Informatization cost intelligent analysis method, system and device and storage medium

Abstract

The embodiment of the invention provides an informatization cost intelligent analysis method, system and device and a storage medium, and the method comprises the steps: extracting first target information and second target information after screening a collected original text through a multi-dimensional classification item, obtaining first content information and first result information through the analysis of parameters in the second target information, and carrying out the analysis of the first content information and the first result information. And generating first storage information with a mapping relationship from the first result information, the corresponding first content information and the multi-dimensional classification item, and executing the input function. According to the embodiment of the invention, a data analysis technology is applied, the case library data is collected, the data is summarized and classified, intelligent screening is realized in the early stage of cost information importing, the importing efficiency and the importing effect are improved, and the workload of manual auditing is reduced. The information data collected through the unified standard provides powerful support for government affair informatization cost estimation, and the cost estimation accuracy is ensured.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of data analysis technology, and specifically to an information-based intelligent cost analysis method, system, device and storage medium. Background Art

[0002] In the construction of government information technology projects, cost estimation is the basic basis for investment decisions. Cost estimation in the early stage of project establishment requires complete information coverage and accurate cost. If the early construction contents are not perfect, it will directly affect the accuracy and completeness of the cost estimation.

[0003] At present, the problems in the cost estimation of government informationization are mainly manifested in the following aspects:

[0004] (1) There is a serious lack of necessary tools for conducting preliminary cost estimation of government information technology, which in turn affects the efficiency and accuracy of assisting users in estimating in the early stages of a project and cannot meet the needs of investment decision-making for government information technology projects.

[0005] (2) The budget expenditure policy documents for government information technology projects issued by various provinces and cities are numerous in number based on administrative divisions, and numerous in number based on construction categories. This can lead to errors in the reference of cost bases, wrong cost items, missing items, and omissions, insufficient policy interpretation, and inconsistent overall output standards.

[0006] (3) In the current implementation of government information technology projects, there is a lack of awareness of cost information collection, and the relevant information of engineering construction has not been uniformly entered into the own system. The cost compilation in the early stage of government information technology did not use the existing software to form a unified template, resulting in the loss of a lot of construction related data and the failure to fully utilize the value of historical data. At present, traditional information collection methods are still used to collect information, which is less accurate and the information is not comprehensive enough, making it impossible to accurately classify and process the information. Summary of the invention

[0007] To this end, embodiments of the present invention provide an information-based intelligent cost analysis method, system, device and storage medium to solve the technical problems of low efficiency and poor accuracy caused by the lack of information data collected according to unified standards in the current government information-based cost estimation.

[0008] In order to achieve the above purpose, the embodiment of the present invention provides the following technical solutions:

[0009] According to a first aspect of an embodiment of the present invention, an information-based intelligent cost analysis method is provided, the method comprising:

[0010] Traversing each original text based on a multi-dimensional classification item, determining whether there is matching first target information in each original text, wherein the multi-dimensional classification item includes at least one first keyword group;

[0011] If there is matching first target information in the current original text, extract the first target information, and determine whether there is second target information in the first target information;

[0012] If there is no matching first target information in the current original text, or there is no second target information in the first target information, the original text is discarded;

[0013] If the second target information exists in the first target information, determining whether the first parameter exists in the second target information;

[0014] If the first parameter exists in the second target information, determining whether the quantity of the first parameter is greater than a first preset threshold;

[0015] If the number of the first parameters is not greater than a first preset threshold, directly dividing the second target information into corresponding first content information according to each first condition corresponding to the first parameter;

[0016] For each piece of first content information, generating first setting information of the first parameter according to the corresponding first condition;

[0017] Combining each piece of first setting information with the corresponding first content information to obtain first result information;

[0018] Each first result information is combined with the corresponding first content information and multi-dimensional classification items to generate first storage information having a mapping relationship and execute an entry function.

[0019] Furthermore, an information-based intelligent cost analysis method provided in an embodiment of the present application also includes:

[0020] If the number of the first parameters is greater than a first preset threshold, combining the first conditions corresponding to the first parameters to obtain a second condition;

[0021] According to the second condition, dividing the second target information into corresponding second content information;

[0022] For each second content information, generating second setting information of the first parameter according to the corresponding second condition;

[0023] Combining each second setting information with the corresponding second content information to obtain second result information;

[0024] Each second result information is combined with the corresponding second content information and multi-dimensional classification items to generate second storage information having a mapping relationship and execute an entry function.

[0025] Furthermore, an information-based intelligent cost analysis method provided in an embodiment of the present application also includes:

[0026] If the first parameter does not exist in the second target information, determining whether the second parameter exists in the second target information;

[0027] If the second target information contains the second parameter, directly using the second target information as the third content information;

[0028] For the third content information, using the second parameter directly as the third setting information;

[0029] combining the third setting information and the third content information to obtain third result information;

[0030] Each piece of third result information is combined with the corresponding third content information and multi-dimensional classification items to generate third storage information having a mapping relationship and execute an entry function.

[0031] Furthermore, an information-based intelligent cost analysis method provided in an embodiment of the present application also includes:

[0032] If the second parameter does not exist in the second target information, determining whether a third parameter exists in the second target information;

[0033] If the third parameter exists in the second target information, directly use the second target information as the fourth content information, and directly obtain fourth result information using the second target information;

[0034] The fourth result information, the corresponding fourth content information and the multi-dimensional classification items are used to generate fourth storage information having a mapping relationship and an entry function is executed.

[0035] Furthermore, an information-based intelligent cost analysis method provided in an embodiment of the present application also includes:

[0036] If the third parameter does not exist in the second target information, determining whether the first target result information exists in the second target information;

[0037] If the first target result information does not exist in the second target information, discard the original text;

[0038] If the first target result information exists in the second target information, directly use the first target result information as the fifth content information;

[0039] The first target result information, the corresponding fifth content information, and the multi-dimensional classification items are used to generate fifth storage information having a mapping relationship and an entry function is executed.

[0040] Further, the input function is executed, including:

[0041] Obtaining the partition information of the corresponding original text for the target to be entered;

[0042] Obtaining a corresponding first partition index and a second partition index based on the partition information;

[0043] Determine whether there is stored data under the first partition index;

[0044] If there is no stored data under the first partition index, directly storing the target to be recorded under the first partition index;

[0045] If there is stored data under the first partition index, determining whether the target to be entered is the same as the second target information corresponding to the stored data;

[0046] If the target to be recorded is the same as the second target information corresponding to the stored data, directly updating the stored data to the target to be recorded;

[0047] If the target to be entered is different from the second target information corresponding to the stored data, all corresponding stored data under the second partition index are obtained as a verification data set;

[0048] Sampling the verification data set and the target to be entered, respectively obtaining a preset number of first result sampling value arrays and corresponding second result sampling values;

[0049] Calculate the average sampling value for each result sampling value array to obtain the corresponding sampling reference value;

[0050] Based on each second result sampling value and the corresponding sampling reference value, the corresponding deviation value is calculated, and the calculation formula of the deviation value is:

[0051]

[0052] Among them, D n is the sampling reference value corresponding to the nth first result sampling value array, M n is the value collected for the nth second result, P n is the nth deviation value;

[0053] Determine whether each deviation value is less than or equal to a second preset threshold;

[0054] If each deviation value is less than or equal to the second preset threshold, the stored data is updated to the target to be entered.

[0055] Furthermore, executing the input function also includes:

[0056] If the current deviation value is greater than a second preset threshold, comparing the target to be entered with the second target information of the currently stored data under the first partition index;

[0057] Determine whether the comparison result meets a preset condition, wherein the preset condition is that the first parameter and the second parameter do not exist in the second target information of the currently stored data, and the first parameter and / or the second parameter do not exist in the second target information of the target to be entered;

[0058] If the comparison result meets the preset condition, the currently stored data is updated to the target to be entered;

[0059] If the comparison result does not meet the preset condition, the target to be entered is discarded and the currently stored data is retained.

[0060] According to a second aspect of an embodiment of the present invention, an information-based intelligent cost analysis system is provided, the system comprising:

[0061] A first verification module is used to traverse each original text based on the multi-dimensional classification items to determine whether there is matching first target information in each original text, wherein the multi-dimensional classification items include at least one group of first keyword groups; if there is no matching first target information in the current original text, the original text is discarded;

[0062] An extraction module, configured to extract the first target information if there is matching first target information in the current original text, and determine whether there is second target information in the first target information; if there is no second target information in the first target information, discard the original text;

[0063] a second verification module, configured to determine whether the first parameter exists in the second target information if the second target information exists; determine whether the number of the first parameters is greater than a first preset threshold if the first parameter exists in the second target information; if the number of the first parameters is not greater than the first preset threshold, directly divide the second target information into corresponding first content information according to each first condition corresponding to the first parameter; generate first setting information of the first parameter according to the corresponding first condition for each first content information; and combine each first setting information with the corresponding first content information to obtain first result information;

[0064] The storage module is used to generate first storage information with a mapping relationship between each first result information and the corresponding first content information and multi-dimensional classification items and execute an entry function.

[0065] According to a third aspect of an embodiment of the present invention, there is provided an information-based construction cost intelligent analysis device, the device comprising: a processor and a memory;

[0066] The memory is used to store one or more program instructions;

[0067] The processor is used to run one or more program instructions to execute the steps of an information-based cost intelligent analysis method as described in any of the above items.

[0068] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of an information-based cost intelligent analysis method as described in any one of the above items are implemented.

[0069] Compared with the prior art, the embodiment of the present application provides an information-based intelligent cost analysis method, system, device and storage medium. After filtering the collected original text using multi-dimensional classification items, the first target information and the second target information are extracted, and the first content information and the first result information are obtained by analyzing the parameters in the second target information. The first result information is combined with the corresponding first content information and multi-dimensional classification items to generate first storage information with a mapping relationship and execute the entry function. The embodiment of the present invention uses data analysis technology to collect case library data, and summarizes and classifies the data, so as to realize intelligent screening in the early stage of cost information import, improve import efficiency and import effect, and reduce the workload of manual review. The information data collected through unified standards provides strong support for government information cost estimation and ensures the accuracy of cost estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0071] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0072] Figure 1A schematic diagram of the logical structure of an information-based cost intelligent analysis system provided by an embodiment of the present invention;

[0073] Figure 2 A schematic diagram of a flow chart of an information-based cost intelligent analysis method provided by an embodiment of the present invention;

[0074] Figure 3 A schematic diagram of a flow chart of an information-based cost intelligent analysis method provided by another embodiment of the present invention;

[0075] Figure 4 A schematic diagram of a flow chart of an information-based cost intelligent analysis method provided by another embodiment of the present invention;

[0076] Figure 5 A schematic diagram of a flow chart of executing an entry function provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0077] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] The purpose of the embodiment of the present invention is to classify and study the cost of software and hardware equipment based on the national, provincial and municipal government information budget expenditure policy documents, establish a cost index library by category, and improve the accuracy of compilation. It provides strong technical support for the development of government information projects and improves the accuracy and work efficiency of cost estimation of government information projects.

[0079] In order to solve the above technical problems, Figure 1 As shown, an embodiment of the present application provides an intelligent analysis system for information-based cost, which is applied to the cost estimation of information-based projects, especially the cost estimation of government information-based projects.

[0080] Specifically, an information-based intelligent cost analysis system provided in an embodiment of the present application includes: a first verification module 1, an extraction module 2, a second verification module 3 and a storage module 4.

[0081] The first verification module 1 is used to traverse each original text based on multidimensional classification items to determine whether there is matching first target information in each original text, and the multidimensional classification items include at least one group of first keyword groups; if there is no matching first target information in the current original text, the original text is discarded.

[0082] The extraction module 2 is used to extract the first target information if there is matching first target information in the current original text, and determine whether there is second target information in the first target information; if there is no second target information in the first target information, discard the original text.

[0083] The second verification module 3 is used to determine whether there is a first parameter in the second target information if the second target information exists; if there is a first parameter in the second target information, determine whether the number of the first parameters is greater than a first preset threshold; if the number of the first parameters is not greater than the first preset threshold, directly divide the second target information into corresponding first content information according to each first condition corresponding to the first parameter; for each first content information, generate first setting information of the first parameter according to the corresponding first condition; combine each first setting information with the corresponding first content information to obtain first result information.

[0084] The storage module 4 is used to generate first storage information having a mapping relationship between each first result information and the corresponding first content information and multi-dimensional classification items and execute an entry function.

[0085] Compared with the prior art, the embodiment of the present application provides an information-based intelligent cost analysis system, which uses multi-dimensional classification items to filter the collected original text, extracts the first target information and the second target information, and obtains the first content information and the first result information by analyzing the parameters in the second target information. The first result information is combined with the corresponding first content information and multi-dimensional classification items to generate first storage information with a mapping relationship and execute an entry function. The embodiment of the present invention uses data analysis technology to collect case library data, summarize and classify the data, and realize intelligent screening in the early stage of cost information import, improve import efficiency and import effect, and reduce the workload of manual review. The information data collected through unified standards provides strong support for government information cost estimation and ensures the accuracy of cost estimation.

[0086] Corresponding to the above-disclosed information-based cost intelligent analysis system, the embodiment of the present invention further discloses an information-based cost intelligent analysis method. The following describes in detail the information-based cost intelligent analysis method disclosed in the embodiment of the present invention in combination with the above-described information-based cost intelligent analysis system.

[0087] In the embodiment of the present application, the cost estimation system of the company's existing information project is used to import the government information cost index library using Java language, MySQL8 database, etc., to realize fuzzy query, data analysis and other functions, so as to achieve the purpose of intelligent and standardized compilation of the cost of government information project. It solves the problems of the company's lack of cost indicators, rough and irregular cost compilation, and slow manual calculation.

[0088] like Figure 2 As shown, the specific steps of an information-based intelligent cost analysis method provided in an embodiment of the present application are described in detail below.

[0089] An embodiment of the present application provides an intelligent analysis method for information technology cost, and the system is applied to the cost estimation of information technology projects, especially the cost estimation of government information technology projects.

[0090] The first verification module 1 traverses each original text based on the multi-dimensional classification items to determine whether there is matching first target information in each original text.

[0091] Furthermore, the original text is the budget expenditure policy document of the government informationization of the state, provinces and cities. In order to standardize the management of the construction, construction cost management, operation and maintenance expenditure management of the national government informationization, the state, provinces and cities have successively issued documents such as: Guobanfa

[2019] No. 57, Caijian

[2016] No. 504, Yucaiyu

[2020] No. 81, Yucaiyu

[2020] No. 67. In the embodiment of the present application, around the contents such as software and hardware purchase fees, system development fees, link rental fees, integration fees, etc., the compilation basis and methods of the main parameters, quantities, unit prices and other constituent elements in the above documents are interpreted in detail to guide the budget expenditure management of government informationization projects, strengthen cost accounting management, and improve the efficiency of fund use.

[0092] As mentioned above, in the national, provincial and municipal government informationization budget expenditure policy documents, government informationization budget expenditure is often released in specific paragraphs, and in these paragraphs, there are some characteristic words. In the embodiment of the present application, a multi-dimensional classification item is formed by using a keyword group, and by traversing various national, provincial and municipal government informationization budget expenditure policy documents, the text paragraph where the government informationization budget expenditure information is located can be matched and queried. The text paragraph where the queried government informationization budget expenditure information is located is the first target information, and the first target information includes the entire paragraph of text information and chart information.

[0093] Furthermore, the multidimensional classification item includes at least one group of first keyword groups, and the multidimensional classification item is a multi-level classification index item formed according to the government informationization budget expenditure. For example, the first-level classification index items of the multidimensional classification item include: hardware, purchase software, development software, and other expenses. The second-level classification index items of the multidimensional classification item are first-level professional classification index items, and the third-level classification index items of the multidimensional classification item are second-level fine-grained professional classification index items. In addition, the hardware is also provided with a fourth-level classification index item. The first keyword group based on the multidimensional classification item is specifically shown in the following table:

[0094] Table 1: First keyword group table based on multi-dimensional classification items

[0095]

[0096]

[0097]

[0098]

[0099] If there is no matching first target information in the current original text, the first verification module 1 discards the original text. If there is matching first target information in the current original text, the extraction module 2 extracts the first target information and determines whether there is second target information in the first target information; if there is no second target information in the first target information, the original text is discarded.

[0100] In the embodiment of the present application, the second target information is specifically: an information-based cost estimation formula or an information-based cost estimation value. If the second target information exists in the first target information, the second verification module 3 determines whether the first parameter exists in the second target information.

[0101] In the embodiment of the present application, the first parameter is specifically: a parameter in the information-based cost estimation formula or the information-based cost estimation value that needs to be graded and valued according to a predetermined rule. If the first parameter exists in the second target information, the second verification module 3 determines whether the number of the first parameter is greater than the first preset threshold.

[0102] If the number of the first parameters is not greater than the first preset threshold, the second verification module 3 performs the following steps: directly according to each first condition corresponding to the first parameter, the second target information is divided into corresponding first content information; for each first content information, the first setting information of the first parameter is generated according to the corresponding first condition; each first setting information is combined with the corresponding first content information to obtain the first result information. Then, the storage module 4 generates the first storage information with the mapping relationship between each first result information, the corresponding first content information and the multi-dimensional classification item and executes the entry function.

[0103] In the embodiment of the present application, the above-mentioned first preset threshold is specifically 1, and the above-mentioned embodiment is the case where there is a first parameter in the second target information. For example, when the multidimensional classification item is "other expenses-design fee-computer room construction", the second target information in the first target information is: design fee unit price (yuan / item) = project direct construction fee × rate × adjustment coefficient, among which only the rate is the above-mentioned first parameter. The rate is specifically graded according to the scale of the project direct construction fee. When the project direct construction fee is ≤3 million yuan, the rate is 5.5%; when 3 million yuan < project direct construction fee ≤5 million yuan, the rate is 4.8%; when 5 million yuan < project direct construction fee ≤10 million yuan, the rate is 4.2%; when 10 million yuan < project direct construction fee ≤20 million yuan, the rate is 3.2%; when the project direct construction fee>20 million yuan, the rate is 2.2%. At this time, there are five first conditions, and the five first conditions are: direct construction cost of the project ≤ 3 million yuan, 3 million yuan < direct construction cost of the project ≤ 5 million yuan, 5 million yuan < direct construction cost of the project ≤ 10 million yuan, 10 million yuan < direct construction cost of the project ≤ 20 million yuan, and direct construction cost of the project > 20 million yuan. The first content information corresponding to the first condition that the direct construction cost of the project is ≤ 3 million yuan is: design fee unit price (yuan / item) = direct construction cost of the project × rate × adjustment coefficient, where, when the direct construction cost of the project is ≤ 3 million yuan, the rate is 5.5% and the adjustment coefficient is 0.6. At this time, the corresponding first setting information is the value of the corresponding rate under the above five first conditions, and the corresponding first result information is: design fee unit price (yuan / item) = direct construction cost of the project × 5.5% × 0.6.

[0104] If the number of the first parameters is greater than the first preset threshold, the second verification module 3 performs the following steps: combining the first conditions corresponding to the first parameters to obtain the second conditions; dividing the second target information into the corresponding second content information according to the second conditions; generating the second setting information of the first parameter according to the corresponding second conditions for each second content information; combining each second setting information with the corresponding second content information to obtain the second result information. Then, the storage module 4 generates the second storage information with the mapping relationship between each second result information, the corresponding second content information and the multi-dimensional classification item and executes the entry function.

[0105] As described above, the above embodiment is a case where there are two or more first parameters in the second target information. For example, when the multi-dimensional classification item is "hardware-server-X86 server-calculated by number of users", the second target information in the first target information is: user unit price = scale factor × concurrency factor × redundancy factor × unit control price, where the scale factor and concurrency factor are both the above first parameters. The scale coefficient is classified according to the relationship between the behavioral entities: when the number of internal users of a unit is ≤5,000, the scale coefficient is 1; when 5,000 people < the number of internal users of a unit ≤10,000 people, the scale coefficient is 0.98; when 10,000 people < the number of internal users of a unit ≤40,000 people, the scale coefficient is 0.88; when 40,000 people < the number of internal users of a unit ≤70,000 people, the scale coefficient is 0.8; when 70,000 people < the number of internal users of a unit ≤100,000 people, the scale coefficient is 0.75; when 100,000 people < the number of internal users of a unit ≤200,000 people, the scale coefficient is 0.65; when it is unit-to-unit, the scale coefficient is 1; when it is unit-to-enterprise, the scale coefficient is 1. When the unit is engaged in business, the scale coefficient is 1; when the unit is for the masses and the number of masses is ≤50,000, the scale coefficient is 1; when the unit is for the masses and 50,000 people < the number of masses ≤100,000 people, the scale coefficient is 0.95; when the unit is for the masses and 100,000 people < the number of masses ≤400,000 people, the scale coefficient is 0.8; when the unit is for the masses and 400,000 people < the number of masses ≤700,000 people, the scale coefficient is 0.53; when the unit is for the masses and 700,000 people < the number of masses ≤1,400,000 people, the scale coefficient is 0.53; when the unit is for the masses and 1,400,000 people < the number of masses ≤2,000,000 people, the scale coefficient is 0.3. The concurrency coefficient is divided into different levels according to the relationship between the actors: when it is within a unit, the concurrency coefficient is 0.7; when it is between units, the concurrency coefficient is 0.5; when it is between units and enterprises, the concurrency coefficient is 0.3; when it is between units and the masses, the concurrency coefficient is 0.1.At this time, there are fourteen first conditions corresponding to the scale coefficient, and the fourteen first conditions corresponding to the scale coefficient are: the number of internal users of the unit ≤ 5,000, 5,000 < the number of internal users of the unit ≤ 10,000, 10,000 < the number of internal users of the unit ≤ 40,000, 40,000 < the number of internal users of the unit ≤ 70,000, 70,000 < the number of internal users of the unit ≤ 100,000, 100,000 < the number of internal users of the unit ≤ 200,000, and the number of internal users of the unit ≤ 400,000. Units to units, units to enterprises, units to the masses and the number of masses ≤ 50,000, units to the masses and 50,000 < the number of masses ≤ 100,000, units to the masses and 100,000 < the number of masses ≤ 400,000, units to the masses and 400,000 < the number of masses ≤ 700,000, units to the masses and 700,000 < the number of masses ≤ 1,400,000, units to the masses and 1,400,000 < the number of masses ≤ 2,000,000. At this time, there are four first conditions corresponding to the concurrency coefficient, and the four first conditions corresponding to the scale coefficient are: within the unit, unit to unit, unit to enterprise, and unit to the masses. Since the scale coefficient and the concurrency coefficient are both graded according to the relationship between the behavioral subjects, the fourteen first conditions corresponding to the above scale coefficient and the four first conditions corresponding to the scale coefficient are combined to obtain fourteen second conditions. The second content information corresponding to the second condition of unit to the masses and 700,000 people < the number of masses ≤ 1,400,000 people is: user unit price = scale coefficient × concurrency coefficient × redundancy coefficient × unit control price, where, when unit to the masses and 700,000 people < the number of masses ≤ 1,400,000 people, the scale coefficient is 0.53; when unit to the masses, the concurrency coefficient is 0.1; the redundancy coefficient is 1.1; the unit control price is 460 yuan / user. At this time, the corresponding first setting information is the values ​​of the scale coefficient, concurrency coefficient, redundancy coefficient, and unit control price corresponding to the above fourteen second conditions, and the corresponding first result information is: user unit price = 0.53 × 0.1 × 1.1 × 460 yuan / user = 26.818 yuan / user.

[0106] refer to Figure 3 If the first parameter does not exist in the second target information, the second verification module 3 performs the following steps: determine whether the second parameter exists in the second target information; if the second parameter exists in the second target information, directly use the second target information as the third content information; for the third content information, use the second parameter directly as the third setting information; combine the third setting information with the third content information to obtain the third result information. Then, the storage module 4 generates the third storage information with a mapping relationship between each third result information and the corresponding third content information and multi-dimensional classification items and executes the entry function.

[0107] In the embodiment of the present application, the second parameter is specifically: a parameter that changes within a preset value range in the information-based cost estimation formula or the information-based cost estimation value. For example, when the multi-dimensional classification item is "hardware / computer room construction / air conditioning and ventilation system-ordinary / general", the second target information in the first target information is: 0.26×0.98 n 10,000 yuan / m 2 (n=computer room area / 100), where n is the second parameter. At this time, the third content information is the same as the second target information, the third setting information is: n=computer room area / 100, and the third result information is also the same as the second target information.

[0108] If the second parameter does not exist in the second target information, the second verification module 3 performs the following steps: determining whether the third parameter exists in the second target information; if the third parameter exists in the second target information, directly using the second target information as the fourth content information, and directly obtaining the fourth result information using the second target information. Then the storage module 4 generates the fourth storage information with a mapping relationship between the fourth result information and the corresponding fourth content information and the multi-dimensional classification item, and executes the entry function.

[0109] In the embodiment of the present application, the third parameter is specifically: a parameter with a fixed value in the information cost estimation formula or the information cost estimation value. For example, when the multi-dimensional classification item is "hardware / computer room construction / power supply system / generality", the second target information in the first target information is: budget standard × preset coefficient (computer room area > 200㎡), where the preset coefficient is 0.9 and the budget standard is 26,000 yuan / m 2 , the preset coefficient is the third parameter mentioned above. At this time, the fourth content information is the same as the second target information, and the fourth result information can be directly obtained from the second target information. The fourth result information is 0.234 million yuan / m 2 .

[0110] refer to Figure 4 If the third parameter does not exist in the second target information, the second verification module 3 performs the following steps: judging whether the first target result information exists in the second target information; judging whether the first target result information exists in the first target information; if the first target result information exists in the first target information, directly taking the first target result information as the fifth content information. Then the storage module 4 generates the fifth storage information with a mapping relationship between the first target result information and the corresponding fifth content information and the multi-dimensional classification item and executes the entry function.

[0111] In the embodiment of the present application, the first target result information is specifically: there is a predetermined value in the information cost estimation formula or the information cost estimation value. The predetermined value can be a standard value. For example, when the multi-dimensional classification item is "hardware / computer room construction / power supply system / generality", the second target information in the first target information is: calculated according to the budget standard (the main computer room area ≤ 200m 2 ), of which the budget standard is 2600 yuan / m 2 , the budget standard is the above-mentioned first target result information. The predetermined value can also be a value agreed upon by the government or a default value. For example, when the multi-dimensional classification item is "purchase software / basic software / server operating system", the second target information in the first target information is: 10,000 yuan / set; when the multi-dimensional classification item is "purchase software / support software / middleware", the second target information in the first target information is: 100,000 yuan / CPU. The above-mentioned "10,000 yuan / set" and "100,000 yuan / CPU" are both the first target result information. At this time, the fifth content information is the same as the first target result information.

[0112] refer to Figure 5 , the specific steps of executing the entry function are described below.

[0113] In the embodiment of the present application, when an execution input function is received, the storage module 4 first obtains the partition information of the corresponding original text for the target to be input; and obtains the corresponding first partition index and second partition index based on the partition information.

[0114] In the embodiment of the present application, the target to be entered is the above-mentioned first storage information, second storage information, third storage information, fourth storage information, and fifth storage information. The partition information of the original text is the release area information of the government informationization budget expenditure policy document, and the first partition index is specifically the provincial administrative region information related to the government informationization budget expenditure policy document, for example, Beijing, Shanghai, Guangzhou, Henan, etc. The second partition index is specifically the regional information above the provincial administrative region related to the government informationization budget expenditure policy document, for example, the three northeastern provinces, North China, Central China, East China, Southeast China, South China, Southwest China, etc.

[0115] Then, the storage module 4 determines whether there is stored data under the first partition index. If there is no stored data under the first partition index, the storage module 4 directly stores the target to be recorded under the first partition index.

[0116] If there is stored data under the first partition index, the storage module 4 determines whether the target to be recorded is the same as the second target information corresponding to the stored data. If the target to be recorded is the same as the second target information corresponding to the stored data, the storage module 4 directly updates the stored data to the target to be recorded.

[0117] If the target to be entered is different from the second target information corresponding to the stored data, the storage module 4 performs the following steps: obtain all corresponding stored data under the second partition index as a verification data set; sample the verification data set and the target to be entered to obtain a preset number of first result sampling value arrays and corresponding second result sampling values, respectively; calculate the sampling value average for each result sampling value array to obtain the corresponding sampling reference value; calculate the corresponding deviation value based on each second result sampling value and the corresponding sampling reference value.

[0118] In an embodiment of the present application, the specific steps of sampling the verification data set and the target to be entered include: analyzing each stored data in the verification data set and the second target information corresponding to the target to be entered, if the first parameter exists in the second target information, taking the same first setting information or the second setting information to obtain the corresponding first result sampling value; if the second parameter exists in the second target information, taking the same third setting information to obtain the corresponding first result sampling value; if the third parameter or the first target result information exists in the second target information, directly obtaining the corresponding first result sampling value.

[0119] In the embodiment of the present application, the calculation formula of the above deviation value is:

[0120]

[0121] Among them, D n is the sampling reference value corresponding to the nth first result sampling value array, M n is the value collected for the nth second result, P n is the nth deviation value.

[0122] Then the storage module 4 determines whether each deviation value is less than or equal to the second preset threshold; if each deviation value is less than or equal to the second preset threshold, the storage module 4 updates the stored data to the target to be entered.

[0123] If the current deviation value is greater than the second preset threshold, the storage module 4 compares the target to be recorded with the second target information of the currently stored data under the first partition index; and determines whether the comparison result meets the preset condition.

[0124] In the embodiment of the present application, the preset condition is that the first parameter and the second parameter do not exist in the second target information of the currently stored data, and the first parameter and / or the second parameter do not exist in the second target information of the target to be entered.

[0125] If the comparison result meets the preset conditions, the storage module 4 updates the currently stored data as the target to be entered; if the comparison result does not meet the preset conditions, the storage module 4 discards the target to be entered and retains the currently stored data.

[0126] Compared with the prior art, the embodiment of the present application provides an information-based intelligent cost analysis method, which uses multi-dimensional classification items to filter the collected original text, extracts the first target information and the second target information, and obtains the first content information and the first result information by analyzing the parameters in the second target information. The first result information is combined with the corresponding first content information and multi-dimensional classification items to generate first storage information with a mapping relationship and execute the entry function. The embodiment of the present invention uses data analysis technology to collect case library data, summarize and classify the data, and realize intelligent screening in the early stage of cost information import, improve import efficiency and import effect, and reduce the workload of manual review. The information data collected through unified standards provides strong support for government information cost estimation and ensures the accuracy of cost estimation.

[0127] In addition, an embodiment of the present invention also provides an information-based intelligent cost analysis device, which includes: a processor and a memory; the memory is used to store one or more program instructions; the processor is used to run one or more program instructions to execute the steps of an information-based intelligent cost analysis method as described in any of the above items.

[0128] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of an information-based cost intelligent analysis method as described in any of the above items are implemented.

[0129] In the embodiment of the present invention, the processor may be an integrated circuit chip having the ability to process signals. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0130] The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware.

[0131] The storage medium may be a memory, which may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0132] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0133] The volatile memory may be a random access memory (RAM) which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus DRAM (DRRAM).

[0134] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0135] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the present invention may be implemented using a combination of hardware and software. When software is used, the corresponding functions may be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. Storage media may be any available media that can be accessed by a general or special-purpose computer.

[0136] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made on the basis of the present invention. Therefore, these modifications or improvements made on the basis of not departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. An information-based intelligent cost analysis method, characterized in that: The method comprises: Traversing each original text based on the multi-dimensional classification item, determining whether there is matching first target information in each original text; If there is matching first target information in the current original text, extract the first target information, and determine whether there is second target information in the first target information; If there is no matching first target information in the current original text, or there is no second target information in the first target information, the original text is discarded; If the second target information exists in the first target information, determining whether the first parameter exists in the second target information; If the first parameter exists in the second target information, determining whether the quantity of the first parameter is greater than a first preset threshold; If the number of the first parameters is not greater than a first preset threshold, directly dividing the second target information into corresponding first content information according to each first condition corresponding to the first parameter; For each piece of first content information, generating first setting information of the first parameter according to the corresponding first condition; Combining each piece of first setting information with the corresponding first content information to obtain first result information; Each first result information is combined with the corresponding first content information and multi-dimensional classification items to generate first storage information having a mapping relationship and execute an entry function.

2. The information-based intelligent cost analysis method according to claim 1, characterized in that: The method further comprises: If the number of the first parameters is greater than a first preset threshold, combining the first conditions corresponding to the first parameters to obtain a second condition; According to the second condition, dividing the second target information into corresponding second content information; For each second content information, generating second setting information of the first parameter according to the corresponding second condition; Combining each second setting information with the corresponding second content information to obtain second result information; Each second result information is combined with the corresponding second content information and multi-dimensional classification items to generate second storage information having a mapping relationship and execute an entry function.

3. The information-based intelligent cost analysis method according to claim 2, characterized in that: The method further comprises: If the first parameter does not exist in the second target information, determining whether the second parameter exists in the second target information; If the second target information contains the second parameter, directly using the second target information as the third content information; For the third content information, using the second parameter directly as the third setting information; combining the third setting information and the third content information to obtain third result information; Each piece of third result information is combined with the corresponding third content information and multi-dimensional classification items to generate third storage information having a mapping relationship and execute an entry function.

4. The information-based intelligent cost analysis method according to claim 3, characterized in that: The method further comprises: If the second parameter does not exist in the second target information, determining whether a third parameter exists in the second target information; If the third parameter exists in the second target information, directly use the second target information as the fourth content information, and directly obtain fourth result information using the second target information; The fourth result information, the corresponding fourth content information and the multi-dimensional classification items are used to generate fourth storage information having a mapping relationship and an entry function is executed.

5. The information-based intelligent cost analysis method according to claim 4, characterized in that: The method further comprises: If the third parameter does not exist in the second target information, determining whether the first target result information exists in the second target information; If the first target result information does not exist in the second target information, discard the original text; If the first target result information exists in the second target information, directly use the first target result information as the fifth content information; The first target result information, the corresponding fifth content information, and the multi-dimensional classification items are used to generate fifth storage information having a mapping relationship and an entry function is executed.

6. The information-based intelligent cost analysis method according to claim 5, characterized in that: Execute the input function, including: Obtaining the partition information of the corresponding original text for the target to be entered; Obtaining a corresponding first partition index and a second partition index based on the partition information; Determine whether there is stored data under the first partition index; If there is no stored data under the first partition index, directly storing the target to be recorded under the first partition index; If there is stored data under the first partition index, determining whether the target to be entered is the same as the second target information corresponding to the stored data; If the target to be recorded is the same as the second target information corresponding to the stored data, directly updating the stored data to the target to be recorded; If the target to be entered is different from the second target information corresponding to the stored data, all corresponding stored data under the second partition index are obtained as a verification data set; Sampling the verification data set and the target to be entered, respectively obtaining a preset number of first result sampling value arrays and corresponding second result sampling values; Calculate the average sampling value for each result sampling value array to obtain the corresponding sampling reference value; Based on each second result sampling value and the corresponding sampling reference value, the corresponding deviation value is calculated, and the calculation formula of the deviation value is: Among them, D n is the sampling reference value corresponding to the nth first result sampling value array, M n is the value collected for the nth second result, P n is the nth deviation value; Determine whether each deviation value is less than or equal to a second preset threshold; If each deviation value is less than or equal to the second preset threshold, the stored data is updated to the target to be entered.

7. The information-based intelligent cost analysis method according to claim 6, characterized in that: Execute the input function, also includes: If the current deviation value is greater than a second preset threshold, comparing the target to be entered with the second target information of the currently stored data under the first partition index; Determine whether the comparison result meets a preset condition, wherein the preset condition is that the first parameter and the second parameter do not exist in the second target information of the currently stored data, and the first parameter and / or the second parameter do not exist in the second target information of the target to be entered; If the comparison result meets the preset condition, the currently stored data is updated to the target to be entered; If the comparison result does not meet the preset conditions, the target to be entered is discarded and the currently stored data is retained.

8. An information-based intelligent cost analysis system, characterized in that: The system comprises: A first verification module is used to traverse each original text based on the multi-dimensional classification items to determine whether there is matching first target information in each original text; if there is no matching first target information in the current original text, the original text is discarded; An extraction module, configured to extract the first target information if there is matching first target information in the current original text, and determine whether there is second target information in the first target information; if there is no second target information in the first target information, discard the original text; a second verification module, configured to determine whether the first parameter exists in the second target information if the second target information exists; determine whether the number of the first parameters is greater than a first preset threshold if the first parameter exists in the second target information; if the number of the first parameters is not greater than the first preset threshold, directly divide the second target information into corresponding first content information according to each first condition corresponding to the first parameter; generate first setting information of the first parameter according to the corresponding first condition for each first content information; and combine each first setting information with the corresponding first content information to obtain first result information; The storage module is used to generate first storage information with a mapping relationship between each first result information and the corresponding first content information and multi-dimensional classification items and execute an entry function.

9. An information-based intelligent cost analysis device, characterized in that: The device comprises: a processor and a memory; The memory is used to store one or more program instructions; The processor is used to run one or more program instructions to execute the steps of an information-based cost intelligent analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of an information-based cost intelligent analysis method as claimed in any one of claims 1 to 7 are implemented.

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