power supply equipment in the enterprise's production data assets and the daily real-time price of the same power supply equipment;

[0090] S3: The data evaluation module is combined with the data recorded in the data recording module to calculate the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise's production data assets, and a preliminary evaluation is performed on the value of different power supply equipment;

[0091] S4: The data calculation module combines the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise production data assets with the data collected by the data collection module to calculate the residual value impact coefficient of different power supply equipment;

[0092] S5: Analyze the status of different power supply devices by combining the residual value impact coefficient thresholds of different power supply devices through the value analysis module, and further evaluate and analyze their current values;

[0093] Through the above technical scheme, this embodiment provides an enterprise data asset evaluation method based on AI and data processing. First, the data recording module uses AI to record the power consumption data and maintenance data of the power supply equipment in the enterprise production data assets since it was put into use. Then, the data acquisition module collects the daily operation status data of the power supply equipment in the enterprise production data assets and the daily real-time price of the same power supply equipment. The data evaluation module combines the data recorded in the data recording module to calculate the historical power consumption change coefficient and the fault influence coefficient of different power supply equipment in the enterprise production data assets, and conducts a preliminary evaluation of the value of different power supply equipment. Subsequently, the data calculation module can be combined with the historical power consumption change coefficient and the fault influence coefficient of different power supply equipment in the enterprise production data assets, and the data collected by the data acquisition module to calculate the residual value influence coefficient of different power supply equipment. Finally, the value analysis module is combined with the residual value influence coefficient threshold of different power supply equipment to analyze the status of different power supply equipment, and further evaluate and analyze its current value;

[0094] Through such settings, the data recording module uses AI to record the electricity consumption data and maintenance data of the power supply equipment in the enterprise's production data assets since it was put into use. The historical electricity consumption change coefficients and fault impact coefficients of different power supply equipment in the enterprise's production data assets can be calculated separately in the future, and a preliminary evaluation can be made on the value of different power supply equipment. After that, by combining the historical electricity consumption change coefficients and fault impact coefficients of different power supply equipment in the enterprise's production data assets with the data collected by the data acquisition module, the residual value impact coefficients of different power supply equipment can be calculated, thereby realizing the evaluation of the value of different power supply equipment. In addition, diversified data can improve the quality of the evaluation data, thereby ensuring the accuracy and reliability of the evaluation results.

[0095] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An enterprise data asset evaluation system based on AI and data processing, characterized in that: The system comprises: The data recording module is used to record the power consumption data and maintenance data of the power supply equipment in the enterprise's production data assets since it was put into use through AI; The data collection module is used to collect the daily operating status data of the power supply equipment in the enterprise's production data assets and the daily real-time price of the same power supply equipment; The data evaluation module is used to combine the data recorded in the data recording module to calculate the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise's production data assets, and conduct a preliminary evaluation of the value of different power supply equipment; The data calculation module is used to combine the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise production data assets with the data collected by the data collection module to calculate the residual value impact coefficient of different power supply equipment; The value analysis module is used to analyze the status of different power supply devices in combination with the residual value impact coefficient thresholds of different power supply devices, and further evaluate and analyze their current values.

2. According to claim 1, an enterprise data asset evaluation system based on AI and data processing is characterized in that: The maintenance data recorded by the data recording module includes: the number of maintenance times, maintenance frequency, maintenance difficulty, material costs and maintenance worker costs for any maintenance; The daily operation status data of the power supply equipment collected by the data collection module includes: daily startup response time, startup current, number of faults and daily production capacity of the power supply equipment.

3. According to claim 2, an enterprise data asset evaluation system based on AI and data processing is characterized in that: The calculation process in the data evaluation module includes: The average hourly power consumption of the ath power supply equipment in the historical data recorded by the data recording module is used to establish the average hourly power consumption change curve of the ath power supply equipment in the enterprise production data assets since it was put into use. a (t); And through the formula Calculate the historical power consumption change coefficient r of the ath power supply equipment a ; Where a is any power supply device, i is any day in the history of the power supply device, n is the total number of days in the history of the power supply device, t1 is the first day since the ath power supply device was put into use, t2 is the last day in the history of the ath power supply device, dl ai is the power consumption of the ath power supply device on the i-th day in history, For all dl ai The average value, sj ai is the number of working hours on the i-th day in the historical use of the a-th power supply equipment.

4. The enterprise data asset evaluation system based on AI and data processing according to claim 3 is characterized in that: The calculation process in the data evaluation module also includes: By formula Calculate the failure influence coefficient s of the ath power supply equipment a ; Among them, jx a is the total maintenance time of the ath power supply equipment, jx b For jx a The standard value of pl a is the maintenance frequency of the ath power supply equipment, pl y is the preset inspection frequency, m is any inspection, w is the total number of inspections, cl am The material cost for the mth maintenance of the ath power supply equipment, rg am The labor cost for the mth maintenance of the ath power supply equipment, nd m is the difficulty influence coefficient of the mth maintenance, wx y The preset total maintenance cost.

5. The enterprise data asset evaluation system based on AI and data processing according to claim 4 is characterized in that: The process of performing a preliminary evaluation on the value of the power supply equipment in the data evaluation module includes: By calculating the failure influence coefficient s of the ath power supply equipment a and the preset fault influence coefficient threshold s 01 Compare and calculate the power consumption change coefficient r of the ath power supply equipment from the time of use to the i-th day a The preset power consumption change coefficient threshold r 01 Make a comparison; If a ≥s 01 And r a ≥r 01 , the system determines that the energy consumption of the ath power supply equipment is high and the depreciation is serious, and determines that the residual value of the power supply equipment is low; If a ≥s 01 And r a <r 01 , the system determines that the energy consumption of the ath power supply equipment is low but the depreciation is serious, and determines that the residual value of the power supply equipment is medium; If a 01 And r a ≥r 01 , the system determines that the energy consumption of the ath power supply equipment is high but the depreciation is not serious, and determines that the residual value of the power supply equipment is medium;​ If a 01 And r a <r 01 , the system determines that the energy consumption of the ath power supply equipment is low and the depreciation is not serious, and determines that the residual value of the power supply equipment is good.​ 6. The enterprise data asset evaluation system based on AI and data processing according to claim 5, characterized in that: The process of data calculation module analysis includes: By formula Calculate the residual value impact coefficient μ of the ath power supply equipment in the enterprise's production data assets when obtaining a data evaluation a ; Among them, kjx a is the startup response time of the ath power supply device on the day of a data evaluation, kjx y is the preset power-on response time, kl a kl is the startup current of the ath power supply device on the day of a data evaluation, y is the preset startup current, gz a is the number of failures of the ath power supply equipment on the day of a data evaluation, gz y is the preset number of failures, gz b For gz a The standard value, f t (x) is a defined function, if f t (x)>1, then let f t (x) = x, if f t (x)≤1, then let f t (x) = 1, cn a is the capacity of the ath power supply equipment on that day during a data evaluation, cn y is the preset capacity, jg a is the purchase price of the ath power supply equipment on the day of a data evaluation, jg y is the actual purchase price of the ath power supply equipment, jg ai is the purchase price of the ath power supply equipment on the i-th day of its historical use, For all jg ai The average value, gz a is the working time of the ath power supply equipment on the day of a data evaluation, gz y The preset working time.

7. The enterprise data asset evaluation system based on AI and data processing according to claim 6 is characterized in that: The analysis process of the value analysis module includes: By calculating the residual value impact coefficient μ of the ath power supply equipment in the enterprise's production data assets during a data evaluation a and the preset residual value impact coefficient threshold μ 01 Make a comparison; If μ a <μ 01 , the system determines that the residual value of the ath power supply equipment is high; If μ a ≥μ 01 , the system determines that the residual value of the ath power supply equipment is low and needs to be replaced.

8. A method for evaluating enterprise data assets based on AI and data processing, the method adopts an enterprise data asset evaluation system based on AI and data processing as described in claims 1-7, characterized in that: The method comprises: S1: The data recording module uses AI to record the power consumption data and maintenance data of the power supply equipment in the enterprise's production data assets since it was put into use; S2: The data collection module collects the daily operation status data of the power supply equipment in the enterprise's production data assets and the daily real-time price of the same power supply equipment; S3: The data evaluation module is combined with the data recorded in the data recording module to calculate the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise's production data assets, and a preliminary evaluation is performed on the value of different power supply equipment; S4: The data calculation module combines the historical power consumption change coefficient and failure impact coefficient of different power supply equipment in the enterprise production data assets with the data collected by the data collection module to calculate the residual value impact coefficient of different power supply equipment; S5: Analyze the status of different power supply devices through the value analysis module in combination with the residual value impact coefficient threshold of different power supply devices, and further evaluate and analyze their current values.

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