CN115271553A - Contract management method and device based on big data, electronic equipment and storage medium - Google Patents
Contract management method and device based on big data, electronic equipment and storage medium Download PDFInfo
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- CN115271553A CN115271553A CN202211177948.5A CN202211177948A CN115271553A CN 115271553 A CN115271553 A CN 115271553A CN 202211177948 A CN202211177948 A CN 202211177948A CN 115271553 A CN115271553 A CN 115271553A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The application discloses a contract management method, a contract management device, electronic equipment and a storage medium based on big data, wherein each contract of a company is subjected to data processing, so that a sample contract is formed in a database by the contract of the company, contract data of a contract to be signed are acquired, the contract data of the contract to be signed are input into a prediction model obtained according to big data correlation technology, and contract risk assessment is given, so that contract signing efficiency is improved, and contract signing risk is reduced.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a contract management method and apparatus based on big data, an electronic device, and a storage medium.
Background
The existing contract is generally drawn up in advance by the first party or by the negotiation of the two parties. For companies with multiple product lines, the unit price of each product is inconsistent, and offline service personnel often encounter the condition that customers require preferential treatment when the business personnel talk about the customers. The off-line business personnel are often primary personnel, do not have the right to modify the price, and often need to ask for a superior leader to decide, the negotiation period is forced to be lengthened, and the emotion of the order signing personnel is easy to be worried and uneasy, so that the negotiation process is abnormal, unsmooth and difficult, and the client is easy to lose. Secondly, even if a company is willing to modify prices, the prices are often modified on the basis of guaranteeing profitability of the company, and a large amount of time and energy are needed to be manually spent on calculation and evaluation to obtain results, so that the time cost of signing an order is high, and the efficiency is low. Thirdly, in the actual production process, the fluctuation of the production cost and the production efficiency is also often obvious, particularly the fluctuation of the human cost and the raw material price, so that the relevant factors need to be considered in the contract signing process, otherwise the expected loss of the bill can be caused by the market fluctuation or the change of the production efficiency.
Disclosure of Invention
In order to improve the signing-in efficiency and reduce the signing-in risk, the application provides a contract management method and device based on big data, an electronic device and a storage medium.
A big data based contract management method, the method comprising:
the method comprises the steps of performing data processing on all contracts of a company in history and summarizing the contracts into sample data, performing big data analysis on the sample data to obtain a prediction model, obtaining contract data of a contract to be signed, inputting the contract data of the contract to be signed into the prediction model to obtain a prediction value, and comparing the contract data of the contract to be signed with the prediction value output by the prediction model to predict the risk degree of the contract to be signed.
The acquired contract data of the contract to be signed comprises a cooperative project name, a cooperative amount and a delivery cycle, and the cooperative project name, the cooperative amount and the delivery cycle are input into the prediction model to predict the predicted delivery cycle and the predicted cost of the contract to be signed;
the cooperation amount is used for comparing with the predicted cost and generating predicted profit, and the predicted profit is used for determining profit rate of the contract to be signed;
the delivery cycle is used for comparing with the predicted delivery cycle to generate predicted delivery data, and the predicted delivery data is used for judging the expected default risk of the contract to be signed.
Further, when the predicted delivery data satisfies the first threshold and the predicted profit satisfies the second threshold, the contract may be directly signed; when the predicted delivery data do not meet the first threshold and the predicted profit meets the second threshold, giving default risk assessment data, if the default risk assessment data meet the third threshold, directly signing a contract, otherwise, feeding back to the upper level for judgment; when the predicted delivery data meet the first threshold and the predicted profit does not meet the second threshold, providing profit risk evaluation data, if the profit risk evaluation data meet the fourth threshold, directly signing a contract, otherwise, feeding back to the upper level for judgment; and when the predicted delivery data does not meet the first threshold and the predicted profit does not meet the second threshold, giving a summary risk assessment, if the summary risk assessment meets a fifth threshold, feeding back to the upper level for judgment, and otherwise, not signing.
Further, the first threshold is determined by the current production efficiency of the company;
the second threshold is determined by the company's current profit margin;
the third threshold is determined by the company's current maximum production efficiency;
the fourth threshold is determined by the lowest profit margin acceptable to the company;
the fifth threshold value is jointly determined by the lowest profit margin acceptable by the company and the current maximum production efficiency;
the previous level is a previous leader level of the order signing personnel to sign the contract.
Further, the predicted cost is composed of a human predicted cost, a raw material predicted cost and an operation predicted cost of the corresponding cooperation project, the human predicted cost is determined by a predicted release pay amount in a delivery cycle of the corresponding cooperation project, the raw material predicted cost is determined by a predicted market purchase unit price, and the operation predicted cost is determined by multiplying the daily operation predicted cost by the delivery cycle.
Further, the predicted payroll amount within the lead period may be obtained by the following formula:
（1）
wherein f (i) is the predicted human cost per day on day i of the delivery cycle of the contract, wherein Y is the predicted payroll amount delivered within the delivery cycle, and wherein h is the number of days of the delivery cycle;
wherein the predicted labor cost for the day i (i) is obtained from a prediction curve fitted to historical data:
(2)
(3)
wherein t is contract signing date, Y (t) is labor cost of t days, n is total days of historical data, an is weight coefficient of historical data of t-n days, a weight system an is set by corresponding historical date, and Zt is noise value; wherein, the longer the weight coefficient an is on-off with the signing time, the smaller the numerical value is.
Furthermore, the predicted market purchase unit price can be directly determined by the current unit price data and the predicted price trend provided by an internet platform, wherein the internet platform is a Chinese price information network platform; the prediction method of the operation prediction cost is the same as the prediction method of the prediction of the released payroll.
In order to further solve the above problem, the present application further provides a contract management apparatus based on big data, the apparatus including:
the authority management module is used for endowing different authorities to the user;
the input module is used for inputting contract data of a contract to be signed;
the contract data of the contract to be signed comprises the name of the cooperation project, the cooperation amount and the delivery cycle;
the big data analysis module is used for carrying out big data analysis on the sample data to obtain a prediction model, acquiring contract data of the contract to be signed, inputting the contract data of the contract to be signed into the prediction model to obtain a prediction value, and predicting the risk degree of the contract to be signed by comparing the contract data of the contract to be signed with the prediction value output by the prediction model;
inputting the name of the cooperative project, the cooperative amount and the delivery cycle into the prediction model, and predicting the predicted delivery cycle and the predicted cost of the contract to be signed;
the cooperation amount is used for comparing with the predicted cost and generating predicted profit, and the predicted profit is used for determining profit rate of the contract to be signed;
the delivery cycle is used for comparing with the predicted delivery cycle to generate predicted delivery data, and the predicted delivery data is used for judging the expected default risk of the contract to be signed.
The output module is used for giving an evaluation result according to the predicted risk degree of the contract to be signed;
and the pushing module is used for pushing the information output by the output module to the upper level.
To further solve the above problem, the present application also provides an electronic device, including:
at least one processor and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the big data based contract management method.
To further solve the above problem, the present application also provides a computer-readable storage medium storing a computer program, which when executed by a processor, implements the big-data based contract management method.
In summary, the present application includes at least one of the following beneficial technical effects:
1. according to the method and the device, contract data such as contract item names, cooperation amount and delivery cycle of the contract to be signed are read, and the cooperation item names, the cooperation amount and the delivery cycle are input into the sample data to be subjected to big data analysis to obtain a prediction model, so that the signing risk under four conditions can be evaluated, orders which can be directly signed, orders which cannot be signed and orders which have risks can be quickly distinguished, the signing efficiency is remarkably improved, the signing risk is reduced, and the benefit maximization of a company is met;
2. the prediction cost is composed of the manpower prediction cost, the raw material prediction cost and the operation prediction cost, and the manpower prediction cost, the raw material prediction cost and the operation prediction cost can be reasonably calculated according to the trend of sample data, so that the prediction cost is dynamic and controllable, the risk assessment has reference significance, and the benefit maximization of a company is facilitated.
3. And contract signing authorities are reasonably configured, so that the communication between upper and lower levels is more convenient, the tension of the signing personnel is effectively relieved, and the success rate of signing is improved.
Drawings
FIG. 1 is a flow chart of a management method in the present application;
fig. 2 is a block diagram of the management device according to the present application.
Detailed Description
The present application is described in further detail below with reference to figures 1-2.
The embodiment of the application discloses a contract management method based on big data. The execution subject of the big data based contract management method includes, but is not limited to, at least one of the electronic devices of the server, the terminal, and the like, which can be configured to execute the method provided by the embodiment of the present application. In other words, the big data-based contract management method may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a big data-based contract management method includes:
the method comprises the steps of carrying out data processing on all contracts of a company historically and summarizing the contracts into sample data, carrying out big data analysis on the sample data to obtain a prediction model, obtaining contract data of a contract to be signed, inputting the contract data of the contract to be signed into the prediction model to obtain a predicted value, and comparing the contract data of the contract to be signed with the predicted value output by the prediction model to predict the risk degree of the contract to be signed.
Further, the acquired contract data of the contract to be signed comprises a cooperative project name, a cooperative amount and a delivery cycle, and the cooperative project name, the cooperative amount and the delivery cycle are input into a prediction model obtained by analyzing big data of the sample data to predict the predicted delivery cycle and the predicted cost of the contract to be signed;
the cooperation amount is used for comparing with the prediction cost and generating prediction profit, and the prediction profit is used for judging the profit rate of the contract to be signed;
the delivery cycle is used for comparing with the predicted delivery cycle of the project to generate predicted delivery data, and the predicted delivery data is used for judging whether the contract to be signed has the expected default risk or not.
Further, the delivery cycle is compared with the predicted delivery cycle to generate predicted delivery data, the cooperation amount is compared with the predicted cost to generate predicted profit, and when the predicted delivery data meets a first threshold value and the predicted profit meets a second threshold value, a contract can be directly signed; when the predicted delivery data do not meet the first threshold and the predicted profit meets the second threshold, default risk assessment data are given, if the default risk assessment data meet the third threshold, a contract can be directly signed, otherwise, the default risk assessment data are fed back to the upper level for judgment; when the predicted delivery data meet the first threshold and the predicted profit does not meet the second threshold, providing profit risk evaluation data, if the profit risk evaluation data meet the fourth threshold, directly signing a contract, and if not, feeding back to the upper level for judgment; and when the predicted delivery data does not meet the first threshold and the predicted profit does not meet the second threshold, giving a summary risk assessment, if the summary risk assessment meets a fifth threshold, feeding back to the upper level for judgment, and otherwise, not signing.
Further, the first threshold is determined by the current production efficiency of the company;
the second threshold is determined by the company's current profit margin;
the third threshold is determined by the company's current maximum production efficiency;
the fourth threshold is determined by the lowest profit margin acceptable to the company;
the fifth threshold value is jointly determined by the lowest profit margin acceptable by the company and the current maximum production efficiency;
the previous level is a previous leader level of the current contract staff to be signed.
Further, the forecast cost is composed of a manpower forecast cost, a raw material forecast cost and an operation forecast cost of the corresponding cooperation project, the manpower forecast cost is determined by a forecast release wage amount in a delivery cycle of the corresponding cooperation project, the raw material forecast cost is determined by a forecast market purchase unit price, and the operation forecast cost is determined by multiplying a daily operation forecast cost by the delivery cycle.
Further, the predicted payroll amount in the lead period can be obtained by the following formula:
（1）
wherein f (i) is the predicted human cost per day on day i of the delivery cycle of the contract, wherein Y is the predicted payroll amount delivered within the delivery cycle, and wherein h is the number of days of the delivery cycle;
wherein the predicted labor cost for the day i (i) is obtained from a prediction curve fitted to historical data:
(2)
(3)
wherein t is contract signing date, Y (t) is labor cost of t days, n is total days of historical data, an is weight coefficient of historical data of t-n days, zt is noise value, and weight system an is set by corresponding historical date, wherein, the longer the weight coefficient an is on-off with signing time, the smaller the value.
Furthermore, the predicted market purchase unit price can be directly determined through current unit price data and price trends provided by an internet platform, and the internet platform is a Chinese price information network platform; the prediction method of the operation prediction cost is the same as the prediction method of the prediction of the released payroll.
Each contract of a company is subjected to data processing and a database is generated, so that the classified management of the contracts is convenient, and when a new contract is signed, the current forecast cost condition and the productivity condition of the company can be directly obtained by acquiring contract data and calling the database by utilizing a big data technology so as to evaluate and guide the execution and profit conditions of the contract, thereby comprehensively planning the feasibility and the risk of signing the contract, improving the signing efficiency of the contract, reducing the signing risk of the contract and maximizing the benefit of the company.
Referring to fig. 2, an embodiment of the present application further discloses a contract management apparatus based on big data, including: the device comprises a right management module, an input module, a reading module, an output module and a pushing module. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and are stored in a memory of the electronic device.
And the authority management module is used for endowing different authorities to the user. The authority management module at least comprises an accessed server and a plurality of client devices, and the server is respectively connected with each client device. The server and client devices may be connected by wireless, wired, or a combination of wireless and wired connections. In addition, the client devices can be connected as required.
After a user logs in a client, client equipment sends an authorization instruction to an authorization source, the authorization source receives the authorization instruction, the authorization source sends an access request to a server, the access request comprises authorization appointments, the server verifies the access request, the server checks the authorization source of the authorization instruction according to the access request, and the server verifies the authority of the authorization instruction to the authorization source.
If the verification is successful, the verification is passed; and if the verification fails, returning to the step that the client segment equipment sends the authorization instruction to the authorization source.
The input module is used for inputting contract data of a contract to be signed; the input module is an existing input module, and is not described herein again.
And the big data analysis module is used for inputting the contract data to be contracted into the prediction model obtained by the big data so as to obtain a predicted value, and the predicted delivery data and the predicted profit are calculated by comparing the contract data to be contracted with the predicted value of the prediction model.
And the output module gives an evaluation result according to the calculated predicted delivery data and the predicted profit.
And the pushing module is used for pushing the information output by the output module to the previous level.
The embodiment of the application also discloses an electronic device, which comprises at least one processor and a memory which is in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a big data based contract management method.
A processor may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., contract management programs based on big data, etc.) stored in the memory and calling data stored in the memory.
The memory includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, and the like. Further, the memory may also include both internal storage units and storage devices of the electronic device. The memory can be used not only for storing application software installed in the electronic device and various types of data such as a code of a contract management program based on big data, etc., but also for temporarily storing data that has been output or is to be output.
The embodiment of the application also discloses a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the contract management method based on big data.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. A contract management method based on big data is characterized in that: the method comprises the following steps:
performing data processing on all contracts of a company in history and summarizing the contracts into sample data, performing big data analysis on the sample data to obtain a prediction model, acquiring contract data of a contract to be signed, inputting the contract data of the contract to be signed into the prediction model to obtain a prediction value, and comparing the contract data of the contract to be signed with the prediction value output by the prediction model to predict the risk degree of the contract to be signed;
the acquired contract data of the contract to be signed comprises a cooperative project name, a cooperative amount and a delivery cycle, and the cooperative project name, the cooperative amount and the delivery cycle are input into the prediction model, so that the predicted delivery cycle and the predicted cost of the contract to be signed can be predicted;
the cooperation amount is used for comparing with the predicted cost and generating predicted profit, and the predicted profit is used for determining profit rate of the contract to be signed;
the delivery cycle is used for comparing with the predicted delivery cycle to generate predicted delivery data, and the predicted delivery data is used for judging the expected default risk of the contract to be signed.
2. The big data-based contract management method according to claim 1, wherein: when the predicted delivery data satisfies a first threshold and the predicted profit satisfies a second threshold, a contract may be directly signed; when the predicted delivery data do not meet a first threshold value and the predicted profit meets a second threshold value, default risk assessment data are given, if the default risk assessment data meet a third threshold value, a contract can be directly signed, otherwise, the default risk assessment data are fed back to the upper level for judgment; when the predicted delivery data meet a first threshold value and the predicted profit does not meet a second threshold value, providing profit risk evaluation data, if the profit risk evaluation data meet a fourth threshold value, directly signing a contract, otherwise, feeding back to the upper level for judgment; and when the predicted delivery data do not meet a first threshold and the predicted profit does not meet a second threshold, giving a summary risk assessment, if the summary risk assessment meets a fifth threshold, feeding back to the upper level for judgment, and otherwise, not signing.
3. The big data-based contract management method according to claim 2, wherein:
the first threshold is determined by the company's current production efficiency;
the second threshold is determined by the company's current profit margin;
the third threshold is determined by the company's current maximum production efficiency;
the fourth threshold is determined by the lowest profit margin acceptable to the company;
the fifth threshold value is jointly determined by the lowest profit margin acceptable by the company and the current maximum production efficiency;
the previous level is a previous leader level of the order signing personnel to sign the contract.
4. The big data-based contract management method according to claim 1, wherein: the forecast cost is composed of a manpower forecast cost, a raw material forecast cost and an operation forecast cost of the cooperative project, the manpower forecast cost is determined by forecast release wage amount in a delivery cycle of the cooperative project, the raw material forecast cost is determined by forecast market purchase unit price, and the operation forecast cost is determined by multiplying daily operation forecast cost by delivery cycle.
5. The big data-based contract management method according to claim 4, wherein: wherein the predicted issuance payroll within the lead period is obtained by the following equation:
（1）
wherein f (i) is the predicted human cost per day on day i of the delivery cycle of the contract, wherein Y is the predicted payroll amount delivered within the delivery cycle, and wherein h is the number of days of the delivery cycle;
wherein the predicted labor cost for the day i, f (i), is obtained from a prediction curve fitted from the following historical data:
(2)
(3)
wherein t is contract signing date, Y (t) is labor cost of t days, n is total days of historical data, an is weight coefficient of the historical data with the historical date of t-n days, a weight system an is set by the corresponding historical date of t-n days, and Zt is noise value.
6. The big-data-based contract management method according to claim 5, wherein: the predicted market purchase unit price is directly determined by the predicted price trend provided by an internet platform, and the internet platform is a Chinese price information network platform; the prediction method of the operation prediction cost is the same as the prediction method of the prediction of the amount of the issued salary.
7. A big data-based contract management device is characterized in that: the device comprises:
the authority management module is used for endowing different authorities to the user;
the input module is used for inputting contract data of a contract to be signed;
the contract data of the contract to be signed comprises a cooperative project name, a cooperative amount and a delivery cycle;
the big data analysis module is used for carrying out big data analysis on the sample data to obtain a prediction model, acquiring contract data of the contract to be signed, inputting the contract data of the contract to be signed into the prediction model to obtain a predicted value, and predicting the risk degree of the contract to be signed by comparing the contract data of the contract to be signed with the predicted value output by the prediction model;
inputting the name of the cooperative project, the cooperative amount and the delivery cycle into the prediction model, and predicting the predicted delivery cycle and the predicted cost of the contract to be signed;
the cooperation amount is used for comparing with the predicted cost and generating predicted profit, and the predicted profit is used for determining profit rate of the contract to be signed;
the delivery cycle is used for comparing with the predicted delivery cycle to generate predicted delivery data, and the predicted delivery data is used for judging the expected default risk of the contract to be signed;
the output module is used for giving an evaluation result according to the predicted risk degree of the contract to be signed;
and the pushing module is used for pushing the information output by the output module to the upper level.
8. An electronic device, characterized in that: the electronic device includes:
at least one processor and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a big data based contract management method according to any of claims 1-6.
9. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements the big-data based contract management method of any of claims 1 to 6.
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| EP3992882A1 (en) * | 2020-11-02 | 2022-05-04 | Vittoria Hub srl | System and method for managing insurance contracts |
| CN113032239A (en) * | 2021-05-28 | 2021-06-25 | 北京宝兰德软件股份有限公司 | Risk prompting method and device, electronic equipment and storage medium |
| CN113554418A (en) * | 2021-07-28 | 2021-10-26 | 国网江苏省电力有限公司物资分公司 | Automatic price linkage method for agreement inventory purchasing material contract |
| CN113822488A (en) * | 2021-09-29 | 2021-12-21 | 平安国际融资租赁有限公司 | Risk prediction method and device for financing lease, computer equipment and storage medium |
| CN113887195A (en) * | 2021-09-29 | 2022-01-04 | 平安国际智慧城市科技股份有限公司 | Contract review method, device, equipment and storage medium based on artificial intelligence |
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