CN117217945B - Enterprise financial flow management platform based on blockchain - Google Patents
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Abstract
The invention belongs to the technical field of financial flow management, and discloses an enterprise financial flow management platform based on a block chain; comprising the following steps: collecting stream data, commodity historical sales data, enterprise public opinion data and enterprise assessment data; verifying an error evaluation value of the running water data based on commodity historical sales data, enterprise public opinion data and enterprise evaluation data, and marking the running water data as qualified running water data when the error evaluation value is smaller than or equal to an error evaluation threshold value; integrating the qualified stream data into block data, and uploading the block data to a block chain; collecting calculation power and idle resource percentage of the nodes, calculating and processing evaluation values, and distributing block chains to the nodes; setting a private key and a public key for an enterprise; and finishing the operation of accessing the block data between enterprises based on the public key and the private key. According to the invention, multiple parameters are collected for data analysis, so that the possibility of financial falsification is effectively reduced, and the safety and reliability of financial data in a block chain are improved.
Description
Technical Field
The invention relates to the technical field of financial flow management, in particular to an enterprise financial flow management platform based on a blockchain.
Background
The blockchain is a traceable value internet, is a decentralized distributed account book technology, and can record the process of transferring a resource transaction. Blockchain technology allows parties to commonly maintain an ever-increasing distributed data record, protecting content and timing through cryptographic techniques, making it difficult for any party to tamper with, repudiate, and counterfeite.
Currently, the concept of blockchain technique de-centering is slowly transitioning to multi-centering. Based on a multi-centralization architecture, transaction rules and conditions are defined through intelligent contract technology, each transaction data between companies is uplink and stored in a plurality of nodes, and each data is traceable and cannot be tampered.
The Chinese patent of the grant bulletin number CN113205415B discloses a financial flow automation method and system based on RPA and blockchain technology, intelligent contracts are used, trigger conditions and rules are formulated through pre-coding, then the intelligent contracts are issued to blockchain nodes, the automation of the financial flow is effectively improved, but financial staff still need to input relevant financial data in specific nodes, whether the manually uploaded financial data is fake or not cannot be judged, and if the financial data is fake, the safety and the credibility of the blockchain cannot be guaranteed.
In view of the above, the present invention proposes a blockchain-based enterprise financial process management platform to solve the above-mentioned problems.
Disclosure of Invention
In order to overcome the above-described deficiencies of the prior art, embodiments of the present invention provide a blockchain-based enterprise financial process management platform.
In order to achieve the above purpose, the present invention provides the following technical solutions: an enterprise financial process management platform based on blockchain, comprising:
the data collection module is used for collecting stream data, commodity historical sales data, enterprise public opinion data and enterprise evaluation data;
the block chain verification module is used for verifying error evaluation values of the running water data based on commodity historical sales data, enterprise public opinion data and enterprise evaluation data, and marking the running water data when the error evaluation values are smaller than or equal to a preset error evaluation threshold value as qualified running water data;
the block chain uploading module is used for uploading the block data to the block chain after integrating the qualified stream data into the block data;
the block chain distribution module is used for collecting the computing power and the idle resource percentage of the nodes; calculating a processing evaluation value based on the calculation power and the idle resource percentage, and distributing the blockchain to the nodes based on the processing evaluation value;
The block chain access module is used for setting a private key and a public key for an enterprise; the public key is used for allowing enterprises to access the block data; the private key is used for marking the enterprise digital tag; and finishing the operation of accessing the block data between enterprises based on the public key and the private key.
Further, the pipeline data comprises expense pipeline data and income pipeline data; the expense flow data comprises transaction amount, transaction flow direction, transaction time and transaction label of each expense of the enterprise; the income flow data comprises the transaction amount of each income of the enterprise, the transaction flow direction, the transaction time and the transaction label;
the transaction amount is a real transaction amount, the transaction flow direction is the flowing direction of the transaction amount from the outflow enterprise A to the inflow enterprise B, the transaction time is the transaction time for completing the transfer of the transaction amount, and the transaction label is the commodity type X and the commodity quantity Y which are completed based on the transaction amount;
the commodity historical sales data is the transaction amount and the transaction label of the commodity of the same type as the commodity type X; the enterprise public opinion data is a total public opinion evaluation value of enterprises within a preset fixed time range before the transaction time.
Further, the total public opinion evaluation value is obtained as follows:
setting a keyword set; the keyword set comprises a plurality of keywords which influence the operation of enterprises; setting a public opinion degree evaluation value YL for the keyword;
Setting a keyword triggering public opinion mechanism, and judging to trigger public opinion when the keyword quantity increasing speed exceeds a preset keyword quantity increasing speed threshold value within a preset fixed time range before the transaction time;
extracting keywords in the online text in real time by using an NLP technology within a preset fixed time range before the transaction time, wherein the increase speed of the number of the keywords is the number of the keywords in the online text extracted by the NLP technology divided by the time length of the keywords; when the keyword triggers public opinion, the keyword passes through the formulaCalculating a total public opinion evaluation value YLPG; in->For +.>Keywords (e.g. Japan)>Is the total number of keywords in the keyword set; />Is->Public opinion evaluation values of the individual keywords; />Is->The number of keywords increases at a rate;
when public opinion is not triggered, YLPG is set to 1.
Further, the enterprise evaluation data is the evaluation ranking percentage of the enterprise in all enterprises of the same type;
the evaluation ranking percentage is obtained by weighted summation of the enterprise scale ranking percentage and the enterprise profit ranking percentage; the enterprise scale ranking percentage weight is 0.4, and the enterprise profit ranking percentage weight is 0.6;
the enterprise-scale ranking percentage is the enterprise-scale ranking of enterprise a divided by the total number of enterprises participating in the enterprise-scale ranking; the enterprise profit ranking percentage is calculated in the same manner as the enterprise scale ranking percentage.
Further, training a machine learning model for calculating transaction amount in real time according to the transaction tag based on commodity historical sales data;
the training mode of the machine learning model is as follows:
taking commodity historical sales data as input of a machine learning model, wherein in the commodity historical sales data, transaction amount is taken as a machine learning label of a transaction label; the machine learning model takes transaction amount as output; taking transaction amount corresponding to the transaction tag in the real-time stream data as a prediction target and taking a minimized machine learning model loss function value as a training target; stopping training when the loss function value of the first machine learning model is smaller than or equal to a preset first target loss value;
the machine learning model loss function is a mean square error; mean square error is determined by multiplying the loss functionTraining a model for the minimum, wherein mse is a loss function value in a loss function, and i is a commodity historical sales data set number; u is the historical sales data group number of the commodity; />Machine learning tag corresponding to the i-th group of transaction tags,>calculating transaction amount for the ith group of transaction tags in real time;
and marking the transaction amount calculated by the machine learning model as a secondary transaction amount.
Further, calculating a primary transaction amount based on the secondary transaction amount and the enterprise evaluation data;
let the secondary transaction amount beEnterprise evaluation data is K;
when the secondary transaction amount is the paid transaction amount, the primary transaction amountThe calculation formula of (2) is as follows:
；
when the secondary transaction amount is the income transaction amountAt the time of first-level transaction amountThe calculation formula of (2) is as follows:
。
further, the transaction amount obtained in real time by stream data is set as，/>Comprises->And->The method comprises the steps of carrying out a first treatment on the surface of the Setting a transaction amount error evaluation value +.>；
Comparing the error evaluation value with a preset error evaluation threshold value; when the error evaluation value is smaller than or equal to a preset error evaluation threshold value, marking the running water data as qualified running water data, and sending the qualified running water data to a block chain uploading module; when the error evaluation value is larger than a preset error evaluation threshold value, marking the running water data as false running water data and refusing, and blading enterprises to submit correct running water data within a specified time range;
when the enterprise does not submit qualified stream data within the specified time range or the number of times of submitting false stream data is larger than the preset submission number threshold, uploading the false stream data to the blockchain, and marking the enterprise as a dishonest enterprise.
Further, the block creation time is used as a time stamp of the block; generating random numbers by taking the time stamps as seeds for generating the random numbers, and adding the random numbers into the blocks;
the chunk includes a timestamp of the chunk, a hash value of a previous chunk, and a transaction record.
Uploading the transaction amount, the transaction flow direction, the transaction time and the transaction tag to a transaction record of the block; and carrying out hash calculation on all data in the block, wherein the obtained numerical value is used as the hash value of the block.
All data in a block is marked as block data.
Further, the calculation power Q and the idle resource percentage Z of the node are collected, and a processing evaluation value is set；
And uploading the new block to the node which does not reach the processing evaluation threshold value when the node processing evaluation value is larger than the preset processing evaluation threshold value.
Further, the process that the enterprise B needs to access the block data of the enterprise a includes:
setting a pair of public key A1 and private key A2 for enterprise A and setting a pair of public key B1 and private key B2 for enterprise B; private key A2 is used to label the block data of enterprise a with a digital tag unique to enterprise a.
Enterprise a will encrypt the block data using public key B1, and send to enterprise B;
after the enterprise B receives the block data, the block data is decrypted through the private key B2, the digital label is verified by using the public key A1, and when the verification is true, the access of the block chain block data is completed.
A blockchain-based enterprise financial process management method, the method comprising:
collecting stream data, commodity historical sales data, enterprise public opinion data and enterprise assessment data;
verifying an error evaluation value of the running water data based on commodity historical sales data, enterprise public opinion data and enterprise evaluation data, and marking the running water data as qualified running water data when the error evaluation value is smaller than or equal to a preset error evaluation threshold value;
after integrating the qualified stream data into block data, uploading the block data to a block chain;
collecting calculation power and idle resource percentage of the node; calculating a processing evaluation value based on the calculation power and the idle resource percentage, and distributing the blockchain to the nodes based on the processing evaluation value;
setting a private key and a public key for an enterprise; the public key is used for allowing enterprises to access the block data; the private key is used for marking the enterprise digital tag; and finishing the operation of accessing the block data between enterprises based on the public key and the private key.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call; the processor executes a blockchain-based enterprise financial process management method as described above by invoking a computer program stored in the memory.
A computer readable storage medium storing instructions that when executed on a computer cause the computer to perform a blockchain-based enterprise financial process management method as described above.
The block chain-based enterprise financial flow management platform has the technical effects and advantages that: collecting stream data, commodity historical sales data, enterprise public opinion data and enterprise assessment data; verifying an error evaluation value of the running water data based on commodity historical sales data, enterprise public opinion data and enterprise evaluation data, and marking the running water data as qualified running water data when the error evaluation value is smaller than or equal to a preset error evaluation threshold value; after integrating the qualified stream data into block data, uploading the block data to a block chain; collecting calculation power and idle resource percentage of the node; calculating a processing evaluation value based on the calculation power and the idle resource percentage, and distributing the blockchain to the nodes based on the processing evaluation value; setting a private key and a public key for an enterprise; the public key is used for allowing enterprises to access the block data; the private key is used for marking the enterprise digital tag; and finishing the operation of accessing the block data between enterprises based on the public key and the private key. According to the invention, multiple parameters are collected for data analysis, so that the possibility of financial falsification is effectively reduced, great benefits are provided for the improvement of inter-enterprise trust and the benign competition cycle among industries, and the safety and reliability of financial data in a blockchain are greatly improved.
Drawings
FIG. 1 is a schematic diagram of a block chain based enterprise financial process management platform system in accordance with the present invention;
FIG. 2 is a block chain schematic diagram of the present invention;
FIG. 3 is a schematic diagram of an electronic device according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a computer-readable storage medium according to one embodiment of the present application;
FIG. 5 is a flow diagram of accessing a blockchain provided by an embodiment of the present application;
FIG. 6 is another flow diagram of accessing a blockchain provided by an embodiment of the present application;
FIG. 7 is a schematic diagram of a block chain based enterprise financial process management platform method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the enterprise financial process management platform based on the blockchain in this embodiment includes a data collection module, a blockchain verification module, a blockchain uploading module, a blockchain allocation module and a blockchain access module. The modules are connected through a wired and/or wireless network.
The data collection module is used for collecting stream data, commodity historical sales data, enterprise public opinion data and enterprise evaluation data.
The flow data comprises expenditure flow data and income flow data;
the expense flow data comprises transaction amount, transaction flow direction, transaction time and transaction label of each expense of the enterprise;
the income flow data comprises the transaction amount of each income of the enterprise, the transaction flow direction, the transaction time and the transaction label;
the transaction amount is a real transaction amount, the transaction flow direction is the flowing direction of the transaction amount from the outflow enterprise A to the inflow enterprise B, the transaction time is the transaction time for completing transfer of the transaction amount, and the transaction label is the commodity type X and the commodity quantity Y which are completed based on the transaction amount. The transaction amount, the transaction flow direction and the transaction time are acquired by a bank transaction data server; the commodity type X in the transaction tag is marked by enterprise purchasing and sales personnel, and the commodity quantity Y in the transaction tag is obtained for an enterprise warehouse list;
it can be understood that the commodity types of enterprises are several, and the commodity types can be purchased raw materials or sold finished products; the commodity type X of the enterprise is taken as an example, and other commodity types are the same, and will not be described in detail.
The commodity historical sales data is transaction amount and transaction label of commodities which are the same as the commodity type X in other enterprises; the method is characterized in that the method is obtained by summarizing the databases of the online sales platform and the offline sales platform.
The enterprise public opinion data is a total public opinion evaluation value of enterprises within a preset fixed time range before transaction time;
the total public opinion evaluation value is obtained in the following manner:
setting a keyword set; the keyword set comprises a plurality of keywords which influence the operation of enterprises; setting a public opinion degree evaluation value YL for the keyword; the public opinion degree evaluation value YL is a rational number and reflects the positive and negative face degree of public opinion, and the larger the public opinion degree evaluation value YL is, the better the positive evaluation of enterprises is, and the opposite is true; illustratively, when the keyword is a word describing advantages such as "good", "reliable", the public opinion evaluation value YL is set to 5; setting the public opinion evaluation value YL to be-5 when the keyword is a word describing defects such as poor quality;
setting a keyword triggering public opinion mechanism, and judging to trigger public opinion when the keyword quantity increasing speed exceeds a preset keyword quantity increasing speed threshold value within a preset fixed time range before the transaction time;
The keyword quantity increasing speed is used as a reference standard for judging and triggering a public opinion mechanism, because public opinion tends to have sudden and explosive increasing trend, the public opinion data is referred to so that the enterprise evaluation can be more accurate, and when the flow data is uploaded to the block chain in the follow-up process, the evaluation is more accurate, and the evaluation degree is more stable.
Extracting keywords in the online text in real time by using an NLP technology within a preset fixed time range before the transaction time, wherein the increase speed of the number of the keywords is the number of the keywords in the online text extracted by the NLP technology divided by the time length of the keywords; when the keyword triggers public opinion, the keyword passes through the formulaCalculating a total public opinion evaluation value YLPG; in->Is the +.>Keywords (e.g. Japan)>Is the total number of keywords in the keyword set; />Is->Public opinion evaluation values of the individual keywords; />Is->The number of individual keywords increases at a rate.
The larger the public opinion rating value is, the larger the keyword quantity increasing speed is, the larger the total public opinion rating value YLPG is, the better the enterprise operation condition is, the consistency and the appreciation of the society are obtained, the smaller the public opinion rating value is negative, the larger the keyword quantity increasing speed is, the smaller the total public opinion rating value YLPG is, and the enterprise is generally not good in the aspect of social public opinion.
The YLPG comprehensively considers the influence of the type of the keywords and the increasing speed of the number of the keywords on the operation of enterprises, so that the enterprise can be evaluated in time when a large number of public opinion bursts, such as the enterprises are endangered to be closed in the public opinion, and fund chains are broken, but a large amount of running water data is generated according to old, and the possibility of fake financial data is higher, and although the blockchain has higher safety and tamper resistance, if the fictional financial data is added into the blockchain, the conservation meaning is not great, the blockchain link point resources are occupied, and other enterprises can also misjudge when inquiring the running water data of the enterprises in the blockchain, so that the benign competition among the enterprises is influenced.
When public opinion is not triggered, setting YLPG to 1;
the online text refers to text contents such as articles, news, research reports and comments which are retrieved from website platforms such as enterprise official networks, information platforms and financial platforms which relate to enterprise public opinion in real time by using a text retrieval technology;
NLP (Natural Language Processing ) is a branch in the field of artificial intelligence, devoting to the ability of computers to understand, analyze, and process natural language. The method mainly relates to the technologies of speech recognition, natural language understanding, text generation, machine translation and the like, and aims to realize natural language communication between a computer and a person. Extracting keywords of text on line by using NLP technology is a conventional technical means in the art, and will not be described herein.
The enterprise evaluation data is the evaluation ranking percentage of the enterprise in all enterprises of the same type;
the evaluation ranking percentage is obtained by weighted summation of the enterprise scale ranking percentage and the enterprise profit ranking percentage; the enterprise scale ranking percentage weight is 0.4, and the enterprise profit ranking percentage weight is 0.6;
the enterprise scale ranking percentage is obtained by enterprise scale ranking, which is the enterprise scale ranking of enterprise A divided by the total number of enterprises participating in the enterprise scale ranking; if enterprise a ranks 10 th in the enterprise scale ranking, and the total number of enterprises participating in the enterprise scale ranking is 100, the enterprise scale ranking percentage of enterprise a is 10/100=10%;
the smaller the enterprise scale ranking percentage, the larger the enterprise scale; the enterprise profit ranking percentage is calculated in the same manner as the enterprise scale ranking percentage, and will not be described in detail.
Enterprise scale ranking and enterprise profit ranking include, but are not limited to, being obtained from industry research reports; industry research reports typically provide data on market share, revenue size, profit margin, etc. for each business, and rank and compare the businesses.
The enterprise evaluation data has significance for verifying the financial counterfeiting possibility of the enterprise, and due to the fact that the same type of commodities are always equal in price under the conventional technical means, it can be understood that when the enterprise scale and profit are larger, larger commodity pricing space and wider commodity pricing range are always possessed, and the enterprise evaluation data are brought into the reference range of blockchain verification, so that the practicability and the accuracy are realized for evaluation and automation of the financial data.
The block chain verification module verifies error evaluation values of the running water data based on commodity historical sales data, enterprise public opinion data and enterprise evaluation data;
training a machine learning model for calculating transaction amount in real time according to the transaction tag based on commodity historical sales data; the training mode of the machine learning model is as follows:
taking commodity historical sales data as input of a machine learning model, wherein in the commodity historical sales data, transaction amount is taken as a machine learning label of a transaction label; the machine learning model takes transaction amount as output; taking transaction amount corresponding to the transaction tag in the real-time stream data as a prediction target and taking a minimized machine learning model loss function value as a training target; and stopping training when the loss function value of the first machine learning model is smaller than or equal to a preset first target loss value.
The machine learning model loss function may be Mean Square Error (MSE) or Cross Entropy (CE);
illustratively, the mean square error (MSE), by applying a loss functionThe model is trained for the purpose of minimization, so that the machine learning model is better fitted with data, and the performance and accuracy of the model are improved; mse in the loss function is a loss function value, and i is a commodity historical sales data group number; u is the historical sales data group number of the commodity; / >Machine learning tag corresponding to the i-th group of transaction tags,>transaction amounts calculated in real time for the ith set of transaction tags.
The machine learning model can be any one of a double-flow convolutional neural network model or a 3D convolutional neural network model; other model parameters of the machine learning model, such as the depth of the network model, the number of neurons in each layer, the activation function used by the network model, the optimization of the loss function and the like, are realized through actual engineering, and are obtained after experimental tuning is continuously carried out.
Marking the transaction amount calculated by the machine learning model as a secondary transaction amount;
calculating a primary transaction amount based on the secondary transaction amount and the enterprise evaluation data;
the secondary transaction amount is obtained by machine learning of commodities of the same type as the commodity type X in other enterprises, is theoretically the average transaction amount of the market, but the secondary transaction amount is inaccurate in practical application, different enterprises have different scales and profit margins, the same commodity is always available for high-end enterprises, the high-end enterprises always have higher bargained right and can determine high prices, meanwhile, the market share is mastered to be more, and the raw materials are always acquired with lower purchase price, so that the primary transaction amount needs to be calculated to obtain more accurate transaction amount, and the accuracy of the blockchain financial data is greatly improved.
Let the secondary transaction amount beEnterprise evaluation data is K;
when the secondary transaction amount is the paid transaction amount, the primary transaction amountThe calculation formula of (2) is as follows:
when the secondary transaction amount is the revenue transaction amount, the primary transaction amountThe calculation formula of (2) is as follows:
let the transaction amount obtained in real time by stream data be，/>Comprises->And->The method comprises the steps of carrying out a first treatment on the surface of the Setting a transaction amount error evaluation value +.>The method comprises the steps of carrying out a first treatment on the surface of the The smaller privot means that the more accurate the collected flow data is, the less the probability of faking, and the calculation of privot fully considers the secondary transaction amount +.>And transaction amount->In order to avoid positive and negative effects, square is selected, and in order to prevent the difference value from being too large, so that the effect of the total public opinion evaluation value is reduced by adopting a tertiary root number, and in order to make the weight of the error evaluation value affecting the transaction amount more gentle when the total public opinion evaluation value is positive, an ln logarithmic function is set so that the change of the error evaluation value is more uniform and smooth, and meanwhile, the influence on the error evaluation value is 0 when no public opinion exists. The privot is selected to well evaluate the correctness of the flow data and the influence of public opinion on the business operation condition of enterprises, so that the possibility of financial falsification is greatly reduced.
Comparing the error evaluation value with a preset error evaluation threshold value; when the error evaluation value is smaller than or equal to a preset error evaluation threshold value, marking the running water data as qualified running water data, and sending the qualified running water data to a block chain uploading module; when the error evaluation value is larger than a preset error evaluation threshold, marking the running water data as false running water data and refusing, and blame enterprises to submit correct running water data within a specified time range, when the enterprises do not submit correct running water data within the specified time range or the number of times of submitting false running water data is larger than a preset number of times of submitting threshold, uploading the false running water data to a blockchain, marking the enterprises as dishonest enterprises, so that all enterprises on the blockchain can inquire the dishonest enterprises, and the transactions with the dishonest enterprises are reduced.
The blockchain uploading module uploads qualified pipeline data to the blockchain.
Taking the block creation time as a time stamp of the block; generating random numbers by taking the time stamps as seeds for generating the random numbers, and adding the random numbers into the blocks; the random number generated under the same time stamp is unique, so that the conflict and repetition of transaction time are avoided;
a block in a blockchain refers to a collection of a set of data records; the chunk includes a version of the chunk, a timestamp, a hash value of a previous chunk, and a transaction record.
Uploading the transaction amount, the transaction time, the transaction flow direction and the transaction tag to a transaction record of the block; and carrying out hash calculation on all data in the block, wherein the obtained numerical value is used as the hash value of the block.
All data in a block is marked as block data.
It will be appreciated that a block contains a plurality of transaction records, and that the blocks are concatenated into a chain to form a blockchain. The blockchain is schematically shown in fig. 2.
The blockchain assignment module assigns blocks to nodes. A node is a computer or device connected to a blockchain network. The blocks are stored on a blockchain to form an immutable chain structure. The node may store processing full blockchain data or partial data.
Collecting computing power Q and idle resource percentage Z of nodes, and setting a processing evaluation value；
The idle resources refer to resources which are not currently used or allocated to specific tasks in the node; computing power is the ability of a node to perform a computing task in a unit of time; the calculation force and the idle resource percentage can be obtained by a system monitoring tool of the node.
And uploading the new block to the node which does not reach the processing evaluation threshold value when the node processing evaluation value is larger than the preset processing evaluation threshold value. The integral safety of the block chain is effectively ensured, so that the financial data of the block chain is more accurate and stable.
The blockchain access module enables each enterprise to access the blockchain to view the blockdata.
Illustratively, as enterprise B needs to access the tile data of enterprise a;
setting a pair of public key A1 and private key A2 for enterprise A, wherein the private key is only stored by the enterprise, and the public key is used for broadcasting to other enterprises for acquisition; setting a pair of public key B1 and private key B2 for enterprise B; private key A2 is used to label the block data of enterprise a with an enterprise a unique digital tag that is a valid proof of the authenticity of the enterprise a's running water data.
Enterprise a will encrypt the block data using public key B1, and send to enterprise B;
After the enterprise B receives the block data, the block data is decrypted through the private key B2, the digital label is verified by using the public key A1, and when the verification is true, the access of the block data is completed once.
The public key and the private key are used for asymmetric encryption, so that financial data transmission is safer, data cannot be disturbed, and if malicious tampering is suffered, the fluctuation of hash values of the upper block and the lower block of the block chain is extremely large, and the hash values are found in time, so that safe and efficient knowledge of data of all parties among enterprises is facilitated. A schematic flow diagram of accessing a blockchain is shown in fig. 5 and 6.
In the embodiment 1, a plurality of parameters are collected for data analysis, so that the possibility of financial falsification is effectively reduced, the trust between enterprises is improved, the benign competition cycle between enterprises is greatly beneficial, and the safety and the reliability of financial data in a blockchain are greatly improved.
Example 2
Referring to fig. 7, the detailed description of the embodiment is not provided in the section of the detailed description of the embodiment, and a method for managing a financial process of an enterprise based on a blockchain is provided, which includes:
collecting stream data, commodity historical sales data, enterprise public opinion data and enterprise assessment data;
verifying an error evaluation value of the running water data based on commodity historical sales data, enterprise public opinion data and enterprise evaluation data, and marking the running water data as qualified running water data when the error evaluation value is smaller than or equal to a preset error evaluation threshold value;
After integrating the qualified stream data into block data, uploading the block data to a block chain;
collecting calculation power and idle resource percentage of the node; calculating a processing evaluation value based on the calculation power and the idle resource percentage, and distributing the blockchain to the nodes based on the processing evaluation value;
setting a private key and a public key for an enterprise; the public key is used for allowing enterprises to access the block data; the private key is used for marking the enterprise digital tag; and finishing the operation of accessing the block data between enterprises based on the public key and the private key.
Example 3
Referring now to FIG. 3, there is also provided a blockchain-based enterprise financial process management platform electronic device 500 in accordance with yet another aspect of the present application. The electronic device 500 may include one or more processors and one or more memories. Wherein the memory has stored therein computer readable code which, when executed by the one or more processors, can perform a blockchain-based enterprise financial flow management platform as described above.
The method or system according to embodiments of the present application may also be implemented by means of the architecture of the electronic device shown in fig. 3. As shown in fig. 3, the electronic device 500 may include a bus 501, one or more CPUs 502, a Read Only Memory (ROM) 503, a Random Access Memory (RAM) 504, a communication port 505 connected to a network, an input/output component 506, a hard disk 507, and the like. A storage device in electronic device 500, such as ROM503 or hard disk 507, may store a blockchain-based enterprise financial process management method provided herein. Further, the electronic device 500 may also include a user interface 508. Of course, the architecture shown in fig. 3 is merely exemplary, and one or more components of the electronic device shown in fig. 3 may be omitted as may be practical in implementing different devices.
Example 4
Referring to FIG. 4, a computer readable storage medium 600 according to one embodiment of the present application is shown. Computer readable storage medium 600 has stored thereon computer readable instructions. When the computer readable instructions are executed by the processor, a blockchain-based enterprise financial process management method according to embodiments of the present application described with reference to the above figures may be performed. Storage medium 600 includes, but is not limited to, for example, volatile memory and/or nonvolatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
In addition, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions executable by a processor to perform instructions corresponding to the method steps provided herein, such as: collecting stream data, commodity historical sales data, enterprise public opinion data and enterprise assessment data; verifying an error evaluation value of the running water data based on commodity historical sales data, enterprise public opinion data and enterprise evaluation data, and marking the running water data as qualified running water data when the error evaluation value is smaller than or equal to a preset error evaluation threshold value; after integrating the qualified stream data into block data, uploading the block data to a block chain; collecting calculation power and idle resource percentage of the node; calculating a processing evaluation value based on the calculation power and the idle resource percentage, and distributing the blockchain to the nodes based on the processing evaluation value; setting a private key and a public key for an enterprise; the public key is used for allowing enterprises to access the block data; the private key is used for marking the enterprise digital tag; and finishing the operation of accessing the block data between enterprises based on the public key and the private key. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU).
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed platform and method may be implemented in other manners. For example, the above-described platform embodiments are merely illustrative, e.g., the partitioning of the elements is merely a blockchain-based enterprise financial flow management platform, and may be implemented in other ways, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. An enterprise financial process management platform based on blockchain, comprising:
the data collection module is used for collecting stream data, commodity historical sales data, enterprise public opinion data and enterprise evaluation data;
the block chain verification module is used for verifying error evaluation values of the running water data based on commodity historical sales data, enterprise public opinion data and enterprise evaluation data, and marking the running water data when the error evaluation values are smaller than or equal to a preset error evaluation threshold value as qualified running water data;
the block chain uploading module is used for uploading the block data to the block chain after integrating the qualified stream data into the block data;
the block chain distribution module is used for collecting the computing power and the idle resource percentage of the nodes; calculating a processing evaluation value based on the calculation power and the idle resource percentage, and distributing the blockchain to the nodes based on the processing evaluation value;
the block chain access module is used for setting a private key and a public key for an enterprise; the public key is used for allowing enterprises to access the block data; the private key is used for marking the enterprise digital tag; completing the operation of accessing block data between enterprises based on the public key and the private key;
the flow data comprises expenditure flow data and income flow data; the expense flow data comprises transaction amount, transaction flow direction, transaction time and transaction label of each expense of the enterprise; the income flow data comprises the transaction amount of each income of the enterprise, the transaction flow direction, the transaction time and the transaction label;
Training a machine learning model for calculating transaction amount in real time according to the transaction tag based on commodity historical sales data;
the training mode of the machine learning model is as follows:
taking commodity historical sales data as input of a machine learning model, wherein in the commodity historical sales data, transaction amount is taken as a machine learning label of a transaction label; the machine learning model takes transaction amount as output; taking transaction amount corresponding to the transaction tag in the real-time stream data as a prediction target and taking a minimized machine learning model loss function value as a training target; stopping training when the loss function value of the first machine learning model is smaller than or equal to a preset first target loss value;
the machine learning model loss function is a mean square error; mean square error is obtained by applying a loss function mse=Training a model for the minimum, wherein mse is a loss function value in a loss function, and i is a commodity historical sales data set number; />Historical sales data sets for the commodity; />Machine learning tag corresponding to the i-th group of transaction tags,>calculating transaction amount for the ith group of transaction tags in real time;
marking the transaction amount calculated by the machine learning model as a secondary transaction amount;
Calculating a primary transaction amount based on the secondary transaction amount and the enterprise evaluation data; let the secondary transaction amount beEnterprise evaluation data is K;
when the secondary transaction amount is the paid transaction amount, the primary transaction amountThe calculation formula of (2) is as follows:
；
when the secondary transaction amount is the revenue transaction amount, the primary transaction amountThe calculation formula of (2) is as follows:
；
the transaction amount obtained by the stream data in real time is，/>Comprises->And->；
The enterprise public opinion data is a total public opinion evaluation value of enterprises within a preset fixed time range before transaction time;
the total public opinion evaluation value is obtained in the following manner:
setting a keyword set; the keyword set comprises a plurality of keywords which influence the operation of enterprises; setting a public opinion evaluation value YL for the keyword;
setting a keyword triggering public opinion mechanism, and judging to trigger public opinion when the keyword quantity increasing speed exceeds a preset keyword quantity increasing speed threshold value within a preset fixed time range before the transaction time;
extracting keywords in the online text in real time by using an NLP technology within a preset fixed time range before the transaction time, wherein the increase speed of the number of the keywords is the number of the keywords in the online text extracted by the NLP technology divided by the time length of the keywords; when the keyword triggers public opinion, the keyword is expressed by the formula YLPG= Calculating a total public opinion evaluation value YLPG; in->Is the +.>Keywords (e.g. Japan)>Is the total number of keywords in the keyword set; />Is->Public opinion evaluation values of the individual keywords; />Is->The number of keywords increases at a rate;
when public opinion is not triggered, setting YLPG to 1; the transaction label is a commodity type X and a commodity quantity Y which are completed based on the transaction amount;
setting a transaction amount error evaluation value privot =-ln(YLPG)；
Comparing the error evaluation value with a preset error evaluation threshold value; when the error evaluation value is smaller than or equal to a preset error evaluation threshold value, marking the running water data as qualified running water data, and sending the qualified running water data to a block chain uploading module; when the error evaluation value is larger than a preset error evaluation threshold value, marking the running water data as false running water data and refusing, and blading enterprises to submit correct running water data within a specified time range;
uploading the false stream data to a blockchain when the enterprise does not submit the qualified stream data within a specified time range or the number of times of submitting the false stream data is larger than a preset submission number threshold, and marking the enterprise as a dishonest enterprise;
taking the block creation time as a time stamp of the block; generating random numbers by taking the time stamps as seeds for generating the random numbers, and adding the random numbers into the blocks;
The block comprises a time stamp of the block, a hash value of the previous block and a transaction record;
uploading the transaction amount, the transaction flow direction, the transaction time and the transaction tag to a transaction record of the block; carrying out hash calculation on all data in the block, wherein the obtained numerical value is used as a hash value of the block;
marking all data in the block as block data;
collecting the calculation force Q of the node and the free resource percentage Z, and setting a processing evaluation value CL=arctan；
When the node processing evaluation value is larger than a preset processing evaluation threshold value, uploading the new block to the node which does not reach the processing evaluation threshold value;
the process of enterprise B accessing the tile data of enterprise a includes:
setting a pair of public key A1 and private key A2 for enterprise A and setting a pair of public key B1 and private key B2 for enterprise B; the private key A2 is used for marking the block data of the enterprise A with a digital tag unique to the enterprise A;
enterprise a will encrypt the block data using public key B1, and send to enterprise B;
after the enterprise B receives the block data, the block data is decrypted through the private key B2, the digital label is verified by using the public key A1, and when the verification is true, the access of the block chain is completed.
2. The blockchain-based business financial process management platform of claim 1, wherein the transaction amount is a real transaction amount, the transaction flow direction is a flow direction of the transaction amount from the outflow business a to the inflow business B, and the transaction time is a transaction time for completing transfer of the transaction amount; the commodity historical sales data is the transaction amount and the transaction tag of the commodity of the same type as the commodity type X.
3. The blockchain-based enterprise financial process management platform of claim 2, wherein the enterprise assessment data is an assessment ranking percentage of the enterprise over all enterprises of the same type;
the evaluation ranking percentage is obtained by weighted summation of the enterprise scale ranking percentage and the enterprise profit ranking percentage;
the enterprise-scale ranking percentage is the enterprise-scale ranking of enterprise a divided by the total number of enterprises participating in the enterprise-scale ranking; the enterprise profit ranking percentage is calculated in the same manner as the enterprise scale ranking percentage.
4. A block chain-based enterprise financial process management method is characterized in that:
collecting stream data, commodity historical sales data, enterprise public opinion data and enterprise assessment data;
verifying an error evaluation value of the running water data based on commodity historical sales data, enterprise public opinion data and enterprise evaluation data, and marking the running water data as qualified running water data when the error evaluation value is smaller than or equal to a preset error evaluation threshold value;
after integrating the qualified stream data into block data, uploading the block data to a block chain;
collecting calculation power and idle resource percentage of the node; calculating a processing evaluation value based on the calculation power and the idle resource percentage, and distributing the blockchain to the nodes based on the processing evaluation value;
Setting a private key and a public key for an enterprise; the public key is used for allowing enterprises to access the block data; the private key is used for marking the enterprise digital tag; completing the operation of accessing block data between enterprises based on the public key and the private key;
the flow data comprises expenditure flow data and income flow data; the expense flow data comprises transaction amount, transaction flow direction, transaction time and transaction label of each expense of the enterprise; the income flow data comprises the transaction amount of each income of the enterprise, the transaction flow direction, the transaction time and the transaction label;
training a machine learning model for calculating transaction amount in real time according to the transaction tag based on commodity historical sales data;
the training mode of the machine learning model is as follows:
taking commodity historical sales data as input of a machine learning model, wherein in the commodity historical sales data, transaction amount is taken as a machine learning label of a transaction label; the machine learning model takes transaction amount as output; taking transaction amount corresponding to the transaction tag in the real-time stream data as a prediction target and taking a minimized machine learning model loss function value as a training target; stopping training when the loss function value of the first machine learning model is smaller than or equal to a preset first target loss value;
The machine learning model loss function is a mean square error; mean square error is obtained by applying a loss function mse=Training a model for the minimum, wherein mse is a loss function value in a loss function, and i is a commodity historical sales data set number; />Historical sales data sets for the commodity; />Machine learning tag corresponding to the i-th group of transaction tags,>calculating transaction amount for the ith group of transaction tags in real time;
marking the transaction amount calculated by the machine learning model as a secondary transaction amount;
calculating a primary transaction amount based on the secondary transaction amount and the enterprise evaluation data; let the secondary transaction amount beEnterprise evaluation data is K;
when the secondary transaction amount is the paid transaction amount, the primary transaction amountThe calculation formula of (2) is as follows:
；
when the secondary transaction amount is the revenue transaction amount, the primary transaction amountThe calculation formula of (2) is as follows:
；
the transaction amount obtained by the stream data in real time is，/>Comprises->And->；
The enterprise public opinion data is a total public opinion evaluation value of enterprises within a preset fixed time range before transaction time;
the total public opinion evaluation value is obtained in the following manner:
setting a keyword set; the keyword set comprises a plurality of keywords which influence the operation of enterprises; setting a public opinion evaluation value YL for the keyword;
Setting a keyword triggering public opinion mechanism, and judging to trigger public opinion when the keyword quantity increasing speed exceeds a preset keyword quantity increasing speed threshold value within a preset fixed time range before the transaction time;
extracting keywords in the online text in real time by using an NLP technology within a preset fixed time range before the transaction time, wherein the increase speed of the number of the keywords is the number of the keywords in the online text extracted by the NLP technology divided by the time length of the keywords; when the keyword triggers public opinion, the keyword is expressed by the formula YLPG=Calculating a total public opinion evaluation value YLPG; in->Is the +.>Keywords (e.g. Japan)>Is the total number of keywords in the keyword set; />Is->Public opinion evaluation values of the individual keywords; />Is->The number of keywords increases at a rate;
when public opinion is not triggered, setting YLPG to 1; the transaction label is a commodity type X and a commodity quantity Y which are completed based on the transaction amount;
setting a transaction amount error evaluation value privot =-ln(YLPG)；
Comparing the error evaluation value with a preset error evaluation threshold value; when the error evaluation value is smaller than or equal to a preset error evaluation threshold value, marking the running water data as qualified running water data, and sending the qualified running water data to a block chain uploading module; when the error evaluation value is larger than a preset error evaluation threshold value, marking the running water data as false running water data and refusing, and blading enterprises to submit correct running water data within a specified time range;
Uploading the false stream data to a blockchain when the enterprise does not submit the qualified stream data within a specified time range or the number of times of submitting the false stream data is larger than a preset submission number threshold, and marking the enterprise as a dishonest enterprise;
taking the block creation time as a time stamp of the block; generating random numbers by taking the time stamps as seeds for generating the random numbers, and adding the random numbers into the blocks;
the block comprises a time stamp of the block, a hash value of the previous block and a transaction record;
uploading the transaction amount, the transaction flow direction, the transaction time and the transaction tag to a transaction record of the block; carrying out hash calculation on all data in the block, wherein the obtained numerical value is used as a hash value of the block;
marking all data in the block as block data;
collecting the calculation force Q of the node and the free resource percentage Z, and setting a processing evaluation value CL=arctan；
When the node processing evaluation value is larger than a preset processing evaluation threshold value, uploading the new block to the node which does not reach the processing evaluation threshold value;
the process of enterprise B accessing the tile data of enterprise a includes:
setting a pair of public key A1 and private key A2 for enterprise A and setting a pair of public key B1 and private key B2 for enterprise B; the private key A2 is used for marking the block data of the enterprise A with a digital tag unique to the enterprise A;
Enterprise a will encrypt the block data using public key B1, and send to enterprise B;
after the enterprise B receives the block data, the block data is decrypted through the private key B2, the digital label is verified by using the public key A1, and when the verification is true, the access of the block chain is completed.
5. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs a blockchain-based enterprise financial process management method as in claim 4 by invoking a computer program stored in the memory.
6. A computer-readable storage medium, characterized by: instructions stored thereon which, when executed on a computer, cause the computer to perform a blockchain-based enterprise financial process management method as in claim 4.
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