Construction operation information storage method and device, electronic equipment and computer medium

Abstract

The embodiment of the invention discloses a construction operation information storage method and device, electronic equipment and a computer medium. According to one specific embodiment, the method comprises the steps that a construction data set is obtained, the construction data set comprises but is not limited to at least one of personnel data, mechanical equipment data, vehicle data, environment data and monitoring data, and the construction data set is a set of various data generated in the building construction process; performing data preprocessing on the construction data set to obtain a processed construction data set; inputting the processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result; displaying the construction operation information set to a construction system so that a user can control and process the construction operation information set to obtain a controlled construction operation information set; and storing the controlled construction operation information set to a construction system. According to the embodiment, the integrity of the construction operation information is improved, the storage speed is increased, and the practicability of the construction system is improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a construction operation information storage method, device, electronic device, and computer medium. Background Art

[0002] Construction operation information storage is designed to store the construction operation information generated during the construction process, and plays a vital role in construction projects. For example, in the field of construction, a large amount of construction operation information is generated after construction operations (such as environmental monitoring operations) are performed on the construction site, and the construction operation information can be directly stored. At present, the construction operation information storage usually adopts the method of manually controlling the construction system to store the operation information.

[0003] However, when the above method is adopted, the following technical problems often occur:

[0004] The manual operation system may cause omissions or large errors in the storage of operation information, resulting in insufficient integrity of the operation information and slow storage speed. When the operation information is stored, the construction system may be less practical due to the poor quality of the operation information.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose construction operation information storage methods, devices, electronic devices and computer media to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for storing construction operation information, the method comprising: obtaining a construction data set, wherein the construction data set includes but is not limited to at least one of the following: personnel data, machinery and equipment data, vehicle data, environmental data, and monitoring data, wherein the construction data set is a collection of various data generated during the construction process; performing data preprocessing on the construction data set to obtain a processed construction data set; inputting the processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result; in response to determining that the quality prediction result represents an abnormal quality prediction result, performing a quality prediction on the construction data corresponding to the abnormal quality prediction result. Generate warning information based on at least one construction data in the collection to obtain a warning information group; generate a sorted warning information sequence based on the warning information group, wherein the sorted warning information sequence is a warning information sequence sorted in chronological order of the warnings; generate a construction operation information set based on the sorted warning information sequence and the processed construction data set, wherein the construction operation information set is a collection of construction information including reported operation information; display the construction operation information set to a construction system so that a user can control and process the construction operation information set to obtain a controlled construction operation information set; store the controlled construction operation information set to the construction system.

[0009] In a second aspect, some embodiments of the present disclosure provide a construction operation information storage device, the device comprising: an acquisition unit, configured to acquire a construction data set, wherein the construction data set includes but is not limited to at least one of the following: personnel data, machinery and equipment data, vehicle data, environmental data, and monitoring data, wherein the construction data set is a collection of various data generated during the construction process; a processing unit, configured to perform data preprocessing on the construction data set to obtain a processed construction data set; an input unit, configured to input the processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result; a first generation unit, configured to, in response to determining that the quality prediction result represents an abnormal quality prediction result, generate a construction data set corresponding to the abnormal quality prediction result; The first generating unit is configured to generate warning information based on at least one construction data in the processing unit to obtain a warning information group; the second generating unit is configured to generate a sorted warning information sequence according to the warning information group, wherein the sorted warning information sequence is a warning information sequence sorted in the chronological order of the warning times; the third generating unit is configured to generate a construction operation information set according to the sorted warning information sequence and the processed construction data set, wherein the construction operation information set is a collection of construction information including reported operation information; the display unit is configured to display the construction operation information set to the construction system so that the user can control and process the construction operation information set to obtain a controlled construction operation information set; the storage unit is configured to store the controlled construction operation information set to the construction system.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0012] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the construction operation information storage method of some embodiments of the present disclosure, the integrity of the construction operation information is improved, the storage speed is improved, and the practicality of the construction system is improved. Specifically, the reasons for the insufficient integrity of the operation information and the slow storage speed, and the low practicality of the construction system are: the manual operation of the construction system to store the operation information may cause omissions or large errors, resulting in insufficient integrity of the operation information and a slow storage speed. When the operation information is stored, the practicality of the construction system may be low due to the poor quality of the operation information. Based on this, the construction operation information storage method of some embodiments of the present disclosure, first, obtains a construction data set, wherein the above-mentioned construction data set includes but is not limited to at least one of the following: personnel data, mechanical equipment data, vehicle data, environmental data, monitoring data, wherein the above-mentioned construction data set is a collection of various data generated during the construction process. Thus, the construction data set can be obtained for subsequent processing. Then, the above-mentioned construction data set is preprocessed to obtain a processed construction data set. Thus, redundant and erroneous data can be removed to ensure the accuracy and integrity of the data. Afterwards, the above-mentioned processed construction data set is input into a pre-trained quality prediction model to obtain a quality prediction result. Thus, the quality of the processed construction data set can be predicted to avoid the low practicality of the construction system due to the poor quality of the operation information when the operation information is subsequently stored. Secondly, in response to determining that the quality prediction result represents the abnormal quality prediction result, at least one construction data in the construction data set corresponding to the abnormal quality prediction result is generated to obtain an early warning information group. Thus, the construction data with poor quality can be warned, which is convenient for timely correction. Thirdly, according to the early warning information group, a sequence of sorted early warning information is generated, wherein the sequence of sorted early warning information is a sequence of early warning information sorted in the order of the time of the early warning. Thus, the order of occurrence of quality problems can be identified, which is convenient for giving priority to the construction data that first detects the quality problems. Then, according to the sequence of sorted early warning information and the processed construction data set, a construction operation information set is generated, wherein the construction operation information set is a set of construction information containing the reported operation information. Thus, the quality of the operation information can be improved through the construction operation information set. The integrity of the operation information can also be improved. Afterwards, the above construction operation information set is displayed to the construction system so that the user can control and process the above construction operation information set to obtain the controlled construction operation information set. Thus, it can be easily controlled through visualization. The above controlled construction operation information set is stored in the above construction system. Thus, the lack of integrity of the operation information and the slow storage speed can be avoided. Therefore, the integrity of the construction operation information improves the storage speed and enhances the practicality of the construction system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flow chart of some embodiments of the construction operation information storage method according to the present disclosure;

[0015] Figure 2 is a schematic diagram of the structure of some embodiments of the construction operation information storage device according to the present disclosure;

[0016] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0018] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0019] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0023] Figure 1This is a process 100 of some embodiments of the construction operation information storage method disclosed in the present invention. The construction operation information storage method includes the following steps:

[0024] Step 101: Obtain construction data set.

[0025] In some embodiments, the executor of the construction operation information storage method (for example, a computing device) can obtain a construction data set through a wired connection or a wireless connection, wherein the above-mentioned construction data set includes but is not limited to at least one of the following: personnel data, machinery and equipment data, vehicle data, environmental data, and monitoring data, wherein the above-mentioned construction data set is a collection of various data generated during the construction process.

[0026] Here, the above-mentioned personnel data may refer to the attribute data of the construction personnel. The above-mentioned attribute data may include but are not limited to at least one of the following: name, height, gender. For example, the above-mentioned personnel data may refer to the name and gender of the construction personnel. The above-mentioned mechanical equipment data may refer to the usage record data of the mechanical equipment. The above-mentioned mechanical equipment may refer to crane equipment. The above-mentioned vehicle data may refer to the model data of the vehicle used in the construction. For example, the above-mentioned vehicle data may refer to the capacity data of the vehicle used in the construction. The above-mentioned environmental data may characterize the data of the surrounding environment during the construction process. For example, the above-mentioned environmental data may include but are not limited to at least one of the following: temperature data, humidity data. The above-mentioned monitoring data may refer to the completion progress data monitored during the construction process. For example, the above-mentioned monitoring data may refer to the completion progress data with a completion rate of 80% detected during the construction process.

[0027] It should be noted that the above-mentioned wireless connection methods may include but are not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0028] It should be noted that the above-mentioned computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. For example, the computing device can be the above-mentioned target terminal. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0029] Step 102: preprocess the construction data set to obtain a processed construction data set.

[0030] In some embodiments, the execution entity may perform data preprocessing on the construction data set to obtain a processed construction data set.

[0031] Here, the processed construction data in the processed construction data set may refer to the construction data after removing redundant and erroneous data. The construction data is data generated during the construction process. For example, the construction data is data on the amount of materials required generated during the construction process.

[0032] Optionally, the execution entity may perform data preprocessing on the construction data set through the following steps to obtain a processed construction data set:

[0033] The first step is to clean the above construction data set to obtain the cleaned construction data set.

[0034] Here, the cleaned construction data in the cleaned construction data set may refer to the construction data after erroneous data and redundant data are removed.

[0035] The second step is to standardize the above cleaned construction data set to obtain a standardized construction data set.

[0036] Here, the standardized construction data in the standardized construction data set may refer to construction data with a mean value of 0 and a standard deviation of 1.

[0037] The third step is to perform data deduplication processing on the above processed construction data set to obtain a deduplicated construction data set.

[0038] Here, the above-mentioned deduplication processing may refer to a removal of duplicate processing.

[0039] The fourth step is to integrate the above-mentioned deduplicated construction data set to obtain an integrated construction data set as the processed construction data set.

[0040] Step 103: input the processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result.

[0041] In some embodiments, the execution entity may input the processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result.

[0042] Here, the quality prediction model may be a model for predicting the quality of each of the processed construction data in the processed construction data set. The quality prediction model may include: a recurrent neural network (RNN) model and a logistic regression model. The logistic regression model may refer to a model for determining the classification result of quality prediction. The classification result may characterize abnormal results and normal results. The processed construction data set is first input into the recurrent neural network model for data processing, and then the processed construction data set after data processing is input into the logistic regression model for prediction result classification, and finally the quality prediction result is obtained. Optionally, the quality prediction model may be used to characterize the correspondence between the processed construction data set and the quality prediction result. The quality prediction model may be a classification model with the processed construction data set as input and the quality prediction result as output. The quality prediction model may compare the processed construction data set with multiple groups of preset processed construction data sets in a preset quality prediction relationship table in sequence. The preset quality prediction relationship table may be a correspondence relationship table created based on the analysis of a large number of preset processed construction data sets, storing the correspondence between the preset processed construction data set and the preset quality prediction result. Each set of preset processed construction data sets corresponds to a preset quality prediction result. The preset quality prediction result may be a pre-set quality prediction result.

[0043] As an example, the above-mentioned recurrent neural network (RNN) model may include: an input layer, a hidden layer and an output layer. The above-mentioned input layer receives the processed construction data in the processed construction data set according to a preset time step, and performs data standardization on the processed construction data set to obtain a standardized construction data set. The above-mentioned preset time step may refer to 0.1 seconds. The above-mentioned hidden layer may include a simple recurrent neural network (SRN) unit and a long short-term memory network (LSTM) unit. The above-mentioned simple recurrent neural network unit is used to transfer each standardized construction data in the standardized construction data set. The above-mentioned long short-term memory network unit is used to control the flow of each standardized construction data. The above-mentioned output layer is used to output the processed construction data set after data processing. Here, the above-mentioned logistic regression model may include: an input layer and a logistic regression layer. The above-mentioned input layer is used to receive the output result from the above-mentioned recurrent neural network model. The above-mentioned logistic regression layer may be used to map the processed construction data set after the input data is processed to a probability value between 0 and 1. The activation function of the above-mentioned logistic regression layer may refer to a sigmoid function.

[0044] Step 104, in response to determining that the quality prediction result represents an abnormal quality prediction result, generating warning information for at least one construction data in the construction data set representing the abnormal quality prediction result to obtain a warning information group.

[0045] In some embodiments, the execution entity may generate warning information for at least one construction data in the construction data set corresponding to the abnormal quality prediction result in response to determining that the quality prediction result represents an abnormal quality prediction result, and obtain a warning information group.

[0046] Here, the above-mentioned abnormal quality prediction results can represent poor quality prediction results.

[0047] As an example, the execution subject may annotate at least one construction data in the construction data set corresponding to the abnormal result of the quality prediction with an abnormal mark to obtain an annotated construction data set. The abnormal mark may refer to "!". Then, the annotated construction data in the annotated construction data set are bound with warning identification information to obtain a bound warning information group as the warning information group. The warning identification information may refer to "ERROR".

[0048] Optionally, the execution subject may generate warning information for at least one construction data in the construction data set corresponding to the abnormal quality prediction result in response to determining that the quality prediction result represents an abnormal quality prediction result, to obtain a warning information group through the following steps:

[0049] In the first step, at least one construction data representing an abnormal quality prediction result in the above construction data sets is determined as an abnormal construction data set.

[0050] In the second step, data features are extracted for each abnormal construction data in the above abnormal construction data set to generate an abnormal construction feature data group, thereby obtaining an abnormal construction feature data group set, wherein the abnormal construction feature data group in the above abnormal construction feature data group set may include but is not limited to at least one of the following: time feature data, equipment status feature data.

[0051] Here, the time characteristic data may refer to the time of abnormal construction data during the construction process. For example, the time characteristic data may refer to 9:30 am on September 20, 2024.

[0052] As an example, the execution entity may extract data features from each abnormal construction data in the abnormal construction data set through data extraction to generate an abnormal construction feature data group, thereby obtaining an abnormal construction feature data group set.

[0053] The third step is to perform anomaly detection on each abnormal construction feature data group in the abnormal construction feature data group set to generate a post-detection feature data group, thereby obtaining a post-detection feature data group set.

[0054] As an example, the execution entity may use an isolation forest (iForest) algorithm to perform anomaly detection on each abnormal construction feature data group in the abnormal construction feature data group set to generate a post-detection feature data group and obtain a post-detection feature data group set.

[0055] The fourth step is to input the above-mentioned post-detection feature data set into the quality scoring model to obtain a feature data scoring set, wherein each feature data score in the above-mentioned feature data scoring set represents the score of the corresponding post-detection feature data set in the above-mentioned post-detection feature data set.

[0056] Here, the quality scoring model can be used to score the quality of each of the post-detection feature data sets in the post-detection feature data set. The input of the quality scoring model can refer to the post-detection feature data set. The output can refer to the feature data scoring set. Optionally, the quality scoring model can be used to characterize the correspondence between the post-detection feature data set and the feature data scoring set. The quality scoring model can be a classification model that takes the post-detection feature data set as input and takes the feature data scoring set as output. The quality scoring model can compare the post-detection feature data set with multiple groups of preset post-detection feature data sets in the preset quality scoring relationship table in sequence. The preset quality scoring relationship table can be a correspondence relationship table created based on the analysis of a large number of preset post-detection feature data sets, storing the correspondence between the preset post-detection feature data sets and the feature data scoring set. Each group of preset post-detection feature data sets corresponds to a preset feature data scoring set. The preset feature data scoring set can be a preset feature data scoring set.

[0057] As an example, the quality scoring model may refer to a linear regression model. The linear regression model may include an input layer, a weight layer, a linear combination layer and an output layer. The input layer is used to receive a post-detection feature data set. The weight layer is used to determine the weight of each post-detection feature data set in the post-detection feature data set using a gradient descent algorithm. The linear combination layer is used to determine the weighted sum of the post-detection feature data set. The output layer is used to output a feature data scoring set, and the output of the output layer is a continuous numerical value.

[0058] In the fifth step, in response to determining that there is a feature data score greater than a preset score threshold in the feature data score set, the construction data corresponding to at least one feature data score greater than the preset score threshold is abnormally typed to obtain a construction data abnormal type group.

[0059] Here, the preset scoring threshold may refer to a preset maximum score value. For example, the preset scoring threshold may refer to 0.7. The abnormal type may refer to a timeout type.

[0060] The sixth step is to score and sort each abnormal construction data type in the above abnormal construction data type group to obtain an abnormal construction data score sequence.

[0061] Here, the above-mentioned score sorting may refer to sorting the scores from large to small. The above-mentioned abnormal construction data score sequence may refer to the sequence of abnormal construction data after the scores are sorted from large to small.

[0062] The seventh step is to annotate the above abnormal construction data scoring sequence with symbol information to obtain an annotated construction data information group as an early warning information group.

[0063] Here, the above-mentioned symbol information may refer to ERROR.

[0064] As an example, the execution entity may use a labeling tool (Label Studio) to label the abnormal construction data scoring sequence with symbol information, and obtain a labeled construction data information group as a warning information group.

[0065] Step 105: Generate a sorted warning information sequence based on the warning information group.

[0066] In some embodiments, the execution entity may generate a sorted warning information sequence based on the warning information group, wherein the sorted warning information sequence is a warning information sequence sorted in chronological order of the warning times.

[0067] As an example, the execution subject may use python to determine the timestamp of each warning information in the warning information group to generate the timestamp information of the warning information and obtain the timestamp information set of the warning information. Then, the timestamp information set of the warning information is sorted to obtain the sorted warning information sequence. The timestamp is determined at the moment when the warning information is generated. The timestamp information of the warning information in the timestamp information set of the warning information is in the ISO 8601 format. For example, the timestamp information of the warning information in the timestamp information set of the warning information is in the format of 2024-10-24T14:30:00Z. The sorting may refer to sorting in the order of the time stamps.

[0068] Step 106: Generate a construction operation information set based on the sorted warning information sequence and the processed construction data set.

[0069] In some embodiments, the execution entity may generate a construction operation information set based on the sorted warning information sequence and the processed construction data set, wherein the construction operation information set is a collection of construction information including reported operation information.

[0070] Here, the construction operation information set is a set of construction information including submitted operation information. The submitted operation information may refer to suggestion information.

[0071] As an example, the execution subject may compare the correlation between the sorted warning information sequence and each processed construction data in the processed construction data set through cluster analysis to obtain the construction data sequence corresponding to the sorted warning information. Then, the reporting information is determined for the construction data sequence corresponding to the sorted warning information to obtain a reporting operation information set as the construction operation information set.

[0072] In the process of adopting technical solutions to solve the problems mentioned in the background technology, the following problems are often accompanied:

[0073] When generating the construction operation information set, since the warning information sequence after sorting and the construction data set after processing are relatively complex and may be redundant, large errors are easily generated, resulting in low integrity of the generated construction operation information set. In addition, since it is impossible to associate the warning information sequence and construction data set within the same time period, the generated construction operation information is relatively simple, resulting in poor quality of the construction operation information.

[0074] In the face of the above technical problems, the inventors decided to adopt the following solutions:

[0075] Optionally, the execution entity may generate a construction operation information set according to the sorted warning information sequence and the processed construction data set through the following steps:

[0076] In the first step, data cleaning is performed on the sorted warning information sequence and the processed construction data set to obtain a cleaned warning information sequence and a cleaned construction data set.

[0077] Optionally, the above sorted warning information sequence is generated by the following steps:

[0078] In the first sub-step, the time information of the above-mentioned warning information group is determined to obtain the time information group corresponding to the warning information.

[0079] Here, the time information group corresponding to the warning information may refer to an information group obtained by binding the warning information with the time information generated by the warning information.

[0080] In the second sub-step, in response to determining that the same warning information corresponding to time information exists in the above warning information corresponding to time information group, capacity information sorting is performed on at least one warning information with the same time information to obtain a warning information sequence after capacity information sorting.

[0081] Here, the above-mentioned capacity information sorting may refer to sorting the capacity information from small to large.

[0082] The third sub-step is to sort the warning information group in time series according to the warning information sequence after the capacity information is sorted and the time information group corresponding to the warning information, so as to obtain the sorted warning information sequence.

[0083] Here, the time series sorting may refer to sorting the time from the earliest to the latest. For example, the time series sorting may refer to sorting the time in the order of "10:00 am, 10:09 am, 10:20 am".

[0084] In the second step, each post-cleaning warning information in the above-mentioned post-cleaning warning information sequence is aligned with each post-cleaning construction data in the above-mentioned post-cleaning construction data set to obtain a warning information sequence of the same duration and a construction data set of the same duration, wherein the warning information of the same duration in the above-mentioned warning information sequence of the same duration and the construction data of the same duration in the above-mentioned construction data set of the same duration are consistent within the same duration range.

[0085] For example, within the same duration of five seconds, the warning information of the same duration is "the equipment needs to be restarted", and the construction data of the same duration is "the operator data responsible for restarting the equipment".

[0086] The third step is to determine the construction operation information set based on the above warning information sequence with the same duration and the above construction data set with the same duration.

[0087] As an example, the execution entity may first extract the keyword information of the warning information sequence of the same duration and the keyword information of the construction data of the same duration for each warning information of the same duration in the warning information sequence of the same duration and each construction data of the same duration in the construction data set of the same duration, and then determine the operation information of each extracted keyword information to obtain the operation information. Finally, determine each obtained operation information as a construction operation information set. For example, the keyword information of the warning information sequence of the same duration may be "equipment restart", the keyword information of the construction data of the same duration may be "Zhang San is responsible for equipment restart", and the operation information may be "Zhang San operates equipment restart".

[0088] The relevant contents in the first step to the third step above are an inventive point of the present disclosure, which solves the following technical problem "leading to poor quality of construction operation information". The factors that cause poor quality of construction operation information are often as follows: when generating a construction operation information set, since the sequence of warning information after sorting and the construction data set after processing are relatively complex and may be redundant, large errors are easily generated, resulting in low integrity of the generated construction operation information set. In addition, since it is impossible to associate the sequence of warning information and the construction data set within the same time length, the generated construction operation information is relatively simple, resulting in poor quality of construction operation information. If the above factors are solved, the effect of improving the practicality of the construction system can be achieved. In order to achieve this effect, in the first step, the sequence of warning information after sorting and the construction data set after processing are cleaned to obtain the sequence of warning information after cleaning and the construction data set after cleaning. In this way, redundancy in the sequence of warning information after sorting and the construction data set after processing can be avoided, and errors are reduced. The sequence of warning information after sorting is generated by the following steps: In the first sub-step, the time information of the warning information group is determined to obtain the time information group corresponding to the warning information. In the second sub-step, in response to determining that there is the same warning information corresponding to the time information group in the above-mentioned warning information corresponding to the time information group, the capacity information of at least one warning information with the same time information is sorted to obtain a warning information sequence after the capacity information is sorted. In the third sub-step, according to the warning information sequence after the capacity information is sorted and the time information group corresponding to the above-mentioned warning information, the time series of the above-mentioned warning information group is sorted to obtain a sorted warning information sequence. In the second step, each post-cleaning warning information in the above-mentioned post-cleaning warning information sequence is aligned with each post-cleaning construction data in the above-mentioned post-cleaning construction data set to obtain a warning information sequence of the same duration and a construction data set of the same duration, wherein the same duration warning information in the above-mentioned same duration warning information sequence and the same duration construction data in the above-mentioned same duration construction data set are consistent within the same duration range. Thus, it is conducive to improving the integrity of the generated construction operation information set. At the same time, associating the warning information sequence and the construction data set within the same duration can improve the quality of the construction operation information. In the third step, the construction operation information set is determined according to the above-mentioned same duration warning information sequence and the above-mentioned same duration construction data set. Therefore, data cleaning can avoid redundancy in the sorted warning information sequence and the processed construction data set, and reduce errors. Associating the warning information sequence and the construction data set within the same time period is conducive to improving the integrity of the generated construction operation information set. At the same time, associating the warning information sequence and the construction data set within the same time period can improve the quality of the construction operation information.

[0089] In the process of adopting technical solutions to solve the problems mentioned in the background technology, the following problems are often accompanied:

[0090] When generating the construction operation information set, due to the complexity of the sorted warning information sequence, the correlation between the sorted warning information sequence and the processed construction data set cannot be fully considered, resulting in low integrity of the generated construction operation information set, poor quality of the construction operation information, and low practicality of the construction system.

[0091] In the face of the above technical problems, the inventors decided to adopt the following solutions:

[0092] Optionally, the execution entity may generate a construction operation information set according to the sorted warning information sequence and the processed construction data set through the following steps:

[0093] The first step is to classify the warning level of the above-mentioned sorted warning information sequence to obtain a classified warning information set, wherein the above-mentioned classified warning information set includes: level one warning information, level two warning information and level three warning information, the above-mentioned level one warning information is the warning information generated within a preset first time period, the level two warning information is the warning information generated between the preset first time period and the preset second time period, and the level three warning information is the warning information generated outside the preset second time period.

[0094] Here, the above-mentioned preset first duration may refer to 5 seconds. The above-mentioned preset second duration may refer to 10 seconds. For example, the above-mentioned first-level warning information may refer to the equipment status warning information generated within 5 seconds. The above-mentioned second-level warning information may refer to the environmental warning information generated between 5 seconds and 10 seconds. The above-mentioned first-level warning information may refer to the personnel change warning information generated beyond 10 seconds.

[0095] As an example, the execution entity may divide the sorted warning information sequence according to a preset duration interval to obtain a divided warning information set. Then, the warning level of each divided warning information in the divided warning information set is determined to generate divided warning level information, and a divided warning level information set is obtained as a classified warning information set. Here, the preset duration interval may refer to a duration range interval divided from a preset duration. For example, the preset duration interval may refer to a range interval that divides 20 seconds into [0 seconds, 5 seconds], [5 seconds, 10 seconds], [10 seconds, 20 seconds].

[0096] The second step is to perform correlation matching on each classified warning information in the classified warning information set and the processed construction data in the processed construction data set to generate a matched warning information group and obtain a matched warning information group set.

[0097] Here, the matched post-warning information group in the matched post-warning information group set may refer to a combination of classified post-warning information and related construction data. For example, the classified post-warning information is first-level warning information, and the related construction data includes: related operator data and warning time data, then the matched post-warning information group is a combination of equipment status warning information, related operator data and warning time data.

[0098] As an example, the above-mentioned execution entity can use the association rule (Apriori) algorithm to perform correlation matching on each classified warning information in the above-mentioned classified warning information set with the processed construction data in the above-mentioned processed construction data set to generate a matched warning information group and obtain a matched warning information group set.

[0099] The third step is to integrate the data of each post-matching warning information group in the above post-matching warning information group set to generate integrated warning information and obtain an integrated warning information set.

[0100] As an example, the execution entity may perform data integration on each post-matching warning information in the post-matching warning information group in the post-matching warning information group set to generate integrated warning information and obtain an integrated warning information set.

[0101] The fourth step is to determine the reporting information of the above integrated warning information set to obtain the reporting information set.

[0102] Here, the notification information may refer to suggestion information. For example, the notification information may refer to "suggesting that the device be restarted".

[0103] The fifth step is to perform resource information analysis on the above-mentioned submitted information set to obtain a resource information analysis result set, wherein the above-mentioned resource information analysis result set represents a collection of resource information results required by each submitted information in the submitted information set.

[0104] Here, the resource information may include but is not limited to at least one of the following: load information, memory information. The resource information result may represent the required result of the resource information. For example, the resource information result may refer to the result that the memory information requires 30 GB.

[0105] As an example, the execution subject may extract the keywords corresponding to each reported information in the reported information set to generate reported information keywords and obtain a reported information keyword set. Then, the reported information keyword set is subjected to resource information determination to obtain a determined result set as a resource analysis result set. For example, the reported information keyword in the reported information keyword set may refer to "device restart". The determined result in the determined result set may refer to "memory allocation 30GB".

[0106] The sixth step is to dynamically adjust the resource allocation of the above construction system according to the above resource analysis result set to obtain a resource allocation result set.

[0107] As an example, the execution entity may perform the following allocation steps for each resource analysis result in the resource analysis result set: allocate resources to the construction system to obtain a resource allocation result. In response to determining that the resource information status corresponding to the resource allocation result is a released status, continue to perform the allocation steps. Determine each resource allocation result whose resource information status is a released status as a resource allocation result set. The resource allocation result may represent the result of whether the resource information allocation is completed. For example, the resource allocation result may refer to the result that 30GB of allocated memory information is being allocated. The resource information status corresponding to the resource allocation result may refer to the resource information status of whether the resource allocation result is in a completed status. The released status may represent a completed status.

[0108] The seventh step is to perform real-time detection on the resource allocation result set to obtain a detection result set.

[0109] Here, the test results in the above test result set can represent normal test results and abnormal test results.

[0110] In the eighth step, in response to determining that the detection result set indicates that the detection is normal, the resource allocation result set is determined as a construction operation information set.

[0111] The relevant contents in the first step to the eighth step above are an inventive point of the present disclosure, which solves the following technical problem "causing the construction system to have low practicality". The factors that cause the construction system to have low practicality are often as follows: when generating the construction operation information set, due to the complexity of the sequence of warning information after sorting, the correlation between the sequence of warning information after sorting and the processed construction data set cannot be fully considered, resulting in low integrity of the generated construction operation information set, making the quality of the construction operation information poor, resulting in low practicality of the construction system. If the above factors are solved, the effect of improving the practicality of the construction system can be achieved. In order to achieve this effect, in the first step, the warning level of the above-mentioned sequence of warning information after sorting is classified to obtain the classified warning information set, wherein the above-mentioned classified warning information set includes: first-level warning information, second-level warning information and third-level warning information, the above-mentioned first-level warning information is the warning information generated within the preset first duration, the second-level warning information is the warning information generated between the preset first duration and the preset second duration, and the third-level warning information is the warning information generated outside the preset second duration. Thus, it can provide convenience for subsequent processing. The second step is to match the correlation between each classified warning information in the above-mentioned classified warning information set and the processed construction data in the above-mentioned processed construction data set to generate a matched warning information group and obtain a matched warning information group set. In this way, the correlation between the warning information and the construction data can be fully considered, which is convenient for improving the integrity of the construction operation information. The third step is to integrate the data of each matched warning information group in the above-mentioned matched warning information group set to generate integrated warning information and obtain an integrated warning information set. In this way, the correlation between the warning information and the construction data can be integrated, which is convenient for improving the integrity of the construction operation information. The fourth step is to determine the reporting information of the above-mentioned integrated warning information set to obtain a reporting information set. In this way, the integrated warning information set can be corrected to improve the quality of the operation information. The fifth step is to perform resource analysis on the above-mentioned reporting information set to obtain a resource analysis result set, wherein the above-mentioned resource analysis result set represents the set of resource information results required for each reporting information in the reporting information set. The sixth step is to dynamically adjust the resource allocation of the above-mentioned construction system according to the above-mentioned resource analysis result set to obtain a resource allocation result set. In this way, the quality of operation information can be improved and the practicality of the construction system can be improved. In the seventh step, the above-mentioned resource allocation result set is detected in real time to obtain a detection result set. In this way, anomalies can be avoided and the practicality of the construction system can be improved. In the eighth step, in response to determining that the above-mentioned detection result set represents that the detection is normal, the above-mentioned resource allocation result set is determined as a construction operation information set. Therefore, the correlation matching can fully consider the correlation between the early warning information and the construction data, which is convenient for improving the integrity of the construction operation information, and determining that the reported information can improve the quality of the operation information, thereby improving the practicality of the construction system.

[0112] Step 107, displaying the above construction operation information set to the construction system, so that the user can control and process the above construction operation information set to obtain a controlled construction operation information set.

[0113] In some embodiments, the execution entity may display the construction operation information set to a construction system so that a user can control and process the construction operation information set to obtain a controlled construction operation information set.

[0114] Here, the above-mentioned construction system may refer to a system used for building construction.

[0115] As an example, the execution subject may display the construction operation information set to the construction system so that the user can verify the construction operation information set and obtain a verification result. Then, in response to determining that the verification result indicates that the verification is passed, the user can adjust the construction speed of the construction operation information set to obtain an adjusted construction operation information set as the controlled construction operation information set.

[0116] Step 108, storing the above-mentioned post-control construction operation information set in the above-mentioned construction system.

[0117] In some embodiments, the execution entity may store the post-control construction operation information set in the construction system.

[0118] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the construction operation information storage method of some embodiments of the present disclosure, the integrity of the construction operation information is improved, the storage speed is improved, and the practicality of the construction system is improved. Specifically, the reasons for the insufficient integrity of the operation information and the slow storage speed, and the low practicality of the construction system are: the manual operation of the construction system to store the operation information may cause omissions or large errors, resulting in insufficient integrity of the operation information and a slow storage speed. When the operation information is stored, the practicality of the construction system may be low due to the poor quality of the operation information. Based on this, the construction operation information storage method of some embodiments of the present disclosure, first, obtains a construction data set, wherein the above-mentioned construction data set includes but is not limited to at least one of the following: personnel data, mechanical equipment data, vehicle data, environmental data, monitoring data, wherein the above-mentioned construction data set is a collection of various data generated during the construction process. Thus, the construction data set can be obtained for subsequent processing. Then, the above-mentioned construction data set is preprocessed to obtain a processed construction data set. Thus, redundant and erroneous data can be removed to ensure the accuracy and integrity of the data. Afterwards, the above-mentioned processed construction data set is input into a pre-trained quality prediction model to obtain a quality prediction result. Thus, the quality of the processed construction data set can be predicted to avoid the low practicality of the construction system due to the poor quality of the operation information when the operation information is subsequently stored. Secondly, in response to determining that the quality prediction result represents the abnormal quality prediction result, at least one construction data in the construction data set corresponding to the abnormal quality prediction result is generated to obtain an early warning information group. Thus, the construction data with poor quality can be warned, which is convenient for timely correction. Thirdly, according to the early warning information group, a sequence of sorted early warning information is generated, wherein the sequence of sorted early warning information is a sequence of early warning information sorted in the order of the time of the early warning. Thus, the order of occurrence of quality problems can be identified, which is convenient for giving priority to the construction data that first detects the quality problems. Then, according to the sequence of sorted early warning information and the processed construction data set, a construction operation information set is generated, wherein the construction operation information set is a set of construction information containing the reported operation information. Thus, the quality of the operation information can be improved through the construction operation information set. The integrity of the operation information can also be improved. Afterwards, the above construction operation information set is displayed to the construction system so that the user can control and process the above construction operation information set to obtain the controlled construction operation information set. Thus, it can be easily controlled through visualization. The above controlled construction operation information set is stored in the above construction system. Thus, the lack of integrity of the operation information and the slow storage speed can be avoided. Therefore, the integrity of the construction operation information improves the storage speed and enhances the practicality of the construction system.

[0119] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a method for storing construction operation information. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0120] like Figure 2 As shown, the construction operation information storage device 200 of some embodiments includes: an acquisition unit 201, a processing unit 202, an input unit 203, a first generation unit 204, a second generation unit 205, a third generation unit 206, a display unit 207 and a storage unit 208. The acquisition unit 201 is configured to acquire a construction data set, wherein the construction data set includes but is not limited to at least one of the following: personnel data, machinery and equipment data, vehicle data, environmental data, and monitoring data, wherein the construction data set is a collection of various data generated during the construction process; the processing unit 202 is configured to perform data preprocessing on the construction data set to obtain a processed construction data set; the input unit 203 is configured to input the processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result; the first generation unit 204 is configured to generate early warning information for at least one construction data in the construction data set corresponding to the quality prediction abnormal result in response to determining that the quality prediction result represents an abnormal quality prediction result. Generate, and obtain a warning information group; the second generating unit 205 is configured to generate a sorted warning information sequence according to the above warning information group, wherein the sorted warning information sequence is a warning information sequence sorted in the chronological order of the warning time; the third generating unit 206 is configured to generate a construction operation information set according to the sorted warning information sequence and the above processed construction data set, wherein the above construction operation information set is a collection of construction information including reported operation information; the display unit 207 is configured to display the above construction operation information set to the construction system, so that the user can control and process the above construction operation information set to obtain the controlled construction operation information set; the storage unit 208 is configured to store the above controlled construction operation information set to the above construction system.

[0121] It can be understood that the units recorded in the construction operation information storage device 200 and the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the construction operation information storage device 200 and the units contained therein, and will not be repeated here.

[0122] Reference below Figure 3 , which shows a structural schematic diagram of an electronic device (such as a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0123] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 304. In RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, ROM 302, and RAM 304 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0124] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0125] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the functions defined in the method of some embodiments of the present disclosure are executed.

[0126] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0127] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0128] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains a construction data set, wherein the above-mentioned construction data set includes but is not limited to at least one of the following: personnel data, mechanical equipment data, vehicle data, environmental data, monitoring data, wherein the above-mentioned construction data set is a collection of various data generated during the construction process; performs data preprocessing on the above-mentioned construction data set to obtain a processed construction data set; inputs the above-mentioned processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result; in response to determining that the above-mentioned quality prediction result represents an abnormal quality prediction result, the abnormal quality prediction result is represented. Generate warning information for at least one construction data in the corresponding construction data set to obtain a warning information group; generate a sorted warning information sequence based on the warning information group, wherein the sorted warning information sequence is a warning information sequence sorted in chronological order of the warning time; generate a construction operation information set based on the sorted warning information sequence and the processed construction data set, wherein the construction operation information set is a collection of construction information including reported operation information; display the construction operation information set to the construction system so that the user can control and process the construction operation information set to obtain a controlled construction operation information set; store the controlled construction operation information set to the construction system.

[0129] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0130] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0131] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be set in a processor. For example, they may be described as: a processor including: an acquisition unit, a processing unit, an input unit, a first generation unit, a second generation unit, a third generation unit, a display unit, and a storage unit. The names of these units do not constitute limitations on the units themselves in certain cases. For example, the processing unit may also be described as "a unit that performs data preprocessing on the above-mentioned construction data set to obtain a processed construction data set."

[0132] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0133] The above descriptions are only some preferred embodiments of the present disclosure and the explanation of the technical principles used above. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for storing construction operation information, comprising: Acquire a construction data set, wherein the construction data set includes but is not limited to at least one of the following: personnel data, machinery and equipment data, vehicle data, environmental data, and monitoring data, wherein the construction data set is a collection of various data generated during the construction process; Performing data preprocessing on the construction data set to obtain a processed construction data set; Inputting the processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result; In response to determining that the quality prediction result represents an abnormal quality prediction result, generating warning information for at least one construction data in the construction data set representing the abnormal quality prediction result to obtain a warning information group; Generate a sorted warning information sequence according to the warning information group, wherein the sorted warning information sequence is a warning information sequence sorted in the order of the warning time; Generate a construction operation information set according to the sorted warning information sequence and the processed construction data set, wherein the construction operation information set is a collection of construction information including the reported operation information; Displaying the construction operation information set to a construction system so that a user can control and process the construction operation information set to obtain a controlled construction operation information set; The post-control construction operation information set is stored in the construction system.

2. The method according to claim 1, wherein: The performing data preprocessing on the construction data set to obtain a processed construction data set includes: Performing data cleaning on the construction data set to obtain a cleaned construction data set; Performing standardization processing on the cleaned construction data set to obtain a standardized construction data set; Performing data deduplication processing on the processed construction data set to obtain a deduplicated construction data set; The deduplicated construction data set is integrated to obtain an integrated construction data set as a processed construction data set.

3. The method according to claim 1, wherein: In response to determining that the quality prediction result represents an abnormal quality prediction result, generating warning information for at least one construction data in the construction data set corresponding to the abnormal quality prediction result to obtain a warning information group includes: Determining at least one construction data in the construction data set that represents an abnormal quality prediction result as an abnormal construction data set; Performing data feature extraction on each abnormal construction data in the abnormal construction data set to generate an abnormal construction feature data group, thereby obtaining an abnormal construction feature data group set, wherein the abnormal construction feature data group in the abnormal construction feature data group set may include but is not limited to at least one of the following: time feature data, equipment status feature data; Performing anomaly detection on each abnormal construction feature data group in the abnormal construction feature data group set to generate a post-detection feature data group, thereby obtaining a post-detection feature data group set; Inputting the post-detection feature data set into a quality scoring model to obtain a feature data scoring set, wherein each feature data score in the feature data scoring set represents a score of a corresponding post-detection feature data set in the post-detection feature data set; In response to determining that there is a feature data score greater than a preset score threshold in the feature data score set, determining an abnormal type of construction data corresponding to at least one feature data score greater than the preset score threshold to obtain a construction data abnormal type group; Score and sort each abnormal construction data type in the abnormal construction data type group to obtain an abnormal construction data score sequence; The abnormal construction data scoring sequence is annotated with symbol information to obtain an annotated construction data information group as an early warning information group.

4. A construction operation information storage device, comprising: an acquisition unit configured to acquire a construction data set, wherein the construction data set includes but is not limited to at least one of the following: personnel data, machinery and equipment data, vehicle data, environmental data, and monitoring data, wherein the construction data set is a collection of various data generated during the construction process; A processing unit is configured to perform data preprocessing on the construction data set to obtain a processed construction data set; An input unit is configured to input the processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result; A first generating unit is configured to generate warning information for at least one construction data in the construction data set corresponding to the quality prediction abnormal result in response to determining that the quality prediction result represents a quality prediction abnormal result, so as to obtain a warning information group; A second generating unit is configured to generate a sorted warning information sequence according to the warning information group, wherein the sorted warning information sequence is a warning information sequence sorted in the order of the warning time; A third generating unit is configured to generate a construction operation information set according to the sorted warning information sequence and the processed construction data set, wherein the construction operation information set is a set of construction information including the reporting operation information; A display unit is configured to display the construction operation information set to the construction system so that the user can control and process the construction operation information set to obtain a controlled construction operation information set; The storage unit is configured to store the post-control construction operation information set in the construction system.

5. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 3.

6. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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