A method and system for engineering cost storage and analysis based on big data

Abstract

The present invention relates to the field of data processing technology, and specifically to a method and system for engineering cost storage and analysis based on big data, which obtains access frequency characteristics of different types of engineering cost data in multiple monitoring time periods, obtains the access importance of engineering cost data in combination with the access frequency characteristics, obtains the access concentration of engineering cost data according to the difference between the number of accesses to engineering cost data in different engineering stages; obtains the data importance of engineering cost data according to the access importance and access concentration; and determines the storage priority based on the data importance of each type of engineering cost data. The present invention does not store all types of engineering cost data together, but determines the storage priority of engineering cost data in combination with the actual data situation of each type of engineering cost data, so that the corresponding storage priority can be determined for engineering cost data in different situations in a targeted manner, thereby improving the reliability of data storage.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for storing and analyzing engineering costs based on big data. Background Art

[0002] Engineering cost data refers to all data and information related to project construction involved in the process of engineering construction, including direct costs, indirect costs and other related expenses. With the continuous development of the construction industry, the challenges faced by engineering cost management are becoming increasingly severe. Especially in large-scale engineering projects, traditional cost management methods cannot effectively cope with the processing and complex analysis of massive data, resulting in problems such as information islands, delayed decision-making, and difficulty in cost control. Therefore, the engineering cost storage and analysis method based on big data technology came into being. It can solve the bottlenecks faced by existing technologies through intelligent analysis and efficient storage technology, and improve the efficiency and accuracy of engineering cost management.

[0003] At present, the engineering cost storage and analysis methods based on big data usually include several key steps, such as data collection, data storage, data cleaning and preprocessing, data analysis, and data visualization. Although the engineering cost storage and analysis methods based on big data have improved the management efficiency of engineering projects to a certain extent, there are still some shortcomings in data storage: the existing engineering cost storage methods usually do not consider the type of engineering cost data, or the priority of engineering cost data, but directly store all types of engineering cost data together, or directly store different types of data in the same storage, resulting in poor data storage reliability. Summary of the invention

[0004] In order to solve the technical problem of poor storage reliability of existing engineering cost data, the purpose of the present invention is to provide an engineering cost storage and analysis method and system based on big data. The technical solution adopted is as follows:

[0005] In a first aspect of the present invention, a method for storing and analyzing construction costs based on big data is provided, comprising:

[0006] Obtain access frequency characteristics of different types of engineering cost data in multiple monitoring time periods;

[0007] Obtaining access importance of the construction cost data according to the number of accesses to the construction cost data in the plurality of monitoring time periods and the access frequency characteristics;

[0008] According to the difference between the access times of the engineering cost data in different engineering stages, the access concentration of the engineering cost data is obtained;

[0009] According to the access importance and access concentration of the engineering cost data, the data importance of the engineering cost data is obtained;

[0010] Based on the data importance of each type of engineering cost data, the storage priority of each type of engineering cost data is determined.

[0011] In an exemplary embodiment, obtaining access frequency characteristics of different types of engineering cost data in multiple monitoring time periods includes:

[0012] Obtain the number of visits to each type of engineering cost data in each monitoring period, and obtain the proportion of the number of visits;

[0013] Obtain the time interval between two adjacent accesses to the same type of engineering cost data in each monitoring time period, and obtain the average time interval between the accesses to each type of engineering cost data in each monitoring time period;

[0014] According to the proportion of access times of each type of engineering cost data in each monitoring time period and the mean access time interval, the access frequency characteristics of each type of engineering cost data in each monitoring time period are obtained. The access frequency characteristics are directly proportional to the proportion of access times and inversely proportional to the mean access time interval.

[0015] In an exemplary embodiment, obtaining the access importance of the construction cost data according to the number of accesses of the construction cost data in the plurality of monitoring time periods and the access frequency characteristics includes:

[0016] Obtain the number of accesses to the project cost data in each monitoring period, as well as the number of alarm information prompts in each monitoring period;

[0017] According to the number of accesses to the engineering cost data in each monitoring time period and the number of alarm information prompts in each monitoring time period, the real access characteristics of the engineering cost data in each monitoring time period are obtained;

[0018] The access importance of the engineering cost data is obtained according to the real access characteristics of the engineering cost data in each monitoring time period and the access frequency characteristics of the engineering cost data in the multiple monitoring time periods.

[0019] In an exemplary embodiment, the calculation formula of the access importance of the construction cost data is as follows:

[0020] ;

[0021] in, represents the access importance of the i-th type of construction cost data, represents the average value of the access frequency characteristics of the i-th type of construction cost data in each monitoring time period; It represents the number of accesses to the i-th type of construction cost data in the m-th monitoring period; Indicates the number of alarm information prompts for the i-th type of engineering cost data in the m-th monitoring time period; M represents the number of monitoring time periods; Represents the normalization function.

[0022] In an exemplary embodiment, the access concentration of the construction cost data is obtained according to the difference between the access times of the construction cost data in different engineering stages, including:

[0023] Obtain the maximum number of accesses to the construction cost data in each engineering stage;

[0024] The access concentration of the engineering cost data is obtained according to the difference between the maximum access number and the remaining access numbers; the remaining access numbers are the access numbers of the engineering cost data in each engineering stage except the maximum access number.

[0025] In an exemplary embodiment, the calculation formula of access concentration is:

[0026] ;

[0027] in, is the access concentration of the i-th type of engineering cost data, is the maximum number of accesses to the i-th type of construction cost data, B is the number of the remaining engineering stages except the engineering stage corresponding to the maximum number of accesses, b is the index of the remaining engineering stages except the engineering stage corresponding to the maximum number of accesses, is the number of accesses to the i-th type of engineering cost data in the b-th engineering stage.

[0028] In an exemplary embodiment, the calculation formula of the data importance is:

[0029] ;

[0030] in, is the data importance of the i-th type of engineering cost data.

[0031] In an exemplary embodiment, the construction cost storage and analysis method further includes:

[0032] Obtaining the engineering phase associated with the engineering cost data and the engineering importance of the associated engineering phase, thereby obtaining the engineering importance of the engineering cost data;

[0033] The step of determining the storage priority of each type of engineering cost data based on the data importance of each type of engineering cost data includes:

[0034] The data importance and engineering importance of various types of engineering cost data are integrated to obtain the comprehensive importance of various types of engineering cost data;

[0035] According to the comprehensive importance of each type of engineering cost data, the storage priority of each type of engineering cost data is determined. The higher the comprehensive importance, the higher the storage priority.

[0036] In an exemplary embodiment, the storage priority of each type of engineering cost data is determined according to the comprehensive importance of each type of engineering cost data, including:

[0037] Comparing the comprehensive importance of each type of engineering cost data with a preset first threshold and a second threshold, wherein the first threshold is greater than the second threshold;

[0038] Determine the storage priority of the construction cost data corresponding to the comprehensive importance greater than the first threshold as a high storage priority, and configure a first storage medium;

[0039] Determine the storage priority of the construction cost data corresponding to the comprehensive importance less than or equal to the first threshold and greater than or equal to the second threshold as the medium storage priority, and configure a second storage medium;

[0040] Determine the storage priority of the construction cost data corresponding to the comprehensive importance less than the second threshold as a low storage priority, and configure a third storage medium;

[0041] The reading and writing speeds of the first storage medium, the second storage medium and the third storage medium decrease in sequence.

[0042] In the second aspect of the present invention, a construction cost storage and analysis system based on big data is provided, comprising: a memory and a processor; the memory is connected to the processor; the memory is used to store program instructions; the processor is used to implement the above-mentioned construction cost storage and analysis method based on big data when the program instructions are executed.

[0043] The present invention has the following beneficial effects: the present invention first obtains the access importance of each type of engineering cost data based on the access frequency, access times and other information of different types of engineering cost data in multiple monitoring time periods; then, according to the difference between the access times of engineering cost data in different engineering stages, obtains the access concentration of each type of engineering cost data; thus, according to the access importance and access concentration of engineering cost data, obtains the data importance of engineering cost data; finally, based on the data importance of each type of engineering cost data, determines the storage priority of each type of engineering cost data. The present invention does not store all types of engineering cost data together, but determines the storage priority of each type of engineering cost data in combination with the actual data situation of each type of engineering cost data, so that the corresponding storage priority can be determined for engineering cost data in different situations in a targeted manner, thereby improving the reliability of data storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flowchart of a method for storing and analyzing construction costs based on big data provided by an embodiment of the present invention;

[0045] Figure 2 It is a specific flow chart of step 1 of the present invention;

[0046] Figure 3 It is a specific flow chart of step 2 of the present invention;

[0047] Figure 4 It is a specific flow chart of step 3 of the present invention;

[0048] Figure 5 It is a specific flow chart of step 5 of the present invention;

[0049] Figure 6 It is a specific flow chart of step 5-2 of the present invention. DETAILED DESCRIPTION

[0050] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0051] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0052] Project cost data refers to all cost data and information related to project construction involved in the process of project construction, including direct costs, indirect costs and other related expenses, etc. In an exemplary embodiment, the sources of project cost data are mainly the following: Project Management System (PMS): including budget list, cost details, project contract, construction plan, etc.; third-party data platform: such as material price database, labor price market, equipment rental price website; real-time collection of construction site: obtaining environmental parameters, equipment operation status, real-time site monitoring video, etc. through Internet of Things devices; historical case library: historical cases of project cost disclosed within the enterprise or in the industry.

[0053] For different data sources, standardized data interfaces can be designed to ensure that data can be automatically acquired and updated. This embodiment can use API (Application Programming Interface) or EDI (Electronic datainterchange) technology to connect data with external databases and systems to obtain background access record data of all engineering cost data during the project. In an exemplary embodiment, all engineering cost data are temporarily stored in the background system. This embodiment provides a method for storing and analyzing engineering cost based on big data (hereinafter referred to as the engineering cost storage and analysis method), which can obtain relevant data information about the engineering cost data being called and accessed during execution. In an exemplary embodiment, an API interface is used to obtain access records of all engineering cost data within a period of time from the background system.

[0054] like Figure 1 As shown, the engineering cost storage analysis method includes the following steps:

[0055] Step 1: Obtain the access frequency characteristics of different types of construction cost data in multiple monitoring time periods.

[0056] It should be understood that the construction cost data includes many types of data, such as budget list data, cost detail data, project contract data, construction plan data, etc. This embodiment classifies the construction cost data to obtain different types of construction cost data, and the construction cost data can be classified manually. Subsequently, the data will be given a certain storage priority according to the importance level of different types of construction cost data to improve the real-time and availability of data updates.

[0057] Multiple monitoring time periods are preset. In an exemplary embodiment, the multiple monitoring time periods may include a current monitoring time period and at least one historical monitoring time period, wherein the number of historical monitoring time periods is set according to actual needs. Moreover, the time length of each monitoring time period is set according to actual needs. The longer the time length, the more data volume, the more accurate the data analysis result, but the more complex the data processing; the shorter the time length, the less complex the data processing, but the smaller the data volume, the less accurate the data analysis result. Therefore, in order to determine the accuracy of the data analysis results, the monitoring time period should not be set too short, and can be set longer. Moreover, the setting of the monitoring time period needs to ensure that there are multiple data accesses within the monitoring time period, and each type of engineering cost data has at least two access times within the monitoring time period. The monitoring time period can be set to 30 minutes or 1 hour.

[0058] To obtain access frequency characteristics of different types of engineering cost data in multiple monitoring time periods, in an exemplary embodiment, Figure 2 As shown, the following steps are included:

[0059] Step 1-1: Obtain the number of visits to each type of engineering cost data in each monitoring time period, and obtain the proportion of the number of visits.

[0060] Take the current monitoring period as an example: for any type of engineering cost data, for the sake of convenience, it is defined as a candidate type of engineering cost data.

[0061] Get the number of accesses to the candidate type of engineering cost data in the current monitoring period, recorded as g, and then get the sum of the number of accesses to all types of engineering cost data in the current monitoring period, recorded as G. Calculate the ratio of g and G to get the proportion of the number of accesses to the candidate type of engineering cost data in the current monitoring period. Thus, the proportion of the number of accesses to each type of engineering cost data in each monitoring period is obtained. The larger the proportion of the number of accesses, the more frequent the accesses, and the higher the access frequency characteristics.

[0062] Step 1-2: Obtain the time interval between two adjacent accesses to the same type of engineering cost data in each monitoring time period, and obtain the average value of the time interval between accesses to each type of engineering cost data in each monitoring time period.

[0063] Since the candidate type of engineering cost data will be accessed multiple times in the current monitoring time period, and each access has a corresponding access time, then the access time interval between two adjacent accesses of the candidate type of engineering cost data in the current monitoring time period is obtained, and then the average value of the access time interval of the candidate type of engineering cost data in the current monitoring time period is calculated as the mean value of the access time interval of the candidate type of engineering cost data in the current monitoring time period. Thus, the mean value of the access time interval of each type of engineering cost data in each monitoring time period is obtained. The smaller the mean value of the access time interval, the more frequent the access and the higher the access frequency characteristic.

[0064] Step 1-3: According to the proportion of access times of each type of engineering cost data in each monitoring time period and the mean access time interval, the access frequency characteristics of each type of engineering cost data in each monitoring time period are obtained.

[0065] According to the proportion of visit times of the candidate type of engineering cost data in the current monitoring time period and the mean visit time interval, the visit frequency characteristics of the candidate type of engineering cost data in the current monitoring time period are obtained. The access frequency characteristics are proportional to the proportion of visit times and inversely proportional to the mean visit time interval.

[0066] In an exemplary embodiment, the calculation formula of the access frequency feature is given as follows:

[0067] ;

[0068] Among them, i represents the i-th type of engineering cost data, It represents the access frequency characteristics of the i-th type of engineering cost data in the current monitoring period. represents the number of accesses to the i-th type of engineering cost data in the current monitoring period; G represents the sum of the number of accesses to all types of engineering cost data in the current monitoring period; It represents the average time interval of access to the i-th type of engineering cost data in the current monitoring period. Normally, Greater than 0, however, to further ensure There are cases where the value is 0 and the calculation cannot be performed normally, so a non-zero constant is used. To correct the logic, Represents a preset positive number, such as 0.1. Represents a normalization function. The normalization in this embodiment may be a linear normalization method, or may be performed in the following general manner: . Where n is the input data, It is an exponential function with the natural constant e as its base.

[0069] The larger the value of is, the higher the access frequency of the i-th type of engineering cost data in the current monitoring period is, that is, The bigger.

[0070] By adopting the above method, the access frequency characteristics of various types of engineering cost data in each monitoring time period are obtained.

[0071] Step 2: According to the number of accesses to the engineering cost data in multiple monitoring time periods and the access frequency characteristics, the access importance of the engineering cost data is obtained.

[0072] Normally, engineering cost data with a higher response level has a higher access frequency; however, data with a high access frequency does not necessarily have a higher degree of importance.

[0073] If the access frequency of data suddenly increases during a monitoring period, it may indicate that the importance of this type of engineering cost data is relatively high, or it may be a "false" importance caused by repeated accesses in a short period of time; therefore, it is necessary to re-evaluate the importance of each type of engineering cost data.

[0074] In an exemplary embodiment, the specific process of obtaining the access importance of the construction cost data is given as follows: Figure 3 As shown, including:

[0075] Step 2-1: Obtain the number of accesses to the project cost data in each monitoring time period, and the number of alarm information prompts in each monitoring time period.

[0076] Get the number of times the candidate type of engineering cost data is accessed in the current monitoring time period. It should be understood that at each access, an erroneous call may occur during the access. If an erroneous call occurs, the system will usually give an alarm message prompt. If an alarm message prompt is obtained once, the alarm message prompt is marked once. Then, the number of alarm message prompts obtained is marked as many times as the alarm message prompts are obtained. Get the number of marks obtained in the current monitoring time period, and get the number of alarm message prompts for the candidate type of engineering cost data in the current monitoring time period, recorded as h.

[0077] Step 2-2: According to the number of accesses to the construction cost data in each monitoring time period and the number of alarm information prompts in each monitoring time period, the real access characteristics of the construction cost data in each monitoring time period are obtained.

[0078] According to the number of accesses to the candidate type of engineering cost data in the current monitoring time period, and the number of alarm information prompts in the current monitoring time period, the access real feature of the candidate type of engineering cost data in the current monitoring time period is obtained. Since the alarm information prompt may appear only when accessing, the number of alarm information prompts is less than or equal to the number of accesses. Then, in an exemplary embodiment, the difference between the number of accesses and the number of alarm information prompts is used as the access real feature. The larger the access real feature, the larger the difference between the number of accesses and the number of alarm information prompts, and the more real the importance of the engineering cost data.

[0079] Step 2-3: According to the real access characteristics of the engineering cost data in each monitoring time period and the access frequency characteristics of the engineering cost data in multiple monitoring time periods, the access importance of the engineering cost data is obtained.

[0080] In an exemplary embodiment, the calculation formula of the access importance of the construction cost data is as follows:

[0081] ;

[0082] in, represents the access importance of the i-th type of construction cost data, represents the average value of the access frequency characteristics of the i-th type of construction cost data in each monitoring time period; It represents the number of accesses to the i-th type of construction cost data in the m-th monitoring period; It indicates the number of alarm information prompts for the i-th type of engineering cost data in the m-th monitoring time period; M indicates the number of monitoring time periods.

[0083] The smaller the value is, it means that in the actual access process, for the i-th type of engineering cost data, there are multiple access situations in the monitoring period, but many of the access situations are accompanied by system prompts or error call information, which means that in the monitoring period, the high frequency of access to the i-th type of engineering cost data may be of "false" importance, such as the high frequency of the i-th type of engineering cost data caused by repeated operations or system errors.

[0084] The larger the product of is, the higher the access frequency of the i-th type of engineering cost data is and the access behavior is normal in multiple monitoring time periods, which means that the access importance of the i-th type of engineering cost data is higher, that is, The bigger.

[0085] Step 3: According to the difference between the access times of the engineering cost data in different engineering stages, the access concentration of the engineering cost data is obtained.

[0086] The construction cost data with high access importance is not necessarily important for the entire project, because if the access importance of this type of construction cost data is only reflected in a specific time period, then the importance of this type of construction cost data will be weakened to a certain extent.

[0087] The main purpose of this step is to further analyze the access to the engineering cost data, and then to determine whether the access to the engineering cost data shows concentrated access in a specific period of time. Because the project is progressing in stages, if the engineering cost data is only frequently accessed in a specific period of time, then its reference value will also decrease, that is, the engineering cost data has high-frequency access, but its importance is relatively low.

[0088] In an exemplary embodiment, Figure 4 As shown in the figure, the access concentration of construction cost data includes:

[0089] Step 3-1: Obtain the maximum number of accesses to the construction cost data in each engineering stage.

[0090] The phases of a general engineering project include multiple engineering phases, such as: decision-making and project establishment phase, design phase, bidding and procurement phase, construction preparation phase, construction phase, completion acceptance phase, etc. In an exemplary embodiment, the time period of each engineering phase can be divided manually to obtain multiple engineering phases. Each type of engineering cost data will have a certain number of data accesses in each engineering phase. Then, the number of accesses to the candidate type of engineering cost data in each engineering phase is obtained, so as to obtain the maximum number of accesses among the number of accesses to the candidate type of engineering cost data in each engineering phase.

[0091] Step 3-2: According to the difference between the maximum access number and the remaining access numbers, the access concentration of the engineering cost data is obtained.

[0092] After obtaining the maximum number of accesses to the candidate type of engineering cost data in each engineering stage, the other access numbers except the maximum number of accesses are set as the remaining access numbers, and the engineering stages corresponding to the other access numbers except the maximum number of accesses are set as the remaining engineering stages.

[0093] In an exemplary embodiment, the calculation formula of access concentration is:

[0094] ;

[0095] in, is the access concentration of the i-th type of engineering cost data, is the maximum number of accesses to the i-th type of engineering cost data, B is the number of other engineering stages corresponding to the i-th type of engineering cost data, and b is the index of other engineering stages. is the number of accesses to the i-th type of engineering cost data in the b-th engineering stage.

[0096] The larger the value of , the greater the maximum number of accesses in the engineering phase where the i-th type of engineering cost data is located is, which means that the concentration of the i-th type of engineering cost data is higher, that is, The larger the value is, the higher the access importance of the i-th type of engineering cost data may be only reflected in a specific engineering stage. For the entire project, the i-th type of engineering cost data may have a relatively low importance, at least lower than the access data with high access importance in multiple engineering stages.

[0097] Step 4: According to the access importance and access concentration of the engineering cost data, the data importance of the engineering cost data is obtained.

[0098] Since the higher the access importance, the higher the data importance of the engineering cost data; the lower the access concentration, the higher the data importance of the engineering cost data. Therefore, data importance is proportional to access importance and inversely proportional to access concentration.

[0099] In an exemplary embodiment, the calculation formula of data importance is:

[0100] ;

[0101] in, is the data importance of the i-th type of engineering cost data.

[0102] The larger the value of , the higher the importance of the i-th type of engineering cost data, and it is not only concentrated in a specific engineering stage. Therefore, the i-th type of engineering cost data is more important to the entire project, that is, the data reuse is relatively high. The larger the size, the more likely it is that you will need to access the i-th type of engineering cost data at each stage of the project.

[0103] Step 5: Based on the data importance of each type of engineering cost data, determine the storage priority of each type of engineering cost data.

[0104] According to the data importance of each type of engineering cost data obtained above, the storage priority of each type of engineering cost data is determined, wherein the higher the data importance, the higher the storage priority, that is, the higher the storage priority.

[0105] In an exemplary embodiment, according to the description of step 4, the engineering cost data with high access importance and access status in multiple engineering stages may have relatively high data importance. However, for different engineering stages, if for some insignificant or less important engineering stages, even if the data importance of the engineering cost data is high, it is not enough to increase the importance of such engineering cost data for storage and analysis, that is, it is necessary to balance the importance of the engineering cost data and the engineering stage in which it is located.

[0106] Therefore, we need to analyze the impact and decision-making of different engineering stages on the overall project. At different engineering stages, the priority of the data required for the entire project also changes. By analyzing the importance of engineering stages, storage resources can be allocated more reasonably.

[0107] Accordingly, the construction cost storage and analysis method provided in this embodiment further includes: obtaining the construction phase associated with the construction cost data, and obtaining the construction importance of the associated construction phase, thereby obtaining the construction importance of the construction cost data.

[0108] It should be understood that each type of engineering cost data has the most relevant engineering stage, and the most relevant engineering stage is used as the associated engineering stage of the engineering cost data. In an exemplary embodiment, the associated engineering stage can be the engineering stage that accesses the most engineering cost data of this type. Specifically, the engineering stage corresponding to the maximum number of accesses of the candidate type of engineering cost data in each engineering stage is used as the associated engineering stage associated with it. Thus, the associated engineering stages of each type of engineering cost data are obtained.

[0109] It should be understood that each engineering stage has a corresponding engineering importance, and the engineering importance can be manually determined by the construction management personnel according to the importance of each engineering stage to the entire project. Then the engineering importance of each engineering stage is quantified so that the engineering importance of each engineering stage is a value of 0-1, and the more important the engineering stage is, the greater the corresponding engineering importance value is. In this way, the engineering importance of the associated engineering stage of each type of engineering cost data is obtained.

[0110] For each type of engineering cost data, the storage priority of each type of engineering cost data is divided by balancing the data importance and engineering importance of each type of engineering cost data, thereby improving the storage response speed. Figure 5 As shown, including:

[0111] Step 5-1: Integrate the data importance and engineering importance of each type of engineering cost data to obtain the comprehensive importance of each type of engineering cost data.

[0112] The data importance and engineering importance of the candidate type of engineering cost data are integrated to obtain the comprehensive importance of the candidate type of engineering cost data. In an exemplary embodiment, the calculation of the engineering cost data is shown as follows:

[0113] ;

[0114] in, is the comprehensive importance of the i-th type of engineering cost data, It represents the engineering importance of the i-th type of engineering cost data. Thus, the comprehensive importance of each type of engineering cost data is obtained.

[0115] In order to balance the data importance and engineering importance of the i-th type of engineering cost data, it is mainly to make the i-th type of engineering cost data more reliably allocate resources during storage. That is, the greater the comprehensive importance, the higher the storage priority of the i-th type of engineering cost data. The bigger.

[0116] Step 5-2: Determine the storage priority of each type of engineering cost data based on the comprehensive importance of each type of engineering cost data.

[0117] In an exemplary embodiment, Figure 6 As shown, the specific implementation process is given as follows, including:

[0118] Step 5-2-1: Compare the comprehensive importance of each type of engineering cost data with the preset first threshold and second threshold.

[0119] The first threshold is greater than the second threshold, and the values ​​of the first threshold and the second threshold are set according to actual needs, for example, the first threshold is 0.7, and the second threshold is 0.3.

[0120] Step 5-2-2: Determine the storage priority of the engineering cost data corresponding to the comprehensive importance greater than the first threshold as a high storage priority, and configure the first storage medium.

[0121] Three storage media are preset: a first storage medium, a second storage medium, and a third storage medium. The first storage medium has the highest read and write speed, and the read and write speeds of the first storage medium, the second storage medium, and the third storage medium decrease in sequence. In an exemplary embodiment, the first storage medium may be a high-performance medium (such as an SSD, Solid State Drive), the second storage medium may be a common performance medium (such as an HDD, Hard Disk Drive), and the third storage medium may be a cold data storage medium (such as tape storage or cloud archiving).

[0122] If the comprehensive importance is greater than the first threshold, the corresponding type of engineering cost data is determined as a high storage priority, indicating that the corresponding type of engineering cost data has the highest storage priority, and the first storage medium is configured, that is, it is stored in a high-performance medium (such as SSD) to ensure fast reading and writing and low latency.

[0123] Step 5-2-3: Determine the storage priority of the engineering cost data corresponding to the comprehensive importance that is less than or equal to the first threshold and greater than or equal to the second threshold as the medium storage priority, and configure the second storage medium.

[0124] If the comprehensive importance is less than or equal to the first threshold and greater than or equal to the second threshold, the corresponding type of engineering cost data is determined as medium storage priority, indicating that the corresponding type of engineering cost data has medium storage priority. Engineering cost data with medium storage priority is stored in ordinary performance media (such as HDD) to meet general access requirements.

[0125] Step 5-2-4: Determine the storage priority of the engineering cost data corresponding to the comprehensive importance less than the second threshold as a low storage priority, and configure a third storage medium.

[0126] If the comprehensive importance is less than the second threshold, the corresponding type of engineering cost data is determined as low storage priority, indicating that the corresponding type of engineering cost data has low storage priority. Engineering cost data with low storage priority is stored in cold data storage media (such as tape storage or cloud archive), using compression or block storage to reduce storage space usage, and is only extracted when needed, with acceptable access delay.

[0127] The usage scenarios of engineering cost data may change as the project phase progresses, so it is necessary to dynamically adjust the storage priority and update the priority in real time based on changes in access records (such as frequency and concentration). This can not only maximize the utilization of storage resources as much as possible, but also effectively reduce system costs and improve storage efficiency and accuracy.

[0128] The present invention first obtains the access importance of engineering cost data according to the access frequency, continuity, system prompt information, etc. of the engineering cost data within a certain period of time, and obtains the access concentration of the engineering cost data according to the access concentration of the actual access behavior in a certain type of engineering stage; then, the importance of the engineering cost data is finally obtained in combination with the engineering importance, so as to determine the storage priority, and then effectively ensure the high availability and high reliability of key data.

[0129] This embodiment also provides a construction cost storage and analysis system based on big data, including: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above-mentioned construction cost storage and analysis method embodiment based on big data when the program instructions are executed.

[0130] In an exemplary embodiment, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-mentioned embodiment of the engineering cost storage and analysis method based on big data.

[0131] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0132] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for storing and analyzing engineering cost based on big data, characterized in that: include: Obtain access frequency characteristics of different types of engineering cost data in multiple monitoring time periods; Obtaining access importance of the engineering cost data according to the number of accesses to the engineering cost data in the plurality of monitoring time periods and the access frequency characteristics; According to the difference between the access times of the engineering cost data in different engineering stages, the access concentration of the engineering cost data is obtained; According to the access importance and access concentration of the engineering cost data, the data importance of the engineering cost data is obtained; Determine the storage priority of each type of engineering cost data based on its importance; Obtain access frequency characteristics of different types of construction cost data in multiple monitoring time periods, including: Obtain the number of visits to each type of engineering cost data in each monitoring period, and obtain the proportion of the number of visits; Obtain the time interval between two adjacent accesses to the same type of engineering cost data in each monitoring time period, and obtain the average time interval between the accesses to each type of engineering cost data in each monitoring time period; According to the proportion of access times of each type of engineering cost data in each monitoring time period and the mean access time interval, the access frequency characteristics of each type of engineering cost data in each monitoring time period are obtained. The access frequency characteristics are proportional to the proportion of access times and inversely proportional to the mean access time interval.

2. The method for storing and analyzing construction costs based on big data as claimed in claim 1, characterized in that: According to the number of accesses to the engineering cost data in the plurality of monitoring time periods and the access frequency characteristics, the access importance of the engineering cost data is obtained, including: Obtain the number of accesses to the project cost data in each monitoring period, as well as the number of alarm information prompts in each monitoring period; According to the number of accesses to the engineering cost data in each monitoring time period and the number of alarm information prompts in each monitoring time period, the real access characteristics of the engineering cost data in each monitoring time period are obtained; The access importance of the engineering cost data is obtained according to the real access characteristics of the engineering cost data in each monitoring time period and the access frequency characteristics of the engineering cost data in the plurality of monitoring time periods.

3. A method for storing and analyzing engineering costs based on big data as claimed in claim 2, characterized in that The calculation formula for the access importance of cost data is as follows: ； in, represents the access importance of the i-th type of construction cost data, represents the average value of the access frequency characteristics of the i-th type of construction cost data in each monitoring time period; It represents the number of accesses to the i-th type of construction cost data in the m-th monitoring period; Indicates the number of alarm information prompts for the i-th type of engineering cost data in the m-th monitoring time period; M represents the number of monitoring time periods; Represents the normalization function.

4. The method for storing and analyzing construction costs based on big data as claimed in claim 3 is characterized in that: According to the difference between the access times of the engineering cost data in different engineering stages, the access concentration of the engineering cost data is obtained, including: Obtain the maximum number of accesses to the construction cost data in each engineering stage; The access concentration of the engineering cost data is obtained according to the difference between the maximum access number and the remaining access numbers; the remaining access numbers are the access numbers of the engineering cost data in each engineering stage except the maximum access number.

5. The method for storing and analyzing construction costs based on big data as claimed in claim 4 is characterized in that: The calculation formula for access concentration is: ； in, is the access concentration of the i-th type of engineering cost data, is the maximum number of accesses to the i-th type of construction cost data, B is the number of the remaining engineering stages except the engineering stage corresponding to the maximum number of accesses, b is the index of the remaining engineering stages except the engineering stage corresponding to the maximum number of accesses, is the number of accesses to the i-th type of engineering cost data in the b-th engineering stage.

6. The method for storing and analyzing construction costs based on big data as claimed in claim 5 is characterized in that: The calculation formula for the data importance is: ； in, is the data importance of the i-th type of engineering cost data.

7. The method for storing and analyzing construction costs based on big data as claimed in claim 1, characterized in that: The engineering cost storage and analysis method further comprises: Obtaining the engineering phase associated with the engineering cost data and the engineering importance of the associated engineering phase, thereby obtaining the engineering importance of the engineering cost data; The step of determining the storage priority of each type of engineering cost data based on the data importance of each type of engineering cost data includes: The data importance and engineering importance of various types of engineering cost data are integrated to obtain the comprehensive importance of various types of engineering cost data; According to the comprehensive importance of each type of engineering cost data, the storage priority of each type of engineering cost data is determined. The higher the comprehensive importance, the higher the storage priority.

8. The method for storing and analyzing construction costs based on big data as claimed in claim 7 is characterized in that: According to the comprehensive importance of various types of engineering cost data, the storage priority of various types of engineering cost data is determined, including: Comparing the comprehensive importance of each type of engineering cost data with a preset first threshold and a second threshold, wherein the first threshold is greater than the second threshold; Determine the storage priority of the construction cost data corresponding to the comprehensive importance greater than the first threshold as a high storage priority, and configure a first storage medium; Determine the storage priority of the construction cost data corresponding to the comprehensive importance less than or equal to the first threshold and greater than or equal to the second threshold as the medium storage priority, and configure a second storage medium; Determine the storage priority of the construction cost data corresponding to the comprehensive importance less than the second threshold as a low storage priority, and configure a third storage medium; The reading and writing speeds of the first storage medium, the second storage medium and the third storage medium decrease in sequence.

9. A big data-based engineering cost storage and analysis system, characterized in that: include: Memory and processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is used to implement the engineering cost storage and analysis method based on big data described in any one of claims 1 to 8 when the program instructions are executed.

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