AI-based fuel contract template collaborative editing recommendation method and system

Abstract

The invention relates to the technical field of fuel contract templates, and discloses an AI-based fuel contract template collaborative editing recommendation method and system, and the method comprises the steps: obtaining historical contract editing data of each contract editor in a contract editing team, and determining a plurality of historical contract editing data sequences; dividing the historical contract editing data sequence into a score-adding and score-reducing historical contract editing data sequence, and calculating a score-adding contract editing factor and a score-reducing contract editing factor; calculating a historical contract editing data recommendation factor of the contract editing team according to the score adding contract editing factor and the score reducing contract editing factor; and selecting the contract editing team corresponding to the maximum historical contract editing data recommendation factor, carrying out collaborative editing on the fuel contract template, calculating the historical contract editing data recommendation factors to improve the recommendation precision and recommendation efficiency of the contract editing team, and editing and recommending a low-risk and high-efficiency contract editing team for the fuel contract template. And the comprehensiveness and safety of contract collaborative editing are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel contract templates, and in particular, to an AI-based collaborative editing recommendation method and system for fuel contract templates. Background Art

[0002] A fuel contract is an agreement signed between a purchaser and a supplier to stipulate the purchase and supply of fuel products. The contract content generally includes basic information, product description, delivery terms, settlement methods, quality assurance, liability for breach of contract, etc. When there are many contract terms, involving multiple legal fields or professional knowledge, a contract editing team with different professional backgrounds is required to perform online editing and review to ensure the comprehensiveness and accuracy of the contract. Therefore, how to recommend a low-risk team among many contract editing teams for collaborative editing of fuel contracts has become an urgent technical problem to be solved.

[0003] The current method for recommending contract editing teams usually involves experts creating corresponding risk models for each dimension in collaborative editing operations, such as editing error rate, unauthorized access, etc., based on their experience, and recommending teams according to the risk detection results. Although this method is effective to a certain extent, it has obvious limitations. The risk model highly depends on the quality of the input data. If the data is incorrect, incomplete, outdated, or biased, the recommendation results will be inaccurate or even misleading. Moreover, the risk model requires a large amount of computing resources and time. This high computational complexity makes it difficult to apply the model risk detection in practice. Therefore, it is impossible to quickly feedback the overall situation of the contract editing team, and thus impossible to recommend a low-risk and high-efficiency contract editing team for fuel contract template editing. Summary of the Invention

[0004] Embodiments of the present invention provide an AI-based collaborative editing recommendation method and system for fuel contract templates. The present invention can perform recommendation analysis on the historical contract editing data of the contract editing team, calculate the recommendation factors of the historical contract editing data, improve the recommendation accuracy and efficiency, and thus recommend a low-risk and high-efficiency contract editing team for fuel contract template editing.

[0005] To achieve the above object, the present invention provides an AI-based collaborative editing recommendation method for fuel contract templates, including: Determine a plurality of contract editing teams to be selected, obtain the historical contract editing data of each contract editor in the contract editing team, analyze the historical contract editing data, and determine a plurality of historical contract editing data sequences; Process each historical contract editing data sequence, divide the historical contract editing data sequence into a plus / minus score historical contract editing data sequence, and calculate the plus score contract editing factor and minus score contract editing factor of the historical contract editing data sequence according to the plus / minus score historical contract editing data sequence, where the plus / minus score historical contract editing data sequence includes a plus score historical contract editing data sequence and a minus score historical contract editing data sequence; Calculate the historical contract editing data recommendation factor of the contract editing team according to the plus score contract editing factor and minus score contract editing factor corresponding to each historical contract editing data sequence; Determine the historical contract editing data recommendation factor corresponding to each contract editing team, sort all the historical contract editing data recommendation factors by numerical size, select the contract editing team corresponding to the maximum historical contract editing data recommendation factor based on the sorting result, and perform collaborative editing on the fuel contract template.

[0006] Further, when processing each historical contract editing data sequence, dividing the historical contract editing data sequence into a plus / minus score historical contract editing data sequence, and calculating the plus score contract editing factor and minus score contract editing factor of the historical contract editing data sequence according to the plus / minus score historical contract editing data sequence, it includes: Determine the preset historical contract editing data corresponding to the historical contract editing data sequence; Divide the historical contract editing data sequence according to the preset historical contract editing data. When the historical contract editing data in the historical contract editing data sequence is less than or equal to the preset historical contract editing data, the corresponding historical contract editing data is divided into the plus score historical contract editing data sequence; When the historical contract editing data in the historical contract editing data sequence is greater than the preset historical contract editing data, the corresponding historical contract editing data is divided into the minus score historical contract editing data sequence; Calculate the plus score contract editing factor of the historical contract editing data sequence according to the plus score historical contract editing data sequence; Calculate the minus score contract editing factor of the historical contract editing data sequence according to the minus score historical contract editing data sequence.

[0007] Further, when calculating the plus score contract editing factor of the historical contract editing data sequence according to the plus score historical contract editing data sequence, it includes: Randomly combine the historical contract editing data in the plus score historical contract editing data sequence in pairs to determine multiple groups of historical contract editing data; Determine whether two historical contract editing data groups in the historical contract editing data group are equal. If so, delete the corresponding historical contract editing data group; If not, retain the corresponding historical contract editing data group, and calculate the historical contract editing data sum value of the two historical contract editings in each retained historical contract editing data group; Determine the historical contract editing data variance of all historical contract editing data sum values, and generate a first calculation mark for all historical contract editing data sum values less than or equal to the historical contract editing data variance; Generate a second calculation mark for all historical contract editing data sum values greater than the historical contract editing data variance; Calculate the bonus contract editing factor of the historical contract editing data sequence according to the historical contract editing data sum value, the first calculation mark, and the second calculation mark.

[0008] Further, when calculating the bonus contract editing factor of the historical contract editing data sequence according to the historical contract editing data sum value, the first calculation mark, and the second calculation mark, it includes: Calculate the bonus contract editing factor of the historical contract editing data sequence according to the following formula: ; where t is the bonus contract editing factor of the historical contract editing data sequence, y is the number of retained historical contract editing data groups, p1 is the first calculation coefficient, a u is the historical contract editing data sum value of the u-th retained historical contract editing data group, a max is the maximum historical contract editing data sum value, p2 is the second calculation coefficient, s1 u is a historical contract editing data in the u-th retained historical contract editing data group, s2 u is another historical contract editing data in the u-th retained historical contract editing data group, d1 is the number of the first calculation marks, d2 is the number of the second calculation marks, p1 + p2 = 1, and p1 < p2.

[0009] Further, when calculating the deduction contract editing factor of the historical contract editing data sequence according to the deduction historical contract editing data sequence, it includes: Determine the maximum historical contract editing data and the minimum historical contract editing data from the deduction historical contract editing data sequence; Calculate the deduction contract editing factor of the historical contract editing data sequence according to the maximum historical contract editing data and the minimum historical contract editing data; ; Among them, q is the deduction contract editing factor of the historical contract editing data sequence, w is the number of historical contract editing data in the deduction historical contract editing data sequence, and r e is the e-th historical contract editing data in the deduction historical contract editing data sequence, and r1 e is the weight of the e-th historical contract editing data, and r min is the minimum historical contract editing data, and r max is the maximum historical contract editing data, is all the maximum value of.

[0010] Furthermore, when calculating the historical contract editing data recommendation factor of the contract editing team according to the bonus contract editing factor and the deduction contract editing factor corresponding to each historical contract editing data sequence, it includes: Randomly select a historical contract editing data sequence and extract the corresponding first bonus contract editing factor and the first deduction contract editing factor; Randomly select another historical contract editing data sequence and extract the corresponding second bonus contract editing factor and the second deduction contract editing factor; Calculate the absolute value of the difference between the bonus contract editing factors of the first bonus contract editing factor and the second bonus contract editing factor; Select the maximum bonus contract editing factor and the minimum bonus contract editing factor from all the bonus contract editing factors, and calculate the difference between the maximum bonus contract editing factor and the minimum bonus contract editing factor; Calculate the ratio of the absolute value of the difference between the bonus contract editing factors and the difference between the bonus contract editing factors, and use it as the factor to be calculated for the bonus; Calculate the absolute value of the difference between the deduction contract editing factors of the first deduction contract editing factor and the second deduction contract editing factor; Select the maximum deduction contract editing factor and the minimum deduction contract editing factor from all the deduction contract editing factors, and calculate the difference between the maximum deduction contract editing factor and the minimum deduction contract editing factor; Calculate the ratio of the absolute value of the difference between the deduction contract editing factors and the difference between the deduction contract editing factors, and use it as the factor to be calculated for the deduction; Determine the sum value of the factor to be calculated for the bonus and the factor to be calculated for the deduction, and use it as the comprehensive factor to be calculated; Perform random selection calculations on the remaining historical contract editing data sequences to obtain multiple comprehensive factors to be calculated; Calculate the historical contract editing data recommendation factor of the contract editing team according to all the comprehensive factors to be calculated.

[0011] Further, when calculating the historical contract editing data recommendation factor of the contract editing team according to all the comprehensive factors to be calculated, it includes: Calculate the historical contract editing data recommendation factor of the contract editing team according to the following formula: ; where, g is the historical contract editing data recommendation factor of the contract editing team, h1 is a calculation function, and its value range is (0.1, 0.5), n1 is the number of comprehensive factors to be calculated, k1 is the mean value of all comprehensive factors to be calculated, b i is the i-th comprehensive factor to be calculated, for all is the minimum value.

[0012] Further, when selecting the contract editing team corresponding to the maximum historical contract editing data recommendation factor based on the sorting result and performing collaborative editing on the fuel contract template, it includes: Real-time track the collaborative editing content of the contract editing team on the fuel contract template, and analyze the collaborative editing content according to the sensitive word library. When there are sensitive words, issue an editing content modification instruction; Real-time track the collaborative modification records of the contract editing team on the fuel contract template, and upload the collaborative modification records in real time, where the collaborative modification records include modification timestamps, modifier identities, and modification contents.

[0013] To achieve the above object, the present invention also provides an AI-based collaborative editing recommendation system for fuel contract templates, including: A data analysis module, used to determine multiple contract editing teams to be selected, obtain the historical contract editing data of each contract editor in the contract editing team, analyze the historical contract editing data, and determine multiple historical contract editing data sequences; A first calculation module, used to process each historical contract editing data sequence, divide the historical contract editing data sequence into plus / minus score historical contract editing data sequences, and calculate the plus score contract editing factor and minus score contract editing factor of the historical contract editing data sequence according to the plus / minus score historical contract editing data sequence, where the plus / minus score historical contract editing data sequence includes a plus score historical contract editing data sequence and a minus score historical contract editing data sequence; A second calculation module, used to calculate the historical contract editing data recommendation factor of the contract editing team according to the plus score contract editing factor and minus score contract editing factor corresponding to each historical contract editing data sequence; The team recommendation module is used to determine the historical contract editing data recommendation factors corresponding to each contract editing team, sort all the historical contract editing data recommendation factors by numerical value, select the contract editing team corresponding to the largest historical contract editing data recommendation factor based on the sorting result, and perform collaborative editing on the fuel contract template.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention discloses an AI-based collaborative editing recommendation method and system for fuel contract templates. Historical contract editing data of each contract editor in the contract editing team is obtained to determine multiple historical contract editing data sequences. The historical contract editing data sequences are divided into plus / minus score historical contract editing data sequences, and the plus-score contract editing factor and the minus-score contract editing factor are calculated. The historical contract editing data recommendation factor of the contract editing team is calculated according to the plus-score contract editing factor and the minus-score contract editing factor. The contract editing team corresponding to the largest historical contract editing data recommendation factor is selected to perform collaborative editing on the fuel contract template. By calculating the historical contract editing data recommendation factor, the recommendation accuracy and efficiency of the contract editing team are improved, a contract editing team with low risk and high efficiency is recommended for the fuel contract template editing, and the comprehensiveness and security of the contract collaborative editing are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 It shows a schematic flow chart of an AI-based collaborative editing recommendation method for fuel contract templates in an embodiment of the present invention; Figure 2 It shows a schematic structural diagram of an AI-based collaborative editing recommendation system for fuel contract templates in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The following combines the drawings and embodiments to further describe the specific embodiments of the present invention in detail. The following embodiments are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0017] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0018] The terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0019] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0020] The following is a description of the preferred embodiments of the present invention in conjunction with the drawings.

[0021] As Figure 1 shown, the embodiments of the present invention disclose an AI-based collaborative editing recommendation method for fuel contract templates, including: S110: Determine a plurality of contract editing teams to be selected, obtain the historical contract editing data of each contract editor in the contract editing team, analyze the historical contract editing data, and determine a plurality of historical contract editing data sequences; In this embodiment, the historical contract editing data includes the number of unauthorized accesses, the number of unauthorized edits, the number of malicious edits, the number of malicious modifications, the number of malicious deletions, etc.

[0022] In this embodiment, analyzing the historical contract editing data means dividing all historical contract editing data of the same type into a sequence. For example, determining the historical contract editing data sequence according to all the number of unauthorized accesses, and determining the historical contract editing data sequence according to all the number of unauthorized edits. The data in each historical contract editing data sequence is historical contract editing data of one type.

[0023] S120: Process each historical contract editing data sequence, divide the historical contract editing data sequence into a plus / minus score historical contract editing data sequence, and calculate the plus score contract editing factor and minus score contract editing factor of the historical contract editing data sequence according to the plus / minus score historical contract editing data sequence. Among them, the plus / minus score historical contract editing data sequence includes a plus score historical contract editing data sequence and a minus score historical contract editing data sequence; In some embodiments of the present application, when processing each historical contract editing data sequence, dividing the historical contract editing data sequence into a plus / minus score historical contract editing data sequence, and calculating the plus score contract editing factor and minus score contract editing factor of the historical contract editing data sequence according to the plus / minus score historical contract editing data sequence, it includes: Determine the preset historical contract editing data corresponding to the historical contract editing data sequence; Divide the historical contract editing data sequence according to the preset historical contract editing data. When the historical contract editing data in the historical contract editing data sequence is less than or equal to the preset historical contract editing data, the corresponding historical contract editing data is divided into the plus score historical contract editing data sequence; When the historical contract editing data in the historical contract editing data sequence is greater than the preset historical contract editing data, the corresponding historical contract editing data is divided into the minus score historical contract editing data sequence; Calculate the plus score contract editing factor of the historical contract editing data sequence according to the plus score historical contract editing data sequence; Calculate the minus score contract editing factor of the historical contract editing data sequence according to the minus score historical contract editing data sequence.

[0024] In this embodiment, the preset historical contract editing data corresponds to the historical contract editing data. As described above, the preset historical contract editing data corresponding to the unauthorized access times is 3 times, the preset historical contract editing data corresponding to the unauthorized editing times is 2 times, and the preset historical contract editing data corresponding to the malicious editing times is 2 times. The rest are not shown one by one and can be set according to the actual situation.

[0025] The beneficial effect of the above technical solution is that the present invention can lay a foundation for calculating the plus score contract editing factor and minus score contract editing factor by determining the plus score historical contract editing data sequence and the minus score historical contract editing data sequence.

[0026] In some embodiments of the present application, when calculating the plus score contract editing factor of the historical contract editing data sequence according to the plus score historical contract editing data sequence, it includes: Randomly combine the historical contract editing data in the bonus historical contract editing data sequence in pairs to determine multiple groups of historical contract editing data; Determine whether two groups of historical contract editing data in the historical contract editing data group are equal. If so, delete the corresponding historical contract editing data group; If not, retain the corresponding historical contract editing data group and calculate the sum value of the historical contract editing data of the two historical contract editing data in each retained historical contract editing data group; Determine the variance of the historical contract editing data of all historical contract editing data sum values, and generate a first calculation mark for all historical contract editing data sum values less than or equal to the variance of the historical contract editing data; Generate a second calculation mark for all historical contract editing data sum values greater than the variance of the historical contract editing data; Calculate the bonus contract editing factor of the historical contract editing data sequence according to the historical contract editing data sum value, the first calculation mark, and the second calculation mark.

[0027] In this embodiment, after randomly combining the historical contract editing data in the bonus historical contract editing data sequence in pairs, if there is a single historical contract editing data, just delete this single historical contract editing data.

[0028] In this embodiment, the calculation process of the variance of the historical contract editing data will not be introduced in detail here.

[0029] The beneficial effects of the above technical solution are: The present invention calculates the bonus contract editing factor of the historical contract editing data sequence according to the historical contract editing data sum value, the first calculation mark, and the second calculation mark. The present invention can ensure the calculation accuracy of the bonus contract editing factor, provide a basis for calculating the recommendation factor of the historical contract editing data, and thus ensure the accuracy of the recommendation factor of the historical contract editing data.

[0030] In some embodiments of the present application, when calculating the bonus contract editing factor of the historical contract editing data sequence according to the historical contract editing data sum value, the first calculation mark, and the second calculation mark, it includes: Calculate the bonus contract editing factor of the historical contract editing data sequence according to the following formula: ; where t is the bonus contract editing factor of the historical contract editing data sequence, y is the number of retained historical contract editing data groups, p1 is the first calculation coefficient, a u is the sum value of the historical contract editing data of the u-th retained historical contract editing data group, a maxLet p1 be the maximum historical contract editing data sum value, p2 be the second calculation coefficient, and s1 u be a historical contract editing data in the u-th reserved historical contract editing data group, and s2 u be another historical contract editing data in the u-th reserved historical contract editing data group. Let d1 be the first calculation marker quantity, d2 be the second calculation marker quantity, p1 + p2 = 1, and p1 < p2.

[0031] In some embodiments of the present application, when calculating the deduction contract editing factor of the historical contract editing data sequence according to the deduction historical contract editing data sequence, it includes: Determine the maximum historical contract editing data and the minimum historical contract editing data from the deduction historical contract editing data sequence; Calculate the deduction contract editing factor of the historical contract editing data sequence according to the maximum historical contract editing data and the minimum historical contract editing data; ; Where q is the deduction contract editing factor of the historical contract editing data sequence, w is the number of historical contract editing data in the deduction historical contract editing data sequence, r e is the e-th historical contract editing data in the deduction historical contract editing data sequence, r1 e is the weight of the e-th historical contract editing data, r min is the minimum historical contract editing data, r max is the maximum historical contract editing data, for all is the maximum value.

[0032] The beneficial effects of the above technical solution are: The present invention calculates the deduction contract editing factor of the historical contract editing data sequence according to the maximum historical contract editing data and the minimum historical contract editing data. The present invention can ensure the calculation accuracy of the deduction contract editing factor, provide a basis for calculating the recommendation factor of the historical contract editing data, and further ensure the accuracy of the recommendation factor of the historical contract editing data.

[0033] S130: Calculate the historical contract editing data recommendation factor of the contract editing team according to the addition contract editing factor and the deduction contract editing factor corresponding to each historical contract editing data sequence; In some embodiments of the present application, when calculating the historical contract editing data recommendation factor of the contract editing team according to the addition contract editing factor and the deduction contract editing factor corresponding to each historical contract editing data sequence, it includes: Randomly select a historical contract editing data sequence, and extract the corresponding first addition contract editing factor and the first deduction contract editing factor; Randomly select another historical contract editing data sequence, and extract the corresponding second bonus contract editing factor and second deduction contract editing factor; Calculate the absolute value of the difference between the first bonus contract editing factor and the second bonus contract editing factor; Select the maximum bonus contract editing factor and the minimum bonus contract editing factor from all the bonus contract editing factors, and calculate the difference between the maximum bonus contract editing factor and the minimum bonus contract editing factor; Calculate the ratio of the absolute value of the difference between the bonus contract editing factors and the difference between the bonus contract editing factors, and use it as the factor to be calculated for bonus; Calculate the absolute value of the difference between the first deduction contract editing factor and the second deduction contract editing factor; Select the maximum deduction contract editing factor and the minimum deduction contract editing factor from all the deduction contract editing factors, and calculate the difference between the maximum deduction contract editing factor and the minimum deduction contract editing factor; Calculate the ratio of the absolute value of the difference between the deduction contract editing factors and the difference between the deduction contract editing factors, and use it as the factor to be calculated for deduction; Determine the sum value of the factor to be calculated for bonus and the factor to be calculated for deduction, and use it as the comprehensive factor to be calculated; Perform random selection calculations on the remaining historical contract editing data sequences to obtain multiple comprehensive factors to be calculated; Calculate the recommended factor for the historical contract editing data of the contract editing team based on all the comprehensive factors to be calculated.

[0034] In this embodiment, for the sake of easy distinction, the bonus contract editing factor and the deduction contract editing factor of the first randomly selected historical contract editing data sequence are respectively named the first bonus contract editing factor and the first deduction contract editing factor. The bonus contract editing factor and the deduction contract editing factor of the second randomly selected historical contract editing data sequence are respectively named the second bonus contract editing factor and the second deduction contract editing factor.

[0035] In this embodiment, when performing random selection calculations on the remaining historical contract editing data sequences, if there is a single historical contract editing data sequence, this single historical contract editing data sequence does not participate in the calculation.

[0036] The beneficial effects of the above technical solution are as follows: According to the historical contract editing data sequence, the present invention calculates multiple comprehensive factors to be calculated, and calculates the historical contract editing data recommendation factor of the contract editing team based on all the comprehensive factors to be calculated. Without manual participation in the calculation, the calculation accuracy is guaranteed, and there is no need to build a complex model, thus improving the recommendation efficiency. The historical contract editing data recommendation factor comprehensively reflects the historical editing situation of the contract editing team, providing a basis for the selection of the contract editing team.

[0037] In some embodiments of the present application, when calculating the historical contract editing data recommendation factor of the contract editing team based on all the comprehensive factors to be calculated, it includes: Calculate the historical contract editing data recommendation factor of the contract editing team according to the following formula: ; where g is the historical contract editing data recommendation factor of the contract editing team, h1 is a calculation function, and its value range is (0.1, 0.5), n1 is the number of comprehensive factors to be calculated, k1 is the mean value of all comprehensive factors to be calculated, b i is the i-th comprehensive factor to be calculated, for all is the minimum value.

[0038] S140: Determine the historical contract editing data recommendation factor corresponding to each contract editing team, sort the values of all historical contract editing data recommendation factors, select the contract editing team corresponding to the largest historical contract editing data recommendation factor based on the sorting result, and perform collaborative editing on the fuel contract template.

[0039] In this embodiment, if there are the same largest historical contract editing data recommendation factors, then select according to the working years of the contract editors in the contract editing team, or select according to the actual contract requirements. For example, if this contract is more inclined to legal terms, then select the contract editing team according to the degree of mastery of legal knowledge of the contract editing team, or select according to the contract completion efficiency of the contract editing team. For example, if the contract editing team can complete the contract accurately and in advance every time they edit a contract, then it will be selected first. Specifically, it can be selected according to the actual situation.

[0040] In some embodiments of the present application, when selecting the contract editing team corresponding to the largest historical contract editing data recommendation factor based on the sorting result and performing collaborative editing on the fuel contract template, it includes: Real-time track the collaborative editing content of the contract editing team on the fuel contract template, analyze the collaborative editing content according to the sensitive word library, and issue an editing content modification instruction when there are sensitive words; Track the collaborative modification records of the contract editing team on the fuel contract template in real time and upload the collaborative modification records in real time, where the collaborative modification records include modification timestamps, modifier identities, and modification contents.

[0041] In this embodiment, AI and network technologies are also used to keep the template synchronized immediately during text editing, enabling the contract editing team to see the modifications made to the contract by all participants in a single fuel contract template.

[0042] The beneficial effects of the above technical solutions are as follows: By calculating the recommendation factors for historical contract editing data, the recommendation accuracy and efficiency of the contract editing team are improved, and a contract editing team with low risk and high efficiency is recommended for fuel contract template editing, ensuring the comprehensiveness and security of contract collaborative editing. By tracking and analyzing the edited content in real time, potential problems or errors can be immediately discovered, and thus measures can be quickly taken for correction. This timely feedback mechanism greatly reduces the accumulation of errors and rework caused by information lag. Automatically detecting sensitive words and issuing modification instructions reduces the workload and time cost of manual review. This automated processing not only improves work efficiency but also ensures the compliance and accuracy of contract content. Each modification is recorded and uploaded in real time, forming a complete version history record. This helps contract editors view previous modification situations at any time, understand the evolution process of the contract, and facilitate subsequent management and review. The use of a sensitive word library can effectively prevent the leakage of sensitive information. When sensitive words appear in the edited content, the system will immediately issue a warning and require modification to ensure that all sensitive information is properly handled. The modification records uploaded in real time are not only a record of the current work but also a form of data backup. Even in case of unexpected situations (such as system failures or human errors), the previous state can be quickly restored through these records. By tracking and recording the identity information of modifiers in real time, better permission management can be carried out. Only authorized personnel can access and modify contract content, ensuring the security and integrity of the data. Real-time tracking and recording of modification records enable all contract editors to clearly see the work progress of each person. This transparent management helps enhance trust and cooperation among the team and reduces communication barriers. Each modification has a detailed record, including the modification timestamp and the modifier's identity. This not only helps trace responsibility but also encourages contract editors to complete their tasks more seriously and responsibly. Supporting multiple people to edit the same document online simultaneously greatly improves the efficiency of contract editing team collaboration. Everyone can immediately see the modifications made by others, avoiding duplicate work and conflicts.

[0043] To further elaborate on the technical idea of the present invention, the technical solutions of the present invention will be described in combination with specific application scenarios.

[0044] Correspondingly, as Figure 2As shown in the figure, the present application also provides an AI-based collaborative editing recommendation system for fuel contract templates, including: A data analysis module, configured to determine multiple contract editing teams to be selected, obtain the historical contract editing data of each contract editor in the contract editing team, analyze the historical contract editing data, and determine multiple historical contract editing data sequences; A first calculation module, configured to process each historical contract editing data sequence, divide the historical contract editing data sequence into a plus / minus score historical contract editing data sequence, and calculate the plus score contract editing factor and minus score contract editing factor of the historical contract editing data sequence according to the plus / minus score historical contract editing data sequence, where the plus / minus score historical contract editing data sequence includes a plus score historical contract editing data sequence and a minus score historical contract editing data sequence; A second calculation module, configured to calculate the historical contract editing data recommendation factor of the contract editing team according to the plus score contract editing factor and minus score contract editing factor corresponding to each historical contract editing data sequence; A team recommendation module, configured to determine the historical contract editing data recommendation factor corresponding to each contract editing team, sort the values of all the historical contract editing data recommendation factors, select the contract editing team corresponding to the maximum historical contract editing data recommendation factor based on the sorting result, and perform collaborative editing on the fuel contract template.

[0045] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0046] Although the present invention has been described above with reference to the embodiments, various improvements can be made to it and components therein can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention can be combined with each other in any way, and the situations of these combinations are not all described in this specification only for the sake of saving space and resources.

[0047] Those of ordinary skill in the art can understand that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A fuel contract template collaborative editing and recommendation method based on AI, characterized in that: include: Determine a plurality of contract editing teams to be selected, obtain historical contract editing data of each contract editor in the contract editing team, analyze the historical contract editing data, and determine a plurality of historical contract editing data sequences; Processing each historical contract editing data sequence, dividing the historical contract editing data sequence into a plus-point historical contract editing data sequence and a minus-point historical contract editing data sequence, and calculating a plus-point contract editing factor and a minus-point contract editing factor of the historical contract editing data sequence according to the plus-point historical contract editing data sequence, wherein the plus-point historical contract editing data sequence includes a plus-point historical contract editing data sequence and a minus-point historical contract editing data sequence; Calculate the historical contract editing data recommendation factor of the contract editing team according to the bonus contract editing factor and the deduction contract editing factor corresponding to each historical contract editing data sequence; Determine the historical contract editing data recommendation factor corresponding to each contract editing team, and sort all the historical contract editing data recommendation factors by numerical value. Based on the sorting results, select the contract editing team corresponding to the largest historical contract editing data recommendation factor to collaboratively edit the fuel contract template.

2. The AI-based collaborative editing and recommendation method for fuel contract templates according to claim 1 is characterized in that: When processing each historical contract editing data sequence, dividing the historical contract editing data sequence into a plus-minus-point historical contract editing data sequence, and calculating a plus-point contract editing factor and a minus-point contract editing factor of the historical contract editing data sequence according to the plus-minus-point historical contract editing data sequence, the method includes: Determining preset historical contract editing data corresponding to the historical contract editing data sequence; The historical contract editing data sequence is divided according to the preset historical contract editing data, and when the historical contract editing data in the historical contract editing data sequence is less than or equal to the preset historical contract editing data, the corresponding historical contract editing data is divided into the bonus historical contract editing data sequence; When the historical contract editing data in the historical contract editing data sequence is greater than the preset historical contract editing data, the corresponding historical contract editing data is classified into the deducted historical contract editing data sequence; Calculating the bonus contract editing factor of the historical contract editing data sequence according to the bonus historical contract editing data sequence; The point reduction contract editing factor of the historical contract editing data sequence is calculated according to the point reduction historical contract editing data sequence.

3. The AI-based collaborative editing and recommendation method for fuel contract templates according to claim 2 is characterized in that: When calculating the additional contract editing factor of the historical contract editing data sequence according to the additional historical contract editing data sequence, it includes: Randomly combining the historical contract editing data in the scoring historical contract editing data sequence in pairs to determine a plurality of historical contract editing data groups; Determining whether two historical contract editing data groups in the historical contract editing data groups are equal, and if so, deleting the corresponding historical contract editing data groups; If not, the corresponding historical contract editing data group is retained, and the historical contract editing data and value of two historical contract editing data in each retained historical contract editing data group are calculated; Determine the historical contract editing data variance of all historical contract editing data and values, and generate a first calculation mark for all historical contract editing data and values ​​that are less than or equal to the historical contract editing data variance; generating a second calculation mark for all historical contract editing data and values ​​that are greater than the variance of the historical contract editing data; A bonus contract editing factor of the historical contract editing data sequence is calculated based on the historical contract editing data and value, the first calculation tag and the second calculation tag.

4. The AI-based collaborative editing and recommendation method for fuel contract templates according to claim 3 is characterized in that: When calculating the bonus contract editing factor of the historical contract editing data sequence according to the historical contract editing data and value, the first calculation mark and the second calculation mark, it includes: The bonus contract editing factor of the historical contract editing data series is calculated according to the following formula: ； Where t is the bonus contract editing factor of the historical contract editing data series, y is the number of historical contract editing data groups retained, p1 is the first calculation coefficient, a u The historical contract edit data and value for the u-th retained historical contract edit data group, a max Edit data and value for the largest historical contract, p2 is the second calculation coefficient, s1 u A historical contract edit data in the u-th reserved historical contract edit data group, s2 u It is another historical contract editing data in the u-th retained historical contract editing data group, d1 is the first calculation mark quantity, d2 is the second calculation mark quantity, p1+p2=1, p1<p2.

5. The AI-based collaborative editing and recommendation method for fuel contract templates according to claim 2 is characterized in that: When calculating the point reduction contract editing factor of the historical contract editing data sequence according to the point reduction historical contract editing data sequence, it includes: Determine the maximum historical contract editing data and the minimum historical contract editing data from the deduction historical contract editing data sequence; Calculate the deduction contract editing factor of the historical contract editing data sequence according to the maximum historical contract editing data and the minimum historical contract editing data; ； Where q is the deduction contract editing factor of the historical contract editing data sequence, w is the number of historical contract editing data in the deduction historical contract editing data sequence, r e The e-th historical contract editing data in the historical contract editing data sequence of the deduction, r1 e The weight of editing data for the e-th historical contract, r min Edit data for minimal historical contracts, r max Compile data for the largest historical contracts, For all The maximum value of .

6. The AI-based collaborative editing and recommendation method for fuel contract templates according to claim 1 is characterized in that: When calculating the historical contract editing data recommendation factor of the contract editing team according to the plus contract editing factor and minus contract editing factor corresponding to each historical contract editing data sequence, it includes: Randomly select a historical contract editing data sequence, and extract the corresponding first plus contract editing factor and first minus contract editing factor; Randomly select another historical contract editing data sequence, and extract the corresponding second plus contract editing factor and second minus contract editing factor; Calculating the absolute value of the difference between the first bonus contract editing factor and the second bonus contract editing factor; Selecting a maximum additional contract editing factor and a minimum additional contract editing factor from all additional contract editing factors, and calculating the additional contract editing factor difference between the maximum additional contract editing factor and the minimum additional contract editing factor; Calculate the ratio of the absolute value of the difference in the editing factor of the bonus contract and the difference in the editing factor of the bonus contract, and use it as the factor to be calculated for bonus points; Calculating the absolute value of the difference between the first point reduction contract editing factor and the second point reduction contract editing factor; Selecting a maximum point reduction contract editing factor and a minimum point reduction contract editing factor from all point reduction contract editing factors, and calculating a point reduction contract editing factor difference between the maximum point reduction contract editing factor and the minimum point reduction contract editing factor; Calculate the ratio of the absolute value of the difference between the editing factors of the contract for deduction and the difference between the editing factors of the contract for deduction, and use it as the factor to be calculated for deduction; Determine the sum of the factor to be calculated for adding points and the factor to be calculated for subtracting points, and use it as the comprehensive factor to be calculated; The remaining historical contract editing data sequences are randomly selected and calculated to obtain multiple comprehensive factors to be calculated; The historical contract editing data recommendation factor of the contract editing team is calculated based on all comprehensive factors to be calculated.

7. The AI-based collaborative editing and recommendation method for fuel contract templates according to claim 6 is characterized in that: When calculating the recommended factors of the contract editing team's historical contract editing data based on all comprehensive factors to be calculated, including: The recommendation factor for the contract editing team's historical contract editing data is calculated according to the following formula: ； Among them, g is the recommended factor of the contract editing team's historical contract editing data, h1 is the calculation function, and its value range is (0.1, 0.5), n1 is the number of comprehensive factors to be calculated, k1 is the mean of all comprehensive factors to be calculated, and b i is the i-th comprehensive factor to be calculated, For all The minimum value of .

8. The AI-based collaborative editing and recommendation method for fuel contract templates according to claim 1 is characterized in that: When the contract editing team corresponding to the maximum historical contract editing data recommendation factor is selected based on the sorting results, the fuel contract template is collaboratively edited, including: Track the collaborative editing content of the contract editing team on the fuel contract template in real time, analyze the collaborative editing content according to the sensitive word database, and issue an editing content modification instruction when sensitive words are present; Track the collaborative modification records of the contract editing team on the fuel contract template in real time, and upload the collaborative modification records in real time, wherein the collaborative modification records include modification timestamp, modifier identity, and modification content.

9. An AI-based fuel contract template collaborative editing and recommendation system, applied to the AI-based fuel contract template collaborative editing and recommendation method according to any one of claims 1 to 8, characterized in that: include: A data analysis module, used to determine a plurality of contract editing teams to be selected, obtain historical contract editing data of each contract editor in the contract editing team, analyze the historical contract editing data, and determine a plurality of historical contract editing data sequences; A first calculation module is used to process each historical contract editing data sequence, divide the historical contract editing data sequence into a plus-minus-point historical contract editing data sequence, and calculate a plus-point contract editing factor and a minus-point contract editing factor of the historical contract editing data sequence according to the plus-minus-point historical contract editing data sequence, wherein the plus-minus-point historical contract editing data sequence includes a plus-point historical contract editing data sequence and a minus-point historical contract editing data sequence; A second calculation module is used to calculate the historical contract editing data recommendation factor of the contract editing team according to the plus contract editing factor and minus contract editing factor corresponding to each historical contract editing data sequence; The team recommendation module is used to determine the historical contract editing data recommendation factor corresponding to each contract editing team, and to sort the numerical values ​​of all the historical contract editing data recommendation factors. Based on the sorting results, the contract editing team corresponding to the largest historical contract editing data recommendation factor is selected to collaboratively edit the fuel contract template.

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