Property reconstruction project budget data generation method and device, equipment and medium

Abstract

The invention provides a budget data generation method and device for property transformation projects, equipment and a medium, and the method comprises the steps: associating a first correction coefficient in a historical data set of each transformation project, and representing a deviation ratio between prediction data and actual data through the correction coefficient, determining the first budget data by using the first correction coefficient and the prediction budget data obtained based on the historical data set, thereby realizing the correction of the prediction data; after the transformation plan is started, progress deviation data and a risk score are determined according to the real-time progress data of each transformation project, the progress deviation data are marked when the risk score is greater than a score threshold, and after the transformation plan is completed, each transformation project evaluates the accuracy of the first correction coefficient based on the progress deviation data and the risk score, and updating the first correction coefficient to the second correction coefficient based on the target step value, and associating the second correction coefficient to the historical data set to realize update iteration of the correction coefficient and improve the reliability of the budget data.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a method, device, equipment and medium for generating budget data for a property renovation project. Background Art

[0002] At present, property management often encounters various renovation projects, such as large-scale updates and iterations of property equipment in the community, or the construction of multiple convenience facilities in a specific area. Before implementing the renovation project, relevant personnel need to prepare a draft budget in the property system and have it approved by relevant reviewers. Since the renovation project involves many renovation projects, if the management personnel are relied on to estimate the costs of each project, the efficiency of preparing the draft budget is low.

[0003] In related technologies, it is already possible to predict the budget data of different renovation projects based on historical data, thereby improving the efficiency of budget draft preparation. However, the reference value of historical data of different renovation projects is not necessarily the same. For example, the reference value of labor expenditure is higher, while the reference value of equipment procurement expenditure is lower. The reliability of budget data predicted solely based on historical data cannot be guaranteed, which affects the rationality of budget allocation. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method, device, equipment, and medium for generating budget data for a property renovation project, which can dynamically adjust the correction coefficient associated with each renovation project, correct the predicted budget data through the correction coefficient, and improve the reliability of the budget data.

[0005] In a first aspect, an embodiment of the present invention provides a method for generating budget data for a property renovation project, comprising: Based on any renovation project of the renovation plan, determining forecast budget data according to a pre-associated historical data set, and determining first budget data based on the forecast budget data and a first correction coefficient associated with the historical data set, wherein the renovation project is associated with planned progress data, and the historical data set includes historical budget data corresponding to the renovation project; After the transformation plan is started, whenever the real-time progress data of any of the transformation projects is obtained, the progress deviation data and the risk score are determined based on the planned progress data and the real-time progress data; when the risk score is greater than a preset score threshold, the progress deviation data determined this time is marked; based on the progress deviation data, the first budget data is updated to the second budget data; and the second budget data is applied to the corresponding transformation project, wherein the value of the risk score is positively correlated with the risk probability; After the transformation plan is completed, based on any of the transformation projects, the number of the marked progress deviation data is determined as a target deviation number, the average value of a plurality of risk scores greater than the score threshold is determined as a target score, a target step value is determined based on the target deviation number and the target score, and a second correction coefficient is determined based on the target step value and the first correction coefficient; The first budget data is determined based on the second correction coefficient in a next reconstruction plan.

[0006] According to some embodiments of the present invention, before determining the forecast budget data based on the pre-associated historical data set, the method further includes: Based on any of the transformation projects, determining a plurality of corresponding transformation matters; Traversing a plurality of preset optional data sets based on the plurality of transformation items, wherein each of the optional data sets includes at least one historical budget item, and each of the historical budget items records corresponding item budget data; Determining the successfully matched optional data set as the historical data set; Alternatively, when the corresponding optional data set cannot be matched, the corresponding historical budget items are obtained from multiple optional data sets based on each transformation item and the historical data set is constructed, and the item budget data of each historical budget item are combined into the historical budget data.

[0007] According to some embodiments of the present invention, determining the progress deviation data and the risk score based on the planned progress data and the real-time progress data includes: Determine the transformation project for which the real-time progress data is currently acquired as the target project, determine the target time information of the real-time progress data, and determine the target stage from the planned progress data based on the target time information, wherein the planned progress data records a plurality of progress stages, and each of the progress stages is associated with the planned time information and the planned transformation data; Determine the difference between the real-time progress data and the planned transformation data corresponding to the target stage as the progress deviation data; When the progress deviation data is a negative number, a preset risk redundancy value of the target project is obtained, and the progress deviation data and the risk redundancy value are summed and input into a preset sigmoid function to obtain the project risk probability; Alternatively, when the progress deviation data is a positive number, the project risk probability is set to 0; The numerator of the project risk probability is determined as the risk score.

[0008] According to some embodiments of the present invention, determining a target step value based on the target deviation number and the target score includes: Determining a reference step value from a preset mapping table based on the target deviation number, wherein the preset mapping table records a mapping relationship between optional deviation numbers and optional step values; Determine the target risk probability corresponding to the target score, and determine the target step value as the product of the reference step value and the target risk probability.

[0009] According to some embodiments of the present invention, after determining the first budget data based on the predicted budget data and the first correction coefficient associated with the historical data set, the method further includes: Determine the sum of all the first budget data as planned budget data, determine the inverse of the planned budget data as a first fitness function, and determine the planned resource data preset for the transformation plan, wherein the planned resource data is used to indicate the resource scale of the transformation plan; Based on the planned resource data, the first fitness function and a preset ant colony algorithm, the first resource data corresponding to each of the transformation projects is determined, wherein each of the transformation projects is an ant colony individual in the ant colony algorithm.

[0010] According to some embodiments of the present invention, after updating the first budget data to second budget data based on the progress deviation data, the method further includes: determining a target mapping relationship between each pair of the first resource data and the first budget data; Determine the transformation project for which the real-time progress data is currently acquired as the target project, determine the second resource data based on the second budget data and the corresponding target mapping relationship, and determine the difference between the planned budget data and the second budget data as the adjusted budget data; Determine the reciprocal of the adjusted budget data as a second fitness function, and determine the difference between the planned resource data and the second resource data as adjusted resource data; Removing the ant colony individual corresponding to the target project in the ant colony algorithm, and determining third resource data corresponding to each of the transformation projects except the target project based on the adjusted resource data, the second fitness function and the adjusted ant colony algorithm; Third budget data is determined based on the third resource data and the target mapping relationship, and the third resource data and the third budget data are applied to the corresponding transformation project.

[0011] According to some embodiments of the present invention, after determining the second correction coefficient based on the target step value and the first correction coefficient, the method further includes: Based on any of the transformation projects, determining the second budget data finally updated as new historical budget data, saving the new historical budget data to the corresponding historical data set, and associating the second correction coefficient and the target deviation number to the historical data set; When at least one of the historical budget data is removed from any of the historical data sets, the removed historical budget data is determined as invalid budget data; When the target deviation number associated with the failure budget data is greater than the current target deviation number, the second correction coefficient is updated based on a ratio of the failure budget data to the second budget data.

[0012] In a second aspect, an embodiment of the present invention provides a budget data generation device for a property renovation project, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the budget data generation method for the property renovation project as described in the first aspect above.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a budget data generating device for a property renovation project as described in the second aspect above.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method for generating budget data for a property renovation project as described in the first aspect above.

[0015] The method for generating budget data for a property renovation project according to an embodiment of the present invention has at least the following beneficial effects: based on any renovation project in the renovation plan, predictive budget data is determined according to a pre-associated historical data set, and first budget data is determined based on the predictive budget data and a first correction coefficient associated with the historical data set, wherein the renovation project is associated with planned progress data, and the historical data set includes the corresponding historical budget data of the renovation project; after the renovation plan is started, whenever real-time progress data of any of the renovation projects is obtained, progress deviation data and a risk score are determined based on the planned progress data and the real-time progress data, and when the risk score is greater than a preset score threshold, the current confirmation is marked as The progress deviation data determined, the first budget data is updated to the second budget data based on the progress deviation data, and the second budget data is applied to the corresponding transformation project, wherein the value of the risk score is positively correlated with the risk probability; after the transformation plan is completed, based on any of the transformation projects, the number of the marked progress deviation data is determined as the target deviation number, the average value of the multiple risk scores greater than the score threshold is determined as the target score, the target step value is determined based on the target deviation number and the target score, and the second correction coefficient is determined based on the target step value and the first correction coefficient; in the next transformation plan, the first budget data is determined based on the second correction coefficient. According to the technical solution of the embodiment of the present invention, it is possible to associate the correction coefficient in the historical data set, characterize the deviation ratio between the predicted data and the actual data by the correction coefficient, periodically collect the implementation progress data during the execution of the transformation plan, determine the number of deviations and risk scores of each transformation project under the first budget data, perform budget correction in real time, and dynamically update the correction coefficient after the transformation plan is executed. After multiple iterations, the correction coefficient can more accurately map the budget data predicted based on the historical data to the actual budget data, thereby improving the reliability of the budget data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the principle of a method for generating budget data for a property renovation project provided by an embodiment of the present invention; Figure 2 is a flow chart of a method for generating budget data for a property renovation project provided by another embodiment of the present invention; Figure 3 is a complete flow chart of a method for generating budget data for a property renovation project provided by another embodiment of the present invention; Figure 4 It is a structural diagram of a budget data generating device for a property renovation project provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0017] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0018] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0019] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0020] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0021] The embodiment of the present invention provides a method, device, equipment, and medium for generating budget data for a property renovation project, wherein the method for generating budget data for a property renovation project comprises: determining predicted budget data based on a pre-associated historical data set for any renovation project in a renovation plan, and determining first budget data based on a first correction coefficient associated with the predicted budget data and the historical data set, wherein the renovation project is associated with planned progress data, and the historical data set includes the corresponding historical budget data of the renovation project; after the renovation plan is started, whenever real-time progress data of any of the renovation projects is obtained, determining progress deviation data and a risk score based on the planned progress data and the real-time progress data, and when the risk score is greater than a preset Scoring threshold, marking the progress deviation data determined this time, updating the first budget data to the second budget data based on the progress deviation data, and applying the second budget data to the corresponding transformation project, wherein the value of the risk score is positively correlated with the risk probability; after the transformation plan is completed, based on any of the transformation projects, the number of the marked progress deviation data is determined as the target deviation number, the average value of multiple risk scores greater than the scoring threshold is determined as the target score, the target step value is determined based on the target deviation number and the target score, and the second correction coefficient is determined based on the target step value and the first correction coefficient; in the next transformation plan, the first budget data is determined based on the second correction coefficient. According to the technical solution of the embodiment of the present invention, it is possible to associate the correction coefficient in the historical data set, characterize the deviation ratio between the predicted data and the actual data by the correction coefficient, periodically collect and implement the progress data during the execution of the transformation plan, determine the number of deviations and risk scores of each transformation project under the first budget data, perform budget correction in real time, and dynamically update the correction coefficient after the transformation plan is executed. After multiple iterations, the correction coefficient can more accurately map the budget data predicted based on the historical data to the actual budget data, thereby improving the reliability of the budget data.

[0022] The following is based on the Figure 1 The principle schematic diagram shown further illustrates the technical solution of the embodiment of the present invention.

[0023] Reference Figure 2 , Figure 2 A flowchart of a method for generating budget data for a property renovation project provided by an embodiment of the present invention, the method for generating budget data for a property renovation project includes but is not limited to the following steps: S10, based on any renovation project in the renovation plan, determining forecast budget data according to a pre-associated historical data set, and determining first budget data based on a first correction coefficient associated with the forecast budget data and the historical data set, wherein the renovation project is associated with planned progress data, and the historical data set includes historical budget data of the corresponding renovation project; S20, after the transformation plan is started, whenever the real-time progress data of any transformation project is obtained, the progress deviation data and the risk score are determined based on the planned progress data and the real-time progress data, and when the risk score is greater than a preset score threshold, the progress deviation data determined this time is marked, and the first budget data is updated to the second budget data based on the progress deviation data, and the second budget data is applied to the corresponding transformation project, wherein the value of the risk score is positively correlated with the risk probability; S30, after the transformation plan is completed, based on any transformation project, the number of marked progress deviation data is determined as a target deviation number, the average value of multiple risk scores greater than the score threshold is determined as a target score, a target step value is determined based on the target deviation number and the target score, and a second correction coefficient is determined based on the target step value and the first correction coefficient; S40, determining first budget data based on the second correction coefficient in the next transformation plan.

[0024] It should be noted that the renovation plan can be any major renovation project in the field of property management, such as installing new elevators, renovating convenience facilities, updating water and electricity equipment, upgrading access control equipment, etc., and no further restrictions are made here. Each renovation plan consists of multiple renovation projects, and a renovation project can include multiple renovation matters. Different renovation projects are not related to each other when calculating the budget. Therefore, renovation matters that need to be jointly budgeted can be summarized into one renovation project to facilitate budget calculation.

[0025] For example Figure 1 As shown, Equipment Renovation Plan 1 is the renovation of fitness equipment in the community. The renovation projects include environmental decoration projects and equipment installation projects. The environmental decoration projects include two renovation items: dismantling old equipment and renovating the installation environment. After completing the environmental decoration project, an environment for installing equipment can be provided. Therefore, the two renovation items need to be jointly budgeted, so they can form one renovation project. The equipment installation project includes three renovation items: equipment procurement, equipment installation and equipment debugging, which will not be elaborated here.

[0026] It should be noted that in addition to historical budget data, the historical data set also records correction coefficients. The initial correction coefficient may be a preset value. As the technical solution of this embodiment is continuously iterated, only the latest correction coefficient is saved. For example, the current correction coefficient is the first correction coefficient, which is updated to the second correction coefficient after one iteration of the transformation plan. The second correction coefficient replaces the first correction coefficient in the historical data set, and so on.

[0027] For example, Figure 1As shown, a device historical data set is matched based on the device installation project, and multiple device budgets and correction coefficients X1 are recorded in the device historical data set. Device budget 1 and device budget 2 correspond to the budget data allocated to the device installation project in two different renovation plans. After executing the technical solution of this embodiment, the correction coefficient X1 is updated to the correction coefficient X2, and the correction coefficient X2 is saved in the device historical data set, and the correction coefficient X1 is no longer saved. The same is true for the environmental decoration project, which will not be repeated here.

[0028] It should be noted that after determining the historical data set, determining the predicted budget data based on the historical data set is a technology well known to those skilled in the art, and will not be elaborated here. The renovation plan involves multiple renovation projects, and different renovation projects have different corresponding contents in practice. Since a general prediction model is usually used in the system for data prediction, the prediction model usually extracts relevant features from the historical budget data for prediction, and the prediction scales of different types of data are not necessarily the same, which makes the reference value scales of the predicted budget data predicted by different renovation projects inconsistent. On this basis, this embodiment uses the first correction coefficient associated with each historical data set to correct the predicted budget data. Since the first correction coefficient is bound to the historical data set, the first correction coefficient can be used as the deviation range between the predicted data and the actual data of the corresponding renovation project. After the data is predicted with the general prediction model, the data is corrected with the first correction coefficient that meets the deviation range of various types of data, so that the first budget data is more in line with the actual needs of the corresponding renovation project, and the reliability of the first budget data is improved.

[0029] It should be noted that after determining each first budget data, a simple sum can be performed to obtain the planned budget data of the transformation plan, so as to perform relevant approval. This embodiment does not limit the approval process. After completing the approval, the transformation plan can be started, and the corresponding first budget data can be allocated to each transformation project. The real-time progress data of the transformation project can be obtained periodically, such as updating the equipment installation progress once a day. Those skilled in the art are familiar with how to obtain real-time progress data, and are also familiar with how to preset planned progress data in each transformation project of the transformation plan, such as the number of equipment installations that should be completed every day, etc., and no further restrictions are made here. After obtaining the real-time progress data, the progress deviation data can be determined based on the planned progress data and the implementation progress data. In the case of having actual progress data and planned progress data, those skilled in the art are familiar with how to perform risk assessment. The risks of this embodiment may include overdue risks, budget overrun risks, and resource shortage risks. A risk score can also be calculated for the above-mentioned risks. The specific algorithm of the risk score is not limited here, and it can characterize the abnormal risks of the transformation project.

[0030] For example, Figure 1As shown, the equipment installation project plans to complete 50% of the installation progress within a week, and the actual progress is 40%. The progress deviation data is -10%, and the calculated risk score is 80, that is, there is an 80% probability of overdue risk. The higher the risk score in this embodiment, the higher the probability of risk. For another example, the environmental decoration project plans to complete 30% of the decoration progress within a week, and the actual progress is 40%. The progress deviation data is +10%, so there is no overdue risk, and the risk score is 0 to indicate no risk.

[0031] It should be noted that after determining the progress deviation data and risk score, not all risk scores need to trigger budget changes, such as progress delays caused by accidental events. Therefore, this embodiment sets a score threshold as an abnormality judgment standard. When the risk score is greater than the score threshold, the probability of risk is high, and the budget is adjusted according to the progress deviation data. Of course, the increase in the budget needs to be approved by the relevant authorities. This embodiment does not limit this. This embodiment aims to determine the actual budget data required to complete the transformation project, and use this as a basis to determine the second correction coefficient to achieve the iteration of the correction coefficient and improve the calculation reliability of the next budget data. For example, if the progress deviation data is -10%, the first budget data is increased by 10% to obtain the second budget data. Of course, the conversion ratio can also be adjusted according to actual needs, and no further limitations are made here.

[0032] It should be noted that the real-time progress data of the present embodiment is collected periodically, and the collection period of the real-time progress data of different transformation projects may be different, so the processing of each transformation project is independent of each other. After any transformation project obtains the real-time progress data, step S20 of the present embodiment is executed once, and the data is recorded after the risk score is greater than the scoring threshold. The recorded content includes the progress deviation data and the risk score. After the transformation plan is completed, if the transformation project does not record a set of progress deviation data and risk score, the first correction coefficient can accurately reflect the deviation between the predicted budget data and the actual budget data, and the current first correction coefficient can be continued to be applied. When the transformation project records at least one set of progress deviation data and risk score, the first correction coefficient has a basis for adjustment. By counting the marked progress deviation data as the target deviation number, the average value of the risk score greater than the scoring threshold is determined as the target score, and the target step value is determined from the two dimensions of the target deviation number and the target score. For example, the target deviation number and the target score are used as two variables in the target step value calculation formula, or the target step value is obtained by looking up the table according to the target deviation number and the target score. No limitation is made here. After obtaining the target step value, the target step value is summed with the first correction coefficient to obtain a second correction coefficient, and the second correction coefficient is saved in the corresponding historical data set. In this embodiment, the target step value is calculated based on the data with abnormal risk, and the deviation caused by the first correction coefficient is characterized by the target step value, so that the second correction coefficient obtained after superimposing the target step value can be more accurate.

[0033] For example, Figure 1 As shown in the figure, after completing the renovation plan, taking the equipment installation project as an example, the recorded content includes {(-10%, 80), (-20%, 90), ···}, where -10% and -20% are progress deviation data, 80 and 90 are risk scores, and taking the number of progress deviation data as 2 as an example, the target score is (80+90) / 2=85, and the step value 1 is determined as the target step value. The correction coefficient X1 is added to the step value 1 to obtain the correction coefficient X2, and the correction coefficient X2 is saved to the equipment historical data set, thereby realizing the iterative update of the correction coefficient, so that the next equipment renovation plan 2 can apply the correction coefficient X2, thereby improving the reliability of the budget data.

[0034] In addition, in one embodiment, referring to Figure 3 In step S10, before determining the forecast budget data according to the pre-associated historical data set, the following steps are specifically included but not limited to: S11, based on any of the transformation projects, determining a plurality of corresponding transformation items; S12, traversing a plurality of preset optional data sets based on the plurality of transformation items, wherein each of the optional data sets includes at least one historical budget item, and each of the historical budget items records corresponding item budget data; S13, determining the successfully matched optional data set as the historical data set; S14, when the corresponding optional data set cannot be matched, based on each of the transformation items, corresponding historical budget items are obtained from multiple optional data sets and the historical data set is constructed, and the item budget data of each historical budget item are combined into the historical budget data.

[0035] It should be noted that the historical data set includes the historical budget data of the renovation project. The database can store multiple optional data sets. This embodiment needs to determine the historical data set from multiple optional data sets. The historical budget data is the project budget data that has been allocated to the renovation project in history. The project budget data will be further allocated to each renovation item. The item budget data of each renovation item can also be recorded in the historical budget data. Each optional data set can be indexed by the renovation items in the above embodiment, and the database can be searched through the multiple renovation items carried by the renovation project. If the match is successful, the corresponding optional data set is directly obtained as the historical data set. If the historical data set cannot be accurately matched because the renovation items of different renovation projects have different compositions, each renovation item can be matched from multiple historical data sets to form a historical data set, and then the item budget data of each historical budget item is combined into the historical budget data corresponding to the historical data set.

[0036] It is worth noting that the above example also provides a scenario in which a historical data set is formed by multiple item budget data from different historical data sets. At this time, the average value of the correction coefficients of the multiple historical data sets to which the item budget data belongs can be used as the first correction coefficient. Of course, the maximum value can also be selected as the first correction coefficient. No further restrictions are placed on the specific selection strategy here.

[0037] In addition, in one embodiment, referring to Figure 3 In step S20, the progress deviation data and risk score are determined based on the planned progress data and the real-time progress data, specifically including but not limited to the following steps: S21, determining the transformation project for which the real-time progress data is currently obtained as the target project, determining the target time information of the real-time progress data, and determining the target stage from the planned progress data based on the target time information, wherein the planned progress data records a plurality of progress stages, and each progress stage is associated with the planned time information and the planned transformation data; S22, determining the difference between the real-time progress data and the planned transformation data corresponding to the target stage as progress deviation data; S23, when the progress deviation data is a negative number, obtain the preset risk redundancy value of the target project, sum the progress deviation data and the risk redundancy value, and input them into the preset sigmoid function to obtain the project risk probability; S24, when the progress deviation data is positive, the project risk probability is set to 0; S25, determining the numerator of the project risk probability as the risk score.

[0038] It should be noted that, according to the description of the above embodiment, multiple renovation projects can periodically obtain real-time progress data, and the acquisition frequency and acquisition timing of different renovation projects can be different. The operations performed by each renovation project are the same. This embodiment takes the renovation project that currently obtains real-time progress data as the target project, and explains the principle through the operation of a target project. When multiple renovation projects obtain the corresponding real-time progress data, the technical solutions of this embodiment can be executed separately.

[0039] It should be noted that each renovation project can pre-set planned progress data, and record multiple progress stages and corresponding planned renovation data in the planned progress data. After the real-time progress data is obtained, the target stage is determined based on the target time information at the time of acquisition, and the real-time progress data is subtracted from the planned renovation data of the target stage to obtain the progress deviation data.

[0040] For example, if the renovation project cycle is one month, the granularity of the progress stage is 1 day, the planned renovation data for the first day is 5%, the second day is 8%, and so on, to ensure that the planned renovation data for the last progress stage is 100%. When the real-time progress data obtained on the second day is 5%, the second day is determined as the target stage, the corresponding planned renovation data is 8%, and the progress deviation data is 5%-8%=-3%.

[0041] It should be noted that when the progress deviation data is a positive number, the current real-time progress meets the plan or is ahead of the plan, and the project risk probability can be set to 0 without complex calculations. When the progress deviation data is a negative number, there is a project risk, and certain risks are allowed to occur in the actual transformation process. For example, when the progress is only slightly behind, it is not considered that there is a risk. This embodiment further sets a risk redundancy value, for example, the risk honor value is set to 2% or 3%. After determining the progress deviation data, it is first summed with the risk redundancy value, and then input into the sigmoid function for risk prediction. The sigmoid function can output a value between [0,1]. Those skilled in the art are familiar with how to configure the sigmoid function for risk prediction, and the specific principle is not limited here.

[0042] It should be noted that after obtaining the project risk probability, the maximum value of the project risk probability is 1. Therefore, this embodiment uses the numerator of the project risk probability as the risk score to characterize the probability of the risk. For example, if the project risk probability is 85%, the risk score is 85, and so on.

[0043] In addition, in one embodiment, referring to Figure 3 In step S30, the target step value is determined based on the target deviation number and the target score, including but not limited to the following steps: S31, determining a reference step value from a preset mapping table based on the target deviation number, wherein the preset mapping table records a mapping relationship between optional deviation numbers and optional step values; S32, determining the target risk probability corresponding to the target score, and determining the product of the reference step value and the target risk probability as the target step value.

[0044] It should be noted that after determining the target deviation number and the target score, the present embodiment records the mapping relationship between the optional deviation number and the optional step value through a preset mapping table. The target deviation number is the number of times the risk occurs. If the number of times the risk occurs is large, the deviation of the first correction coefficient is large and a larger adjustment is required, and vice versa. Therefore, the present embodiment looks up the target deviation number in the preset mapping table and determines the matched optional step value as the reference step value. The value of the optional step value increases with the increase of the corresponding value of the optional deviation number. The specific corresponding relationship can be set according to actual needs.

[0045] It should be noted that after obtaining the reference step value, since the reference step value is associated with the frequency of risk occurrence, the reference step value in this embodiment is only used as a base and is not directly applied. The degree of risk is characterized by the target score. Since the target score is the numerator of the probability, the target score is converted into the target risk probability, and the reference step value is multiplied by the target risk probability to obtain the target step value. Since the target risk probability is less than or equal to 1, the larger the target risk probability, the smaller the reference step value is converted, and the larger the target step value is obtained, so that the adjustment range can be related to the number of risk occurrences and the risk frequency, thereby improving the reliability of the coefficient adjustment.

[0046] In addition, in one embodiment, referring to Figure 3 After executing step S10, the following steps are also included but not limited to: S61, determining the sum of all the first budget data as planned budget data, determining the inverse of the planned budget data as a first fitness function, and determining planned resource data preset for the transformation plan, wherein the planned resource data is used to indicate the resource scale of the transformation plan; S62, determining first resource data corresponding to each transformation project based on the planned resource data, the first fitness function and a preset ant colony algorithm, wherein each transformation project is an ant colony individual in the ant colony algorithm.

[0047] It should be noted that after the planned budget data is obtained by summing up multiple first budget data, since the first budget data is not the actual expenditure of the transformation project, but the financial resources of the transformation project, not all the first budget data are necessarily required for each transformation project. The financial expenditure is usually converted into resources of multiple dimensions, such as human resources, material resources, logistics resources, etc. Therefore, after determining the first budget data, the resource allocation of each transformation project will also have a certain impact on the transformation efficiency. For example, in the equipment installation project, the equipment procurement items are insufficient in budget due to price increases, resulting in a stagnation of progress. At this time, more budget amounts need to be allocated to procurement resources to promote the continued execution of equipment procurement items. Therefore, after determining the planned budget data, this embodiment uses the inverse of the planned budget data as the first fitness function, and uses the ant colony algorithm to allocate resources to each transformation project, thereby obtaining the first resource data of each transformation project. The first resource data of this embodiment can be the resource scale allocated to different transformation projects, such as human resources and logistics resources allocated under the first budget data. It is worth noting that using the ant colony algorithm for resource allocation is a conventional technical means in this field, and no further description is given here.

[0048] In addition, in one embodiment, referring to Figure 3 In step S20, after the first budget data is updated to the second budget data based on the progress deviation data, the following steps are also included but not limited to: S63, determining a target mapping relationship between each pair of first resource data and first budget data; S64, determining the transformation project for which the real-time progress data is currently obtained as the target project, determining the second resource data based on the second budget data and the corresponding target mapping relationship, and determining the difference between the planned budget data and the second budget data as the adjusted budget data; S65, determining the inverse of the adjusted budget data as the second fitness function, and determining the difference between the planned resource data and the second resource data as the adjusted resource data; S66, removing the ant colony individuals corresponding to the target project in the ant colony algorithm, and determining third resource data corresponding to each transformation project except the target project based on the adjusted resource data, the second fitness function and the adjusted ant colony algorithm; S67, determining third budget data based on the third resource data and the target mapping relationship, and applying the third resource data and the third budget data to the corresponding transformation project.

[0049] It should be noted that there is a certain mapping relationship between the first resource data and the first budget data of this embodiment. For example, under the first budget data, 10 construction workers and 2 logistics vehicles can be allocated, and different types of resources are converted into standardized data. For example, the resource value of the construction workers is 1, and the resource value of the logistics vehicles is 2. The first resource data is 24, and the ratio between it and the first budget data is the target mapping relationship.

[0050] It should be noted that, according to the description of the above embodiment, after calculating the progress deviation data with a negative value, it can be determined that the first resource data allocated for the target stage does not meet the requirements. After the first budget data is added to the second budget data, this embodiment calculates the second resource data based on the second budget data and the target mapping relationship, and uses the second resource data to represent the number of resources currently needed to mitigate the risk.

[0051] It should be noted that due to the increase in the budget and resources of the target project, the resources of the remaining transformation projects will be reduced accordingly when the total resources remain unchanged. For example, if logistics vehicles are added from another transformation project to the target project, if the remaining transformation projects can be completed without changing the total budget through reasonable resource allocation, the transformation efficiency and capital utilization rate of the transformation plan can be improved. Therefore, this embodiment also determines the difference between the planned budget data and the second budget data as the adjusted budget data, and uses the inverse of the adjusted budget data as the second fitness function to re-allocate resources for the transformation projects other than the target project to obtain multiple third resource data, thereby determining the third resource data required for the remaining transformation projects, and then determining the third budget data based on the third resource data and the target mapping relationship, thereby realizing secondary allocation of resources when the total budget remains unchanged.

[0052] It is worth noting that the adjusted budget data in this embodiment directly uses the planned budget data minus the second budget data, and does not consider whether other renovation projects have consumed part of the budget. If other renovation projects have consumed part of the budget but have not exceeded the third budget data, they will also be identified as operating within the budget. If the update of the third budget data causes any renovation project to stagnate, a prompt message can be sent to the management personnel to instruct them to increase the budget. Therefore, this embodiment can continuously iterate resource allocation before the renovation project reports an error, thereby improving the rationality of resource allocation.

[0053] In addition, in one embodiment, referring to Figure 3 In step S30, after determining the second correction coefficient based on the target step value and the first correction coefficient, the following steps are also included but not limited to: S71, based on any renovation project, determining the second budget data finally updated as new historical budget data, saving the new historical budget data to a corresponding historical data set, and associating the second correction coefficient and the target deviation number to the historical data set; S72, when at least one historical budget data is removed from any historical data set, the removed historical budget data is determined as invalid budget data; S73: When the target deviation number associated with the failure budget data is greater than the current target deviation number, update the second correction coefficient based on the ratio of the failure budget data to the second budget data.

[0054] It should be noted that after the transformation plan is completed, the number of times each transformation project iterates the second budget data is at most equal to the number of times the real-time progress data is collected. This embodiment saves the finally updated second budget data as historical budget data, creates a new object in the corresponding historical data set, and records the second correction coefficient to the historical data set according to the description of the above embodiment. At the same time, the target deviation number can also be saved, and the target deviation number is used to characterize the degree of adjustment risk.

[0055] It should be noted that the data in the historical data set can be updated dynamically, for example, once a year, and the expired data can be deleted from the historical data set. The update can also be triggered manually. For example, if a certain model of equipment is no longer purchased, the relevant historical budget data for the purchase of this model of equipment can be deleted to avoid affecting the accuracy of the forecast.

[0056] It should be noted that after determining the failure budget data, when the target deviation number of the failure budget data is greater than the current target deviation number, the correction coefficient is adjusted significantly in the transformation plan for the failure budget data. The failure budget data has a greater effect on improving the accuracy during prediction. After deleting the failure budget data, this embodiment needs to make certain adjustments to the second correction coefficient so that the second correction coefficient can make up for the accuracy impact after the loss of the failure budget data to a certain extent. This embodiment updates the second correction coefficient with the ratio of the failure budget data to the second budget data. If the ratio is greater than 1, the failure budget data will increase the predicted budget data. Therefore, the predicted budget data obtained after deleting the failure budget data will decrease. The number of times can be compensated to a certain extent by the increased second correction coefficient, and vice versa. It is not repeated here.

[0057] like Figure 4 As shown, Figure 4 The present invention also provides a device for generating budget data for a property renovation project, including: The processor 401 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 402 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 402, and the processor 401 calls and executes the budget data generation method for the property renovation project of the embodiment of this application; Input / output interface 403, used to implement information input and output; Communication interface 404, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.); A bus 405 that transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404); The processor 401 , the memory 402 , the input / output interface 403 and the communication interface 404 are connected to each other in communication within the device via the bus 405 .

[0058] An embodiment of the present application also provides an electronic device, including the budget data generating device for the property renovation project as described above.

[0059] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the budget data generation method for the above-mentioned property renovation project is implemented.

[0060] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0061] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media generally include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0062] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions under the shared conditions without violating the spirit of the present invention. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for generating budget data for a property renovation project, characterized in that: include: Based on any renovation project of the renovation plan, determining forecast budget data according to a pre-associated historical data set, and determining first budget data based on the forecast budget data and a first correction coefficient associated with the historical data set, wherein the renovation project is associated with planned progress data, and the historical data set includes historical budget data corresponding to the renovation project; After the transformation plan is started, whenever the real-time progress data of any of the transformation projects is obtained, the progress deviation data and the risk score are determined based on the planned progress data and the real-time progress data; when the risk score is greater than a preset score threshold, the progress deviation data determined this time is marked; based on the progress deviation data, the first budget data is updated to the second budget data; and the second budget data is applied to the corresponding transformation project, wherein the value of the risk score is positively correlated with the risk probability; After the transformation plan is completed, based on any of the transformation projects, the number of the marked progress deviation data is determined as a target deviation number, the average value of a plurality of risk scores greater than the score threshold is determined as a target score, a target step value is determined based on the target deviation number and the target score, and a second correction coefficient is determined based on the target step value and the first correction coefficient; The first budget data is determined based on the second correction coefficient in a next reconstruction plan.

2. The method for generating budget data for a property renovation project according to claim 1, characterized in that: Before determining the forecast budget data based on the pre-associated historical data set, the method further includes: Based on any of the transformation projects, determining a plurality of corresponding transformation matters; Traversing a plurality of preset optional data sets based on the plurality of transformation items, wherein each of the optional data sets includes at least one historical budget item, and each of the historical budget items records corresponding item budget data; Determining the successfully matched optional data set as the historical data set; Alternatively, when the corresponding optional data set cannot be matched, the corresponding historical budget items are obtained from multiple optional data sets based on each transformation item and the historical data set is constructed, and the item budget data of each historical budget item are combined into the historical budget data.

3. The method for generating budget data for a property renovation project according to claim 1, characterized in that: Determining progress deviation data and a risk score based on the planned progress data and the real-time progress data includes: Determine the transformation project for which the real-time progress data is currently acquired as the target project, determine the target time information of the real-time progress data, and determine the target stage from the planned progress data based on the target time information, wherein the planned progress data records a plurality of progress stages, and each of the progress stages is associated with the planned time information and the planned transformation data; Determine the difference between the real-time progress data and the planned transformation data corresponding to the target stage as the progress deviation data; When the progress deviation data is a negative number, a preset risk redundancy value of the target project is obtained, and the progress deviation data and the risk redundancy value are summed and input into a preset sigmoid function to obtain the project risk probability; Alternatively, when the progress deviation data is a positive number, the project risk probability is set to 0; The numerator of the project risk probability is determined as the risk score.

4. The method for generating budget data for a property renovation project according to claim 3, characterized in that: Determining a target step value based on the target deviation number and the target score includes: Determining a reference step value from a preset mapping table based on the target deviation number, wherein the preset mapping table records a mapping relationship between optional deviation numbers and optional step values; Determine the target risk probability corresponding to the target score, and determine the target step value as the product of the reference step value and the target risk probability.

5. The method for generating budget data for a property renovation project according to claim 1, characterized in that: After determining the first budget data based on the predicted budget data and the first correction coefficient associated with the historical data set, the method further includes: Determine the sum of all the first budget data as planned budget data, determine the inverse of the planned budget data as a first fitness function, and determine the planned resource data preset for the transformation plan, wherein the planned resource data is used to indicate the resource scale of the transformation plan; Based on the planned resource data, the first fitness function and a preset ant colony algorithm, the first resource data corresponding to each of the transformation projects is determined, wherein each of the transformation projects is an ant colony individual in the ant colony algorithm.

6. The method for generating budget data for a property renovation project according to claim 5, characterized in that: After updating the first budget data to second budget data based on the progress deviation data, the method further includes: determining a target mapping relationship between each pair of the first resource data and the first budget data; Determine the transformation project for which the real-time progress data is currently acquired as the target project, determine the second resource data based on the second budget data and the corresponding target mapping relationship, and determine the difference between the planned budget data and the second budget data as the adjusted budget data; Determine the reciprocal of the adjusted budget data as a second fitness function, and determine the difference between the planned resource data and the second resource data as adjusted resource data; Removing the ant colony individual corresponding to the target project in the ant colony algorithm, and determining third resource data corresponding to each of the transformation projects except the target project based on the adjusted resource data, the second fitness function and the adjusted ant colony algorithm; Third budget data is determined based on the third resource data and the target mapping relationship, and the third resource data and the third budget data are applied to the corresponding transformation project.

7. The method for generating budget data for a property renovation project according to claim 1, characterized in that: After determining a second correction coefficient based on the target step value and the first correction coefficient, the method further includes: Based on any of the transformation projects, determining the second budget data finally updated as new historical budget data, saving the new historical budget data to the corresponding historical data set, and associating the second correction coefficient and the target deviation number to the historical data set; When at least one of the historical budget data is removed from any of the historical data sets, the removed historical budget data is determined as invalid budget data; When the target deviation number associated with the failure budget data is greater than the current target deviation number, the second correction coefficient is updated based on the ratio of the failure budget data to the second budget data.

8. A budget data generating device for a property renovation project, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the budget data generation method for a property renovation project as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: Including the budget data generating device for the property renovation project as described in claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method for generating budget data for a property renovation project as described in any one of claims 1 to 7.

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