CN117194480B - Calculation auxiliary system based on scene data verification - Google Patents
Calculation auxiliary system based on scene data verification Download PDFInfo
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- CN117194480B CN117194480B CN202311480104.2A CN202311480104A CN117194480B CN 117194480 B CN117194480 B CN 117194480B CN 202311480104 A CN202311480104 A CN 202311480104A CN 117194480 B CN117194480 B CN 117194480B
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Abstract
The invention discloses a calculation auxiliary system based on field scene data verification, which belongs to the technical field of artificial intelligence, wherein the calculation auxiliary system integrates and classifies calculation formulas commonly used in supervision work, and when the supervision work is carried out on the field, a worker can retrieve and call a proper formula on terminal equipment according to the need to calculate a required result, so that the formula memory requirement on the worker is reduced, the required result is obtained rapidly, and the supervision efficiency is improved; in addition, by mining the relevance between each calculation formula and other calculation formulas, when a user uses one calculation formula in actual operation, the calculation formulas possibly used by the user in the future are recommended according to the relevance between each calculation formula, so that the situation that the user needs to retrieve the corresponding calculation formula again when calculating a certain parameter every time is avoided, and the calculation efficiency is greatly improved.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to a calculation auxiliary system based on field scene data verification.
Background
In the project implementation process, a supervisor needs to use standards such as laws and regulations, technical specifications and the like to calculate to verify the safety, progress, quality, investment and other links of the project, and the project management and control conditions relate to a plurality of empirical formulas, mathematical expressions and the like, so that the improvement of the project supervision professional computing capacity is particularly important. However, due to uneven technical skill levels of the prisoner, many mathematical formulas often require repeated exercises and long-term use by the user to be mastered. And after receiving a great deal of knowledge and information, the user can forget to go and go for a long time. When the formulas are required in the supervision construction site, if error information exists in the temporary internet surfing search, a lot of time is consumed if the specification data is temporarily browsed or the computer is used for inquiring and calculating. Therefore, if a calculator aiming at a built-in formula required by supervision is provided, a supervision person can flexibly and accurately perform calculation verification in the engineering project, objective, real, accurate, timely and reliable data verification can be provided for the engineering project, and the high-quality finishing effect of the project is ensured.
Disclosure of Invention
The invention aims to provide a calculation auxiliary system based on field scene data verification, which solves the problems that in the prior art, a supervision staff in a monitoring working field needs to master a plurality of calculation formulas to perform accurate verification work, has higher professional requirements and has low efficiency.
The aim of the invention can be achieved by the following technical scheme:
a computing assistance system based on scene data verification, comprising:
a user login unit through which a user logs in the system;
the formula library is used for storing a calculation formula, and the calculation formula is divided into a plurality of formula classes according to the application field;
the searching unit is used for matching corresponding calculation formulas in the formula library according to the search keywords and importing the calculation formulas obtained by matching into the workbench;
the acquisition unit is used for inputting observation data by a user;
and the workbench is used for calculating and outputting a corresponding calculated value according to the observation data input in the acquisition unit and a calculation formula imported into the workbench.
As a further aspect of the present invention, the workbench is further configured to recommend a calculation formula used by a user later, where the recommendation method includes the following steps:
s1, a user logs in a system in a user login unit;
when the user calculates and outputs a corresponding calculated value in the workbench through the observation data input in the acquisition unit and the calculation formula imported into the workbench, the corresponding user is considered to finish the use of the corresponding calculation formula;
s2, for a user, acquiring an active time period of the user using the calculation auxiliary system, and finishing the use of each calculation formula in the active time period;
s3, acquiring each time Tw in an active period and a calculation formula corresponding to each time Tw;
sequencing the price calculation formulas according to the sequence of the corresponding time Tw;
marking a calculation formula as a reference formula, acquiring calculation formulas after the reference formula in the corresponding active time period, and marking the calculation formulas as associated formulas of the corresponding reference formula;
sequentially assigning serial numbers 1, 2, 3, … and n to each association formula according to the sequence of the corresponding time Tw; n is a natural number, which is the number of association formulas corresponding to the reference formulas in the corresponding active time period;
acquiring a correlation formula corresponding to a reference formula in each corresponding active time period and a sequence number of each correlation formula for a user;
according to the formulaCalculating to obtain an individual association coefficient Gi between the corresponding reference formula and each association formula;
wherein k1 is the number of active time periods containing the corresponding association formula;
k is the number of active time periods;
xp is the average value of the serial numbers of the corresponding association formulas;
alpha is a preset value, and in one embodiment of the invention, the value of alpha is 1.2;
acquiring association formulas corresponding to the reference formulas in each corresponding active time period and sequence numbers of the association formulas for all users;
calculating to obtain an overall association coefficient Gz between the corresponding reference formula and each association formula;
s4, when the user finishes using the corresponding calculation formula through the workbench, acquiring an integral association coefficient Gz1 between the calculation formula and each association formula and an individual association coefficient Gi1 between the calculation formula and each association formula for the user;
calculating according to the formula g=β1×gz1+β2×gi1 to obtain a relevance coefficient G of each relevance formula for the corresponding user; wherein β1 and β2 are both preset coefficients, and β1+β2=1; recommending each association formula according to the sequence of the association coefficient G from large to small;
as a further aspect of the present invention, the method for determining the active time period of the user using the computing auxiliary system is as follows:
s21, when a user does not use the computing auxiliary system within a continuous preset time t1, the user corresponding to the account is considered to be in a silent state;
s22, when the user is in a silent state, if the user finishes using a calculation formula, the corresponding user is considered to enter an active state;
s23, when the number of corresponding time Tw of the user in one continuous active state is larger than or equal to a preset threshold value, the period covered by the corresponding active state is considered to be an active period.
As a further aspect of the present invention, when there are two or more times Tw for which one calculation formula corresponds in one active period, only the first time Tw is used as the reference formula.
As a further scheme of the invention, when two or more than two identical calculation formulas exist in the association formulas corresponding to one reference formula in one active time period, the corresponding minimum sequence number is selected to calculate the individual association coefficient and the whole association coefficient.
As a further scheme of the invention, the workbench can recommend the corresponding association formula only when the corresponding association coefficient G is larger than or equal to a preset threshold G1.
The invention has the beneficial effects that:
1. according to the invention, through integrating and classifying the common calculation formulas in the supervision work, when the supervision work is carried out on site, the staff can search and call the proper formulas on the terminal equipment according to the needs to calculate the required results, so that the formula memory requirements of the staff are reduced, the required results are obtained rapidly, and the supervision efficiency is improved.
2. According to the invention, by mining the relevance between each calculation formula and other calculation formulas, when a user uses one calculation formula in actual operation, the calculation formulas possibly used by the user in the future are recommended according to the relevance between each calculation formula, so that the situation that the user needs to retrieve the corresponding calculation formula again when calculating a certain parameter every time is avoided, and the calculation efficiency is greatly improved.
3. According to the method and the device, the influence of the personal use habit of the user on the relevance degree of each calculation formula is considered, and the calculation formulas are recommended on the basis of combining the relevance of the integral calculation formulas and the personal habit of the user, so that the accuracy of a recommendation result is ensured.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a framework of a computing assistance system based on scene data verification in accordance with the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A computing assistance system based on scene data verification, as shown in fig. 1, comprising:
the formula library is used for storing a calculation formula, and the calculation formula is divided into a plurality of formula classes according to the application field;
the application fields comprise civil engineering, distribution network, electric, circuit and the like;
in one embodiment of the invention, the formula class corresponding to civil engineering comprises a cast-in-place pile square quantity theory, a construction engineering face brick bonding strength, a field leveling engineering quantity calculation formula, a high-pressure injection grouting foundation slurry consumption calculation formula, a pretensioned prestressing pipe pile, a concrete precast pile core concrete calculation formula and the like;
the searching unit is used for inputting the search keywords by a user, matching corresponding calculation formulas in the formula library according to the search keywords and importing the calculation formulas obtained by matching into the workbench;
the acquisition unit is used for inputting observation data by a user;
the workbench is used for calculating and outputting a corresponding calculated value according to the observation data input in the acquisition unit and a calculation formula imported into the workbench;
the method is also used for recommending a calculation formula possibly used by the user subsequently according to the use record of the user;
a user login unit in which a user logs in the system;
the method for recommending the calculation formula used by the user by the workbench comprises the following steps:
s1, a user logs in a system in a user login unit;
when the user calculates and outputs a corresponding calculated value in the workbench through the observation data input in the acquisition unit and the calculation formula imported into the workbench, the corresponding user is considered to finish the use of the corresponding calculation formula;
s2, for a user, acquiring an active time period of the user using the calculation auxiliary system, and finishing the use of each calculation formula in the active time period;
the method for determining the active time period of the computing auxiliary system for the user comprises the following steps:
s21, when a user does not use the computing auxiliary system within a continuous preset time t1, the user corresponding to the account is considered to be in a silent state;
s22, when the user is in a silent state, if the user finishes using a calculation formula, the corresponding user is considered to enter an active state;
s23, when the number of corresponding time Tw of the user in one continuous active state is larger than or equal to a preset threshold value, the period covered by the corresponding active state is considered to be an active period.
S3, acquiring each time Tw in an active period and a calculation formula corresponding to each time Tw;
sequencing the price calculation formulas according to the sequence of the corresponding time Tw;
marking a calculation formula as a reference formula, acquiring calculation formulas after the reference formula in the corresponding active time period, and marking the calculation formulas as associated formulas of the corresponding reference formula;
sequentially assigning serial numbers 1, 2, 3, … and n to each association formula according to the sequence of the corresponding time Tw; n is a natural number, which is the number of association formulas corresponding to the reference formulas in the corresponding active time period;
it should be noted that, when there are two or more times Tw corresponding to one calculation formula in one active period, only the first time Tw is taken as a reference formula;
acquiring a correlation formula corresponding to a reference formula in each corresponding active time period and a sequence number of each correlation formula for a user;
according to the formulaCalculating to obtain an individual association coefficient Gi between the corresponding reference formula and each association formula;
wherein k1 is the number of active time periods containing the corresponding association formula;
k is the number of active time periods;
xp is the average value of the serial numbers of the corresponding association formulas;
alpha is a preset value, and in one embodiment of the invention, the value of alpha is 1.2;
acquiring association formulas corresponding to the reference formulas in each corresponding active time period and sequence numbers of the association formulas for all users;
calculating to obtain an overall association coefficient Gz between the corresponding reference formula and each association formula;
it should be noted that, in an active period, when two or more than two identical calculation formulas exist in the association formulas corresponding to one reference formula, the corresponding minimum sequence number is selected to calculate the individual association coefficient and the overall association coefficient;
s4, sequentially calculating to obtain an individual association coefficient Gi and an overall association coefficient Gz between each calculation formula and each corresponding association formula when each calculation formula is used as a reference formula according to the method in the step S3;
when a user finishes using a corresponding calculation formula through a workbench, acquiring an integral association coefficient Gz1 between the calculation formula and each association formula and an individual association coefficient Gi1 between the calculation formula and each association formula for the user;
calculating according to the formula g=β1×gz1+β2×gi1 to obtain a relevance coefficient G of each relevance formula for the corresponding user;
wherein β1 and β2 are both preset coefficients, and β1+β2=1;
recommending each association formula through a workbench according to the sequence of the association coefficient G from large to small;
in one embodiment of the present invention, the workbench recommends the corresponding association formula only when the corresponding association coefficient G is equal to or greater than the preset threshold G1;
in another embodiment of the invention, a folding window is arranged when the workbench recommends the association formula, the user selects the association formula after clicking and opening the folding window, and after opening the folding window, the association formulas corresponding to the folding window are ordered according to the order of the association degree coefficient G from large to small;
according to the invention, through integrating and classifying the calculation formulas commonly used in the supervision work, when the supervision work is carried out on site, a worker can search and call the proper formulas on the terminal equipment according to the needs to calculate the required results, so that the formula memory requirements of the worker are reduced, the required results are obtained rapidly, and the supervision efficiency is improved;
according to the invention, by mining the relevance between each calculation formula and other calculation formulas, when a user uses one calculation formula in actual operation, the calculation formulas possibly used by the user in the future are recommended according to the relevance between each calculation formula, so that the situation that the user needs to retrieve the corresponding calculation formula again when calculating a certain parameter every time is avoided, and the calculation efficiency is greatly improved;
in addition, the invention also considers the influence of the personal use habit of the user on the degree of association between the calculation formulas, recommends the calculation formulas on the basis of combining the association between the integral calculation formulas and the personal habit of the user, thereby ensuring the accuracy of the recommendation result.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative and explanatory of the invention, as various modifications and additions may be made to the particular embodiments described, or in a similar manner, by those skilled in the art, without departing from the scope of the invention or exceeding the scope of the invention as defined in the claims.
Claims (5)
1. A computing assistance system based on scene data verification, comprising:
a user login unit through which a user logs in the system;
the formula library is used for storing a calculation formula, and the calculation formula is divided into a plurality of formula classes according to the application field;
the searching unit is used for matching corresponding calculation formulas in the formula library according to the search keywords and importing the calculation formulas obtained by matching into the workbench;
the acquisition unit is used for inputting observation data by a user;
the workbench is used for calculating and outputting a corresponding calculated value according to the observation data input in the acquisition unit and a calculation formula imported into the workbench;
the workbench is also used for recommending a calculation formula used by a user subsequently, and the recommending method comprises the following steps:
s1, a user logs in a system in a user login unit;
when the user calculates and outputs a corresponding calculated value in the workbench through the observation data input in the acquisition unit and the calculation formula imported into the workbench, the corresponding user is considered to finish the use of the corresponding calculation formula;
s2, for a user, acquiring an active time period of the user using the calculation auxiliary system, and finishing the use of each calculation formula in the active time period;
s3, acquiring each time Tw in an active period and a calculation formula corresponding to each time Tw;
sequencing the price calculation formulas according to the sequence of the corresponding time Tw;
marking a calculation formula as a reference formula, acquiring calculation formulas after the reference formula in the corresponding active time period, and marking the calculation formulas as associated formulas of the corresponding reference formula;
sequentially assigning serial numbers 1, 2, 3, … and n to each association formula according to the sequence of the corresponding time Tw; n is a natural number, which is the number of associated companies corresponding to the reference formula in the corresponding active time period;
acquiring a correlation formula corresponding to a reference formula in each corresponding active time period and a sequence number of each correlation formula for a user;
according to the formulaCalculating to obtain an individual association coefficient Gi between the corresponding reference formula and each association formula;
wherein k1 is the number of active time periods containing the corresponding association formula;
k is the number of active time periods;
xp is the average value of the serial numbers of the corresponding association formulas; alpha is a preset value;
acquiring association formulas corresponding to the reference formulas in each corresponding active time period and sequence numbers of the association formulas for all users;
calculating to obtain an overall association coefficient Gz between the corresponding reference formula and each association formula;
s4, when the user finishes using the corresponding calculation formula through the workbench, acquiring an integral association coefficient Gz1 between the calculation formula and each association formula and an individual association coefficient Gi1 between the calculation formula and each association formula for the user;
calculating according to a formula g=β1×gz1+β2×gi1 to obtain a correlation coefficient G of each correlation formula for a corresponding user; wherein β1 and β2 are both preset coefficients, and β1+β2=1; and recommending the association formulas according to the order of the association coefficient G from large to small.
2. The computing assistance system of claim 1, wherein the method for determining the active time period of the computing assistance system is:
s21, when a user does not use the computing auxiliary system within a continuous preset time t1, the user corresponding to the account is considered to be in a silent state;
s22, when the user is in a silent state, if the user finishes using a calculation formula, the corresponding user is considered to enter an active state;
s23, when the number of corresponding time Tw of the user in one continuous active state is larger than or equal to a preset threshold value, the period covered by the corresponding active state is considered to be an active period.
3. The calculation assistance system based on-site scene data verification according to claim 1, wherein when there are two or more times Tw for which one calculation formula corresponds in one active period, only the first time Tw is used as a reference formula.
4. The system of claim 3, wherein when two or more of the same calculation formulas are included in the correlation formulas corresponding to one reference formula in one active period, the corresponding minimum sequence number is selected to calculate the individual correlation coefficient and the overall correlation coefficient.
5. The system of claim 1, wherein the workstation recommends the corresponding association formula only when the corresponding association coefficient G is equal to or greater than a predetermined threshold G1.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110021019A (en) * | 2009-08-25 | 2011-03-04 | (주)윕스 | Apparatus and method for recommending of search formula |
| CN102324072A (en) * | 2011-09-14 | 2012-01-18 | 西安易博软件有限责任公司 | Full three-dimensional digital knowledge base system and application method of knowledge base |
| CN105893338A (en) * | 2015-02-17 | 2016-08-24 | 北京三星通信技术研究有限公司 | Formula input method, formula input device and electronic equipment |
| CN113642307A (en) * | 2021-08-12 | 2021-11-12 | 卡斯柯信号有限公司 | Python-based rail transit vehicle-mounted safety parameter automatic verification method and system |
| CN115186063A (en) * | 2022-07-15 | 2022-10-14 | 中国银行股份有限公司 | Service data processing method, device, equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9965533B2 (en) * | 2015-08-28 | 2018-05-08 | 6Connect, Inc. | Reverse application programming interface (API) endpoint creation process and standardized schema |
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- 2023-11-08 CN CN202311480104.2A patent/CN117194480B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110021019A (en) * | 2009-08-25 | 2011-03-04 | (주)윕스 | Apparatus and method for recommending of search formula |
| CN102324072A (en) * | 2011-09-14 | 2012-01-18 | 西安易博软件有限责任公司 | Full three-dimensional digital knowledge base system and application method of knowledge base |
| CN105893338A (en) * | 2015-02-17 | 2016-08-24 | 北京三星通信技术研究有限公司 | Formula input method, formula input device and electronic equipment |
| CN113642307A (en) * | 2021-08-12 | 2021-11-12 | 卡斯柯信号有限公司 | Python-based rail transit vehicle-mounted safety parameter automatic verification method and system |
| CN115186063A (en) * | 2022-07-15 | 2022-10-14 | 中国银行股份有限公司 | Service data processing method, device, equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 公路工程施工综合管理信息系统的研究与实现;杨薛明;陈勇;张金凯;苑津莎;;计算机工程(11);第230-232页 * |
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