Construction Operation Information Storage Method, Device, Electronic Device and Computer Medium

Abstract

Embodiments of the present disclosure disclose a method, apparatus, electronic device, and computer medium for storing construction operation information. A specific implementation of the method includes: obtaining a construction data set, where the construction data set includes, but is not limited to, at least one of the following: personnel data, mechanical equipment data, vehicle data, environmental data, and monitoring data, and the construction data set is a set of various data generated during 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 the construction system for the user to perform control processing on the construction operation information set to obtain a controlled construction operation information set; and storing the controlled construction operation information set in the construction system. This implementation improves the integrity of the construction operation information, increases the storage speed, and enhances the practicality of the construction system.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technologies, and more particularly, to a method, an apparatus, an electronic device, and a computer medium for storing construction operation information. Background Art

[0002] The storage of construction operation information aims to store the construction operation information generated during the construction process of a building, which plays a crucial role in a construction project. For example, in the field of building construction, after performing construction operations (such as environmental monitoring operations) on a construction site, a large amount of construction operation information is generated, and the construction operation information can be directly stored. Currently, the commonly adopted method for storing construction operation information is: manually controlling a construction system to store the operation information.

[0003] However, when adopting the above method, the following technical problems often exist:

[0004] Manually controlling a construction system to store operation information may result in omissions or large errors, leading to insufficient integrity of the operation information and a slow storage speed. When storing the operation information, the practicability of the construction system may be low due to the poor quality of the operation information.

[0005] The above information disclosed in this background art section is only used to enhance the understanding of the background of the inventive concept, and thus, it may include information that does not constitute the prior art known to those of ordinary skill in the art in this country. Summary of the Invention

[0006] This content part of the present disclosure is used to briefly introduce the inventive concepts, which will be described in detail in the following detailed implementation part. This content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose a method, an apparatus, an electronic device, and a computer medium for storing construction operation information to solve one or more of the technical problems mentioned in the above background art section.

[0008] 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, mechanical equipment data, vehicle data, environmental data, monitoring data, wherein the construction data set is a collection of various data generated during 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; in response to determining that the quality prediction result represents a quality prediction abnormal result, generating warning information for at least one construction data in the construction data set corresponding to the quality prediction abnormal result to obtain a warning information group; generating 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 warnings; generating 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 reported operation information; displaying the construction operation information set to a construction system for a user to perform control processing on the construction operation information set to obtain a controlled construction operation information set; storing the controlled construction operation information set in the construction system.

[0009] 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, mechanical equipment data, vehicle data, environmental data, monitoring data, wherein the construction data set is a set of various data generated during the building 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 a quality prediction abnormal result, generate warning information for at least one construction data in the construction data set corresponding to the quality prediction abnormal result to obtain a warning information group; a second generation unit 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 warnings; a third generation unit 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 reported operation information; a display unit configured to display the construction operation information set to a construction system for a user to perform control processing on the construction operation information set to obtain a controlled construction operation information set; and a storage unit configured to store the controlled construction operation information set in the construction system.

[0010] Third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having stored thereon one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation manner of the first aspect above.

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

[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 increased, and the practicability of the construction system is enhanced. Specifically, the reasons for the insufficient integrity of the operation information, the slow storage speed, and the low practicability of the construction system are as follows: Manual control of the construction system to store the operation information may result in omissions or large errors, resulting in insufficient integrity of the operation information and a slow storage speed. When storing the operation information, the practicability of the construction system may be low due to the poor quality of the operation information. Based on this, in the construction operation information storage method of some embodiments of the present disclosure, first, a construction data set is obtained, where 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, and monitoring data, and the above-mentioned construction data set is a collection of various data generated during the building 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 incorrect data can be removed to ensure the accuracy and integrity of the data. After that, 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 above-mentioned processed construction data set can be predicted to avoid the low practicability of the construction system that may be caused by the poor quality of the operation information when storing the operation information subsequently. Second, in response to determining that the above-mentioned quality prediction result represents a quality prediction abnormal result, at least one construction data in the construction data set corresponding to the quality prediction abnormal result is used to generate a warning information group, and a warning information group is obtained. Thus, the construction data with poor quality can be warned to facilitate timely correction. Third, according to the above-mentioned warning information group, a sorted warning information sequence is generated, where the above-mentioned sorted warning information sequence is a warning information sequence sorted in the order of the warning time. Thus, the order in which the quality problems occur can be identified to facilitate the priority processing of the construction data with the quality problems detected first. Then, according to the above-mentioned sorted warning information sequence and the above-mentioned processed construction data set, a construction operation information set is generated, where the above-mentioned construction operation information set is a set of construction information including 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. After that, the above-mentioned construction operation information set is displayed on the construction system for the user to control and process the above-mentioned construction operation information set to obtain a controlled construction operation information set. Thus, it is easy to control through visualization. The above-mentioned controlled construction operation information set is stored in the above-mentioned construction system. Thus, the insufficient integrity of the operation information and the slow storage speed can be avoided. Therefore, the integrity of the construction operation information is improved, the storage speed is increased, and the practicability of the construction system is enhanced. Description of the Drawings

[0013] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and the elements and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flowchart of some embodiments of a method for storing construction operation information according to the present disclosure;

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

[0016] Figure 3 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. Specific Embodiments

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

[0018] In addition, it should be noted that for the sake of convenience of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments 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 mutual dependence relationship of the functions performed by these devices, modules or units.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified 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 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 drawings and in combination with the embodiments.

[0023] Figure 1It is the process 100 of some embodiments of the construction operation information storage method of the present disclosure. The construction operation information storage method includes the following steps:

[0024] Step 101, obtain a construction data set.

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

[0026] Here, the above personnel data may refer to the attribute data of construction personnel. The above attribute data may include, but is not limited to, at least one of the following: name, height, gender. For example, the above personnel data may refer to the name and gender of construction personnel. The above mechanical equipment data may refer to the usage record data of mechanical equipment. The above mechanical equipment may refer to a crane device. The above vehicle data may refer to the model data of the vehicles used in the construction. For example, the above vehicle data may refer to the capacity data of the vehicles used in the construction. The above environmental data may be data characterizing the surrounding environment during the building construction process. For example, the above environmental data may include, but is not limited to, at least one of the following: temperature data, humidity data. The above monitoring data may refer to the completion progress data monitored during the construction process. For example, the above 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 wireless connection methods may include, but are not limited to, 3G / 4G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future-developed wireless connection methods.

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

[0029] Step 102, perform data preprocessing on the above construction data set to obtain a processed construction data set.

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

[0031] Here, the processed construction data in the above-mentioned processed construction data set may refer to the construction data after removing redundant and incorrect data. The above-mentioned construction data is the data generated during the building construction process. For example, the above-mentioned construction data is the data on the required material usage generated during the building construction process.

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

[0033] In the first step, perform data cleaning on the above-mentioned construction data set to obtain a cleaned construction data set.

[0034] Here, the cleaned construction data in the above-mentioned cleaned construction data set may refer to the construction data after removing incorrect data and redundant data.

[0035] In the second step, perform standardization processing on the above-mentioned cleaned construction data set to obtain a standardized processed construction data set.

[0036] Here, the standardized processed construction data in the above-mentioned standardized processed construction data set may refer to the construction data with a mean of 0 and a standard deviation of 1 for the distribution.

[0037] In the third step, perform data deduplication processing on the above-mentioned processed construction data set to obtain a deduplicated construction data set.

[0038] Here, the above-mentioned data deduplication processing may refer to removing duplicates.

[0039] In the fourth step, perform data integration on 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 above-mentioned processed construction data set into a pre-trained quality prediction model to obtain a quality prediction result.

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

[0042] Here, the above quality prediction model can be a model for predicting the quality of each of the processed construction data in the above processed construction data set. The above quality prediction model can include: a Recurrent Neural Network (RNN) model and a Logistic Regression model. The above Logistic Regression model can be a model for judging the classification result of quality prediction. The above classification result can represent an abnormal result and a normal result. The above 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 above quality prediction model can be used to represent the corresponding relationship between the processed construction data set and the quality prediction result. The quality prediction model can be a classification model with the processed construction data set as the input and the quality prediction result as the output. The above quality prediction model can compare the processed construction data set with multiple groups of preset processed construction data sets in the preset quality prediction relationship table in sequence. The above preset quality prediction relationship table can be a corresponding relationship table created based on the analysis of a large number of preset processed construction data sets and storing the corresponding relationship between the preset processed construction data sets and the preset quality prediction results. Each group of preset processed construction data sets corresponds to a preset quality prediction result. The above preset quality prediction result can be a pre-set quality prediction result.

[0043] As an example, the above Recurrent Neural Network (RNN) model can include: an input layer, a hidden layer, and an output layer. Among them, the above input layer receives the processed construction data in the processed construction data set according to a preset time step, and normalizes the processed construction data set to obtain a normalized construction data set. The above preset time step can be 0.1 second. The above hidden layer can include Simple Recurrent Network (SRN) units and Long Short-Term Memory (LSTM) units. The above Simple Recurrent Network units are used to transfer each of the normalized construction data in the normalized construction data set. The above Long Short-Term Memory units are used to control the flow of each of the normalized construction data. The above output layer is used to output the processed construction data set after data processing. Here, the above Logistic Regression model can include: an input layer and a Logistic Regression layer. The above input layer is used to receive the output result from the above Recurrent Neural Network model. The above Logistic Regression layer can be used to map the input processed construction data set after data processing to a probability value between 0 and 1. The activation function of the above Logistic Regression layer can be the sigmoid function.

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

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

[0046] Here, the above quality prediction abnormal result may represent a result with poor quality prediction.

[0047] As an example, the above execution entity may perform abnormal marking on at least one construction data in the construction data set corresponding to the quality prediction abnormal result, to obtain a post-marked construction data set. The above abnormal mark may refer to "!". Then, bind warning identification information to the post-marked construction data in the above post-marked construction data set, to obtain a post-bound warning information group, as the warning information group. The above warning identification information may refer to "ERROR".

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

[0049] First step, determine at least one construction data in the above construction data set that represents a quality prediction abnormal result as an abnormal construction data set.

[0050] Second step, extract data features for each abnormal construction data in the above abnormal construction data set, to generate a group of abnormal construction feature data, to obtain a set of groups of abnormal construction feature data, where the groups of abnormal construction feature data in the above set of groups of abnormal construction feature data may include but are not limited to at least one of the following: time feature data, equipment status feature data.

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

[0052] As an example, the above execution entity may perform data feature extraction on each abnormal construction data in the above abnormal construction data set through Data Extraction, to generate a group of abnormal construction feature data, to obtain a set of groups of abnormal construction feature data.

[0053] In the third step, anomaly detection is performed on each abnormal construction feature data group in the above abnormal construction feature data group set to generate a post-detection feature data group, and a post-detection feature data group set is obtained.

[0054] As an example, the above execution entity can use the Isolation Forest (iForest) algorithm to perform anomaly detection on each abnormal construction feature data group in the above abnormal construction feature data group set to generate a post-detection feature data group, and a post-detection feature data group set is obtained.

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

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

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

[0058] In the fifth step, in response to determining that there is feature data scoring greater than a preset scoring threshold in the above-mentioned feature data scoring set, determine the abnormal type of the construction data corresponding to at least one feature data scoring greater than the preset scoring threshold, and obtain a construction data abnormal type group.

[0059] Here, the above-mentioned preset scoring threshold may refer to the maximum value of the preset scoring. For example, the above-mentioned preset scoring threshold may refer to 0.7. The above-mentioned abnormal type may refer to the overtime type.

[0060] In the sixth step, perform scoring sorting on each construction data abnormal type in the above-mentioned construction data abnormal type group to obtain an abnormal construction data scoring sequence.

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

[0062] In the seventh step, perform symbol information annotation on the above-mentioned abnormal construction data scoring sequence to obtain an annotated construction data information group, which is used as a warning information group.

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

[0064] As an example, the above-mentioned execution subject can use a labeling tool (Label Studio) to perform symbol information annotation on the above-mentioned abnormal construction data scoring sequence to obtain an annotated construction data information group, which is used as a warning information group.

[0065] Step 105, generate a sorted warning information sequence according to the above-mentioned warning information group.

[0066] In some embodiments, the above-mentioned execution subject can generate a sorted warning information sequence according to the above-mentioned warning information group, where the above-mentioned sorted warning information sequence is a warning information sequence sorted in the order of the time of warning.

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

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

[0069] In some embodiments, the above-mentioned execution entity may generate a construction operation information set according to the sorted early warning information sequence and the processed construction data set described above, where the construction operation information set is a set of construction information including submission operation information.

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

[0071] As an example, the above-mentioned execution entity may perform a correlation comparison between the sorted early warning information sequence and each processed construction data in the processed construction data set through cluster analysis to obtain a construction data sequence corresponding to the sorted early warning information. Then, determine the submission information for the construction data sequence corresponding to the sorted early warning information to obtain a submission 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 art, the following problems often arise:

[0073] When generating the construction operation information set, due to the complexity of the sorted early warning information sequence and the processed construction data set and the possible redundancy, it is easy to produce large errors, resulting in a low integrity of the generated construction operation information set. Also, due to the inability to associate the early warning information sequence and the construction data set within the same time period, the generated construction operations are relatively single, making the quality of the construction operation information poor.

[0074] Facing the above technical problems, the inventor decides to adopt the following solutions:

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

[0076] First step: Perform data cleaning on the sorted early warning information sequence and the processed construction data set to obtain a cleaned early warning information sequence and a cleaned construction data set.

[0077] Optionally, the sorted early warning information sequence is generated through the following steps:

[0078] First sub-step: Determine the time information for the above-mentioned early warning information group to obtain an early warning information corresponding time information group.

[0079] Here, the early warning information corresponding time information group may refer to the information group after binding the early warning information and the time information generated by the early warning information.

[0080] A second sub-step, in response to determining that there are identical warning message corresponding time information in the above warning message corresponding time information group, sorting at least one warning message with the same time information according to capacity information, to obtain a warning message sequence after capacity information sorting.

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

[0082] A third sub-step, according to the warning message sequence after the above capacity information sorting and the above warning message corresponding time information group, performing time series sorting on the above warning message group, to obtain a sorted warning message sequence.

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

[0084] The second step is to perform data alignment processing on each of the above cleaned warning messages in the cleaned warning message sequence and each of the above cleaned construction data in the cleaned construction data set, to obtain a warning message sequence with the same duration and a construction data set with the same duration, where the warning messages with the same duration in the above warning message sequence with the same duration and the construction data with the same duration in the above construction data set with the same duration are consistent in data within the same duration range.

[0085] For example, within the same duration of five seconds, the above warning message with the same duration is "The device needs to be restarted", and the above construction data with the same duration is "The operator data responsible for device restart".

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

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

[0088] The relevant content in the above first to third steps is an inventive point of the present disclosure, which solves the following technical problem: "resulting in poor quality of construction operation information". The factors that cause poor quality of construction operation information are often as follows: when generating the construction operation information set, due to the complexity and possible redundancy of the sorted warning information sequence and the processed construction data set, large errors are easily generated, resulting in low integrity of the generated construction operation information set. Also, since the warning information sequence and the construction data set within the same time period cannot be associated, the generated construction operation information is relatively single, making the quality of the construction operation information poor. If the above factors are solved, the practicability of the construction system can be improved. To achieve this effect, first, perform data cleaning 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. Thereby, redundancy in the sorted warning information sequence and the processed construction data set can be avoided, and errors can be reduced. The sorted warning information sequence is generated through the following steps: the first sub-step is to determine the time information of the warning information group to obtain the warning information corresponding time information group. The second sub-step is to, in response to determining that there is the same warning information corresponding time in the warning information corresponding time information group, perform capacity information sorting on at least one warning information with the same time information to obtain a capacity information sorted warning information sequence. The third sub-step is to perform time series sorting on the warning information group according to the capacity information sorted warning information sequence and the warning information corresponding time information group to obtain a sorted warning information sequence. Second, perform data alignment on each cleaned warning information in the cleaned warning information sequence and each cleaned construction data in the cleaned construction data set to obtain a same-duration warning information sequence and a same-duration construction data set, where the same-duration warning information in the same-duration warning information sequence and the same-duration construction data in the same-duration construction data set are consistent within the same time period range. Thereby, it is beneficial to improve 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. Third, determine the construction operation information set according to the same-duration warning information sequence and the 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 beneficial 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 often arise:

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

[0091] Facing the above technical problems, the inventor decided to adopt the following solutions:

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

[0093] In the first step, classify the warning levels of the above-mentioned sorted early warning information sequence to obtain a classified early warning information set. Among them, the above-mentioned classified early warning information set includes: first-level early warning information, second-level early warning information, and third-level early warning information. The above-mentioned first-level early warning information is the early warning information generated within a preset first time period, the second-level early warning information is the early warning information generated between the preset first time period and the preset second time period, and the third-level early warning information is the early warning information generated outside the preset second time period.

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

[0095] As an example, the above-mentioned execution entity can divide the above-mentioned sorted early warning information sequence according to a preset time period interval to obtain a divided early warning information set. Then, determine the warning level of each divided early warning information in the above-mentioned divided early warning information set to generate divided warning level information, and obtain a divided warning level information set as the classified early warning information set. Here, the above-mentioned preset time period interval can refer to the time range interval divided by a preset period of time. For example, the above-mentioned preset time period interval can refer to dividing 20 seconds into range intervals of [0 seconds, 5 seconds], [5 seconds, 10 seconds], and [10 seconds, 20 seconds].

[0096] In the second step, perform correlation matching between each classified early warning information in the above-mentioned classified early warning information set and the processed construction data in the above-mentioned processed construction data set to generate a matched early warning information group, and obtain a set of matched early warning information groups.

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

[0098] As an example, the above-mentioned execution entity can perform correlation matching 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 through the Apriori algorithm to generate a post-matching warning information group and obtain a post-matching warning information group set.

[0099] In the third step, for each post-matching warning information group in the above-mentioned post-matching warning information group set, data integration is performed to generate integrated warning information and obtain an integrated warning information set.

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

[0101] In the fourth step, the submission information is determined for the above-mentioned integrated warning information set to obtain a submission information set.

[0102] Here, the above-mentioned submission information may refer to a suggestion information. For example, the above-mentioned submission information may refer to "suggest that the equipment be restarted".

[0103] In the fifth step, resource information analysis is performed on the above-mentioned submission information set to obtain a resource information analysis result set, where the above-mentioned resource information analysis result set represents the set of resource information results required for each submission information in the submission information set.

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

[0105] As an example, the above-mentioned execution entity can extract the keywords corresponding to each submission information in the above-mentioned submission information set to generate submission information keywords and obtain a submission information keyword set. Then, resource information determination is performed on the above-mentioned submission information keyword set to obtain a determined result set as the resource analysis result set. For example, the submission information keyword in the above-mentioned submission information keyword set may refer to "equipment restart". The determined result in the above-mentioned determined result set may refer to "allocate 30GB of memory".

[0106] Step 6: 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 above execution entity may perform the following allocation steps for each resource analysis result in the above resource analysis result set: perform resource allocation on the above construction system to obtain a resource allocation result. In response to determining that the resource information status corresponding to the above resource allocation result is the released state, continue to perform the above allocation steps. Determine each resource allocation result with the resource information status being the released state as the resource allocation result set. The above resource allocation result may represent the result of whether the resource information allocation is completed. For example, the above resource allocation result may refer to the result that 30 GB of memory information is being allocated. The resource information status corresponding to the above resource allocation result may refer to the resource information status of whether the resource allocation result is in the completed state. The above released state may represent the completed state.

[0108] Step 7: Perform real-time detection on the above resource allocation result set to obtain a detection result set.

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

[0110] Step 8: In response to determining that the above detection result set represents normal detection, determine the above resource allocation result set as the construction operation information set.

[0111] The relevant content in the above first step - eighth step is an inventive point of the present disclosure, which solves the following technical problem: "resulting in relatively low practicality of the construction system". The factors causing relatively low practicality of the construction system are often as follows: when generating the construction operation information set, due to the relatively complex sorted early warning information sequence, the correlation relationship between the sorted early warning information sequence and the processed construction data set cannot be fully considered, resulting in relatively low integrity of the generated construction operation information set, poor quality of the construction operation information, and relatively 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. To achieve this effect, first, classify the sorted early warning information sequence by early warning level to obtain the classified early warning information set. Among them, the classified early warning information set includes: first-level early warning information, second-level early warning information, and third-level early warning information. The first-level early warning information is the early warning information generated within the preset first time period, the second-level early warning information is the early warning information generated between the preset first time period and the preset second time period, and the third-level early warning information is the early warning information generated outside the preset second time period. Thus, it can facilitate subsequent processing. Second, perform correlation matching between each classified early warning information in the classified early warning information set and the processed construction data in the processed construction data set to generate a matched early warning information group, and obtain a set of matched early warning information groups. Thus, the correlation relationship between the early warning information and the construction data can be fully considered, which is convenient for improving the integrity of the construction operation information. Third, for each matched early warning information group in the set of matched early warning information groups, perform data integration on the matched early warning information group to generate integrated early warning information, and obtain a set of integrated early warning information. Thus, the correlation relationship between the early warning information and the construction data can be integrated, which is convenient for improving the integrity of the construction operation information. Fourth, determine the submission information for the set of integrated early warning information to obtain a set of submission information. Thus, the set of integrated early warning information can be corrected, and the quality of the operation information can be improved. Fifth, perform resource analysis on the set of submission information to obtain a set of resource analysis results. Among them, the set of resource analysis results represents the set of resource information results required for each submission information in the set of submission information. Sixth, dynamically adjust the resource allocation of the construction system according to the set of resource analysis results to obtain a set of resource allocation results. Thus, the quality of the operation information can be improved, and the practicality of the construction system can be improved. Seventh, perform real-time detection on the set of resource allocation results to obtain a set of detection results. Thus, anomalies can be avoided, and the practicality of the construction system can be improved. Eighth, in response to determining that the set of detection results represents normal detection, determine the set of resource allocation results as the construction operation information set. Therefore, correlation matching can fully consider the correlation relationship between the early warning information and the construction data, which is convenient for improving the integrity of the construction operation information. Determining the submission information can improve the quality of the operation information, thereby improving the practicality of the construction system.

[0112] Step 107: Display the above construction operation information set to the construction system for the user to perform control processing on the above construction operation information set, and obtain a controlled construction operation information set.

[0113] In some embodiments, the above execution entity may display the above construction operation information set to the construction system for the user to perform control processing on the above construction operation information set, and obtain a controlled construction operation information set.

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

[0115] As an example, the above execution entity may display the above construction operation information set to the construction system for the user to perform verification processing on the above construction operation information set, and obtain a verification result. Then, in response to determining that the above verification result indicates that the verification is passed, the user is provided with the ability to perform construction speed adjustment processing on the above construction operation information set, and obtain an adjusted construction operation information set as the controlled construction operation information set.

[0116] Step 108: Store the above controlled construction operation information set in the above construction system.

[0117] In some embodiments, the above execution entity may store the above controlled construction operation information set in the above 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 construction operation information is improved, the storage speed is increased, and the practicability of the construction system is enhanced. Specifically, the reasons for the insufficient integrity of operation information, slow storage speed, and low practicability of the construction system are as follows: Manual control of the construction system to store operation information may result in omissions or large errors, leading to insufficient integrity of operation information and slow storage speed. When storing operation information, the practicability of the construction system may be low due to poor quality of the operation information. Based on this, in the construction operation information storage method of some embodiments of the present disclosure, first, a construction data set is obtained, where the construction data set includes, but is not limited to, at least one of the following: personnel data, mechanical equipment data, vehicle data, environmental data, and monitoring data, and the construction data set is a collection of various data generated during the building construction process. Thus, obtaining the construction data set facilitates subsequent processing. Then, data preprocessing is performed on the construction data set to obtain a processed construction data set. Thus, redundant and incorrect data can be removed to ensure the accuracy and integrity of the data. After that, the 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 practicability of the construction system that may be caused by poor quality of operation information when storing operation information subsequently. Secondly, in response to determining that the quality prediction result represents a quality prediction abnormal result, at least one construction data in the construction data set corresponding to the quality prediction abnormal result is used to generate warning information to obtain a warning information group. Thus, warning can be given to construction data with poor quality for timely correction. Thirdly, according to the warning information group, a sorted warning information sequence is generated, where the sorted warning information sequence is a warning information sequence sorted in the order of the time of warning. Thus, the order in which quality problems occur can be identified to facilitate the priority processing of construction data with quality problems detected first. Then, according to the sorted warning information sequence and the processed construction data set, a construction operation information set is generated, where the construction operation information set is a set of construction information containing reported operation information. Thus, the quality of operation information can be improved through the construction operation information set. The integrity of operation information can also be improved. After that, the construction operation information set is displayed on the construction system for the user to perform control processing on the construction operation information set to obtain a controlled construction operation information set. Thus, it is easy to control through visualization. The controlled construction operation information set is stored in the construction system. Thus, the insufficiency of the integrity of operation information and the slow storage speed can be avoided. Therefore, the integrity of construction operation information is improved, the storage speed is increased, and the practicability of the construction system is enhanced.

[0119] Further reference is made to 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 correspond to Figure 1 the method embodiments shown, and the device can be specifically applied to various electronic devices.

[0120] As shown in Figure 2 , 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. Among them, the acquisition unit 201 is configured to acquire a construction data set, where the 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, and the construction data set is a collection of various data generated during the building 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, in response to determining that the quality prediction result represents a quality prediction abnormal result, generate a warning information group for at least one construction data in the construction data set corresponding to the quality prediction abnormal result; the second generation unit 205 is configured to generate a sorted warning information sequence according to the warning information group, where the sorted warning information sequence is a warning information sequence sorted in the chronological order of warnings; the third generation unit 206 is configured to generate a construction operation information set according to the sorted warning information sequence and the processed construction data set, where the construction operation information set is a set of construction information including reported operation information; the display unit 207 is configured to display the construction operation information set to the construction system for the user to perform control processing on the construction operation information set to obtain a controlled construction operation information set; the storage unit 208 is configured to store the controlled construction operation information set in the construction system.

[0121] It can be understood that the various units described in the construction operation information storage device 200 correspond to the respective steps in the method described with reference to Figure 1 . Therefore, the operations, features, and beneficial effects described above for the method also apply to the construction operation information storage device 200 and the units included therein, and will not be repeated here.

[0122] Next, reference is made to Figure 3 , which shows a schematic structural 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 merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present disclosure.

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

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

[0125] Specifically, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. 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 program codes for performing the methods shown in the flowcharts. In such some embodiments, the computer program may 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 methods of some embodiments of the present disclosure are executed.

[0126] It should be noted that the computer-readable media described in some embodiments of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having 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 that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, 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, which carries computer-readable program code. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can 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 a computer-readable medium may be transmitted using any appropriate 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 digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0128] The above computer-readable medium may be included in the above electronic device; or it may exist independently and not be assembled into the electronic device. The above computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device is caused to: obtain a construction data set, where the construction data set includes, but is not limited to, at least one of the following: personnel data, mechanical equipment data, vehicle data, environmental data, and monitoring data, and the construction data set is a set of various data generated during the building construction process; perform data preprocessing on the construction data set to obtain a processed construction data set; input 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 a quality prediction abnormal result, generate warning information for at least one construction data in the construction data set corresponding to the quality prediction abnormal result to obtain a warning information group; generate a sorted warning information sequence according to the warning information group, where the sorted warning information sequence is a warning information sequence sorted in the order of the time of warning; generate a construction operation information set according to the sorted warning information sequence and the processed construction data set, where the construction operation information set is a set of construction information including reporting operation information; display the construction operation information set to the construction system for the user to perform control processing on the construction operation information set to obtain a controlled construction operation information set; and store the controlled construction operation information set in 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 combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0130] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions labeled in the blocks may occur in a different order than labeled in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0131] The units described in some embodiments of the present disclosure can be implemented in software or in hardware. The described units can also be provided in a processor. For example, it can be described as: a processor includes: 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. Among them, the names of these units do not constitute a limitation to the unit itself in some cases. For example, the processing unit can also be described as "the unit that performs data preprocessing on the above construction data set to obtain the processed construction data set".

[0132] The functions described above can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), Systems on Chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0133] The above description is only some preferred embodiments of the present disclosure and an explanation of the technical principles applied 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, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for storing construction operation information, comprising: Obtaining a construction data set, where the construction data set includes, but is not limited to, at least one of the following: personnel data, mechanical equipment data, vehicle data, environmental data, and monitoring data, and the construction data set is a collection of various data generated during 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, where the quality prediction model is a model for predicting the quality of each processed construction data in the processed construction data set; In response to determining that the quality prediction result represents a quality prediction abnormal result, generating warning information for at least one construction data in the construction data set corresponding to the quality prediction abnormal result to obtain a warning information group, where at least one construction data in the construction data set corresponding to the quality prediction abnormal result is marked with an abnormal mark to obtain a marked construction data set, and warning identification information is bound to the marked construction data in the marked construction data set to obtain a bound warning information group as the warning information group; Generating a sorted warning information sequence according to the warning information group, where the sorted warning information sequence is a warning information sequence sorted in the chronological order of warnings; Generating a construction operation information set according to the sorted warning information sequence and the processed construction data set, where the construction operation information set is a collection of construction information including reported operation information; Displaying the construction operation information set to a construction system for a user to perform control processing on the construction operation information set to obtain a controlled construction operation information set; Storing the controlled construction operation information set in the construction system; Wherein, the generating of the construction operation information set according to the sorted warning information sequence and the processed construction data set includes: Performing data cleaning processing 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; The sorted warning information sequence is generated through the following steps: Determining time information for the warning information group to obtain a warning information corresponding time information group, where the warning information corresponding time information group refers to an information group after binding the warning information with the time information generated by the warning information; In response to determining that there is the same warning information corresponding time information in the warning information corresponding time information group, performing capacity information sorting on at least one warning information with the same time information to obtain a capacity information sorted warning information sequence; Performing time series sorting on the warning information group according to the capacity information sorted warning information sequence and the warning information corresponding time information group to obtain a sorted warning information sequence; Align each of the post-cleaning warning information in the post-cleaning warning information sequence with each of the post-cleaning construction data in the post-cleaning construction dataset to obtain a warning information sequence and a construction dataset of the same duration, where the warning information of the same duration in the warning information sequence of the same duration and the construction data of the same duration in the construction dataset of the same duration are consistent within the same duration range; Determine a construction operation information set according to the warning information sequence of the same duration and the construction dataset 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 dataset of the same duration, 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, and determine operation information for each of the extracted keyword information to obtain operation information.

2. The method according to claim 1, wherein The data preprocessing of the construction dataset to obtain a processed construction dataset includes: Perform data cleaning on the construction dataset to obtain a post-cleaning construction dataset; Perform standardization processing on the post-cleaning construction dataset to obtain a post-standardization processing construction dataset; Perform data deduplication processing on the processed construction dataset to obtain a post-deduplication construction dataset; Perform data integration on the post-deduplication construction dataset to obtain an integrated construction dataset as the processed construction dataset.

3. The method according to claim 1, wherein, In response to determining that the quality prediction result represents a quality prediction abnormal result, generate warning information for at least one construction data in the construction dataset corresponding to the quality prediction abnormal result to obtain a warning information group, including: Determine at least one construction data representing a quality prediction abnormal result in the construction dataset as an abnormal construction dataset; Extract data features for each abnormal construction data in the abnormal construction dataset to generate a group of abnormal construction feature data, obtaining a set of groups of abnormal construction feature data, where the group of abnormal construction feature data in the set of groups of abnormal construction feature data includes at least one of the following: time feature data, equipment status feature data; Perform anomaly detection on each group of abnormal construction feature data in the set of groups of abnormal construction feature data to generate a group of detected feature data, obtaining a set of groups of detected feature data; Input the set of groups of detected feature data into a quality scoring model to obtain a set of feature data scores, where each feature data score in the set of feature data scores represents the score of the corresponding group of detected feature data in the set of groups of detected feature data; In response to determining that there is a feature data score greater than a preset score threshold in the set of feature data scores, determine the abnormal type of the construction data corresponding to at least one feature data score greater than the preset score threshold to obtain a group of construction data abnormal types; Sort the scores of each construction data abnormal type in the group of construction data abnormal types to obtain a sequence of abnormal construction data scores; Perform symbol information annotation on the sequence of abnormal construction data scores to obtain an annotated group of construction data information as the warning information group.

4. A construction operation information storage device, comprising: An acquisition unit configured to acquire a construction data set, where the 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, and the construction data set is a collection of various data generated during the building 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, and the quality prediction model is a model for predicting the quality of each processed construction data in the processed construction data set; A first generation unit configured to, in response to determining that the quality prediction result represents a quality prediction abnormal result, generate warning information for at least one construction data in the construction data set corresponding to the quality prediction abnormal result to obtain a warning information group. Among them, at least one construction data in the construction data set corresponding to the quality prediction abnormal result is marked with an abnormality label to obtain a labeled construction data set, and warning identification information is bound to the labeled construction data in the labeled construction data set to obtain a bound warning information group, which is used as the warning information group; A second generation unit configured to generate a sorted warning information sequence according to the warning information group, where the sorted warning information sequence is a warning information sequence sorted in the order of the time of warning; A third generation unit configured to generate a construction operation information set according to the sorted warning information sequence and the processed construction data set, where the construction operation information set is a collection of construction information including reported operation information; A display unit configured to display the construction operation information set to a construction system for a user to perform control processing on the construction operation information set to obtain a controlled construction operation information set; A storage unit configured to store the controlled construction operation information set in the construction system; Among them, the third generation unit is configured to: Perform data cleaning processing 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; The sorted warning information sequence is generated through the following steps: Determine time information for the warning information group to obtain a warning information corresponding time information group, where the warning information corresponding time information group refers to an information group after the warning information is bound to the time information generated by the warning information; In response to determining that there is the same warning information corresponding time information in the warning information corresponding time information group, perform capacity information sorting on at least one warning information with the same time information to obtain a capacity information sorted warning information sequence; According to the capacity information sorted warning information sequence and the warning information corresponding time information group, perform time series sorting on the warning information group to obtain a sorted warning information sequence; Align each of the post-cleaning warning information sequences with each of the post-cleaning construction data in the post-cleaning construction data set to obtain a warning information sequence with the same duration and a construction data set with the same duration, where the warning information with the same duration in the warning information sequence with the same duration is consistent with the construction data with the same duration in the construction data set with the same duration within the same duration range; Determine a construction operation information set according to the warning information sequence with the same duration and the construction data set with the same duration. For each piece of warning information with the same duration in the warning information sequence with the same duration and each piece of construction data with the same duration in the construction data set with the same duration, extract the keyword information of the warning information sequence with the same duration and the keyword information of the construction data with the same duration, and determine operation information for each of the extracted keyword information to obtain operation information.

5. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; 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, The program, when executed by the processor, implements the method according to any one of claims 1 to 3.

Images