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CN-122022899-A - Enterprise dynamic cost accounting method based on Internet of things perception driving

CN122022899ACN 122022899 ACN122022899 ACN 122022899ACN-122022899-A

Abstract

The invention belongs to the technical field of Internet of things and enterprise cost accounting, and particularly relates to an enterprise dynamic cost accounting method based on Internet of things perception driving, which comprises the steps of collecting multidimensional original data sources in real time through an Internet of things perception node and transmitting the multidimensional original data sources to an edge computing gateway, preprocessing the original data sources, extracting 6 types of cost dynamic factors, constructing a judging matrix by adopting a analytic hierarchy process, carrying out consistency test and computing to obtain final weight values of the cost dynamic factors, accounting 6 types of sub-item costs based on the final weight values, summarizing to obtain real-time dynamic total cost of an enterprise, computing cost deviation rate and dynamically adjusting related parameters according to a threshold value judging result, computing fitting degree, judging accounting effectiveness according to a fitting degree result, and circularly optimizing until the result is up to standard if the result is not qualified. The invention builds a full-flow closed-loop accounting system, improves the instantaneity, the accuracy and the intellectualization level of cost accounting, reduces the cost control difficulty and provides reliable support for enterprise cost control decisions.

Inventors

  • CHEN JIUHUI
  • LONG MIN
  • CHEN SHANQIAO

Assignees

  • 成都锦城学院

Dates

Publication Date
20260512
Application Date
20260415

Claims (9)

  1. 1. The enterprise dynamic cost accounting method based on the perception driving of the Internet of things is characterized by comprising the following steps of: s1, acquiring a multi-dimensional original data source required by enterprise dynamic cost accounting in real time through each sensing node of the Internet of things, and transmitting the multi-dimensional original data source to an edge computing gateway in real time; S2, after receiving the multidimensional original data source transmitted by each sensing node of the Internet of things, the edge computing gateway performs preprocessing and then performs classified storage according to the three-dimensional index rule of cost category-acquisition time-sensing node ID; s3, extracting cost dynamic factors corresponding to the dimension data, and carrying out weight distribution on the cost dynamic factors to obtain final weight values of the cost dynamic factors; s4, carrying out dynamic allocation accounting on the total cost of the enterprise based on the final weight value of each cost dynamic factor, carrying out item-by-item accounting on raw material cost, equipment loss cost, energy consumption cost, storage cost, manpower cost and logistics cost respectively, and summarizing each item cost to obtain the real-time dynamic total cost of the enterprise; S5, comparing the real-time dynamic total cost of the enterprise with a preset standard cost threshold value, and calculating whether the cost deviation rate is less than or equal to the preset deviation threshold value, if so, maintaining the current cost accounting parameters unchanged, otherwise, adjusting the weight value of the corresponding cost dynamic factor and the parameters in the itemized cost accounting formula until the absolute value of the cost deviation rate is less than or equal to the preset deviation threshold value; And S6, selecting recent historical perception data of an enterprise as a verification sample, calculating the fitting degree of the accounting result and the actual cost data, if the fitting degree is more than or equal to a preset threshold value, confirming that the cost accounting result is effective, if the fitting degree is less than the preset threshold value, re-executing the step S2, optimizing the standardized conversion parameters and the cost cause identification rule, and repeating the steps S3 to S5 until the fitting degree is more than or equal to the preset threshold value.
  2. 2. The enterprise dynamic cost accounting method based on the internet of things perception driving of claim 1, wherein the specific process of step S3 is as follows: S31, based on the preprocessed multidimensional data source, matching and extracting core cost dynamic factors corresponding to each dimensional data, and defining specific corresponding relations of 6 types of cost dynamic factors, wherein the specific corresponding relations comprise raw material storage and loss data corresponding to raw material consumption dynamic factors, equipment operation duration data corresponding to equipment loss dynamic factors, energy consumption dynamic factors corresponding to production energy consumption data, storage dynamic factors corresponding to storage cargo turnover rate data, manual operation man-hour data corresponding to manual dynamic factors, logistics transportation mileage corresponding to logistics dynamic factors and time consumption data; And S32, weight distribution is carried out.
  3. 3. The enterprise dynamic cost accounting method based on the internet of things perception driving according to claim 2, wherein the specific process of step S32 is as follows: s321, constructing a cost trend judgment matrix, namely constructing an n-order cost trend judgment matrix A, n=6 and corresponding 6 types of cost trends by taking enterprise historical cost accounting data and industry standard data as references; S322, carrying out consistency test, and verifying the rationality of the judgment matrix; and S323, calculating weight values, namely solving the weight values of the cost factors after the consistency test is passed.
  4. 4. The method for accounting the dynamic cost of the enterprise based on the perception driving of the internet of things according to claim 3, wherein the specific calculation process of step S323 is as follows: s3231, calculating the sum of each column of the judgment matrix, and S j : ;i,j=1,2,...,n; Wherein n is the number of cost causes, i is the row number of the judgment matrix, j is the column number of the judgment matrix, a ij is the element in the judgment matrix A, and represents the importance scale of the ith cause relative to the jth cause; S3232, carrying out normalization processing on the judgment matrix to obtain a normalized matrix B, wherein the elements in the B are B ij : ;i,j=1,2,...,n; s3233, calculating a weight value w i of each cost factor: ;i,j=1,2,...,n; and S3234, carrying out normalization processing on the weight values to obtain final weight values w i : 。
  5. 5. The enterprise dynamic cost accounting method based on the internet of things perception driving of claim 4, wherein the specific process of step S4 is as follows: S41, final weight w i of each cost factor comprises equipment loss factor weight w 1 , energy consumption factor weight w 2 , warehouse factor weight w 3 , manpower factor weight w 4 and logistics factor weight w 5 , wherein raw material consumption factor has no additional weight value and is directly calculated according to actual consumption; S42, performing sub-term cost accounting, wherein the sub-term cost accounting comprises raw material cost C mat , equipment loss cost C eqp , energy consumption cost C ene , storage cost C war , labor cost C lab and logistics cost C log ; S43, carrying out enterprise real-time dynamic total cost accounting, namely summarizing the 6 types of sub-item cost to obtain enterprise real-time dynamic total cost C total , wherein the formula is as follows: C total =C mat +C eqp +C ene +C war +C lab +C log 。
  6. 6. the method for accounting the dynamic cost of the enterprise based on the perception driving of the internet of things according to claim 5, wherein the specific process of the step S42 is as follows: s421, raw material cost accounting is as follows: C mat =Q in ×P mat -Q loss ×P mat ; Wherein, C mat is the cost of raw materials, Q in is the warehouse-in quantity of raw materials, Q loss is the loss quantity of raw materials, and P mat is the unit purchase price of raw materials; S422, accounting equipment loss cost, wherein the formula is as follows: C eqp =T eqp ×P eqp-loss ×w 1 *; Wherein, C eqp is the equipment loss cost, T eqp is the equipment operation time, P eqp-loss is the equipment unit time loss amount, and w 1 is the equipment loss factor weight value; S423, energy consumption cost accounting, wherein the formula is as follows: C ene =E prod ×P ene ×w 2 *; Wherein, C ene is energy consumption cost, E prod is energy consumption data, P ene is energy consumption unit price, and w 2 is energy consumption factor final weight value; S424, accounting the storage cost, wherein the formula is as follows: C war =R war ×P war ×w 3 *; Wherein, C war is the storage cost, R war is the turnover rate of storage goods, P war is the storage unit cost, and w 3 is the final weight value of storage factor; S425, accounting the labor cost, wherein the formula is as follows: C lab =T lab ×P lab ×w 4 *; Wherein, C lab is the cost of man-power, T lab is man-power working hours, P lab is the compensation of man-power unit working hours, and w 4 is the final weight value of man-power factor; S426, accounting the stream cost, wherein the formula is as follows: C log =(L log ×P log-dis +T log ×P log-time )×w 5 *; Wherein, C log is logistics cost, L log is logistics transportation mileage, P log-dis is unit mileage transportation fee, T log is logistics transportation time consumption, P log-time is unit duration service fee, and w 5 is final weight value of logistics cause.
  7. 7. The enterprise dynamic cost accounting method based on the internet of things perception driving of claim 5, wherein the specific process of step S5 is as follows: s51, confirming deviation basic data, wherein the deviation basic data comprises enterprise real-time dynamic total cost C total , a preset standard cost threshold C std and a preset deviation threshold delta; s52, calculating a cost deviation rate, wherein the formula is as follows: η=(C total -C std )/C std ×100%; Wherein η is a cost deviation rate, C total is a real-time dynamic total cost of the enterprise obtained in the step S4, and C std is a preset standard cost threshold; S53, carrying out deviation threshold judgment and case division processing: if the eta delta is less than or equal to delta, maintaining the current cost accounting parameters unchanged; If |eta| > delta, namely the absolute value of the cost deviation rate exceeds a preset deviation threshold value, starting a dynamic adjustment flow.
  8. 8. The enterprise dynamic cost accounting method based on the internet of things perception driving of claim 7, wherein if |η| > δ, the specific process of starting dynamic adjustment is as follows: s531, calculating a deviation value delta C k of each sub-term cost; s532, selecting the first 3 types of sub-term cost with the maximum |delta C k | and the corresponding cost factor is the core cost factor generated by deviation; s533, combining the latest data acquired by the sensing nodes of the Internet of things in real time, and carrying out targeted adjustment on the final weight value corresponding to the core cost dynamic factor and the related parameters in the corresponding subentry cost accounting formula; And S534, after the parameter adjustment is completed, re-executing the sub-term cost accounting of the step S4 and the enterprise real-time dynamic total cost summary to obtain a new real-time dynamic total cost C total ', repeating the steps S532-S533, calculating a new cost deviation rate eta ' and carrying out threshold judgment until the eta ' is less than or equal to delta, and stopping adjustment.
  9. 9. The enterprise dynamic cost accounting method based on the internet of things perception driving of claim 1, wherein the specific process of step S6 is as follows: s61, randomly dividing historical perception data of an enterprise for 3 months and corresponding actual cost data into k equal-scale subsets, wherein the value of k is 5-10, and recording the k as a subset D 1 ,D 2 ,...,D k ; S62, sequentially selecting each subset as a verification set, using the remaining k-1 subsets as training sets, namely, using D t as the verification set in the t-th verification, substituting the historical perception data in the verification set D t into the cost accounting flow adjusted in the step S5, and calculating to obtain the accounting cost data of the verification set I is the sample number in the verification set; S63, repeating the step S62, and summarizing all accounting cost data obtained by k times of verification Matching and aligning with the corresponding actual cost data y i ; S64, adopting a determination coefficient R 2 as a fitting degree evaluation index, and quantifying the fitting degree of the accounting result and the actual cost data; s65, judging the fitting degree, and processing according to the situation: If R 2 is more than or equal to 95%, confirming that the dynamic cost accounting result adjusted in the step S5 is effective, and according to the fixed format of the real-time accounting value-history contrast value-deviation analysis report, finishing the effective accounting result to finish final enterprise dynamic cost accounting; if R 2 is less than 95%, starting a cyclic optimization flow, namely returning to the step S2, preprocessing the original data source acquired by the sensing node of the Internet of things again, optimizing standardized conversion parameters and cost cause identification rules, eliminating invalid data interference, and after optimization is finished, sequentially repeating the steps S3-S5 until the verification flow of the step S6 is executed again, so that R 2 is more than or equal to 95%, and finishing final enterprise dynamic cost accounting.

Description

Enterprise dynamic cost accounting method based on Internet of things perception driving Technical Field The invention belongs to the technical field of Internet of things technology, data processing and enterprise cost management, and particularly relates to an enterprise dynamic cost accounting method based on Internet of things perception driving. Background In the enterprise production and management process, cost accounting is a core foundation for cost control and decision making, and accounting accuracy and instantaneity of the cost accounting directly influence resource allocation efficiency, profit level and market competitiveness of an enterprise. Currently, enterprise cost accounting mainly relies on traditional manual statistics and a semi-automatic system auxiliary mode, static or semi-dynamic accounting is performed by combining historical data, and multi-dimensional, high-frequency and dynamic cost management and control requirements of modern enterprises are difficult to adapt, so that a plurality of technical defects exist. Firstly, the existing cost accounting method has low efficiency and larger error in the data acquisition link. In the traditional mode, raw material consumption, equipment operation loss, production energy consumption and other multidimensional cost data depend on manual input and manual statistics, so that a large amount of labor cost is consumed, hysteresis exists in data acquisition, dynamic cost change in the production and operation process cannot be captured in real time, meanwhile, the problems of neglected recording, misrecording and the like easily occur in manual input, basic data of cost accounting are distorted, and accuracy of accounting results is further affected. Secondly, cost causes are not accurately identified, and weight distribution lacks scientificity. In the prior art, most enterprises simply divide a few types of cost factors, the corresponding relation between each dimension data and the cost factors is not defined by the production practice of the enterprises, the weight distribution is dependent on experience judgment, quantitative analysis and industry standard support are lacked, and even if a hierarchical analysis method is adopted in part, a standardized judgment matrix construction and consistency inspection flow is not formed, so that the weight distribution deviation is larger, the cost allocation is unreasonable, and the actual composition of various costs cannot be truly reflected. Again, the dynamics of the cost accounting is insufficient and the bias adjustment lags. Most of the existing cost accounting is periodic accounting, dynamic allocation accounting cannot be carried out based on real-time data, when abnormal deviation occurs to cost, core cost causes generated by the deviation are difficult to quickly locate, the adjustment process lacks systematicness, only single parameters can be finely adjusted, the cooperative adjustment of cost cause weights and sub-term accounting parameters cannot be realized, the deviation is difficult to quickly correct, and the timeliness of cost management and control is affected. Finally, verification mechanism of the accounting result is imperfect. In the prior art, simple historical data comparison is adopted for verification of cost accounting results, a scientific cross verification method is not adopted, the fitting degree of the accounting results and actual cost data cannot be effectively quantified, the problem that the accounting results are effective but are disjointed with actual production easily occurs, and when verification is unqualified, a clear circulation optimization flow is lacking, key links such as data preprocessing and cost cause identification are difficult to be optimized in a targeted mode, so that the accounting accuracy is difficult to improve. In addition, with the rapid development of the internet of things technology, various sensing devices are widely applied to various links of enterprise production, storage, logistics and the like, and technical support is provided for real-time acquisition of multi-dimensional cost data, but a closed loop system of sensing acquisition, data preprocessing, dynamic factor identification, dynamic accounting, deviation adjustment and result verification is not formed at present, the advantages of the internet of things technology are not fully exerted, and the practical requirements of enterprise refinement and dynamic cost management and control cannot be met. Therefore, developing an enterprise dynamic cost accounting method which is based on the perception driving of the internet of things, accurate in accounting, real-time and efficient, dynamically adjustable and complete in verification becomes a technical problem to be solved currently. Disclosure of Invention The invention aims to provide an enterprise dynamic cost accounting method based on the perception drive of the Internet of things, which is used for solving the tech